Abdi, P, Hamedani-Golshan, M-E, Alhelou, HH & Milano, F 2022, 'A PMU-Based Method for On-Line Thévenin Equivalent Estimation', IEEE Transactions on Power Systems, vol. 37, no. 4, pp. 2796-2807.
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Abdin, Z, Khalilpour, K & Catchpole, K 2022, 'Projecting the levelized cost of large scale hydrogen storage for stationary applications', Energy Conversion and Management, vol. 270, pp. 116241-116241.
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Hydrogen as an energy vector is considered to be an attractive solution for sustainable energy systems - provided, of course, that the energy is from renewable resources. As for all energy systems, this would require energy storage to alleviate the supply and demand disparity within the energy value chain. Despite a great deal of effort to reduce the cost of hydrogen generation, there has been relatively little attention paid to the cost of hydrogen storage. This article determines the levelized cost of hydrogen storage (LCHS) for seven technologies based on the projected capital expenditure (CapEx), operational expenditure (OpEx), and decommissioning cost. Our analysis quantitatively demonstrates the impact of different storage cycle lengths on storage system economics, with LCHS dramatically increasing for long-term storage despite a radical decrease in OpEx cost. For example, the LCHS of above-ground compressed gaseous storage for a daily and 4-monthly storage cycle length is ∼$0.33 and ∼$25.20 per kg of H2, respectively. On the other hand, globally, most green hydrogen is produced by low-carbon electricity primarily based on intermittent solar and wind, and the average levelized cost of hydrogen production ranges from ∼$3.2 to ∼$7.7 per kg of H2. Thus, the storage costs are much higher than the generation cost for long-term storage. Storage in salt caverns exhibits the lowest LCHS at ∼$0.14/kg of H2 for daily storage, followed by above-ground compressed gaseous storage. On the other hand, ammonia has the highest LCHS ∼$3.51/kg of H2, followed by methanol ∼$2.25/kg of H2. These costs are expected to stay relatively high; our CapEx prediction suggests that by 2050 the LCHS of ammonia and methanol could decrease by 20–25%. Furthermore, storage efficiency for ammonia is the lowest at ∼42%, followed by methanol at ∼50%, while compressed gaseous shows the highest storage efficiency, at ∼92%. Overall the analysis shows that the cost of hydrogen storage wou...
Abdollahi, A, Liu, Y, Pradhan, B, Huete, A, Dikshit, A & Nguyen Tran, N 2022, 'Short-time-series grassland mapping using Sentinel-2 imagery and deep learning-based architecture', The Egyptian Journal of Remote Sensing and Space Sciences, vol. 25, no. 3, pp. 673-685.
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In the present work, a deep learning-based network called LeNet is applied for accurate grassland map production from Sentinel-2 data for the Greater Sydney region, Australia. First, we apply the technique to the base date Sentinel-2 data (non-seasonal) to make the vegetation maps. Then, we combine short time-series (seasonal) data and enhanced vegetation index (EVI) information to the base date imagery to improve the classification results and generate high-resolution grassland maps. The proposed model obtained an overall accuracy (OA) of 88.36% for the mono-temporal data, and 92.74% for the multi-temporal data. The experimental products proved that, by combining the short time-series images and EVI information to the base date, the classification maps' accuracy is increased by 4.38%. Moreover, the Sentinel-2 produced grassland maps are compared with the pre-existing maps such as Australian Land Use and Management (ALUM) 50 m resolution and Dynamic Land Cover Dataset (DLCD) with 250 m resolution as well as some traditional machine learning methods such as Support Vector Machine (SVM) and Random Forest (RF). The results show the effect of the LeNet network's performance and efficiency for grassland map production from short time-series data. As a result, decision-makers and urban planners can benefit from this work in terms of grassland change identification, monitoring, and planning assessment.
Abdollahi, A, Pradhan, B & Alamri, A 2022, 'SC-RoadDeepNet: A New Shape and Connectivity-Preserving Road Extraction Deep Learning-Based Network From Remote Sensing Data', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, no. 99, pp. 1-15.
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Existing automated road extraction approaches concentrate on regional accuracy rather than road shape and connectivity quality. Most of these techniques produce discontinuous outputs caused by obstacles, such as shadows, buildings, and vehicles. This study proposes a shape and connectivity-preserving road identification deep learning-based architecture called SC-RoadDeepNet to overcome the discontinuous results and the quality of road shape and connectivity. The proposed model comprises a state-of-the-art deep learning-based network, namely, the recurrent residual convolutional neural network, boundary learning (BL), and a new measure based on the intersection of segmentation masks and their (morphological) skeleton called connectivity-preserving centerline Dice (CPclDice). The recurrent residual convolutional layers accumulate low-level features for segmentation tasks, thus allowing for better feature representation. Such representation enables us to construct a UNet network with the same number of network parameters but improved segmentation effectiveness. BL also aids the model in improving the road’s boundaries by penalizing boundary misclassification and fine-tuning the road form. Furthermore, the CPclDice method aids the model in maintaining road connectivity and obtaining accurate segmentations. We demonstrate that CPclDice ensures connection preservation for binary segmentation, thereby allowing for efficient road network extraction at the end. The proposed model improves F1 score accuracy to 5.49%, 4.03%, 3.42%, and 2.27% compared with other comparative models, such as LinkNet, ResUNet, UNet, and VNet, respectively. Furthermore, qualitative and quantitative assessments demonstrate that the proposed SC-RoadDeepNet can improve road extraction by tackling shadow and occlusion-related interruptions. These assessments can also produce high-resolution results, particularly in the area of road network completeness.
Abdollahi, A, Pradhan, B & Alamri, AM 2022, 'An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images', Geocarto International, vol. 37, no. 12, pp. 3355-3370.
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Building objects is one of the principal features that are essential for updating the geospatial database. Extracting building features from high-resolution imagery automatically and accurately is challenging because of the existence of some obstacles in these images, such as shadows, trees, and cars. Although deep learning approaches have shown significant improvements in the results of image segmentation in recent years, most deep neural networks still cannot achieve highly accurate results with correct segmentation map when processing high-resolution remote sensing images. Therefore, we implemented a new deep neural network named Seg–Unet method, which is a composition of Segnet and Unet techniques, to exploit building objects from high-resolution aerial imagery. Results obtained 92.73% accuracy carried on the Massachusetts building dataset. The proposed technique improved the performance to 0.44%, 1.17%, and 0.14% compared with fully convolutional neural network (FCN), Segnet, and Unet methods, respectively. Results also confirmed the superiority of the proposed method in building extraction.
Abdollahi, M, Ashtari, S, Abolhasan, M, Shariati, N, Lipman, J, Jamalipour, A & Ni, W 2022, 'Dynamic Routing Protocol Selection in Multi-Hop Device-to-Device Wireless Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 8, pp. 8796-8809.
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Abdullah, Faye, I & Islam, MR 2022, 'EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives', Bioengineering, vol. 9, no. 12, pp. 726-726.
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Communication, neuro-prosthetics, and environmental control are just a few applications for disabled persons who use robots and manipulators that use brain-computer interface (BCI) systems. The brain’s motor imagery (MI) signal is an essential input for a brain-related task in BCI applications. Due to their noninvasive, portability, and cost-effectiveness, electroencephalography (EEG) signals are the most widely used input in BCI systems. The EEG data are often collected from more than 100 different locations in the brain; channel selection techniques are critical for selecting the optimum channels for a given application. However, when analyzing EEG data, the principal purpose of channel selection is to reduce computational complexity, improve classification accuracy by avoiding overfitting, and reduce setup time. Several channel selection assessment algorithms, both with and without classification-based methods, extracted appropriate channel subsets using defined criteria. Therefore, based on the exhaustive analysis of the EEG channel selection, this manuscript analyses several existing studies to reduce the number of noisy channels and improve system performance. We review several existing works to find the most promising MI-based EEG channel selection algorithms and associated classification methodologies on various datasets. Moreover, we focus on channel selection methods that choose fewer channels with great precision. Finally, our main finding is that a smaller channel set, typically 10–30% of total channels, provided excellent performance compared to other existing studies.
Abdullah, NHB, Mijan, NA, Taufiq-Yap, YH, Ong, HC & Lee, HV 2022, 'Environment-friendly deoxygenation of non-edible Ceiba oil to liquid hydrocarbon biofuel: process parameters and optimization study', Environmental Science and Pollution Research, vol. 29, no. 34, pp. 51143-51152.
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Abharian, S, Sarfarazi, V, Marji, MF & Rasekh, H 2022, 'Experimental and numerical evaluation of the effects of interaction between multiple small holes and a single notch on the mechanical behavior of artificial gypsum specimens', Theoretical and Applied Fracture Mechanics, vol. 121, pp. 103462-103462.
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The mechanical behavior of cubic gypsum specimens containing five small circular holes in a linear configuration and a single notch under uniaxial compression test were studied to evaluate interactions between these flaws during crack development under loading. Multiple angles between the line of holes and the horizontal axis were evaluated (15°, 45°, and 75°), as were different notch apertures (2, 4, 6 and 8 mm). Acoustic emission (AE) data were used to evaluate the fracture development process in each case. Following the experiments, numerical simulations of the tests were conducted using the particle flow code (PFC2D). The compressive strengths of the specimens were found to be associated with the failure mechanism and fracturing geometry, which were in turn controlled by the geometric attributes of the flaws considered. The compressive strength of specimens were affected by the number of tensile cracks. The induced tensile cracked number were increased by decreasing the joint length. Only few AE events were detected in the initial phase of loading, but then AE hits grew rapidly prior to reaching the peak stress. The AE hits increased by increasing the filling thickness. Failure pattern and compressive strength of specimens were nearly similar in both numerical and experimental approaches.
Abharian, S, Sarfarazi, V, Rasekh, H & Behzadinasab, M 2022, 'Effects of concrete/gypsum bedding layers and their inclination angles on the tensile failure mechanism: Experimental and numerical studies', Case Studies in Construction Materials, vol. 17, pp. e01272-e01272.
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This paper investigates the influence of concrete/gypsum bedding layers and their orientation angles on the tensile failure mechanism in the three-point bending test based on experiments and numerical simulations. Rectangular samples containing different combinations of concrete and gypsum layers were prepared, i.e. one layer of gypsum and one layer of concrete, one layer of gypsum and two layers of concrete, and two layers of gypsum and two layers of concrete. In each configuration, bedding layer angles varied between 0° and 90° with increment of 30°. A total of 36 specimens including 12 configurations were prepared and tested. In addition, numerical simulations were conducted on the concrete/gypsum bedding layers at different angles of 0°, 15°, 30°, 45°, 60°, 75°, and 90°. Results show that the bedding layer orientation and bedding layer thickness affect the observed tensile failure process including the failure pattern and tensile strength. A pure tensile failure occurred when the bedding layer angle was 0°, while a sliding failure evolved by increasing the joint angle. When the bedding layer angle was 90°, the failure in boundary of layer was observed. Specimens with one layer of concrete and one layer of gypsum at 0° inclination angle had the highest tensile strength. However, increasing the number of layers and inclination angles decreased the tensile strength of specimens as the number of weak layers in the direction of loading increased.
Aboulkheyr Es, H, Aref, AR & Warkiani, ME 2022, 'Generation and Culture of Organotypic Breast Carcinoma Spheroids for the Study of Drug Response in a 3D Microfluidic Device', Methods in Molecular Biology, vol. 2535, pp. 49-57.
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Breast cancer (BC) is a leading cause of cancer death among women worldwide. To better understand and predict therapeutic response in BC patient developing a fast, low-cost, and reliable preclinical tumor from patient's tumor specimen is needed. Here, we describe the development of a preclinical model of BC through the generation and ex vivo culture of patient-derived organotypic tumor spheroids (PDOTS) in a 3D microfluidic device. Moreover, the real-time screening of conventional chemotherapy agents on cultured PDOTS is also described.
Aboutorab, H, Hussain, OK, Saberi, M & Hussain, FK 2022, 'A reinforcement learning-based framework for disruption risk identification in supply chains', Future Generation Computer Systems, vol. 126, pp. 110-122.
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Risk management is one of the critical activities which needs to be done well to ensure supply chain activities operate smoothly. The first step in risk management is risk identification, in which the risk manager identifies the risk events of interest for further analysis. The timely identification of risk events in the risk identification step is crucial for the risk manager to be proactive in managing the supply chain risks in its operations. Undertaking this step manually, however, is tedious and time-consuming. With the increased sophistication and capability of advanced computing algorithms, various eminent supply chain researchers have called for the use of artificial intelligence techniques to increase efficiency and efficacy when performing their tasks. In this paper, we demonstrate how reinforcement learning, which is one of the recent artificial intelligence techniques, can assist risk managers to proactively identify the risks to their operations. We explain the working of our proposed Reinforcement Learning-based approach for Proactive Risk Identification (RL-PRI) and its various steps. We then show the performance accuracy of RL-PRI in identifying the risk events of interest by comparing its output with the risk events which are manually identified by professional risk managers.
Abraham, MT, Satyam, N & Pradhan, B 2022, 'Effect of data splitting and selection of machine learning algorithms for landslide susceptibility mapping'.
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<p>Landslide susceptibility maps (LSMs) are inevitable parts of regional scale landslide forecasting models. The susceptibility maps can provide the spatial probability of occurrence of landslides and have crucial role in the development and planning activities of any region. With the wide availability of satellite-based data and advanced computational facilities, data driven LSMs are being developed for different regions across the world. Since a decade, machine learning (ML) algorithms have gained wide acceptance for developing LSMs and the performance of such maps depends highly on the quality of input data and the choice of ML algorithm. This study employs a k fold cross validation technique for evaluating the performance of five different ML models, viz., Na&#239;ve Bayes (NB), Logistic Regression (LR), Random Forest (RF), K Nearest Neighbors (KNN) and Support Vector Machines (SVM), to develop LSMs, by varying the train to test ratio. The ratio is varied by changing the number folds used for k fold cross validation from 2 to 10, and its effect on each algorithm is assessed using Receiver Operating Characteristic (ROC) curves and accuracy values. The method is tested for Wayanad district, Kerala, India, which is highly affected by landslides during monsoon. The results show that RF algorithm performs better among all the five algorithms considered, and the maximum accuracy values were obtained with the value of k as 8, for all cases. The variation between the minimum and maximum accuracy values were found to be 0.6 %, 0.74 %, 1.71 %, 1.92 % and 1.83 % for NB, LR, KNN, RF and SVM respectively.</p>
Abraham, MT, Satyam, N, Pradhan, B & Segoni, S 2022, 'Proposing an easy-to-use tool for estimating landslide dimensions using a data-driven approach', All Earth, vol. 34, no. 1, pp. 243-258.
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Abraham, MT, Satyam, N, Pradhan, B & Tian, H 2022, 'Debris flow simulation 2D (DFS 2D): Numerical modelling of debris flows and calibration of friction parameters', Journal of Rock Mechanics and Geotechnical Engineering, vol. 14, no. 6, pp. 1747-1760.
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Abraham, MT, Satyam, N, Pradhan, B, Segoni, S & Alamri, A 2022, 'Developing a prototype landslide early warning system for Darjeeling Himalayas using SIGMA model and real-time field monitoring', Geosciences Journal, vol. 26, no. 2, pp. 289-301.
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Rainfall thresholds are commonly utilized to forecast landslides using the historical relationship between occurrence of slope failures and rainfall in an area. SIGMA (Sistema Integrato Gestione Monitoraggion Allerta) is a rainfall threshold model, which uses the statistical distribution of rainfall for forecasting the occurrence of landslides. The threshold curves are functions of standard deviation of the cumulated rainfall data, taking into account both long-term and short term-rainfall. To overcome the limitations of statistical rainfall threshold, the real-time monitoring data from MicroElectroMechanical Systems (MEMS) tilt sensors have been integrated with SIGMA model using a decisional algorithm for a test site (Kalimpong) in Darjeeling Himalayas, in the northeastern part of India. Three different models, the SIGMA model, tilt meter readings and the combination of both are compared quantitatively using the precipitation and landslide data of Kalimpong town between July 2017 and September 2020. The results indicate that the integration of tilt meter readings has lowered the number of false alarms issued by SIGMA model from 70 to 38 in the studied period, with an increase in the likelihood ratio from 18.10 to 20.23. The Receiver Operating Characteristic (ROC) curves indicate that the combined approach has the best performance among the models considered in this study, with an area under the curve 0.976. The proposed method was found to have better performance than the other rainfall thresholds derived for Kalimpong region so far, and the prototypal model can be further fine-tuned to develop an operational Landslide Early Warning System (LEWS) for the region.
Abualigah, L, Elaziz, MA, Khasawneh, AM, Alshinwan, M, Ibrahim, RA, Al-qaness, MAA, Mirjalili, S, Sumari, P & Gandomi, AH 2022, 'Meta-heuristic optimization algorithms for solving real-world mechanical engineering design problems: a comprehensive survey, applications, comparative analysis, and results', Neural Computing and Applications, vol. 34, no. 6, pp. 4081-4110.
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Real-world engineering design problems are widespread in various research disciplines in both industry and industry. Many optimization algorithms have been employed to address these kinds of problems. However, the algorithm’s performance substantially reduces with the increase in the scale and difficulty of problems. Various versions of the optimization methods have been proposed to address the engineering design problems in the literature efficiently. In this paper, a comprehensive review of the meta-heuristic optimization methods that have been used to solve engineering design problems is proposed. We use six main keywords in collecting the data (meta-heuristic, optimization, algorithm, engineering, design, and problems). It is worth mentioning that there is no survey or comparative analysis paper on this topic available in the literature to the best of our knowledge. The state-of-the-art methods are presented in detail over several categories, including basic, modified, and hybrid methods. Moreover, we present the results of the state-of-the-art methods in this domain to figure out which version of optimization methods performs better in solving the problems studied. Finally, we provide remarkable future research directions for the potential methods. This work covers the main important topics in the engineering and artificial intelligence domain. It presents a large number of published works in the literature related to the meta-heuristic optimization methods in solving various engineering design problems. Future researches can depend on this review to explore the literature on meta-heuristic optimization methods and engineering design problems.
Abualigah, L, Elaziz, MA, Khodadadi, N, Forestiero, A, Jia, H & Gandomi, AH 2022, 'Aquila Optimizer Based PSO Swarm Intelligence for IoT Task Scheduling Application in Cloud Computing', vol. 1038, pp. 481-497.
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This paper introduces IAO, a new swarm intelligence approach for addressing the challenge of task scheduling in cloud computing. The proposed method uses conventional Aquila Optimizer (AO) and Particle Swarm Optimizer (PSO) as a hybrid method based on a novel transition mechanism. The proposed hybrid method, IAO, combined the AO and PSO to avoid the weaknesses they face; these weaknesses are trapped in the local search area and have low solution diversity. The proposed transition mechanism is proposed to acquire proper changes between the search operators in order to keep the improvements; it changes between them when any algorithm gets stuck or the solutions diversity decreases. Several scenarios are conducted and tested to validate the suggested method’s ability to address the task scheduling problem; these scenarios contain various tasks (i.e., 600, 1000, and 2000). The obtained results are compared with other well-known methods in terms of Max, Mean, Min of the Expected Complete Time (ECT), Friedman ranking test, and Wilcoxon signed-rank test. The proposed IAO method achieved better results and promising compared to other comparative methods; it is an excellent scheduling approach for solving any related scheduling problem.
Abualigah, L, Elaziz, MA, Sumari, P, Geem, ZW & Gandomi, AH 2022, 'Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer', Expert Systems with Applications, vol. 191, pp. 116158-116158.
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This paper proposes a novel nature-inspired meta-heuristic optimizer, called Reptile Search Algorithm (RSA), motivated by the hunting behaviour of Crocodiles. Two main steps of Crocodile behaviour are implemented, such as encircling, which is performed by high walking or belly walking, and hunting, which is performed by hunting coordination or hunting cooperation. The mentioned search methods of the proposed RSA are unique compared to other existing algorithms. The performance of the proposed RSA is evaluated using twenty-three classical test functions, thirty CEC2017 test functions, ten CEC2019 test functions, and seven real-world engineering problems. The obtained results of the proposed RSA are compared to various existing optimization algorithms in the literature. The results of the tested three benchmark functions revealed that the proposed RSA achieved better results than the other competitive optimization algorithms. The results of the Friedman ranking test proved that the RSA is a significantly superior method than other comparative methods. Finally, the results of the examined engineering problems showed that the RSA obtained better results compared to other various methods. Source codes of RSA are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/101385-reptile-search-algorithm-rsa-a-nature-inspired-optimizer
Abualigah, L, Elaziz, MA, Sumari, P, Khasawneh, AM, Alshinwan, M, Mirjalili, S, Shehab, M, Abuaddous, HY & Gandomi, AH 2022, 'Black hole algorithm: A comprehensive survey', Applied Intelligence, vol. 52, no. 10, pp. 11892-11915.
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This paper provides an in-depth literature review of the Black Hole Algorithm (BHA) which is considered as a recent metaheuristic. BHA has been proven to be very efficient in different applications. There has been several modifications and variants of this algorithm in the literature, so this work reviews various variants of the BHA. The applications of BHA in engineering problems, clustering, task scheduling, image processing, etc. have been thoroughly reviewed as well. This review article sheds lights on the pros and cons of this algorithm and enables finding a right variant of this algorithm for a certain application area. The paper concludes with an in-depth future direction.
Abualigah, L, Zitar, RA, Almotairi, KH, Hussein, AM, Abd Elaziz, M, Nikoo, MR & Gandomi, AH 2022, 'Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques', Energies, vol. 15, no. 2, pp. 578-578.
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Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. Computational Intelligence (CI) techniques have been recognized as effective methods in generating and optimizing renewable tools. The complexity of this variety of energy depends on its coverage of large sizes of data and parameters, which have to be investigated thoroughly. This paper covered the most resent and important researchers in the domain of renewable problems using the learning-based methods. Various types of Deep Learning (DL) and Machine Learning (ML) algorithms employed in Solar and Wind energy supplies are given. The performance of the given methods in the literature is assessed by a new taxonomy. This paper focus on conducting comprehensive state-of-the-art methods heading to performance evaluation of the given techniques and discusses vital difficulties and possibilities for extensive research. Based on the results, variations in efficiency, robustness, accuracy values, and generalization capability are the most obvious difficulties for using the learning techniques. In the case of the big dataset, the effectiveness of the learning techniques is significantly better than the other computational methods. However, applying and producing hybrid learning techniques with other optimization methods to develop and optimize the construction of the techniques is optionally indicated. In all cases, hybrid learning methods have better achievement than a single method due to the fact that hybrid methods gain the benefit of two or more techniques for providing an accurate forecast. Therefore, it is suggested to utilize hybrid learning techniques in the future to deal with energy generation problems.
Abughalwa, M, Tuan, HD, Nguyen, DN, Poor, HV & Hanzo, L 2022, 'Finite-Blocklength RIS-Aided Transmit Beamforming', IEEE Transactions on Vehicular Technology, vol. 71, no. 11, pp. 12374-12379.
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This paper considers the downlink of an ultra-reliable low-latency communication (URLLC) system in which a base station (BS) serves multiple single-antenna users in the short (finite) blocklength (FBL) regime with the assistance of a reconfigurable intelligent surface (RIS). In the FBL regime, the users' achievable rates are complex functions of the beamforming vectors and of the RIS's programmable reflecting elements (PREs). We propose the joint design of the transmit beamformers and PREs to maximize the geometric mean (GM) of these rates (GM-rate) and show that this approach provides fair rate distribution and thus reliable links to all users. A novel computational algorithm is developed, which is based on closed forms to generate improved feasible points. Simulations show the merit of our solution.
AbuSalim, S, Zakaria, N, Islam, MR, Kumar, G, Mokhtar, N & Abdulkadir, SJ 2022, 'Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review', Healthcare, vol. 10, no. 10, pp. 1892-1892.
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Within the ever-growing healthcare industry, dental informatics is a burgeoning field of study. One of the major obstacles to the health care system’s transformation is obtaining knowledge and insightful data from complex, high-dimensional, and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, heterogeneous, poorly documented, and generally unstructured electronic health records, imaging, sensor data, and text. There were still certain restrictions even after many current techniques were used to extract more robust and useful elements from the data for analysis. New effective paradigms for building end-to-end learning models from complex data are provided by the most recent deep learning technology breakthroughs. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for dental informatics problems and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to some drawbacks and the need for better technique development and provide new perspectives about this exciting new development in the field.
Adak, A, Pradhan, B & Shukla, N 2022, 'Sentiment Analysis of Customer Reviews of Food Delivery Services Using Deep Learning and Explainable Artificial Intelligence: Systematic Review', Foods, vol. 11, no. 10, pp. 1500-1500.
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During the COVID-19 crisis, customers’ preference in having food delivered to their doorstep instead of waiting in a restaurant has propelled the growth of food delivery services (FDSs). With all restaurants going online and bringing FDSs onboard, such as UberEATS, Menulog or Deliveroo, customer reviews on online platforms have become an important source of information about the company’s performance. FDS organisations aim to gather complaints from customer feedback and effectively use the data to determine the areas for improvement to enhance customer satisfaction. This work aimed to review machine learning (ML) and deep learning (DL) models and explainable artificial intelligence (XAI) methods to predict customer sentiments in the FDS domain. A literature review revealed the wide usage of lexicon-based and ML techniques for predicting sentiments through customer reviews in FDS. However, limited studies applying DL techniques were found due to the lack of the model interpretability and explainability of the decisions made. The key findings of this systematic review are as follows: 77% of the models are non-interpretable in nature, and organisations can argue for the explainability and trust in the system. DL models in other domains perform well in terms of accuracy but lack explainability, which can be achieved with XAI implementation. Future research should focus on implementing DL models for sentiment analysis in the FDS domain and incorporating XAI techniques to bring out the explainability of the models.
Adak, A, Pradhan, B, Shukla, N & Alamri, A 2022, 'Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique', Foods, vol. 11, no. 14, pp. 2019-2019.
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The demand for food delivery services (FDSs) during the COVID-19 crisis has been fuelled by consumers who prefer to order meals online and have it delivered to their door than to wait at a restaurant. Since many restaurants moved online and joined FDSs such as Uber Eats, Menulog, and Deliveroo, customer reviews on internet platforms have become a valuable source of information about a company’s performance. FDS organisations strive to collect customer complaints and effectively utilise the information to identify improvements needed to enhance customer satisfaction. However, only a few customer opinions are addressed because of the large amount of customer feedback data and lack of customer service consultants. Organisations can use artificial intelligence (AI) instead of relying on customer service experts and find solutions on their own to save money as opposed to reading each review. Based on the literature, deep learning (DL) methods have shown remarkable results in obtaining better accuracy when working with large datasets in other domains, but lack explainability in their model. Rapid research on explainable AI (XAI) to explain predictions made by opaque models looks promising but remains to be explored in the FDS domain. This study conducted a sentiment analysis by comparing simple and hybrid DL techniques (LSTM, Bi-LSTM, Bi-GRU-LSTM-CNN) in the FDS domain and explained the predictions using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). The DL models were trained and tested on the customer review dataset extracted from the ProductReview website. Results showed that the LSTM, Bi-LSTM and Bi-GRU-LSTM-CNN models achieved an accuracy of 96.07%, 95.85% and 96.33%, respectively. The model should exhibit fewer false negatives because FDS organisations aim to identify and address each and every customer complaint. The LSTM model was chosen over the other two DL models, Bi-LSTM and Bi-GRU-LSTM-CNN...
Adhikari, S, Thapa, S, Naseem, U, Singh, P, Huo, H, Bharathy, G & Prasad, M 2022, 'Exploiting linguistic information from Nepali transcripts for early detection of Alzheimer's disease using natural language processing and machine learning techniques.', Int. J. Hum. Comput. Stud., vol. 160, pp. 102761-102761.
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Adibi, T, Adibi, O & Saha, S 2022, 'Evaluation of the optimum pressures of gas turbines based on energy and exergy analyses', Energy Equipment and Systems, vol. 10, no. 2, pp. 169-181.
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The irreversible Brayton cycle is usually used in gas turbine-based power plants. In this study, energy and exergy analysis has been performed for an irreversible Brayton cycle with a regenerator, reheater, and intercooler for the first time. The influence of different parameters such as the efficiency of the cycle's components is examined based on the first and the second laws of thermodynamics. The lost exergy in different components and the total exergy loss of the irreversible Brayton cycle are calculated for various conditions. The optimum pressure of the intercooler and the reheater is obtained for different cases. An irreversible Brayton cycle with regenerator, reheater, and intercooler is simulated in engineering equation solver software and the optimum pressure in each simulation is determined based on the first and the second laws of thermodynamics. Furthermore, the obtained optimum pressures are compared with the geometric mean of the low and the high pressure of the cycle in each simulation. The results show that the optimum pressure of the intercooler changes between 0.69 (formula presented) and 0.98 (formula presented) based on exergy analyses and changes between 0.98 (formula presented) and 2.97 (formula presented) based on energy analyses.
Adibi, T, Razavi, SE, Ahmed, SF, Amrikachi, A & Saha, SC 2022, 'Characteristic-Based Fluid Flow Modeling between Two Eccentric Cylinders in Laminar and Turbulent Regimes', Geofluids, vol. 2022, pp. 1-9.
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Determining the flow between eccentric cylinders is crucial in a wide range of industries. The governing equations for the flow between eccentric cylinders cannot be solved analytically. Therefore, three-dimensional incompressible viscous fluid flow between eccentric and concentric cylinders has numerically been simulated in this paper to investigate them using a characteristic-based approach. The first-order characteristic-based scheme is used to calculate convective terms, whereas the second-order averaging technique is used to calculate viscous fluxes. The Taylor number, eccentricity distance, Reynolds number, and radius ratio are considered the controlling parameters of fluid flow between the cylinders. The influence of flow between cylinders on flow patterns is presented in terms of velocity, pressure, and flow contours. It is found that at a constant Taylor number, the asymmetric centrifugal forces produce the Taylor vortices on the right of the internal rotating cylinder as the eccentric distance increases. When the eccentric distance increases, the magnitude of shear stress and its fluctuation on the cylinder wall, as well as the pressure on the cylinder wall, rise. The numerical results obtained were validated by comparing them to previously published experimental results, which showed a high level of agreement.
Adibi, T, Sojoudi, A & Saha, SC 2022, 'Modeling of thermal performance of a commercial alkaline electrolyzer supplied with various electrical currents', International Journal of Thermofluids, vol. 13, pp. 100126-100126.
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Hydrogen produced by solar and other clean energy sources is an essential alternative to fossil fuels. In this stuy, a commercial alkaline electrolyzer with different cell numbers and electrode areas are simulated for different pressure, temperature, thermal resistance, and electrical current. This alkaline electrolyzer is considered unsteady in simulations, and different parameters such as temperature are obtained in terms of time. The obtained results are compared with similar results in the literature, and good agreement is observed. Various characteristics of this alkaline electrolyzer as thermoneutral voltage, faraday efficiency and cell voltage are calculated and displayed. The outlet heat rate and generated heat rate are obtained as well. The pressure and the temperature in the simulations are between 1 and 100 bar and between 300 and 360 Kelvin respectively. The results show that the equilibrium temperature is reached 2–3 h after the time when the Alkaline electrolyzer starts to work.
Aditya, L, Mahlia, TMI, Nguyen, LN, Vu, HP & Nghiem, LD 2022, 'Microalgae-bacteria consortium for wastewater treatment and biomass production', Science of The Total Environment, vol. 838, no. Pt 1, pp. 155871-155871.
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The diversity of microalgae and bacteria allows them to form a complementary consortium for efficient wastewater treatment and nutrient recovery. This review highlights the potential of wastewater-derived microalgal biomass as a renewable feedstock for producing animal feed, biofertilisers, biofuel, and many valuable biochemicals. Data corroborated from this review shows that microalgae and bacteria can thrive in many environments. Microalgae are especially effective at utilising nutrients from the water as they grow. This review also consolidates the current understanding of microalgae characteristics and their interactions with bacteria in a consortium system. Recent studies on the performance of only microalgae and microalgae-bacteria wastewater treatment are compared and discussed to establish a research roadmap for practical implementation of the consortium systems for various wastewaters (domestic, industrial, agro-industrial, and landfill leachate wastewater). In comparison to the pure microalgae system, the consortium system has a higher removal efficiency of up to 15% and shorter treatment time. Additionally, this review addresses a variety of possibilities for biomass application after wastewater treatment.
Afrane, S, Ampah, JD, Agyekum, EB, Amoh, PO, Yusuf, AA, Fattah, IMR, Agbozo, E, Elgamli, E, Shouran, M, Mao, G & Kamel, S 2022, 'Integrated AHP-TOPSIS under a Fuzzy Environment for the Selection of Waste-To-Energy Technologies in Ghana: A Performance Analysis and Socio-Enviro-Economic Feasibility Study', International Journal of Environmental Research and Public Health, vol. 19, no. 14, pp. 8428-8428.
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Energy recovery from waste presents a promising alternative for several countries, including Ghana, which has struggled with unsustainable waste treatment methods and an inadequate power supply for several decades. The current study adopts a comprehensive multi-criteria decision-making approach for the selection of an optimal waste-to-energy (WtE) technology for implementation in Ghana. Four WtE technologies are evaluated against twelve selection criteria. An integrated AHP-fuzzy TOPSIS method is applied to estimate the criteria’s weights and rank the WtE alternatives. From the AHP results, technical criteria obtained the highest priority weight, while social criteria emerged as the least important in the selection process. The overall ranking order of WtE technologies obtained by fuzzy TOPSIS is as follows: anaerobic digestion > gasification > pyrolysis > plasma gasification. The sensitivity analysis indicates highly consistent and sturdy results regarding the optimal selection. This study recommends adopting a hybrid system of anaerobic digestion and gasification technologies, as this offers a well-balanced system under all of the evaluation criteria compared to the standalone systems. The results of the current study may help the government of Ghana and other prospective investors select a suitable WtE technology, and could serve as an index system for future WtE research in Ghana.
Afroz, F & Braun, R 2022, 'Empirical Analysis of Extended QX-MAC for IOT-Based WSNS', Electronics, vol. 11, no. 16, pp. 2543-2543.
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The Internet of Things (IoT) connects our world in more ways than we imagine. Wireless sensor network (WSN) technology is at the core of implementing IoT architectures. Although WSN applications give us enormous opportunities, their deployment is challenging because of the energy constraint in sensor nodes. The primary design objective of WSNs is therefore to maximize energy efficiency. Enhancing network quality of service (QoS), such as latency, is another crucial factor, particularly for different delay-sensitive applications. Medium access control (MAC) protocols are of paramount importance to achieve these targets. Over the years, several duty-cycled MAC protocols were proposed. Among them, the strobed preamble approach introduced in X-MAC has gained much interest in IoT field because of its several theoretical advantages. However, X-MAC is highly efficient only under light traffic. Under heavy traffic, X-MAC incurs high per-packet overhead and extra delay. In addition, X-MAC has several design flaws that can significantly degrade network performance. In this paper, we point out some specific malfunctions in the original X-MAC design and propose alternatives to reduce their impact. We present an energy-efficient, traffic-adaptive MAC protocol called QX-MAC that addresses the foreseen shortcomings in X-MAC. QX-MAC integrates Q-learning and the more bit scheme to enable the nodes to adapt the active period and duty cycle in accordance with incoming traffic. Finally, the performance of QX-MAC is thoroughly analyzed compared with other reference protocols to validate its efficacy. Our QX-MAC simulation results demonstrate substantial improvements in overall network performance in terms of energy consumption, packet loss, delay, or throughput.
Afroz, F, Braun, R & Chaczko, Z 2022, 'XX-MAC and EX-MAC: Two Variants of X-MAC Protocol for Low Power Wireless Sensor Networks', Ad Hoc and Sensor Wireless Networks, vol. 51, no. 4, pp. 285-314.
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The strobed preamble approach introduced in the X-MAC protocol minimises long preamble duration, overhearing, and per-hop latency in conventional wireless sensor networks (WSNs). However, it incurs high per-packet overhead and extra delay under high traffic scenarios as it operates only in the unsynchronised state. In this paper, we model a variant of X-MAC, namely XX-MAC, which employs an adaptive dutycycling algorithm to address this issue in low data rate WSNs with short, fixed inter-packet arrival time. Furthermore, we identify the shortcoming of XX-MAC as well as propose a request-based MAC protocol, namely EX-MAC, targeting WSNs in dynamic traffic scenarios. Simulations show that at optimum slot duration, XX-MAC reduces the per-packet delay by 13.53% and 48.86% than the delay experienced by X-MAC and B-MAC, respectively. XX-MAC, on average, can deliver 92.5% of packets to the receiver, whereas X-MAC and B-MAC respectively support 91.66% and 82.91% packet delivery. XX-MAC also reduces the energy consumption per received packet by 2.61% than X-MAC, and by 65.31% than the B-MAC protocol. Experimental results also demonstrate that under variable traffic conditions, EX-MAC offers the lowest packet loss (8.55%), whilst XX-MAC and X-MAC experience 13.1% and 18.3% packet loss, respectively. EX-MAC decreases per-packet network energy consumption (3.056mJ/packet) compared with XX-MAC (3.107mJ/ packet) and X-MAC (3.424mJ/packet). Furthermore, EX-MAC minimises the mean delay per received packet by 5.758% and 10.457% (approximately) than that of XX-MAC and X-MAC, respectively.
Afroz, S, Nguyen, QD, Zhang, Y, Kim, T & Castel, A 2022, 'Evaluation of cracking potential parameters for low to high grade concrete with fly ash or slag', Construction and Building Materials, vol. 350, pp. 128891-128891.
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Cracking induced by early age restrained shrinkage can lead to durability issues in concrete structures and premature deterioration that reduce service life of reinforced concrete members. Despite the increasing demand for using Supplementary Cementitious Materials (SCMs) in concrete, the effect of binders and strength grades on early-age restrained concrete cracking has not been fully understood. This study investigated 21 concrete mixes with 30 % fly ash, 40 % and 60 % slag having compressive strengths ranging between 25 MPa and 100 MPa using a restrained ring test. Their cracking potential was evaluated by considering three different methods. The results showed that the time to cracking was short for high grade concretes and concretes with slag. Fly ash delayed the cracking for all strength grades. Though all cracking estimators used in this study did not outstandingly predict the potential of early-age cracking in concrete, the stress rate method performed the best. The cracking strain method and the R ratio method were significantly influenced by the supplementary cementitious materials. The most dominant factor governing the cracking potential of concrete was the rate of stress or strain development for all strength grade and binder types.
Afroz, S, Zhang, Y, Nguyen, QD, Kim, T & Castel, A 2022, 'Effect of limestone in General Purpose cement on autogenous shrinkage of high strength GGBFS concrete and pastes', Construction and Building Materials, vol. 327, pp. 126949-126949.
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This work investigates the autogenous shrinkage of pastes and concretes prepared using General Purpose cement and GGBFS (40% and 60%) up to 100 days. To determine the underlying factors influencing autogenous shrinkage, hydration progression and evolution of microstructure were investigated. Results showed that autogenous shrinkage of GGBFS blends continuously increased after 28 days (until 100 days) whereas the control samples reached a plateau after about 28 days. Late reaction between limestone from the General Purpose cement and alumina from GGBFS progressed untill 90 days forming a high amount of monocarboaluminates. General Purpose cement blends with high GGBFS content can behave as ternary blends and not as binary blends due to the small amount of limestone usually added to General Purpose cements. These long term reactions lead to a significant refinement of the pore structure which is responsible of the late autogenous shrinkage in GGBFS blends.
Afsari, M, Ghorbani, AH, Asghari, M, Shon, HK & Tijing, LD 2022, 'Computational fluid dynamics simulation study of hypersaline water desalination via membrane distillation: Effect of membrane characteristics and operational parameters', Chemosphere, vol. 305, pp. 135294-135294.
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Afzal, MU, Esselle, KP & Koli, MNY 2022, 'A Beam-Steering Solution With Highly Transmitting Hybrid Metasurfaces and Circularly Polarized High-Gain Radial-Line Slot Array Antennas', IEEE Transactions on Antennas and Propagation, vol. 70, no. 1, pp. 365-377.
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Afzali Naniz, M, Askari, M, Zolfagharian, A, Afzali Naniz, M & Bodaghi, M 2022, '4D printing: a cutting-edge platform for biomedical applications', Biomedical Materials, vol. 17, no. 6, pp. 062001-062001.
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Abstract Nature’s materials have evolved over time to be able to respond to environmental stimuli by generating complex structures that can change their functions in response to distance, time, and direction of stimuli. A number of technical efforts are currently being made to improve printing resolution, shape fidelity, and printing speed to mimic the structural design of natural materials with three-dimensional printing. Unfortunately, this technology is limited by the fact that printed objects are static and cannot be reshaped dynamically in response to stimuli. In recent years, several smart materials have been developed that can undergo dynamic morphing in response to a stimulus, thus resolving this issue. Four-dimensional (4D) printing refers to a manufacturing process involving additive manufacturing, smart materials, and specific geometries. It has become an essential technology for biomedical engineering and has the potential to create a wide range of useful biomedical products. This paper will discuss the concept of 4D bioprinting and the recent developments in smart materials, which can be actuated by different stimuli and be exploited to develop biomimetic materials and structures, with significant implications for pharmaceutics and biomedical research, as well as prospects for the future.
Agarwal, A, Leslie, WD, Nguyen, TV, Morin, SN, Lix, LM & Eisman, JA 2022, 'Performance of the Garvan Fracture Risk Calculator in Individuals with Diabetes: A Registry-Based Cohort Study', Calcified Tissue International, vol. 110, no. 6, pp. 658-665.
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Diabetes increases fracture and falls risks. We evaluated the performance of the Garvan fracture risk calculator (FRC) in individuals with versus without diabetes. Using the population-based Manitoba bone mineral density (BMD) registry, we identified individuals aged 50-95 years undergoing baseline BMD assessment from 1 September 2012, onwards with diabetes and self-reported falls in the prior 12 months. Five-year Garvan FRC predictions were generated from clinical risk factors, with and without femoral neck BMD. We identified non-traumatic osteoporotic fractures (OF) and hip fractures (HF) from population-based data to 31 March 2018. Fracture risk stratification was assessed from area under the receiver operating characteristic curves (AUROC). Cox regression analysis was performed to examine the effect of diabetes on fractures, adjusted for Garvan FRC predictions. The study population consisted of 2618 women with and 14,064 without diabetes, and 636 and 2201 men with and without the same, respectively. The Garvan FRC provided significant OF and HF risk stratification in women with diabetes, similar to those without diabetes. Analyses of OF in men were limited by smaller numbers; no significant difference was evident by diabetes status. Cox regression showed that OF risk was 23% greater in women with diabetes adjusted for Garvan FRC including BMD (hazard ratio [HR] 1.23, 95% confidence interval [CI] 1.01-1.49), suggesting it slightly underestimated risk; a non-significant increase in diabetes-related HF risk was noted (HR 1.37, 95% CI 0.88-2.15). Garvan FRC shows similar fracture risk stratification in individuals with versus without diabetes, but may underestimate this risk.
Agarwal, A, Leslie, WD, Nguyen, TV, Morin, SN, Lix, LM & Eisman, JA 2022, 'Predictive performance of the Garvan Fracture Risk Calculator: a registry-based cohort study', Osteoporosis International, vol. 33, no. 3, pp. 541-548.
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UNLABELLED: The G arvan Fracture Risk Calculator predicts risk of osteoporotic fractures. We evaluated its predictive performance in 16,682 women and 2839 men from Manitoba, Canada, and found significant risk stratification, with a strong gradient across scores. The tool outperformed clinical risk factors and bone mineral density for fracture risk stratification. INTRODUCTION: The optimal model for fracture risk estimation to guide treatment decision-making remains controversial. Our objective was to evaluate the predictive performance of the Garvan Fracture Risk Calculator (FRC) in a large clinical registry from Manitoba, Canada. METHODS: Using the population-based Manitoba Bone Mineral Density (BMD) registry, we identified women and men aged 50-95 years undergoing baseline BMD assessment from September 1, 2012, onwards. Five-year Garvan FRC predictions were generated from clinical risk factors (CRFs) with and without femoral neck BMD. We identified incident non-traumatic osteoporotic fractures (OFs) and hip fractures (HFs) from population-based healthcare data sources to March 31, 2018. Fracture risk was assessed from area under the receiver operating characteristic curve (AUROC). Cox regression analysis and calibration ratios (5-year observed/predicted) were assessed for risk quintiles. All analyses were sex stratified. RESULTS: We included 16,682 women (mean age 66.6 + / - SD 8.7 years) and 2839 men (mean age 68.7 + / - SD 10.2 years). During a mean observation time of 2.6 years, incident OFs were identified in 681 women and 140 men and HFs in 199 women and 22 men. AUROC showed significant fracture risk stratification with the Garvan FRC. Tool predictions without BMD were better than from age or decreasing weight, and the tool with BMD performed better than BMD alone. Garvan FRC with BMD performed better than without BMD, especially for HF prediction (AUROC 0.86 in women, 0.82 in men). There was a strong gradient of increasing risk across Garvan FRC...
Agrawal, D, Minocha, S, Namasudra, S & Gandomi, AH 2022, 'A robust drug recall supply chain management system using hyperledger blockchain ecosystem', Computers in Biology and Medicine, vol. 140, pp. 105100-105100.
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Drug recall is a critical issue for manufacturing companies, as a manufacturer might face criticism and severe business downfall due to a defective drug. A defective drug is a highly detrimental issue, as it can cost several lives. Therefore, recalling the drug becomes one of the most sensitive issues in the pharmaceutical industry. This paper presents a blockchain-enabled network that allows manufacturers to effectively monitor a drug while in the supply chain with improved security and transparency throughout the process. The study also tries to minimize the cost and time sustained by the manufacturing company to transfer the drug to the end-user by proposing forward and backward supply chain mathematical models. Specifically, the forward chain model supports drug delivery from the manufacturer to the end-user in less time with a reliable transport mode. The backward supply chain model explicitly focuses on reducing the extra time and cost incurred to the manufacturer in pursuit of recalling the defective drug. Moreover, a real-time implementation of the proposed blockchain-enabled supply chain management system using the Hyperledger Composer is done to demonstrate the transparency of the process.
Ahmad, FB, Kalam, MA, Zhang, Z & Masjuki, HH 2022, 'Sustainable production of furan-based oxygenated fuel additives from pentose-rich biomass residues', Energy Conversion and Management: X, vol. 14, pp. 100222-100222.
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Ahmadi, H, Zakertabrizi, M, Hosseini, E, Cha-Umpong, W, Abdollahzadeh, M, Korayem, AH, Chen, V, Shon, HK, Asadnia, M & Razmjou, A 2022, 'Heterogeneous asymmetric passable cavities within graphene oxide nanochannels for highly efficient lithium sieving', Desalination, vol. 538, pp. 115888-115888.
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Lithium is a critical energy element that plays a pivotal role in transitions to sustainable energy. Numerous two-dimensional (2D) membranes have been developed to extract Li+ from different resources. However, their Li+ extraction efficacy is not high enough to meet industrial requirements. Here, we introduce an approach that boosts Li+ selectivity of 2D membranes by inducing asymmetricity in the morphology and chemistry of their nanochannels. Our approach provides an opportunity to manipulate cation hydration shells via a sudden change in the nanochannel size. Then, the addition of nucleophilic traps in the nanochannel intersections results in high Li+ selectivity. Our design leads to a new ion transport mechanism named “Energy Surge Baffle” (ESB) that substantially enriches Li+ in the feed by increasing the monovalent/lithium-ion selectivity up to six times that of other graphene oxide-based membranes. Our approach can be extended to other 2D materials, creating a platform for designing advanced membranes.
Ahmadianfar, I, Heidari, AA, Noshadian, S, Chen, H & Gandomi, AH 2022, 'INFO: An efficient optimization algorithm based on weighted mean of vectors', Expert Systems with Applications, vol. 195, pp. 116516-116516.
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This study presents the analysis and principle of an innovative optimizer named weIghted meaN oF vectOrs (INFO) to optimize different problems. INFO is a modified weight mean method, whereby the weighted mean idea is employed for a solid structure and updating the vectors’ position using three core procedures: updating rule, vector combining, and a local search. The updating rule stage is based on a mean-based law and convergence acceleration to generate new vectors. The vector combining stage creates a combination of obtained vectors with the updating rule to achieve a promising solution. The updating rule and vector combining steps were improved in INFO to increase the exploration and exploitation capacities. Moreover, the local search stage helps this algorithm escape low-accuracy solutions and improve exploitation and convergence. The performance of INFO was evaluated in 48 mathematical test functions, and five constrained engineering test cases including optimal design of 10-reservoir system and 4-reservoir system. According to the literature, the results demonstrate that INFO outperforms other basic and advanced methods in terms of exploration and exploitation. In the case of engineering problems, the results indicate that the INFO can converge to 0.99% of the global optimum solution. Hence, the INFO algorithm is a promising tool for optimal designs in optimization problems, which stems from the considerable efficiency of this algorithm for optimizing constrained cases. The source codes of INFO algorithm are publicly available at https://imanahmadianfar.com. and https://aliasgharheidari.com/INFO.html.
Ahmed, F, Afzal, MU, Hayat, T, Esselle, KP & Thalakotuna, DN 2022, 'A Near-Field Meta-Steering Antenna System With Fully Metallic Metasurfaces', IEEE Transactions on Antennas and Propagation, vol. 70, no. 11, pp. 10062-10075.
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Ahmed, F, Afzal, MU, Hayat, T, Esselle, KP & Thalakotuna, DN 2022, 'Self-Sustained Rigid Fully Metallic Metasurfaces to Enhance Gain of Shortened Horn Antennas', IEEE Access, vol. 10, pp. 79644-79654.
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Ahmed, N, Hoque, MA-A, Arabameri, A, Pal, SC, Chakrabortty, R & Jui, J 2022, 'Flood susceptibility mapping in Brahmaputra floodplain of Bangladesh using deep boost, deep learning neural network, and artificial neural network', Geocarto International, vol. 37, no. 25, pp. 8770-8791.
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Ahmed, N, Hoque, MA-A, Howlader, N & Pradhan, B 2022, 'Flood risk assessment: role of mitigation capacity in spatial flood risk mapping', Geocarto International, vol. 37, no. 25, pp. 8394-8416.
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Ahmed, SF, Kumar, PS, Rozbu, MR, Chowdhury, AT, Nuzhat, S, Rafa, N, Mahlia, TMI, Ong, HC & Mofijur, M 2022, 'Heavy metal toxicity, sources, and remediation techniques for contaminated water and soil', Environmental Technology & Innovation, vol. 25, pp. 102114-102114.
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Ahmed, SF, Mehejabin, F, Momtahin, A, Tasannum, N, Faria, NT, Mofijur, M, Hoang, AT, Vo, D-VN & Mahlia, TMI 2022, 'Strategies to improve membrane performance in wastewater treatment', Chemosphere, vol. 306, pp. 135527-135527.
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Membrane technology has rapidly gained popularity in wastewater treatment due to its cost-effectiveness, environmentally friendly tools, and elevated productivity. Although membrane performance in wastewater treatment has been reviewed in several past studies, the key techniques for improving membrane performance, as well as their challenges, and solutions associated with the membrane process, were not sufficiently highlighted in those studies. Also, very few studies have addressed hybrid techniques to improve membrane performance. The present review aims to fill those gaps and achieve public health benefits through safe water processing. Despite its higher cost, membrane performance can result in a 36% reduction in flux degradation. The issue with fouling has been identified as one of the key challenges of membrane technology. Chemical cleaning is quite effective in removing accumulated foulant. Fouling mitigation techniques have also been shown to have a positive effect on membrane photobioreactors that handle wastewater effluent, resulting in a 50% and 60% reduction in fouling rates for backwash and nitrogen bubble scouring techniques. Membrane hybrid approaches such as hybrid forward-reverse osmosis show promise in removing high concentrations of phosphorus, ammonium, and salt from wastewater. The incorporation of the forward osmosis process can reject 99% of phosphorus and 97% of ammonium, and the reverse osmosis approach can achieve a 99% salt rejection rate. The control strategies for membrane fouling have not been successfully optimized yet and more research is needed to achieve a realistic, long-term direct membrane filtering operation.
Ahmed, SF, Mofijur, M, Chowdhury, SN, Nahrin, M, Rafa, N, Chowdhury, AT, Nuzhat, S & Ong, HC 2022, 'Pathways of lignocellulosic biomass deconstruction for biofuel and value-added products production', Fuel, vol. 318, pp. 123618-123618.
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As the world attempts to transition from fossil fuels, lignocellulosic biomass (LCB) serves as a promising alternative due to its high abundance. Hydrolysing LCB can generate various bioproducts, such as biofuels and value-added chemicals. However, the presence of lignin inhibits the solubilization of LCBs, presenting a major techno-economic challenge in the biorefinery concept. Therefore, this paper addresses the gaps left by most of the recent review works that fail to comprehensively review different pretreatment methods and the full scope of applications of LCBs, and do not incorporate techno-economic considerations of the technologies, the latter being the greatest bottleneck in the commercialization of the processes. The literature review revealed that while many of the physical and chemical pretreatment methods exhibit great effectiveness, they have a huge dependence on energy, chemicals, water, and/or specialized equipment, and produce harmful waste and inhibitory compounds. The pretreatment of lignocellulosic biomass can account for 40% of total production costs. Biological pretreatment can address these challenges but is limited by long incubation times. For instance, the bacterial pretreatment can noticeably reduce sawdust cellulose, hemicelluloses, and lignin contents by 35.8%, 37.1%, and 46.2%, respectively. Recently, integrated/coupling (hybrid) methods, such as chemical-assisted liquid hot water/steam and microwave or ultrasound-assisted alkaline pretreatment, have been gaining popularity due to their potential to improve chemical yield, but at the expense of the high cost of operation. To make pretreatment processes more techno-economically feasible, there is a need for process integration and the standardization and optimization of process parameters.
Ahmed, SF, Mofijur, M, Islam, N, Parisa, TA, Rafa, N, Bokhari, A, Klemeš, JJ & Indra Mahlia, TM 2022, 'Insights into the development of microbial fuel cells for generating biohydrogen, bioelectricity, and treating wastewater', Energy, vol. 254, pp. 124163-124163.
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Bio-electrochemical systems, such as microbial fuel cells (MFCs), serve as greener alternatives to conventional fuel energy. Despite the burgeoning review works on MFCs, comprehensive discussions are lacking on MFC designs and applications. This review paper provides insights into MFC applications, substrates used in MFC and the various design, technological, and chemical factors affecting MFC performance. MFCs have demonstrated efficacy in wastewater treatment of at least 50% and up to 98%. MFCs have been reported to produce ∼30 W/m2 electricity and ∼1 m3/d of biohydrogen, depending on the design and feedstock. Electricity generation rates of up to 5.04 mW/m−2–3.6 mW/m−2, 75–513 mW/m−2, and 135.4 mW/m−2 have been found for SCMFCs, double chamber MFCs, and stacked MFCs with the highest being produced by the single/hybrid single-chamber type using microalgae. Hybrid MFCs may emerge as financially promising technologies worth investigating due to their low operational costs, integrating low-cost proton exchange membranes such as PVA-Nafion-borosilicate, and electrodes made of natural materials, carbon, metal, and ceramic. MFCs are mostly used in laboratories due to their low power output and the difficulties in assessing the economic feasibility of the technology. The MFCs can generate incomes of as much as $2,498.77 × 10−2/(W/m2) annually through wastewater treatment and energy generation alone. The field application of MFC technology is also narrow due to its microbiological, electrochemical, and technological limitations, exacerbated by the gap in knowledge between laboratory and commercial-scale applications. Further research into novel and economically feasible electrode and membrane materials, the improvement of electrogenicity of the microbes used, and the potential of hybrid MFCs will provide opportunities to launch MFCs from the laboratory to the commercial-scale as a bid to improve the global energy security in an eco-friendly way.
Ahmed, SF, Mofijur, M, Nahrin, M, Chowdhury, SN, Nuzhat, S, Alherek, M, Rafa, N, Ong, HC, Nghiem, LD & Mahlia, TMI 2022, 'Biohydrogen production from wastewater-based microalgae: Progresses and challenges', International Journal of Hydrogen Energy, vol. 47, no. 88, pp. 37321-37342.
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Microalgae originating from wastewater has been exhibiting particularly promising results in terms of biohydrogen production and wastewater treatment. This paper aims to review the factors affecting production, pretreatment techniques to improve synthesis, advanced technologies utilized for enhancing biohydrogen production, and techno-economic feasibility evaluation of the processes at a commercial scale. Microalgae possess metabolic components to synthesize biohydrogen using photobiological and fermentative processes but must undergo pretreatment for efficient biohydrogen production. The efficiency of these processes is influenced by factors such as the microalgae species, light intensity, cell density, pH, temperature, substrates, and the type of bioreactors. Moreover, many limitations, such as oxygen sensitivity, altered thylakoid constitution, low photon conversion efficiency, light capture disruption, and the evolution of harmful by-products hinder the sustainability of biohydrogen production processes. High operational and maintenance costs serve as the major bottleneck in the scaling up of the process as an industrial technology. Therefore, future research needs to be directed towards increasing optimization of the processes by reducing energy and resource demand, recycling metabolic wastes and process components, genetically engineered microalgae to adopt more efficient routes, and conducting pilot studies for commercialization.
Ahmed, SF, Mofijur, M, Parisa, TA, Islam, N, Kusumo, F, Inayat, A, Le, VG, Badruddin, IA, Khan, TMY & Ong, HC 2022, 'Progress and challenges of contaminate removal from wastewater using microalgae biomass', Chemosphere, vol. 286, no. Pt 1, pp. 131656-131656.
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The utilization of microalgae in treating wastewater has been an emerging topic focussed on finding an economically sustainable and environmentally friendly approach to treating wastewater. Over the last several years, different types of con microalgae and bacteria consortia have been experimented with to explore their potential in effectively treating wastewater from different sources. The basic features considered while determining efficiency is their capacity to remove nutrients including nitrogen (N) and phosphorus (P) and heavy metals like arsenic (As), lead (Pb), and copper (Cu). This paper reviews the efficiency of microalgae as an approach to treating wastewater from different sources and compares conventional and microalgae-based treatment systems. The paper also discusses the characteristics of wastewater, conventional methods of wastewater treatment that have been used so far, and the technological mechanisms for removing nutrients and heavy metals from contaminated water. Microalgae can successfully eliminate the suspended nutrients and have been reported to successfully remove N, P, and heavy metals by up to 99.6 %, 100 %, and 13%-100 % from different types of wastewater. However, although a microalgae-based wastewater treatment system offers some benefits, it also presents some challenges as outlined in the last section of this paper. Performance in eliminating nutrients from wastewater is affected by different parameters such as temperature, biomass productivity, osmotic ability, pH, O2 concentration. Therefore, the conducting of pilot-scale studies and exploration of the complexities of contaminants under complex environmental conditions is recommended.
Ahmed, SF, Mofijur, M, Rafa, N, Chowdhury, AT, Chowdhury, S, Nahrin, M, Islam, ABMS & Ong, HC 2022, 'Green approaches in synthesising nanomaterials for environmental nanobioremediation: Technological advancements, applications, benefits and challenges', Environmental Research, vol. 204, no. Pt A, pp. 111967-111967.
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Green synthesis approaches of nanomaterials (NMs) have received considerable attention in recent years as it addresses the sustainability issues posed by conventional synthesis methods. However, recent works of literature do not present the complete picture of biogenic NMs. This paper addresses the previous gaps by providing insights into the stability and toxicity of NMs, critically reviewing the various biological agents and solvents required for synthesis, sheds light on the factors that affect biosynthesis, and outlines the applications of NMs across various sectors. Despite the advantages of green synthesis, current methods face challenges with safe and appropriate solvent selection, process parameters that affect the synthesis process, nanomaterial cytotoxicity, bulk production and NM morphology control, tedious maintenance, and knowledge deficiencies. Consequently, the green synthesis of NMs is largely trapped in the laboratory phase. Nevertheless, the environmental friendliness, biocompatibility, and sensitivities of the resulting NMs have wider applications in biomedical science, environmental remediation, and consumer industries. To the scale-up application of biogenic NMs, future research should be focused on understanding the mechanisms of the synthesis processes, identifying more biological and chemical agents that can be used in synthesis, and developing the practicality of green synthesis at the industrial scale, and optimizing the factors affecting the synthesis process.
Akbal, E, Barua, PD, Dogan, S, Tuncer, T & Acharya, UR 2022, 'DesPatNet25: Data encryption standard cipher model for accurate automated construction site monitoring with sound signals', Expert Systems with Applications, vol. 193, pp. 116447-116447.
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Akbal, E, Barua, PD, Tuncer, T, Dogan, S & Acharya, UR 2022, 'Development of novel automated language classification model using pyramid pattern technique with speech signals', Neural Computing and Applications, vol. 34, no. 23, pp. 21319-21333.
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Akter, N, Fletcher, J, Perry, S, Simunovic, MP, Briggs, N & Roy, M 2022, 'Glaucoma diagnosis using multi-feature analysis and a deep learning technique', Scientific Reports, vol. 12, no. 1.
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AbstractIn this study, we aimed to facilitate the current diagnostic assessment of glaucoma by analyzing multiple features and introducing a new cross-sectional optic nerve head (ONH) feature from optical coherence tomography (OCT) images. The data (n = 100 for both glaucoma and control) were collected based on structural, functional, demographic and risk factors. The features were statistically analyzed, and the most significant four features were used to train machine learning (ML) algorithms. Two ML algorithms: deep learning (DL) and logistic regression (LR) were compared in terms of the classification accuracy for automated glaucoma detection. The performance of the ML models was evaluated on unseen test data, n = 55. An image segmentation pilot study was then performed on cross-sectional OCT scans. The ONH cup area was extracted, analyzed, and a new DL model was trained for glaucoma prediction. The DL model was estimated using five-fold cross-validation and compared with two pre-trained models. The DL model trained from the optimal features achieved significantly higher diagnostic performance (area under the receiver operating characteristic curve (AUC) 0.98 and accuracy of 97% on validation data and 96% on test data) compared to previous studies for automated glaucoma detection. The second DL model used in the pilot study also showed promising outcomes (AUC 0.99 and accuracy of 98.6%) to detect glaucoma compared to two pre-trained models. In combination, the result of the two studies strongly suggests the four features and the cross-sectional ONH cup area trained using deep learning have a great potential for use as an initial screening tool for glaucoma which will assist clinicians in making a precise decision.
Akter, S, Zakia, MA, Mofijur, M, Ahmed, SF, Vo, D-VN, Khandaker, G & Mahlia, TMI 2022, 'SARS-CoV-2 variants and environmental effects of lockdowns, masks and vaccination: a review', Environmental Chemistry Letters, vol. 20, no. 1, pp. 141-152.
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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously evolving and four variants of concern have been identified so far, including Alpha, Beta, Gamma and Delta variants. Here we review the indirect effect of preventive measures such as the implementation of lockdowns, mandatory face masks, and vaccination programs, to control the spread of the different variants of this infectious virus on the environment. We found that all these measures have a considerable environmental impact, notably on waste generation and air pollution. Waste generation is increased due to the implementation of all these preventive measures. While lockdowns decrease air pollution, unsustainable management of face mask waste and temperature-controlled supply chains of vaccination potentially increases air pollution.
AL Hunaity, SA, Far, H & Saleh, A 2022, 'Vibration behaviour of cold-formed steel and particleboard composite flooring systems', Steel and Composite Structures, vol. 43, no. 3, pp. 403-417.
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Recently, there has been an increasing demand for buildings that allow rapid assembly of construction elements, have ample open space areas and are flexible in their final intended use. Accordingly, researchers have developed new competitive structures in terms of cost and efficiency, such as cold-formed steel and timber composite floors, to satisfy these requirements. Cold-formed steel and timber composite floors are light floors with relatively high stiffness, which allow for longer spans. As a result, they inherently have lower fundamental natural frequency and lower damping. Therefore, they are likely to undergo unwanted vibrations under the action of human activities such as walking. It is also quite expensive and complex to implement vibration control measures on problematic floors. In this study, a finite element model of a composite floor reported in the literature was developed and validated against four-point bending test results. The validated FE model was then utilised to examine the vibration behaviour of the investigated composite floor. Predictions obtained from the numerical model were compared against predictions from analytical formulas reported in the literature. Finally, the influence of various parameters on the vibration behaviour of the composite floor was studied and discussed.
Alam, M, Lu, DD-C & Siwakoti, YP 2022, 'Time-multiplexed hysteretic control for single-inductor dual-input single-output DC-DC power converter.', Int. J. Circuit Theory Appl., vol. 50, no. 4, pp. 1235-1249.
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Single-inductor multi-input single-output (SI-MISO) switching DC-DC power converter architecture is a cost effective solution to applications where multiple input sources are required to be managed with a limited space and cost. This paper presents a new time-multiplexed hysteretic control (TMHC) scheme for SI-DISO topology to decouple the power sharing among two input sources. Unlike previously reported solutions with discontinuous conduction or pseudo-continuous conduction operation of the inductor, this paper focuses on how to keep the inductor current in a continuous conduction mode (CCM) and proposed a control scheme with considerably lower ripple current with fast transition time upon switching and higher efficiency. The mathematical proof using the expressions of inductor ripple current, comparison between efficiency and transition time from one level to other, is derived. Additionally, a low-cost analog circuitry has been implemented to incorporate the proposed control scheme. Experimental results from the hardware prototype are given to verify the proposed control scheme.
Al‐Canaan, A, Chakib, H, Uzair, M, Toor, S, Al‐Khatib, A & Sultan, M 2022, 'BCI‐control and monitoring system for smart home automation using wavelet classifiers', IET Signal Processing, vol. 16, no. 2, pp. 141-156.
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Alderighi, T, Malomo, L, Auzinger, T, Bickel, B, Cignoni, P & Pietroni, N 2022, 'State of the Art in Computational Mould Design.', Comput. Graph. Forum, vol. 41, no. 6, pp. 435-452.
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AbstractMoulding refers to a set of manufacturing techniques in which a mould, usually a cavity or a solid frame, is used to shape a liquid or pliable material into an object of the desired shape. The popularity of moulding comes from its effectiveness, scalability and versatility in terms of employed materials. Its relevance as a fabrication process is demonstrated by the extensive literature covering different aspects related to mould design, from material flow simulation to the automation of mould geometry design. In this state‐of‐the‐art report, we provide an extensive review of the automatic methods for the design of moulds, focusing on contributions from a geometric perspective. We classify existing mould design methods based on their computational approach and the nature of their target moulding process. We summarize the relationships between computational approaches and moulding techniques, highlighting their strengths and limitations. Finally, we discuss potential future research directions.
Algayyim, S, Yusaf, T, Hamza, N, Wandel, A, Fattah, I, Laimon, M & Rahman, S 2022, 'Sugarcane Biomass as a Source of Biofuel for Internal Combustion Engines (Ethanol and Acetone-Butanol-Ethanol): A Review of Economic Challenges', Energies, vol. 15, no. 22, pp. 8644-8644.
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The objective of this review is to provide a deep overview of liquid biofuels produced from sugarcane bagasse and to address the economic challenges of an ethanol and acetone-butanol-ethanol blend in commercial processes. The chemistry of sugarcane bagasse is presented. Pretreatment technologies such as physical, chemical pretreatment, biological, and combination pretreatments used in the fermentation process are also provided and summarised. Different types of anaerobic bacteria Clostridia (yeast) are discussed to identify the ingredient best suited for sugarcane bagasse, which can assist the industry in commercializing ethanol and acetone-butanol-ethanol biofuel from biomass sugarcane. The use of an acetone-butanol-ethanol mixture and ethanol blend in internal combustion engines is also discussed. The literature then supports the proposal of the best operating conditions for fermentation to enhance ethanol and acetone-butanol-ethanol plant efficiency in the sugar waste industry and its application in internal combustion engines.
Alharbi, SK, Ansari, AJ, Nghiem, LD & Price, WE 2022, 'New transformation products from ozonation and photolysis of diclofenac in the aqueous phase', Process Safety and Environmental Protection, vol. 157, pp. 106-114.
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Alhasnawi, BN, Jasim, BH, Mansoor, R, Alhasnawi, AN, Rahman, ZSA, Haes Alhelou, H, Guerrero, JM, Dakhil, AM & Siano, P 2022, 'A new Internet of Things based optimization scheme of residential demand side management system', IET Renewable Power Generation, vol. 16, no. 10, pp. 1992-2006.
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AbstractThe steady increase in the energy demand and the growing carbon footprint has forced electricity‐based utilities to shift from their use of non‐renewable energy sources to renewable energy sources. Furthermore, there has been an increase in the integration of renewable energy sources in the electric grid. Hence, one needs to manage the energy consumption needs of the consumers, more effectively. Consumers can connect all the devices and houses to the internet by using Internet of Things (IoT) technology. In this study, the researchers have developed and proposed a novel 2‐stage hybrid method that schedules the power consumption of the houses possessing a distributed energy generation and storage system. Stage 1 modeled the non‐identical Home Energy Management Systems (HEMSs) that can contain the DGS like WT and PV. The HEMS organise the controllable appliances after taking into consideration the user preferences, electricity prices and the amount of energy produced /stored. The set of optimal consumption schedules for every HEMS was estimated using a BPSO and BSA. On the other hand, Stage 2 includes a Multi‐Agent‐System (MAS) based on the IoT. The system comprises two portions: software and hardware. The hardware comprises the Base Station Unit (BSU) and many Terminal Units (TUs).
Alhelou, HH & Cuffe, P 2022, 'A Dynamic-State-Estimator-Based Tolerance Control Method Against Cyberattack and Erroneous Measured Data for Power Systems', IEEE Transactions on Industrial Informatics, vol. 18, no. 7, pp. 4990-4999.
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Alhendi, AA, Al-Sumaiti, AS, Elmay, FK, Wescaot, J, Kavousi-Fard, A, Heydarian-Forushani, E & Alhelou, HH 2022, 'Artificial intelligence for water–energy nexus demand forecasting: a review', International Journal of Low-Carbon Technologies, vol. 17, pp. 730-744.
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Abstract Demand forecasting is an essential stage in the plan and management of resources for water and electrical utilities. With the emerging of the concept of water–energy nexus and the dependence of both resources on each other, intelligent approaches are needed for such resources’ prediction in smart communities. Over the past few decades, extensive research has been devoted to develop or improve forecasting techniques to accurately estimate the future demand. The purpose of this paper is to review the most important methods in the demand forecasting of both water and energy, focusing mainly on the most recent advancements and future possible trends, hence providing a guide and insight for future research in the field. With the recent developments in artificial intelligence, it has been observed that most research work in this area highlight the artificial intelligence–based models as promising approaches for short-term demand forecasting in terms of performance evaluation or improvement in accuracy. Finally, all metrics used by researchers to assess the water/energy demand forecast are gathered and compared to provide a solid ground for the future works.
Ali, O, Shrestha, A, Ghasemaghaei, M & Beydoun, G 2022, 'Assessment of Complexity in Cloud Computing Adoption: a Case Study of Local Governments in Australia.', Inf. Syst. Frontiers, vol. 24, no. 2, pp. 595-617.
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This research paper assesses complexity in cloud computing adoption, using the context of the local government sector in Australia. The research utilized both cloud computing adoption literature and an Information Systems Complexity Framework to propose a complexity assessment model for cloud computing adoption. A mixed method approach was used in this research. Firstly, we conducted 21 indepth interviews with IT managers in the local governments in Australia to obtain their insights into the complexity of cloud computing adoption. Secondly, a quantitative method is used in which 480 IT staff from 47 local governments responded to an online survey to validate the proposed assessment model. The findings indicate that structural complexity of an organization (i.e., knowledge management), structural complexity of technology (i.e., technology interoperability, and data processing capability), dynamic complexity of an organization (i.e., business operations), and dynamic complexity of technology (i.e., systems integration, IT infrastructure update, and customization resources) are critical complexity aspects to be considered during cloud computing adoption. These findings provide important implications for both researchers and managers that are trying to understand the complexities involved in cloud computing adoption.
Ali, SMN, Hossain, MJ, Wang, D, Mahmud, MAP, Sharma, V, Kashif, M & Kouzani, AZ 2022, 'Thermally degraded speed estimation of traction machine drive in electric vehicle', IET Electric Power Applications, vol. 16, no. 12, pp. 1464-1475.
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AbstractThe speed of an induction machine drive (IMD) in the electrified powertrain of an electric vehicle (EV) suffers from thermal degradation caused by EV loading, driving cycle schedules, EV operating conditions, traffic state and temperature. It is necessary to estimate this thermal degradation in order to design appropriate control methodologies to address this significant issue that directly affects the EV performance. This study proposes a robust linear parameter varying (LPV) observer to estimate this degradation in IMD as well as EV speed under various thermal and loading conditions in steady state and during large transients. The stability and robustness of LPV methodology is ensured by optimal gains of control and linear matrix inequalities using convex optimisation techniques. The weighting functions in LPV design are optimised by genetic algorithms. The proposed observer performance is compared with that of conventional sensorless field‐oriented control and sliding mode observer. An improved speed performance during EV operation is also presented to validate the robustness of the proposed LPV observer against New European Driving Cycle. The performance analysis is conducted through NI myRIO 1900 controller‐based electrical drive set‐up.
Alibeikloo, M, Khabbaz, H & Fatahi, B 2022, 'Random Field Reliability Analysis for Time-Dependent Behaviour of Soft Soils Considering Spatial Variability of Elastic Visco-Plastic Parameters', Reliability Engineering & System Safety, vol. 219, pp. 108254-108254.
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Low embankment strategy is one of the effective methods to control time-dependent settlement of soft soils in infrastructure construction projects. Spatial variability of soil characteristics is a crucial factor, affecting the reliability of predictions of the long-term settlement in soft soils. In this paper, the time-dependent behaviour of soft soils is analysed incorporating spatial variability of elastic visco-plastic model parameters. Standard Gaussian random fields for correlated elastic-plastic model parameter (λ/V) and the initial creep coefficient (ψ0/V) are generated adopting Karhunen-Loeve expansion method based on the spectral decomposition of correlation function into eigenvalues and eigenfunctions. Then the generated random fields are incorporated in the proposed non-linear elastic visco-plastic (EVP) creep model. The impacts of spatially variable elastic visco-plastic model parameters (i.e. ψ0/V and λ/V) on long-term settlement predictions are evaluated through random field analysis (RF) with different spatial correlation lengths, and results are then compared to a single random variable (SRV) analysis. The probability of failure (PF) is calculated adopting RF and SRV analysis to determine the critical spatial correlation length, resulted in a maximum probability of failure. This study can be employed by design engineers to determine the critical spatial correlation length for safe design in the absence of adequate data to determine the exact spatial correlation length. The results also confirm that SRV analysis is not always the most conservative analysis in predicting time-dependent settlement of soft soils; and it is essential to perform RF analysis considering the spatial correlation length to reduce the risk and increase the reliability of the design to be applied in construction.
Aljaafari, A, Fattah, IMR, Jahirul, MI, Gu, Y, Mahlia, TMI, Islam, MA & Islam, MS 2022, 'Biodiesel Emissions: A State-of-the-Art Review on Health and Environmental Impacts', Energies, vol. 15, no. 18, pp. 6854-6854.
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Biodiesel is an alternative source of fuel for various automotive applications. Because of the increasing demand for energy and the scarcity of fossil fuels, researchers have turned their attention to biodiesel production from various sources in recent years. The production of biofuels from organic materials and waste components allows for the use of these waste resources in transporting resources and people over long distances. As a result, developing sustainable measures for this aspect of life is critical, as knowledge of appropriate fuel sources, corresponding emissions, and health impacts will benefit the environment and public health assessment, which is currently lacking in the literature. This study investigates biodiesel’s composition and production process, in addition to biodiesel emissions and their associated health effects. Based on the existing literature, a detailed analysis of biodiesel production from vegetable oil crops and emissions was undertaken. This study also considered vegetable oil sources, such as food crops, which can have a substantial impact on the environment if suitable growing procedures are not followed. Incorporating biodegradable fuels as renewable and sustainable solutions decreases pollution to the environment. The effects of biodiesel exhaust gas and particulates on human health were also examined. According to epidemiologic studies, those who have been exposed to diesel exhaust have a 1.2–1.5 times higher risk of developing lung cancer than those who have not. In addition, for every 24 parts per billion increase in NO2 concentration, symptom prevalence increases 2.7-fold. Research also suggests that plain biodiesel combustion emissions are more damaging than petroleum diesel fuel combustion emissions. A comprehensive analysis of biodiesel production, emissions, and health implications would advance this field’s understanding.
Aljafari, B, Vishnuram, P, Alagarsamy, S & Haes Alhelou, H 2022, 'Stability Analysis of the Dual Half-Bridge Series Resonant Inverter-Fed Induction Cooking Load Based on Floquet Theory', International Transactions on Electrical Energy Systems, vol. 2022, pp. 1-20.
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Induction heating (IH) applications aided power electronic control and becomes most attractive in recent years. Power control plays a vital role in any IH applications in which the stability of the converter is still a research hot spot due to variable frequency operation. In the proposed work, the stability of the converter is carried out based on the Floquet theory for dual-frequency half-bridge series inverter-fed multiload IH system. The dynamic behaviour of the converter is analyzed by developing a small-signal model of the converter. The system with a dynamic closed-loop controller results in poles and zeros lying outside the unit circle, which has poor closed-loop stability and up-down glitches in the frequency response plot. Hence, a proportional-integral (PI) compensator is used to mitigate the said issue, which results in a better response when compared with the open system and works satisfactorily. However, the system becomes unstable when the frequency is varied and the system also possesses a poor time domain response. Hence, the values of the controller gain are optimized with the Floquet theory, which is based on the Eigenvalues of the time domain model. For the optimized gains, the system possesses better stability for the variations in the switching frequency (20 kHz to 24 kHz), and also, the frequency response of the system is better with minimum time domain specifications. The performance of the system is simulated in MATLAB, and the response is noted for various switching frequencies in open loop, with a PI compensator, and with an optimized PI compensator. The output power is varied from 500 W to 18 W at load 1 and 250 W to 9 W at load 2. It is noted from the output response that the rise time is 0.0085 s, the peak time is 0.0001 s, and the peak overshoot is 0.1% with minimum steady-state error. Furthermore, the IH system is validated using a PIC16F877A microcontroller with the optimized PI controller, and the thermal image ...
Aljarajreh, H, Lu, DD-C, Siwakoti, YP & Tse, CK 2022, 'A Nonisolated Three-Port DC–DC Converter With Two Bidirectional Ports and Fewer Components', IEEE Transactions on Power Electronics, vol. 37, no. 7, pp. 8207-8216.
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This article presents a new nonisolated three-port converter with reduced component count compared with existing reported topologies. This is achieved by developing different power flow graphs and selecting the most appropriate converters arrangement. In addition, as compared to only one bidirectional port in most reported studies, this article considers two bidirectional ports to accommodate applications requiring bidirectional power flow, such as dc microgrid and regenerative braking. The proposed converter is able to work in seven different modes of operation, which cover all possible combinations of power flow among the three ports. Furthermore, seamless and smooth transition, maximum power point tracking, battery protection and output voltage regulation are achieved. Experimental waveforms, particularly for transient responses during mode transition, are reported to verify the proposed TPC.
Al-Juboori, RA, Bakly, S, Bowtell, L, Alkurdi, SSA & Altaee, A 2022, 'Innovative capacitive deionization-degaussing approach for improving adsorption/desorption for macadamia nutshell biochar', Journal of Water Process Engineering, vol. 47, pp. 102786-102786.
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Allahabadi, H, Amann, J, Balot, I, Beretta, A, Binkley, C, Bozenhard, J, Bruneault, F, Brusseau, J, Candemir, S, Cappellini, LA, Chakraborty, S, Cherciu, N, Cociancig, C, Coffee, M, Ek, I, Espinosa-Leal, L, Farina, D, Fieux-Castagnet, G, Frauenfelder, T, Gallucci, A, Giuliani, G, Golda, A, van Halem, I, Hildt, E, Holm, S, Kararigas, G, Krier, SA, Kuhne, U, Lizzi, F, Madai, VI, Markus, AF, Masis, S, Mathez, EW, Mureddu, F, Neri, E, Osika, W, Ozols, M, Panigutti, C, Parent, B, Pratesi, F, Moreno-Sanchez, PA, Sartor, G, Savardi, M, Signoroni, A, Sormunen, H-M, Spezzatti, A, Srivastava, A, Stephansen, AF, Theng, LB, Tithi, JJ, Tuominen, J, Umbrello, S, Vaccher, F, Vetter, D, Westerlund, M, Wurth, R & Zicari, RV 2022, 'Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients', IEEE Transactions on Technology and Society, vol. 3, no. 4, pp. 272-289.
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Almansor, EH, Hussain, FK & Hussain, OK 2022, 'Measuring chatbot quality of service to predict human-machine hand-over using a character deep learning model', International Journal of Web and Grid Services, vol. 18, no. 4, pp. 479-479.
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Almansor, EH, Hussain, OK & Hussain, FK 2022, 'Measuring chatbot quality of service to predict human-machine hand-over using a character deep learning model', International Journal of Web and Grid Services, vol. 18, no. 4, pp. 479-479.
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Almuntashiri, A, Hosseinzadeh, A, Badeti, U, Shon, H, Freguia, S, Dorji, U & Phuntsho, S 2022, 'Removal of pharmaceutical compounds from synthetic hydrolysed urine using granular activated carbon: Column study and predictive modelling', Journal of Water Process Engineering, vol. 45, pp. 102480-102480.
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Human urine contains high concentration of pharmaceuticals, a concern that must be addressed if used as a fertiliser. This study systematically evaluated granular activated carbon (GAC) adsorption in removing five most commonly found pharmaceuticals in the environment – naproxen (NAP), carbamazepine (CBZ), ibuprofen (IBP), acetaminophen (APAP) and metronidazole (MTZ) from hydrolysed urine. Fixed-bed column experiments were conducted to obtain breakthrough curves and assess GAC (1000 m2g−1) performance in the adsorption of pharmaceuticals at different adsorbent mass (4–12 g·L−1), flow rate (1.15–4.32 L·d−1) and adsorption/contact time at ambient room temperature and pH 9. The highest adsorption capacity was observed at a lower adsorbent mass (4 g·L−1) and lower flow rate (1.15 L·d−1) for all micropollutants. The breakthrough curves showed the highest GAC adsorption capacity for CBZ (56.1 mg·g−1) while MTZ (32.2 mg·g−1) with the lowest adsorption will be the design limiting for column adsorption application. Thomas and Yoon-Nelson models fitted well for predicting empirical breakthrough curves for fixed-bed GAC column adsorption. The artificial neural network (ANN) modelling was able to predict the removal effectiveness of over 99% except for APAP at 84.5%. The study showed that the potential application of GAC column adsorption for micropollutant removal is significant although this study was limited in the range of parameters studied.
Alotaibi, AA, Maerz, NH, Boyko, KJ, Youssef, AM & Pradhan, B 2022, 'Temporal LiDAR scanning in quantifying cumulative rockfall volume and hazard assessment: A case study at southwestern Saudi Arabia', The Egyptian Journal of Remote Sensing and Space Science, vol. 25, no. 2, pp. 435-443.
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Rockfalls and unstable slopes pose a serious threat to people and property along roads/highways in the southwestern mountainous regions of Saudi Arabia. In this study, the application of terrestrial light detection and ranging (LiDAR) technology was applied aiming to propose a strategy to analyze and accurately depict the detection of rockfall changes, calculation of rockfall volume, and evaluate rockfall hazards along the Habs Road, Jazan Region, Saudi Arabia. A series of temporal LiDAR scans were acquired at three selected sites. Our results show that these three sites have different degrees of hazard due to their geological differences. The mean volume loss of sites A1, A2, and A3 is 327.1, 424.4, and 3.7 L, respectively. Statistical analysis confirms the significance of the influence of site type on rockfall volume, with a probability value of < 0.0105. The rockfall volume and change detection values are then correlated with precipitation, which is a triggering factor. The study also reveals that the use of terrestrial LiDAR could reduce time and effort, increase accessibility, and produce effective solutions. LiDAR could be an indispensable tool for disaster risk assessment, response and recovery process.
Alsahafi, YA, Gay, V & Khwaji, AA 2022, 'Factors affecting the acceptance of integrated electronic personal health records in Saudi Arabia: The impact of e-health literacy', Health Information Management Journal, vol. 51, no. 2, pp. 98-109.
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Background: National implementation of electronic personal health record (ePHR) systems is of vital importance to governments worldwide because this type of technology promises to promote and enhance healthcare. Although there is widespread agreement as to the advantages of ePHRs, the level of awareness and acceptance of this technology among healthcare consumers has been low. Objective: The aim of this study was to identify the factors that can influence the acceptance and use of an integrated ePHR system in Saudi Arabia. Method: The unified theory of acceptance and use of technology model was extended in this study to include e-health literacy (e-HL) and tested using structural equation modelling. Data were collected via a questionnaire survey, resulting in 794 valid responses. Results: The proposed model explained 56% of the variance in behavioural intention (BI) to use the integrated ePHR system. Findings also highlighted the significance of performance expectancy, effort expectancy, social influence (SI) and e-HL as determinants of Saudi healthcare consumers’ intentions to accept and use the integrated ePHR system. Additionally, assessment of the research model moderators revealed that only gender had a moderating influence on the relationship between SI and BI. Finally, findings showed a low level of awareness among Saudi citizens about the national implementation of an integrated ePHR system, suggesting the need to promote a greater and more widespread awareness of the system and to demonstrate its usefulness. Conclusion: Findings from this study can assist governments, policymakers and developers of health information technologies and systems by identifyin...
Alsalibi, B, Mirjalili, S, Abualigah, L, yahya, RI & Gandomi, AH 2022, 'A Comprehensive Survey on the Recent Variants and Applications of Membrane-Inspired Evolutionary Algorithms', Archives of Computational Methods in Engineering, vol. 29, no. 5, pp. 3041-3057.
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In the last decade, the application of membrane-inspired evolutionary algorithms in real-life problems has attracted much attention due to their flexibility and parallelizability. Almost seven years have passed since the first membrane algorithms survey paper was published in 2014. Considering the importance and ongoing research on such algorithms and their applications in various disciplines, this paper presents a comprehensive review of the published literature and suggests future directions. This review aims to summarize and analyze membrane algorithms based on the used nature-inspired algorithm, membrane structure, membrane rules, and their merits and demerits. Furthermore, an extensive bibliography about their real-world applications is presented.
Alsenwi, M, Abolhasan, M & Lipman, J 2022, 'Intelligent and Reliable Millimeter Wave Communications for RIS-Aided Vehicular Networks', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 21582-21592.
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Utilizing the millimeter-wave (mmWave) frequency is a promising solution to meet fast-growing traffic demand over wireless networks. However, mmWave communications are sensitive to physical obstructions on signal propagation. In this paper, the reconfigurable intelligent surfaces (RISs) are investigated to overcome the limitations of mmWave communications. Particularly, an RIS is deployed to reflect the mmWave signals towards vehicular users who experience direct link blockages that may occur due to static or dynamic obstacles. To this end, a risk-averse optimization problem is designed to optimize the Base Station (BS) precoding matrix and the RIS phase shifts under stochastic link blockages. A solution approach is developed in two phases: the BS precoding optimization and the RIS phase shift control phases. In the first phase, a Decomposition and Relaxation-based Precoding Optimization (DRPO) algorithm is developed to obtain the optimal precoding matrix. In the second phase, a learning-based method is introduced to dynamically adjust the direction of reflected signals under channel uncertainty. Extensive simulations are presented to validate the efficacy of the developed algorithms. The obtained results show that the developed algorithms can ensure reliable transmissions to users in non-LoS areas and improve network performance.
Alshahrani, AA, Al-Zoubi, H, Alotaibi, SE, Hassan, HMA, Alsohaimi, IH, Alotaibi, KM, Alshammari, MS, Nghiem, L & Panhuis, MIH 2022, 'Assessment of commercialized nylon membranes integrated with thin layer of MWCNTs for potential use in desalination process', Journal of Materials Research and Technology, vol. 21, pp. 872-883.
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Alsolbi, I, Wu, M, Zhang, Y, Joshi, S, Sharma, M, Tafavogh, S, Sinha, A & Prasad, M 2022, 'Different approaches of bibliometric analysis for data analytics applications in non-profit organisations', Journal of Smart Environments and Green Computing, vol. 2, no. 3, pp. 90-104.
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Aim: Profitable companies that used data analytics have a double gain in cost reduction, demand prediction, and decision-making. However, using data analysis in non-profit organisations (NPOs) can help understand and identify more patterns of donors, volunteers, and anticipated future cash, gifts, and grants. This article presents a bibliometric study of 2673 to discover the use of data analytics in different NPOs and understand its contribution. Methods: We characterise the associations between data analysis techniques and NPOs using, Bibliometrics R tool, a co-term analysis and scientific evolutionary pathways analysis, as well as identify the research topic changes in this field throughout time. Results: The findings revealed three key conclusions may be drawn from the findings: (1) In the sphere of NPOs, robust and conventional statistical methods-based data analysis procedures are dominantly common at all times; (2) Healthcare and public affairs are two crucial sectors that involve data analytics to support decision-making and problem-solving; (3) Artificial Intelligence (AI) based data analytics is a recently emerging trending, especially in the healthcare-related sector; however, it is still at an immature stage, and more efforts are needed to nourish its development. Conclusion: The research findings can leverage future research and add value to the existing literature on the subject of data analytics.
Alsufyani, N & Gill, AQ 2022, 'Digitalisation performance assessment: A systematic review', Technology in Society, vol. 68, pp. 101894-101894.
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Organisations are showing a keen interest in digitalisation. However, they are uncertain about how to determine the impact of digitalisation on organisation performance outcomes. This places decision-makers in a challenging position to assess the feasibility and intended performance outcomes of digitalisation. This paper aims to address this important research need and provides the performance indicators, measures, metrics and scales based on a systematic review of 30 selected papers. The results from this review were synthesised using the “adaptive enterprise architecture”, and “results and determinants” frameworks as theoretical lenses. This work will benefit researchers and practitioners interested in studying the impact of digitalisation on organisational performance.
Altulyan, M, Yao, L, Wang, X, Huang, C, Kanhere, SS & Sheng, QZ 2022, 'A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions', The Computer Journal, vol. 65, no. 8, pp. 2098-2132.
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Abstract Recommendation is a critical tool for developing and promoting the benefits of the Internet of Things (IoT). In recent years, recommender systems have attracted considerable attention in many IoT-related fields such as smart health, smart home, smart tourism and smart marketing. However, traditional recommender system approaches fail to exploit ever-growing, dynamic and heterogeneous IoT data in building recommender systems for the IoT (RSIoT). This article aims to provide a comprehensive review of state-of-the-art RSIoT, including the related techniques, applications and a discussion on the limitations of applying recommendation systems to IoT. Finally, we propose a reference framework for comparing existing studies to guide future research and practices.
Al-Zainati, N, Subbiah, S, Yadav, S, Altaee, A, Bartocci, P, Ibrar, I, Zhou, J, Samal, AK & Fantozzi, F 2022, 'Experimental and theoretical work on reverse osmosis - Dual stage pressure retarded osmosis hybrid system', Desalination, vol. 543, pp. 116099-116099.
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Two-pass reverse osmosis desalination is a common process to treat high-salinity feed solution and provides a low-salinity permeate solution. This study investigated the significance of the energy generated by the dual-stage pressure retarded osmosis (DSPRO) from the reverse osmosis (RO) brine stream. The main components of the DSPRO-RO hybrid system are RO, pressure retarded osmosis (PRO), and energy recovery device, and their models are determined. Dymola software, using Modelica modelling language, was utilized for solving the hybrid system models. Two different flowsheets were built; the first included a two-pass RO, while the second is a hybrid of a two-pass RO (2RO)-DSPRO system. Seawater salinities of 40 and 45 g/L were the RO feed solution, and 1 g/L tertiary treated wastewater was the feed solution of the DSPRO process. The net specific energy consumption was calculated for the 2RO and 2RO-DSPRO systems for 40 and 45 g/L salinities. At a 47% recovery rate and 40 g/L seawater salinity, the 2RO-DSPRO system was 14.7% more energy efficient than the 2RO system. The corresponding energy saving at a 47% recovery rate and 45 g/L seawater salinity was 17.5%. The desalination energy for the 2RO system was between 3.25 and 3.49 kWh/m3, and for the 2RO-DSPRO system was between 2.91 and 2.97 kWh/m3. The results demonstrate the great potential of integrating the 2RO with the DSPRO to reduce desalination's energy consumption and environmental impacts.
AlZainati, N, Yadav, S, Altaee, A, Subbiah, S, Zaidi, SJ, Zhou, J, Al-Juboori, RA, Chen, Y & Shaheed, MH 2022, 'Impact of hydrodynamic conditions on optimum power generation in dual stage pressure retarded osmosis using spiral-wound membrane', Energy Nexus, vol. 5, pp. 100030-100030.
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Alzoubi, YI & Gill, AQ 2022, 'Can Agile Enterprise Architecture be Implemented Successfully in Distributed Agile Development? Empirical Findings', Global Journal of Flexible Systems Management, vol. 23, no. 2, pp. 221-235.
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A potential solution to the high failure rate in distributed agile development and enhance the success of projects is through implementing agile enterprise architecture, though the success is still to be established. The present paper empirically investigates the gap, by defining the role and commitment of implementing agile enterprise architecture on distributed agile development. The data were collected by interviewing 12 key team members and observing four team meetings over 2 months and analyzing using thematic analysis. The present study suggests that implementing agile enterprise architecture is possible in distributed agile development and may have a positive impact on project success. However, many questions demand further investigation.
Alzoubi, YI, Gill, A & Mishra, A 2022, 'A systematic review of the purposes of Blockchain and fog computing integration: classification and open issues', Journal of Cloud Computing, vol. 11, no. 1, p. 80.
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AbstractThe fog computing concept was proposed to help cloud computing for the data processing of Internet of Things (IoT) applications. However, fog computing faces several challenges such as security, privacy, and storage. One way to address these challenges is to integrate blockchain with fog computing. There are several applications of blockchain-fog computing integration that have been proposed, recently, due to their lucrative benefits such as enhancing security and privacy. There is a need to systematically review and synthesize the literature on this topic of blockchain-fog computing integration. The purposes of integrating blockchain and fog computing were determined using a systematic literature review approach and tailored search criteria established from the research questions. In this research, 181 relevant papers were found and reviewed. The results showed that the authors proposed the combination of blockchain and fog computing for several purposes such as security, privacy, access control, and trust management. A lack of standards and laws may make it difficult for blockchain and fog computing to be integrated in the future, particularly in light of newly developed technologies like quantum computing and artificial intelligence. The findings of this paper serve as a resource for researchers and practitioners of blockchain-fog computing integration for future research and designs.
Al-zqebah, R, Hoffmann, F, Bennett, N, Deuse, J & Clemon, L 2022, 'Layout optimisation for production systems in the wool industry using discrete event simulation', Journal of Industrial Engineering and Management, vol. 15, no. 2, pp. 296-296.
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Purpose: Computer-aided production engineering simulation is a common approach in the search for improvements to real systems. They are used in various industrial sectors and are a basis for optimization. Such production simulations have found limited use in the wool industry. This study aims to compare the performance of different woolshed layouts (curved vs linear). Design/methodology/approach: A discrete event simulation is constructed for both considered layouts in Siemens Technomatix Plant Simulation software. Data from an in-field observational visit to a working woolshed is used to validate the simulation model. The different layouts are compared in their base configuration and with equipment and worker changes to evaluate the impacts on throughput.Findings: In the base configurations, the curved layout reduces some worker travel time which increases production by 11 fleeces per day over the linear layout. The addition of an extra skirting table in the curved layout further increases throughout by 30 fleeces per day. The addition of more wool handlers does not have as large of an impact indicating that processing limits occur due to equipment capacity and shearer speed.Practical implications: This verifies the proposed curved shed layout improves production and gives farmers the ability to compute the long-term economic impact. The results also highlight that other processing stages in the shed need adjustment for more system gains.Originality/value: This is the first application of discrete event simulation to evaluate woolsheds operations and introduce multiple improvement scenarios.
Al-Zu'bi, MM, Mohan, AS, Plapper, PW & Ling, SH 2022, 'Intrabody Molecular Communication via Blood-Tissue Barrier for Internet of Bio-Nano Things.', IEEE Internet Things J., vol. 9, no. 21, pp. 21802-21810.
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Amini, E, Mehdipour, H, Faraggiana, E, Golbaz, D, Mozaffari, S, Bracco, G & Neshat, M 2022, 'Optimization of hydraulic power take-off system settings for point absorber wave energy converter', Renewable Energy, vol. 194, pp. 938-954.
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Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 2022, 'Remote Water Salinity Sensor Using Metamaterial Perfect Absorber', IEEE Transactions on Antennas and Propagation, vol. 70, no. 8, pp. 6785-6794.
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Controlling water salinity plays a key role in farming efficiency. Current sensors are mostly expensive and need regular maintenance. In addition, they require electrical connections or extra power supply that leads to difficult and costly implementation in remote-sensing scenarios. In this article, an accurate and low-profile sensor is developed using a metamaterial perfect absorber (MPA) structure. The proposed sensor works based on the level and frequency of the absorbed signals. Hence, there is no need for electrical connections, which enables remote-sensing applications. Square-shaped channels have been created in a regular FR-4 substrate to facilitate sensing of water salinity levels. A 7 × 7 array with a total size of 140 mm × 160 mm has been fabricated that shows a resolution of 10 MHz per percentage of water salinity. The absorption frequency shifts from f=3.12 to 3.59 GHz for salinity level from 0% to 50%. A strong correlation between measurement and simulation results validates the design procedure.
Ampah, JD, Yusuf, AA, Agyekum, EB, Afrane, S, Jin, C, Liu, H, Fattah, IMR, Show, PL, Shouran, M, Habil, M & Kamel, S 2022, 'Progress and Recent Trends in the Application of Nanoparticles as Low Carbon Fuel Additives—A State of the Art Review', Nanomaterials, vol. 12, no. 9, pp. 1515-1515.
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The first part of the current review highlights the evolutionary nuances and research hotspots in the field of nanoparticles in low carbon fuels. Our findings reveal that contribution to the field is largely driven by researchers from Asia, mainly India. Of the three biofuels under review, biodiesel seems to be well studied and developed, whereas studies regarding vegetable oils and alcohols remain relatively scarce. The second part also reviews the application of nanoparticles in biodiesel/vegetable oil/alcohol-based fuels holistically, emphasizing fuel properties and engine characteristics. The current review reveals that the overall characteristics of the low carbon fuel–diesel blends improve under the influence of nanoparticles during combustion in diesel engines. The most important aspect of nanoparticles is that they act as an oxygen buffer that provides additional oxygen molecules in the combustion chamber, promoting complete combustion and lowering unburnt emissions. Moreover, the nanoparticles used for these purposes exhibit excellent catalytic behaviour as a result of their high surface area-to-volume ratio—this leads to a reduction in exhaust pollutants and ensures an efficient and complete combustion. Beyond energy-based indicators, the exergy, economic, environmental, and sustainability aspects of the blends in diesel engines are discussed. It is observed that the performance of the diesel engine fuelled with low carbon fuels according to the second law of efficiency improves under the influence of the nano-additives. Our final part shows that despite the benefits of nanoparticles, humans and animals are under serious threats from the highly toxic nature of nanoparticles.
An, Y, Lam, H-K & Ling, SH 2022, 'Auto-Denoising for EEG Signals Using Generative Adversarial Network.', Sensors, vol. 22, no. 5, pp. 1750-1750.
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The brain–computer interface (BCI) has many applications in various fields. In EEG-based research, an essential step is signal denoising. In this paper, a generative adversarial network (GAN)-based denoising method is proposed to denoise the multichannel EEG signal automatically. A new loss function is defined to ensure that the filtered signal can retain as much effective original information and energy as possible. This model can imitate and integrate artificial denoising methods, which reduces processing time; hence it can be used for a large amount of data processing. Compared to other neural network denoising models, the proposed model has one more discriminator, which always judges whether the noise is filtered out. The generator is constantly changing the denoising way. To ensure the GAN model generates EEG signals stably, a new normalization method called sample entropy threshold and energy threshold-based (SETET) normalization is proposed to check the abnormal signals and limit the range of EEG signals. After the denoising system is established, although the denoising model uses the different subjects’ data for training, it can still apply to the new subjects’ data denoising. The experiments discussed in this paper employ the HaLT public dataset. Correlation and root mean square error (RMSE) are used as evaluation criteria. Results reveal that the proposed automatic GAN denoising network achieves the same performance as the manual hybrid artificial denoising method. Moreover, the GAN network makes the denoising process automatic, representing a significant reduction in time.
Anand, U, Li, X, Sunita, K, Lokhandwala, S, Gautam, P, Suresh, S, Sarma, H, Vellingiri, B, Dey, A, Bontempi, E & Jiang, G 2022, 'SARS-CoV-2 and other pathogens in municipal wastewater, landfill leachate, and solid waste: A review about virus surveillance, infectivity, and inactivation', Environmental Research, vol. 203, pp. 111839-111839.
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This review discusses the techniques available for detecting and inactivating of pathogens in municipal wastewater, landfill leachate, and solid waste. In view of the current COVID-19 pandemic, SARS-CoV-2 is being given special attention, with a thorough examination of all possible transmission pathways linked to the selected waste matrices. Despite the lack of works focused on landfill leachate, a systematic review method, based on cluster analysis, allows to analyze the available papers devoted to sewage sludge and wastewater, allowing to focalize the work on technologies able to detect and treat pathogens. In this work, great attention is also devoted to infectivity and transmission mechanisms of SARS-CoV-2. Moreover, the literature analysis shows that sewage sludge and landfill leachate seem to have a remote chance to act as a virus transmission route (pollution-to-human transmission) due to improper collection and treatment of municipal wastewater and solid waste. However due to the incertitude about virus infectivity, these possibilities cannot be excluded and need further investigation. As a conclusion, this paper shows that additional research is required not only on the coronavirus-specific disinfection, but also the regular surveillance or monitoring of viral loads in sewage sludge, wastewater, and landfill leachate. The disinfection strategies need to be optimized in terms of dosage and potential adverse impacts like antimicrobial resistance, among many other factors. Finally, the presence of SARS-CoV-2 and other pathogenic microorganisms in sewage sludge, wastewater, and landfill leachate can hamper the possibility to ensure safe water and public health in economically marginalized countries and hinder the realization of the United Nations' sustainable development goals (SDGs).
Andaryani, S, Nourani, V, Pradhan, B, Jalali Ansarudi, T, Ershadfath, F & Torabi Haghighi, A 2022, 'Spatiotemporal evaluation of future groundwater recharge in arid and semi-arid regions under climate change scenarios', Hydrological Sciences Journal, vol. 67, no. 6, pp. 979-995.
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Angeloski, A, Price, JR, Ennis, C, Smith, K, McDonagh, AM, Dowd, A, Thomas, P, Cortie, M, Appadoo, D & Bhadbhade, M 2022, 'Thermosalience Revealed on the Atomic Scale: Rapid Synchrotron Techniques Uncover Molecular Motion Preceding Crystal Jumping', Crystal Growth & Design, vol. 22, no. 3, pp. 1951-1959.
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The solid-state phase transformation in nickel(II) bis(diisopropyldithiocarbonate) is analyzed using a combination of high-speed in situ single-crystal diffraction, terahertz spectroscopy, optical microscopy, thermal analysis, and density functional theory. We show that the monoclinic P21/c structure of this compound undergoes a displacive phase change at about 3 °C. The monoclinic angles and unit cell volumes change reversibly between 110.3°/2265 Å3 and 103.8°/2168 Å3. An analysis of atomic positions using high-resolution in situ synchrotron X-ray diffraction data revealed details of the atomic displacements that show a change in order that precedes and accompanies the change in structure. The structural changes are rapid and are manifested as reversible macroscale crystal movement and jumping (thermosalience) and represent the first case of thermosalience in dithiocarbamate complexes.
Angerschmid, A, Zhou, J, Theuermann, K, Chen, F & Holzinger, A 2022, 'Fairness and Explanation in AI-Informed Decision Making', Machine Learning and Knowledge Extraction, vol. 4, no. 2, pp. 556-579.
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AI-assisted decision-making that impacts individuals raises critical questions about transparency and fairness in artificial intelligence (AI). Much research has highlighted the reciprocal relationships between the transparency/explanation and fairness in AI-assisted decision-making. Thus, considering their impact on user trust or perceived fairness simultaneously benefits responsible use of socio-technical AI systems, but currently receives little attention. In this paper, we investigate the effects of AI explanations and fairness on human-AI trust and perceived fairness, respectively, in specific AI-based decision-making scenarios. A user study simulating AI-assisted decision-making in two health insurance and medical treatment decision-making scenarios provided important insights. Due to the global pandemic and restrictions thereof, the user studies were conducted as online surveys. From the participant’s trust perspective, fairness was found to affect user trust only under the condition of a low fairness level, with the low fairness level reducing user trust. However, adding explanations helped users increase their trust in AI-assisted decision-making. From the perspective of perceived fairness, our work found that low levels of introduced fairness decreased users’ perceptions of fairness, while high levels of introduced fairness increased users’ perceptions of fairness. The addition of explanations definitely increased the perception of fairness. Furthermore, we found that application scenarios influenced trust and perceptions of fairness. The results show that the use of AI explanations and fairness statements in AI applications is complex: we need to consider not only the type of explanations and the degree of fairness introduced, but also the scenarios in which AI-assisted decision-making is used.
Ansari, M, Jones, B & Guo, YJ 2022, 'Spherical Luneburg Lens of Layered Structure With Low Anisotropy and Low Cost', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4307-4318.
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A spherical Luneburg lens made of parallel planar layers of lightweight foam with embedded conducting cylindrical inserts on a uniform hexagonal grid centered in each layer is presented. This work draws on the authors' previous paper (Ansari et al., 2020) describing a Luneburg lens that uses cubic conducting inserts on a uniform cubic grid. This previous lens, while being of lightweight and economical construction, suffered from anisotropy resulting in a focal length that varied with the inclination of the beam relative to the orientation of the cubic grid. The lens described here largely overcomes this problem and allows for simpler and more economical construction. A prototype lens designed for the 3.3-3.8 GHz band with a diameter of 400 mm and a beamwidth of 14° was tested. Radiation patterns at wide scanning angles were nearly identical, and cross-polarization for slant incident polarization was below -25 dB on boresight and below -18 dB for all angles. A characteristic of this lens construction is its extremely high efficiency. The measured gain at the mid-band was 21.6 dBi, agreeing with simulated gain based on lossless materials to within measurement error. It is shown that wider bandwidths are obtainable if the thickness of the layers is reduced.
Anwar, A, Kanwal, S, Tahir, M, Saqib, M, Uzair, M, Rahmani, MKI & Ullah, H 2022, 'Image Aesthetic Assessment: A Comparative Study of Hand-Crafted & Deep Learning Models', IEEE Access, vol. 10, pp. 101770-101789.
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Automatic image aesthetics assessment is a computer vision problem dealing with categorizing images into different aesthetic levels. The categorization is usually done by analyzing an input image and computing some measure of the degree to which the image adheres to the fundamental principles of photography such as balance, rhythm, harmony, contrast, unity, look, feel, tone, and texture. Due to its diverse applications in many areas, automatic image aesthetic assessment has gained significant research attention in recent years. This article presents a comparative study of different automatic image aesthetics assessment techniques from the year 2005 to 2021. A number of conventional hand-crafted as well as modern deep learning-based approaches are reviewed and analyzed for their performance on various publicly available datasets. Additionally, critical aspects of different features and models have also been discussed to analyze their performance and limitations in different situations. The comparative analysis reveals that deep learning based approaches excel hand-crafted based techniques in image aesthetic assessment.
Anwar, MJ, Gill, AQ, Fitzgibbon, AD & Gull, I 2022, 'PESTLE+ risk analysis model to assess pandemic preparedness of digital ecosystems.', Secur. Priv., vol. 5, no. 1.
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AbstractCOVID‐19 pandemic has affected every country in many ways. Its substantial economic impacts are causing businesses to fade, pushing many nations into an economic downturn. This exposes organizations worldwide to unique risks which cannot be foreseen with conventional methods of risk analysis. This research is part of a broader action design research project conducted in collaboration with industry partner to answer an important research question: How to extend PESTLE risk analysis model to assess pandemic preparedness? In this context, the health factor is added to extend the traditional PESTLE risk analysis model. Furthermore, the interdependence between PESTLE factors has also been investigated, which has not been discussed before. The contribution of this research is the novel PESTLE+ risk analysis model that will help individuals and businesses to improve their understanding of the health crisis, such as the COVID‐19, adjust accordingly and eventually endure the ongoing crisis, which is driving most businesses into liquidation.
Apers, S, Gawrychowski, P & Lee, T 2022, 'Finding the KT Partition of a Weighted Graph in Near-Linear Time', Leibniz International Proceedings in Informatics Lipics, vol. 245, no. -, pp. 1-14.
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In a breakthrough work, Kawarabayashi and Thorup (J. ACM'19) gave a near-linear time deterministic algorithm to compute the weight of a minimum cut in a simple graph G = (V, E). A key component of this algorithm is finding the (1 + ε)-KT partition of G, the coarsest partition {P1, ..., Pk} of V such that for every non-trivial (1 + ε)-near minimum cut with sides {S, S̅} it holds that Pi is contained in either S or S̅, for i = 1, ..., k. In this work we give a near-linear time randomized algorithm to find the (1 + ε)-KT partition of a weighted graph. Our algorithm is quite different from that of Kawarabayashi and Thorup and builds on Karger's framework of tree-respecting cuts (J. ACM'00). We describe a number of applications of the algorithm. (i) The algorithm makes progress towards a more efficient algorithm for constructing the polygon representation of the set of near-minimum cuts in a graph. This is a generalization of the cactus representation, and was initially described by Benczúr (FOCS'95). (ii) We improve the time complexity of a recent quantum algorithm for minimum cut in a simple graph in the adjacency list model from Oe(n3/2) to Oe(√mn), when the graph has n vertices and m edges. (iii) We describe a new type of randomized algorithm for minimum cut in simple graphs with complexity O(m + nlog6 n). For graphs that are not too sparse, this matches the complexity of the current best O(m+nlog2 n) algorithm which uses a different approach based on random contractions. The key technical contribution of our work is the following. Given a weighted graph G with m edges and a spanning tree T of G, consider the graph H whose nodes are the edges of T, and where there is an edge between two nodes of H iff the corresponding 2-respecting cut of T is a non-trivial near-minimum cut of G. We give a O(mlog4 n) time deterministic algorithm to com...
Arabameri, A, Santosh, M, Moayedi, H, Tiefenbacher, JP, Pal, SC, Nalivan, OA, Costache, R, Ahmed, N, Hoque, MA-A, Chakrabortty, R & Cerda, A 2022, 'Application of the novel state-of-the-art soft computing techniques for groundwater potential assessment', Arabian Journal of Geosciences, vol. 15, no. 10.
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Arachchige, CMK, Indraratna, B, Qi, Y, Vinod, JS & Rujikiatkamjorn, C 2022, 'Deformation and degradation behaviour of Rubber Intermixed Ballast System under cyclic loading', Engineering Geology, vol. 307, pp. 106786-106786.
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Arachchige, CMK, Indraratna, B, Qi, Y, Vinod, JS & Rujikiatkamjorn, C 2022, 'Geotechnical characteristics of a Rubber Intermixed Ballast System', Acta Geotechnica, vol. 17, no. 5, pp. 1847-1858.
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This study aims to promote the concept of using rubber granules from waste tyres as elastic aggregates blended with traditional ballast particles for better performance of rail tracks, i.e. a Rubber Intermixed Ballast System (RIBS). This paper describes the mechanical and compressibility characteristics of RIBS under monotonic loads and a criterion designed to determine the optimum rubber content in the proposed RIBS. The most interesting findings of this study embrace how the rubber granules in the blended rockfill assembly significantly reduce the dilation and modulus degradation, and the breakage of ballast aggregates. RIBS with more than 10% of rubber demonstrates a seemingly consistent reduction in dilation under changing confining pressures. Increased deviator stress and larger effective confining pressure compress the rubber particles within the RIBS which may cause relatively large initial settlements in the ballast layer, if the rubber content becomes excessive. It is also evident from the results that rubber particles ranging from 9.5 to 19 mm with similar angularity to ballast aggregates is advantageous, because, they reduce the breakage of load-bearing larger aggregates, thus effectively controlling ballast fouling within the granular matrix.
Arakawa, K, Kono, N, Malay, AD, Tateishi, A, Ifuku, N, Masunaga, H, Sato, R, Tsuchiya, K, Ohtoshi, R, Pedrazzoli, D, Shinohara, A, Ito, Y, Nakamura, H, Tanikawa, A, Suzuki, Y, Ichikawa, T, Fujita, S, Fujiwara, M, Tomita, M, Blamires, SJ, Chuah, J-A, Craig, H, Foong, CP, Greco, G, Guan, J, Holland, C, Kaplan, DL, Sudesh, K, Mandal, BB, Norma-Rashid, Y, Oktaviani, NA, Preda, RC, Pugno, NM, Rajkhowa, R, Wang, X, Yazawa, K, Zheng, Z & Numata, K 2022, '1000 spider silkomes: Linking sequences to silk physical properties', Science Advances, vol. 8, no. 41.
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Spider silks are among the toughest known materials and thus provide models for renewable, biodegradable, and sustainable biopolymers. However, the entirety of their diversity still remains elusive, and silks that exceed the performance limits of industrial fibers are constantly being found. We obtained transcriptome assemblies from 1098 species of spiders to comprehensively catalog silk gene sequences and measured the mechanical, thermal, structural, and hydration properties of the dragline silks of 446 species. The combination of these silk protein genotype-phenotype data revealed essential contributions of multicomponent structures with major ampullate spidroin 1 to 3 paralogs in high-performance dragline silks and numerous amino acid motifs contributing to each of the measured properties. We hope that our global sampling, comprehensive testing, integrated analysis, and open data will provide a solid starting point for future biomaterial designs.
Araujo, AM, Abaurrea, A, Azcoaga, P, López-Velazco, JI, Manzano, S, Rodriguez, J, Rezola, R, Egia-Mendikute, L, Valdés-Mora, F, Flores, JM, Jenkins, L, Pulido, L, Osorio-Querejeta, I, Fernández-Nogueira, P, Ferrari, N, Viera, C, Martín-Martín, N, Tzankov, A, Eppenberger-Castori, S, Alvarez-Lopez, I, Urruticoechea, A, Bragado, P, Coleman, N, Palazón, A, Carracedo, A, Gallego-Ortega, D, Calvo, F, Isacke, CM, Caffarel, MM & Lawrie, CH 2022, 'Stromal oncostatin M cytokine promotes breast cancer progression by reprogramming the tumor microenvironment', Journal of Clinical Investigation, vol. 132, no. 7, p. e148667.
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The tumor microenvironment (TME) is reprogrammed by cancer cells and participates in all stages of tumor progression. The contribution of stromal cells to the reprogramming of the TME is not well understood. Here, we provide evidence of the role of the cytokine oncostatin M (OSM) as central node for multicellular interactions between immune and nonimmune stromal cells and the epithelial cancer cell compartment. OSM receptor (OSMR) deletion in a multistage breast cancer model halted tumor progression. We ascribed causality to the stromal function of the OSM axis by demonstrating reduced tumor burden of syngeneic tumors implanted in mice lacking OSMR. Single-cell and bioinformatic analysis of murine and human breast tumors revealed that OSM expression was restricted to myeloid cells, whereas OSMR was detected predominantly in fibroblasts and, to a lower extent, cancer cells. Myeloid-derived OSM reprogrammed fibroblasts to a more contractile and tumorigenic phenotype and elicited the secretion of VEGF and proinflammatory chemokines CXCL1 and CXCL16, leading to increased myeloid cell recruitment. Collectively, our data support the notion that the stromal OSM/OSMR axis reprograms the immune and nonimmune microenvironment and plays a key role in breast cancer progression.
Archer, NS, Bluff, A, Eddy, A, Nikhil, CK, Hazell, N, Frank, D & Johnston, A 2022, 'Odour enhances the sense of presence in a virtual reality environment', PLOS ONE, vol. 17, no. 3, pp. e0265039-e0265039.
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Virtual reality (VR) headsets provide immersive audio-visual experiences for users, but usually neglect to provide olfactory cues that can provide additional information about our environment in the real world. This paper examines whether the introduction of smells into the VR environment enhances users’ experience, including their sense of presence through collection of both psychological and physiological measures. Using precise odour administration with an olfactometer, study participants were exposed to smells while they were immersed in the popular PlayStation VR game “Resident Evil 7”. A within-subject study design was undertaken where participants (n = 22) walked-through the same VR environment twice, with or without the introduction of associated congruent odour stimuli. Directly after each gameplay, participants completed a questionnaire to determine their sense of presence from the overall gameplay and their sense of immersion in each of the virtual scenes. Additionally, physiological measurements (heart rate, body temperature and skin electrodermal activity) were collected from participants (n = 11) for each gameplay. The results showed the addition of odours significantly increased participants’ sense of spatial presence in the VR environment compared to VR with no odour. Participants also rated the realism of VR experience with odour higher compared to no odour, however odour addition did not result in change in emotional state of participants (arousal, pleasure, dominance). Further, the participants’ physiological responses were impacted by the addition of odour. Odour mediated physiological changes were dependent on whether the VR environment was novel, as the effect of odour on physiological response was lost when participants experienced the aroma on the second gameplay. Overall, the results indicate the addition of odours to a VR environment had a significant effect on both the psychological and physiological experience showing...
Areerachakul, N, Prongnuch, S, Longsomboon, P & Kandasamy, J 2022, 'Quantitative Precipitation Estimation (QPE) Rainfall from Meteorology Radar over Chi Basin', Hydrology, vol. 9, no. 10, pp. 178-178.
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This study of the Quantitative Estimation Precipitation (QEP) of rainfall, detected by two Meteorology Radars over Chi Basin, North-east Thailand, used data from the Thai Meteorological Department (TMD). The rainfall data from 129 rain gauge stations in the Chi Basin area, covering a period of two years, was also used. The study methodology consists of: firstly, deriving the QPE between radar and rainfall based on meteorological observations using the Marshall Palmer Stratiform, the Summer Deep Convection, and Regression Model and calibrating with rain gauge station data; secondly, Bias Correction using statistical method; thirdly, determining spatial variation using three methods, namely Kriging, Inverse Distance Weight (IDW), and the Minimum Curvature Method. The results of the study demonstrated the accuracy of estimating precipitation using meteorological radar. Estimated precipitation compared against an equivalent of 2 years of rain station measurement had a probability of detection (POD) of 0.927, where a value of 1 indicated perfect agreement, demonstrating the effectiveness of the method used to calibrate the radar data. The bias correction method gave high accuracy compared with measured rainfall. Furthermore, of the spatial estimation of rainfall methods, the Kriging methodology showed the best fit between estimation of rainfall distribution and measured rainfall distribution. Therefore, the results of this study showed that the rainfall estimation, using data from a meteorology radar, has good accuracy and can be useful, especially in areas where it is not possible to install and operate rainfall measurement stations, such as in heavily forested areas and/or in steep terrain. Additionally, good accuracy rainfall data derived from radar data can be integrated with other data used for water management and natural disasters for applications to reduce economic losses, as well as losses of life and property.
Arivalagan, J, Indraratna, B, Rujikiatkamjorn, C & Warwick, A 2022, 'Effectiveness of a Geocomposite-PVD system in preventing subgrade instability and fluidisation under cyclic loading', Geotextiles and Geomembranes, vol. 50, no. 4, pp. 607-617.
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Arjmandi, A, Peyravi, M, Arjmandi, M & Altaee, A 2022, 'Taking advantage of large water-unstable Zn4O(BDC)3 nanoparticles for fabricating the PMM-based TFC FO membrane with improved water flux in desalination process', Chemical Engineering Research and Design, vol. 186, pp. 112-124.
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Armaghani, DJ, Harandizadeh, H, Momeni, E, Maizir, H & Zhou, J 2022, 'An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity', Artificial Intelligence Review, vol. 55, no. 3, pp. 2313-2350.
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Arnaz, A, Lipman, J, Abolhasan, M & Hiltunen, M 2022, 'Toward Integrating Intelligence and Programmability in Open Radio Access Networks: A Comprehensive Survey', IEEE Access, vol. 10, pp. 67747-67770.
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Open RAN is an emerging vision and an advancement of the Radio Access Network (RAN). Its purpose is to implement a vendor and network-generation agnostic RAN, provide networking solutions across all service requests, and implement artificial intelligence solutions in different stages of an end-to-end communication path. The 5th Generation (5G) and beyond the 5th Generation (B5G) of networking introduce and support new use cases, such as tactile internet and autonomous driving. The complexity and innovative nature of these use cases require continuous innovation at a high pace in the RAN. The traditional approach of building end-to-end RAN solutions by only one vendor hampers the speed of innovation - furthermore, the lack of a standard approach to implementing artificial intelligence complicates the compatibility of products with the RAN ecosystem. O-RAN Alliance, a community of industry and academic experts in RAN, works on writing Open RAN specifications on top of the 3rd Generation Partnership Project (3GPP) standards. Founded on these specifications, the aim of this paper is to introduce open research topics in Open RAN that overlap the interests of both AI and telecommunication researchers. The paper provides an overview of the architecture and components of Open RAN, then explores AI use cases in Open RAN. Also, this survey includes some plausible AI deployment scenarios that the specifications have not covered. Open RAN in future cities creates opportunities for various use cases across different sectors, including engineering, operations, and research that this paper addresses.
Arnett, S, Sanchez, SJ, Downing, J, Boggild, M, Sun, J & Broadley, SA 2022, 'Low vitamin D levels do not predict risk of autoimmune disease following alemtuzumab treatment for multiple sclerosis', Multiple Sclerosis and Related Disorders, vol. 59, pp. 103511-103511.
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Arora, S, Nag, A, Kalra, A, Sinha, V, Meena, E, Saxena, S, Sutaria, D, Kaur, M, Pamnani, T, Sharma, K, Saxena, S, Shrivastava, SK, Gupta, AB, Li, X & Jiang, G 2022, 'Successful application of wastewater-based epidemiology in prediction and monitoring of the second wave of COVID-19 with fragmented sewerage systems–a case study of Jaipur (India)', Environmental Monitoring and Assessment, vol. 194, no. 5, p. 342.
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The present study tracked the city-wide dynamics of severe acute respiratory syndrome-corona virus 2 ribonucleic acids (SARS-CoV-2 RNA) in the wastewater from nine different wastewater treatment plants (WWTPs) in Jaipur during the second wave of COVID-19 out-break in India. A total of 164 samples were collected weekly between February 19th and June 8th, 2021. SARS-CoV-2 was detected in 47.2% (52/110) influent samples and 37% (20/54) effluent samples. The increasing percentage of positive influent samples correlated with the city's increasing active clinical cases during the second wave of COVID-19 in Jaipur. Furthermore, wastewater-based epidemiology (WBE) evidence clearly showed early detection of about 20 days (9/9 samples reported positive on April 20th, 2021) before the maximum cases and maximum deaths reported in the city on May 8th, 2021. The present study further observed the presence of SARS-CoV-2 RNA in treated effluents at the time window of maximum active cases in the city even after tertiary disinfection treatments of ultraviolet (UV) and chlorine (Cl2) disinfection. The average genome concentration in the effluents and removal efficacy of six commonly used treatments, activated sludge process + chlorine disinfection (ASP + Cl2), moving bed biofilm reactor (MBBR) with ultraviolet radiations disinfection (MBBR + UV), MBBR + chlorine (Cl2), sequencing batch reactor (SBR), and SBR + Cl2, were compared with removal efficacy of SBR + Cl2 (81.2%) > MBBR + UV (68.8%) > SBR (57.1%) > ASP (50%) > MBBR + Cl2 (36.4%). The study observed the trends and prevalence of four genes (E, RdRp, N, and ORF1ab gene) based on two different kits and found that prevalence of N > ORF1ab > RdRp > E gene suggested that the effective genome concentration should be calculated based on the presence/absence of multiple genes. Hence, it is imperative to say that using a combination of different detection genes (E, N, RdRp, & ORF1ab genes) increases the sensitivity in WBE.
Arsalanloo, A, Abbasalizadeh, M, Khalilian, M, Saniee, Y, Ramezanpour, A & Islam, MS 2022, 'A computational approach to understand the breathing dynamics and pharmaceutical aerosol transport in a realistic airways', Advanced Powder Technology, vol. 33, no. 7, pp. 103635-103635.
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Arunprasad, J, Krishna, AN, Radha, D, Singh, M, Surakasi, R & Gidebo, TD 2022, '[Retracted] Nanometal‐Based Magnesium Oxide Nanoparticle with C. vulgaris Algae Biodiesel in Diesel Engine', Journal of Nanomaterials, vol. 2022, no. 1, pp. 1-9.
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Many researchers are interested in biofuels because it isenvironmentally friendly and potentially reduce global warming. Incorporating nanoparticles into biodiesel has increased its performance and emission characteristics. The current study examines the influence of magnesium oxide nanoadditions on the performance and emissions of a diesel engine that runs on C. vulgaris algae biodiesel. The transesterification process produced methyl ester from C. vulgaris algae biodiesel.The morphology of nanoadditives was studied using scanning electron microscopy, transmission electron microscopy, and energy‐dispersive X‐ray spectroscopy. The fuel sample consisted of biodiesel blends with and without magnesium oxide nanoadditives. The fuel properties of the prepared C. vulgaris methyl ester were found to conform with the ASTM standards. The experimental results were determined by running a single‐cylinder four‐stroke diesel engine at different load conditions. When compared to B20, a B20 blend containing 100 ppm magnesium oxide nanoparticles enhanced brake thermal efficiency while reducing specific fuel consumption, according to the research. When MgO nanoparticles were introduced to B20, engine emissions of HC, CO, and smoke were decreased.
Aryal, B, Gurung, R, Camargo, AF, Fongaro, G, Treichel, H, Mainali, B, Angove, MJ, Ngo, HH, Guo, W & Puadel, SR 2022, 'Nitrous oxide emission in altered nitrogen cycle and implications for climate change', Environmental Pollution, vol. 314, pp. 120272-120272.
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Natural processes and human activities play a crucial role in changing the nitrogen cycle and increasing nitrous oxide (N2O) emissions, which are accelerating at an unprecedented rate. N2O has serious global warming potential (GWP), about 310 times higher than that of carbon dioxide. The food production, transportation, and energy required to sustain a world population of seven billion have required dramatic increases in the consumption of synthetic nitrogen (N) fertilizers and fossil fuels, leading to increased N2O in air and water. These changes have radically disturbed the nitrogen cycle and reactive nitrogen species, such as nitrous oxide (N2O), and have impacted the climatic system. Yet, systematic and comprehensive studies on various underlying processes and parameters in the altered nitrogen cycle, and their implications for the climatic system are still lacking. This paper reviews how the nitrogen cycle has been disturbed and altered by anthropogenic activities, with a central focus on potential pathways of N2O generation. The authors also estimate the N2O-N emission mainly due to anthropogenic activities will be around 8.316 Tg N2O-N yr-1 in 2050. In order to minimize and tackle the N2O emissions and its consequences on the global ecosystem and climate change, holistic mitigation strategies and diverse adaptations, policy reforms, and public awareness are suggested as vital considerations. This study concludes that rapidly increasing anthropogenic perturbations, the identification of new microbial communities, and their role in mediating biogeochemical processes now shape the modern nitrogen cycle.
Asadniaye Fardjahromi, M, Nazari, H, Ahmadi Tafti, SM, Razmjou, A, Mukhopadhyay, S & Warkiani, ME 2022, 'Metal-organic framework-based nanomaterials for bone tissue engineering and wound healing', Materials Today Chemistry, vol. 23, pp. 100670-100670.
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Over the past decade, tremendous growth has been witnessed in the synthesis of scaffolds fabricated by natural or synthetic, composite, or hybrid biomaterials to enhance wound healing, repair of bone fractures, and pathological loss of bones. However, the current limitations of using these scaffolds in tissue engineering are impaired cellular proliferation, poor differentiation, low mechanical stability, and bioactivity. Recent advances in the fabrication of nanoscale metal-organic framework (nano-MOF) scaffolds have provided golden opportunities to enhance the properties of scaffolds in bone and wound tissue engineering. In the past few years, studies have shown that incorporating nano-MOFs into scaffolds can be highly favorable in the regeneration of imperfect tissues owing to their unique properties such as high internal surface areas, high porosity, good mechanical stability, biocompatibility, and tunability. Moreover, the nanoscale structural and topological properties of nano-MOFs enhance the physicochemical properties of scaffolds, enrich them with drug-loading and ion-releasing capacity, and regulate stem cell attachment, proliferation, and differentiation after transplantation. This review initially introduces the various nano-MOFs incorporated into scaffolds for tissue engineering. Recent applications of nanoMOFs for bone and wound healing are comprehensively discussed. The unique properties of nano-MOFs for improving osteoconductivity, osteoinductivity, and wound healing, such as high antibacterial activity, high drug loading capacity (i.e., bioactive molecules and growth factors), and controlled drug release, are discussed. Finally, challenges, clinical barriers, and considerations for implementing these nanomaterials in different scaffolds, tissue-like structures, implants, fillers, and dressers in the orthopedic and wound clinics are comprised.
Aseeri, M & Kang, K 2022, 'Big data, oriented-organizational culture, and business performance: A socio-technical approach', Problems and Perspectives in Management, vol. 20, no. 4, pp. 52-66.
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This paper experimentally examines the impact of oriented-organizational culture that could support big data analytics (BDA) in higher education institutions (HEIs) in Saudi Arabia. Specifically, this study analyzed the effect of oriented-organizational culture (OC) on big data tasks (BDTs) toward improving decision-making (DM) and organization performance (OP). The study hinged on the theory of socio-technical systems to investigate BDA elements in higher education decision-making in Saudi Arabia. The analysis was conducted using a quantitative survey research design where data were collected from 270 IT staff working in Saudi Arabian HEIs using Qualtrics. PLS-SEM was applied to validate the research data and explore the relationship between the proposed hypotheses. The findings show that oriented-organizational culture positively affected big data tasks, i.e., storing, analyzing, and visualizing. Similarly, oriented-organizational culture positively affects improving decision-making by top management in Saudi Arabian universities. OC also positively influences the performance of Saudi Arabian universities. Improving decision-making by top management has a positive impact on enhancing the overall university’s performance. However, big data tasks, i.e., storing, analyzing, and visualizing, negatively affect improving decision-making by top management in Saudi Arabian HEIs. One of the study limitations is the small sample size; future studies should include private and public universities to alter the expected outcomes. Additional technological elements, such as IT infrastructure at Saudi Arabia’s private and public HEIs, are recommended to be considered in future studies to establish the competence of respective IT infrastructure.AcknowledgmentThe authors wish to thank the Problems and Perspectives in Management Journal editors for their valuable time and assistance in improving the manuscript.
Ashtari, S, Abdollahi, M, Abolhasan, M, Shariati, N & Lipman, J 2022, 'Performance analysis of multi-hop routing protocols in SDN-based wireless networks', Computers & Electrical Engineering, vol. 97, pp. 107393-107393.
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Wireless cellular networks have rapidly evolved to be software-defined in nature. This has created opportunities to improve their performance. One such opportunity is through enabling programming and integration of multi-hop device-to-device (MD2D) at the edge. However, efficient integration of MD2D at the edge requires a highly adaptable and scalable routing protocol, where its development is underpinned through understanding of which type of current routing characteristics and architectures are suitable over dynamic networking conditions. To develop such understanding, we conducted a detailed analysis and performance study on three routing protocols, namely virtual ad-hoc routing protocol-source based (VARP-S) Abolhasan et al. (2018), SDN-based multi-hop device-to-device routing protocol (SMDRP) Abdollahi et al. (2019) and hybrid SDN architecture for wireless distributed networks (HSAW) Abolhasan et al. (2015). Our investigations illustrate that VARP-S and SMDRP perform best in terms of energy consumption and cellular routing overhead. However, HSAW shows better performance in terms of end-to-end (E2E) delay and packet loss over lower network and traffic densities.
ashtari, S, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2022, 'Joint Mobile Node Participation and Multihop Routing for Emerging Open Radio-Based Intelligent Transportation System', IEEE Access, vol. 10, pp. 85228-85242.
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This paper proposes joint mobile node participation and routing protocol for multi-hop device-to-device (MD2D) networking in intelligent transportation systems, called fuzzy-based participation and routing protocol for MD2D (FPRM). Our proposed protocol is designed to operate over future open-radio access networks (O-RANs). We introduce a sub-layer at the network layer that can determine nodes with the highest participation probability in routing using a fuzzy logic system, thus building a framework to create more stable routes. To ensure the participating nodes are capable of handling the data traffic, two constraints are proposed, mobility and coverage constraints. The former enables the creation of sustainable communication links, and the latter enforces the communication service to the entire MD2D network. Simulation results show that our approach can increase the network lifetime, decrease the end-to-end (E2E) delay, and increase the packet delivery ratio (PDR) compared to the existing proactive routing protocol. Our protocol outperforms the benchmarked MD2D protocols and other investigated ad hoc protocols.
Ashtari, S, Zhou, I, Abolhasan, M, Shariati, N, Lipman, J & Ni, W 2022, 'Knowledge-defined networking: Applications, challenges and future work', Array, vol. 14, pp. 100136-100136.
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Future 6G wireless communication systems are expected to feature intelligence and automation. Knowledge-defined networking (KDN) is an evolutionary step toward autonomous and self-driving networks. The building blocks of the KDN paradigm in achieving self-driving networks are software-defined networking (SDN), packet-level network telemetry, and machine learning (ML). The KDN paradigm intends to integrate intelligence to manage and control networks automatically. In this study, we first introduce the disadvantages of current network technologies. Then, the KDN and associated technologies are explored with three possible KDN architectures for heterogeneous wireless networks. Furthermore, a thorough investigation of recent survey studies on different wireless network applications was conducted. The aim is to identify and review suitable ML-based studies for KDN-based wireless cellular networks. These applications are categorized as resource management, network management, mobility management, and localization. Resource management applications can be further classified as spectrum allocation, power management, quality-of-service (QoS), base station (BS) switching, cache, and backhaul management. Within network management configurations, routing strategies, clustering, user/BS association, traffic classification, and data aggregation were investigated. Applications in mobility management include user mobility prediction and handover management. To improve the accuracy of positioning in indoor environments, localization techniques were discussed. We classify existing research into the respective KDN architecture and identify how the knowledge obtained will enhance future networks; as a result, researchers can extend their work to empower intelligence and self-organization in the network using the KDN paradigm. Finally, the requirements, motivations, applications, challenges, and open issues are presented.
Asteris, PG, Gavriilaki, E, Touloumenidou, T, Koravou, E, Koutra, M, Papayanni, PG, Pouleres, A, Karali, V, Lemonis, ME, Mamou, A, Skentou, AD, Papalexandri, A, Varelas, C, Chatzopoulou, F, Chatzidimitriou, M, Chatzidimitriou, D, Veleni, A, Rapti, E, Kioumis, I, Kaimakamis, E, Bitzani, M, Boumpas, D, Tsantes, A, Sotiropoulos, D, Papadopoulou, A, Kalantzis, IG, Vallianatou, LA, Armaghani, DJ, Cavaleri, L, Gandomi, AH, Hajihassani, M, Hasanipanah, M, Koopialipoor, M, Lourenço, PB, Samui, P, Zhou, J, Sakellari, I, Valsami, S, Politou, M, Kokoris, S & Anagnostopoulos, A 2022, 'Genetic prediction of ICU hospitalization and mortality in COVID‐19 patients using artificial neural networks', Journal of Cellular and Molecular Medicine, vol. 26, no. 5, pp. 1445-1455.
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AbstractThere is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease‐19 (COVID‐19). We aimed to a) identify complement‐related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement‐related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID‐19. Through targeted next‐generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH‐related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID‐19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID‐19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID‐19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that ge...
Asteris, PG, Lourenço, PB, Roussis, PC, Elpida Adami, C, Armaghani, DJ, Cavaleri, L, Chalioris, CE, Hajihassani, M, Lemonis, ME, Mohammed, AS & Pilakoutas, K 2022, 'Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques', Construction and Building Materials, vol. 322, pp. 126500-126500.
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Atamewoue Tsafack, S, Wen, S, Onasanya, BO & Feng, Y 2022, 'Skew polynomial superrings', Soft Computing, vol. 26, no. 21, pp. 11277-11286.
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The purpose of this paper is to define the skew polynomial superrings arising from Krasner (hyperfields) hyperrings and study their ideals and some of their properties. In this work, we showed that the skew polynomial superrings are principal superideal superdomains which are not necessarily commutative. We also proved the division theorem for these skew polynomial superrings and provided an algorithm for the decomposition of a polynomial to a product of some irreducible polynomials.
Atgur, V, Manavendra, G, Desai, GP, Rao, BN, Fattah, IMR, Mohamed, BA, Sinaga, N & Masjuki, HH 2022, 'Thermogravimetric and combustion efficiency analysis of Jatropha curcas biodiesel and its derivatives', Biofuels, vol. 13, no. 9, pp. 1069-1079.
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Thermal behavior of diesel, Jatropha curcas methyl ester (JOME), and its B20 blend (20% biodiesel and 80% diesel) are examined from the profiles of thermogravimetry–differential scanning calorimetry (TG-DSC) under air. TG profiles of samples indicate the mass loss steps to volatilization and combustion of methyl esters. Due to the higher temperature combustion of the intermediate stable compounds that are formed, the peak temperature of combustion is high for JOME compared to diesel and B20 blend. DSC profiles of diesel and B20 JOME indicate an endothermic peak associated with the vaporization of methyl esters for B20 JOME and the volatilization of a small fraction of the diesel. The ignition temperature for diesel and B20 blend is 128 °C, whereas JOME has an ignition temperature of 220 °C. The burnout temperatures for the diesel, JOME, and B20 blend are 283.24, 470.02, and 376.92 °C, respectively. The ignition index for the B20 blend was found to be 73.73% more compared to diesel. The combustion index for the B20 blend was found to be 37.81% higher compared to diesel. The B20 blend exhibits high enthalpy, better thermal stability, and a reduced peak temperature of combustion, with an improved combustion index and an intensity of combustion making it nearly comparable with diesel.
Atif, Y, Soulaimani, A, Ait lamqadem, A, Pour, AB, Pradhan, B, Nouamane, EA, Abdelali, K, Muslim, AM & Hossain, MS 2022, 'Identifying hydrothermally altered rocks using ASTER satellite imageries in Eastern Anti-Atlas of Morocco: a case study from Imiter silver mine', International Journal of Image and Data Fusion, vol. 13, no. 4, pp. 337-361.
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Atique, MN, Imran, S, Razzaq, L, Mujtaba, MA, Nawaz, S, Kalam, MA, Soudagar, MEM, Hussain, A, Veza, I & Arshad, A 2022, 'Hydraulic characterization of Diesel, B50 and B100 using momentum flux', Alexandria Engineering Journal, vol. 61, no. 6, pp. 4371-4388.
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Augustine, R, S, A, Nayeem, A, Salam, SA, Augustine, P, Dan, P, Maureira, P, Mraiche, F, Gentile, C, Hansbro, PM, McClements, L & Hasan, A 2022, 'Increased complications of COVID-19 in people with cardiovascular disease: Role of the renin–angiotensin-aldosterone system (RAAS) dysregulation', Chemico-Biological Interactions, vol. 351, pp. 109738-109738.
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The rapid spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19), has had a dramatic negative impact on public health and economies worldwide. Recent studies on COVID-19 complications and mortality rates suggest that there is a higher prevalence in cardiovascular diseases (CVD) patients. Past investigations on the associations between pre-existing CVDs and susceptibility to coronavirus infections including SARS-CoV and the Middle East Respiratory Syndrome coronavirus (MERS-CoV), have demonstrated similar results. However, the underlying mechanisms are poorly understood. This has impeded adequate risk stratification and treatment strategies for CVD patients with SARS-CoV-2 infections. Generally, dysregulation of the expression of angiotensin-converting enzyme (ACE) and the counter regulator, angiotensin-converting enzyme 2 (ACE2) is a hallmark of cardiovascular risk and CVD. ACE2 is the main host receptor for SARS-CoV-2. Although further studies are required, dysfunction of ACE2 after virus binding and dysregulation of the renin-angiotensin-aldosterone system (RAAS) signaling may worsen the outcomes of people affected by COVID-19 and with preexisting CVD. Here, we review the current knowledge and outline the gaps related to the relationship between CVD and COVID-19 with a focus on the RAAS. Improved understanding of the mechanisms regulating viral entry and the role RAAS may direct future research with the potential to improve the prevention and management of COVID-19.
Aung, TWW, Wan, Y, Huo, H & Sui, Y 2022, 'Multi-triage: A multi-task learning framework for bug triage', Journal of Systems and Software, vol. 184, pp. 111133-111133.
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Assigning developers and allocating issue types are two important tasks in the bug triage process. Existing approaches tackle these two tasks separately, which is time-consuming due to repetition of effort and negating the values of correlated information between tasks. In this paper, a multi-triage model is proposed that resolves both tasks simultaneously via multi-task learning (MTL). First, both tasks can be regarded as a classification problem, based on historical issue reports. Second, performances on both tasks can be improved by jointly interpreting the representations of the issue report information. To do so, a text encoder and abstract syntax tree (AST) encoder are used to extract the feature representation of bug descriptions and code snippets accordingly. Finally, due to the disproportionate ratio of class labels in training datasets, the contextual data augmentation approach is introduced to generate syntactic issue reports to balance the class labels. Experiments were conducted on eleven open-source projects to demonstrate the effectiveness of this model compared with state-of-the-art methods.
Aurlene, N, Tadakamadla, J, Arora, A, Sun, J & Tadakamadla, SK 2022, 'The Role of Parenting Practices on the Parent Perceived Impact of Child Oral Health on Family Wellbeing', International Journal of Environmental Research and Public Health, vol. 19, no. 3, pp. 1680-1680.
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Background: Family characteristics and parenting practices could significantly influence child oral health and the impact of child oral health on family wellbeing. Aim: To determine the association between parenting practices and parent-perceived impact of children’s oral health condition on family wellbeing. Design: A cross-sectional survey was conducted among 1539 school children in India. Parents answered the short form of FIS (Family Impact Scale), and PCRQ (parent–child relationship questionnaire) to assess the impact of the child’s oral health on family and parenting practices, respectively. Two factors emerged on factor analysis of PCRQ: ‘Positive parenting’ and ‘Power assertive parenting’. The intra-oral examination was conducted for children to assess their dental caries experience and gingival health status. Unadjusted linear regression and hierarchical multiple regression analysis were used to determine the influence of parenting practices on FIS. Results: An increase in power assertion (B = 1.16) parenting was associated with increased FIS scores indicating a higher adverse impact of the child’s oral health on family wellbeing when parents used more power assertive parenting practices. On the other hand, an increase in positive parenting (B = −1.27) was associated with decreased FIS scores, indicating a lesser impact of child’s oral health on family wellbeing when parents used more positive parenting practices. Conclusions: Parenting practices were associated with parents’ perceptions of the effect of children’s oral health on family wellbeing.
Awang, MSN, Mohd Zulkifli, NW, Abbas, MM, Zulkifli, MSA, Kalam, MA, Mohd Yusoff, MNA, Ahmad, MH & Wan Daud, WMA 2022, 'Effect of plastic pyrolytic oil and waste cooking biodiesel on tribological properties of palm biodiesel–diesel fuel blends', Industrial Lubrication and Tribology, vol. 74, no. 8, pp. 932-942.
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PurposeThe purpose of this paper was to investigate the lubricity of palm biodiesel (PB)–diesel fuel with plastic pyrolysis oil (PPO) and waste cooking biodiesel (WCB).Design/methodology/approachThree quaternary fuels were prepared by mechanical stirring. B10 (10% PB in diesel) fuel was blended with 5%, 10% and 15% of both PPO and WCB. The results were compared to B30 (30% PB in diesel) and B10. The lubricity of fuel samples was determined using high-frequency reciprocating rig in accordance with ASTM D6079. The tribological behavior of all fuels was assessed by using scanning electron microscopy on worn steel plates to determine wear scar diameter (WSD) and surface morphology. The reported WSD is the average of the major and minor axis of the wear scar.FindingsThe addition of PPO and WCB to B10 had improved its lubricity while lowering wear and friction coefficients. Among the quaternary fuels, B40 showed the greatest reduction in coefficient of friction and WSD, with 7.63% and 44.5%, respectively, when compared to B10. When compared to B30a, the quaternary fuel mixes (B40, B30b and B20) exhibited significant reduction in WSD by 49.66%, 42.84% and 40.24%, respectively. Among the quaternary fuels, B40 exhibited the best overall lubricating performance, which was supported by surface morphology analysis. The evaluation of B40 indicated a reduced adhesive wear and tribo-oxidation, as well as a smoother metal surface, as compared to B20 and B30b.Originality/valueIncorporation of PPO and WCB in PB–diesel blend as a quaternary fuel blend in diesel engines has not been reported. O...
Awang, MSN, Zulkifli, NWM, Abbas, MM, Zulkifli, SA, Kalam, MA, Yusoff, MNAM, Daud, WMAW & Ahmad, MH 2022, 'Effect of diesel-palm biodiesel fuel with plastic pyrolysis oil and waste cooking biodiesel on tribological characteristics of lubricating oil', Alexandria Engineering Journal, vol. 61, no. 9, pp. 7221-7231.
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Aydemir, E, Dogan, S, Baygin, M, Ooi, CP, Barua, PD, Tuncer, T & Acharya, UR 2022, 'CGP17Pat: Automated Schizophrenia Detection Based on a Cyclic Group of Prime Order Patterns Using EEG Signals', Healthcare, vol. 10, no. 4, pp. 643-643.
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Background and Purpose: Machine learning models have been used to diagnose schizophrenia. The main purpose of this research is to introduce an effective schizophrenia hand-modeled classification method. Method: A public electroencephalogram (EEG) signal data set was used in this work, and an automated schizophrenia detection model is presented using a cyclic group of prime order with a modulo 17 operator. Therefore, the presented feature extractor was named as the cyclic group of prime order pattern, CGP17Pat. Using the proposed CGP17Pat, a new multilevel feature extraction model is presented. To choose a highly distinctive feature, iterative neighborhood component analysis (INCA) was used, and these features were classified using k-nearest neighbors (kNN) with the 10-fold cross-validation and leave-one-subject-out (LOSO) validation techniques. Finally, iterative hard majority voting was employed in the last phase to obtain channel-wise results, and the general results were calculated. Results: The presented CGP17Pat-based EEG classification model attained 99.91% accuracy employing 10-fold cross-validation and 84.33% accuracy using the LOSO strategy. Conclusions: The findings and results depicted the high classification ability of the presented cryptologic pattern for the data set used.
Azadi, M, Emrouznejad, A, Ramezani, F & Hussain, FK 2022, 'Efficiency Measurement of Cloud Service Providers Using Network Data Envelopment Analysis', IEEE Transactions on Cloud Computing, vol. 10, no. 1, pp. 348-355.
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IEEE An increasing number of organizations and businesses around the world use cloud computing services to improve their performance in the competitive marketplace. However, one of the biggest challenges in using cloud computing services is performance measurement and the selection of the best cloud service providers (CSPs) based on quality of service (QoS) requirements (Duan, 2017). To address this shortcoming in this article we propose a network data envelopment analysis (DEA) method in measuring the efficiency of CSPs. When network dimensions are taken into consideration, a more comprehensive analysis is enabled where divisional efficiency is reflected in overall efficiency estimates. This helps managers and decision makers in organizations to make accurate decisions in selecting cloud services. In the current study, variable returns to scale (VRS), the non-oriented network slacks-based measure (SBM) model and input-oriented and output-oriented SBM models are applied to measure the performance of 18 CSPs. The obtained results show the superiority of the network DEA model and they also demonstrate that the proposed model can evaluate and rank CSPs much better than compared to traditional DEA models.
Azam, MA, Khan, KB, Salahuddin, S, Rehman, E, Khan, SA, Khan, MA, Kadry, S & Gandomi, AH 2022, 'A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics', Computers in Biology and Medicine, vol. 144, pp. 105253-105253.
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BACKGROUND AND OBJECTIVES: Over the past two decades, medical imaging has been extensively apply to diagnose diseases. Medical experts continue to have difficulties for diagnosing diseases with a single modality owing to a lack of information in this domain. Image fusion may be use to merge images of specific organs with diseases from a variety of medical imaging systems. Anatomical and physiological data may be included in multi-modality image fusion, making diagnosis simpler. It is a difficult challenge to find the best multimodal medical database with fusion quality evaluation for assessing recommended image fusion methods. As a result, this article provides a complete overview of multimodal medical image fusion methodologies, databases, and quality measurements. METHODS: In this article, a compendious review of different medical imaging modalities and evaluation of related multimodal databases along with the statistical results is provided. The medical imaging modalities are organized based on radiation, visible-light imaging, microscopy, and multimodal imaging. RESULTS: The medical imaging acquisition is categorized into invasive or non-invasive techniques. The fusion techniques are classified into six main categories: frequency fusion, spatial fusion, decision-level fusion, deep learning, hybrid fusion, and sparse representation fusion. In addition, the associated diseases for each modality and fusion approach presented. The quality assessments fusion metrics are also encapsulated in this article. CONCLUSIONS: This survey provides a baseline guideline to medical experts in this technical domain that may combine preoperative, intraoperative, and postoperative imaging, Multi-sensor fusion for disease detection, etc. The advantages and drawbacks of the current literature are discussed, and future insights are provided accordingly.
Azimian, M, Amir, V, Javadi, S, Siano, P & Alhelou, HH 2022, 'Enabling demand response for optimal deployment of multi‐carrier microgrids incorporating incentives', IET Renewable Power Generation, vol. 16, no. 3, pp. 547-564.
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AbstractThis paper inspects customer multi‐carrier microgrid deployments' techno‐economic viability and assists investors in deciding whether or not to invest in multi‐carrier microgrid installations equipped with smart demand‐side technologies. The solution of the proposed model determines the optimal mix and size of distributed energy resources, and identifies the ideal participation rate of potential responsive customers within the multi‐carrier microgrid. The objective of the proposed model is to minimize the overall deployment cost comprising the investment and replacement of distributed energy resources, demand‐side smart measurement and informing appliances, loan payoff, operation, maintenance, peak demand charge, energy demand shifting reward or penalty, emission, and unserved energy while ensuring the desired levels of reliability and online reserve. The model also considers incentive policies to encourage customers to install demand‐side smart technologies to participate in demand response programs actively. The planning problem is formulated by mixed‐integer programming. The proposed model is applied to an industrial zone as an aggregate load. Numerical simulations exhibit the model's efficacy and scrutinize in‐depth, the effect of a variety of factors on multi‐carrier microgrid planning results, including the extents of the capital investment fund and loan in addition to demand response enabling technology cost.
Baba, AA, Hashmi, RM, Attygalle, M, Esselle, KP & Borg, D 2022, 'Ultrawideband Beam Steering at mm-Wave Frequency With Planar Dielectric Phase Transformers', IEEE Transactions on Antennas and Propagation, vol. 70, no. 3, pp. 1719-1728.
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Babakian, A, Monclus, P, Braun, R & Lipman, J 2022, 'A Retrospective on Workload Identifiers: From Data Center to Cloud-Native Networks', IEEE Access, vol. 10, pp. 105518-105527.
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As applications move to multiple clouds, the network has become a reactive element to support cloud consumption and application needs. Through each generation of network architectures, identifiers and the use of dynamic locators evolved in different levels of the protocol stack. The identifiers and locators type is defined by the isolation boundary and how the architecture considers semantic overload in the IP address. Each solution is an outcome of incrementalism, resulting in application delivery outgrowing the underlying network. This paper contributes an industrial retrospective of how the schemes and mechanisms for identification and location of network entities have evolved in traditional data centers and how they match cloud-native application requirements. Specifically, there is an evaluation of each application artifact that forced necessary changes in the identifiers and locators. Finally, the common themes are highlighted from observations to determine the investigation areas that may play an essential role in the future of cloud-native networking.
Bachosz, K, Vu, MT, Nghiem, LD, Zdarta, J, Nguyen, LN & Jesionowski, T 2022, 'Enzyme-based control of membrane biofouling for water and wastewater purification: A comprehensive review', Environmental Technology & Innovation, vol. 25, pp. 102106-102106.
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Bachosz, K, Zdarta, J, Nghiem, LD & Jesionowski, T 2022, 'Multienzymatic conversion of monosaccharides from birch biomass after pretreatment', Environmental Technology & Innovation, vol. 28, pp. 102874-102874.
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Badeti, U, Jiang, J, Almuntashiri, A, Pathak, N, Dorji, U, Volpin, F, Freguia, S, Ang, WL, Chanan, A, Kumarasingham, S, Shon, HK & Phuntsho, S 2022, 'Impact of source-separation of urine on treatment capacity, process design, and capital expenditure of a decentralised wastewater treatment plant', Chemosphere, vol. 300, pp. 134489-134489.
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In this study, the impact of urine diversion on the treatment capacity, treatment process, and capital costs of a decentralised wastewater treatment plant (WWTP) was simulated using BioWin. The data for simulation including for economic analysis were obtained from a real decentralised WWTP at Sydney. Simulation was conducted for two alternative process design scenarios of a WWTP: membrane bioreactor (MBR) without denitrification and anaerobic MBR in place of aerobic MBR and compared to existing process design. The simulation shows that with about 75% urine diversion (through source separation), the treatment capacity of the existing WWTP can be doubled although above 40% urine diversion, the impact appears less rapid. When the urine diversion exceeds 75%, it was found that the anoxic tank for biological denitrification becomes redundant and the current wastewater treatment process could be replaced with a simpler and much less aeration intensive membrane bioreactor (MBR) producing similar effluent quality with a 24% reduction in capital expenditure (footprint) cost. Anaerobic MBR can be a potential alternative to aerobic MBR although pre-treatment becomes essential before reverse osmosis treatment for water reuse applications. Sensitivity analysis has revealed that by operating the bioreactor at higher mixed liquor suspended solids concentrations (9 g/L instead of 5 g/L) could help increase the WWTP treatment capacity by about 3.5 times at 75% urine diversion. Hence, urine diversion (until nitrogen-limiting conditions occur above 75% urine diversion) can increase the treatment capacity of an existing WWTP and reduce the capital expenses due to reduced plant footprint.
Bagherimehrab, M, Sanders, YR, Berry, DW, Brennen, GK & Sanders, BC 2022, 'Nearly Optimal Quantum Algorithm for Generating the Ground State of a Free Quantum Field Theory', PRX Quantum, vol. 3, no. 2, p. 020364.
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We devise a quasilinear quantum algorithm for generating an approximation for the ground state of a quantum field theory (QFT). Our quantum algorithm delivers a superquadratic speedup over the state-of-the-art quantum algorithm for ground-state generation, overcomes the ground-state-generation bottleneck of the prior approach and is optimal up to a polylogarithmic factor. Specifically, we establish two quantum algorithms - Fourier-based and wavelet-based - to generate the ground state of a free massive scalar bosonic QFT with gate complexity quasilinear in the number of discretized QFT modes. The Fourier-based algorithm is limited to translationally invariant QFTs. Numerical simulations show that the wavelet-based algorithm successfully yields the ground state for a QFT with broken translational invariance. Furthermore, the cost of preparing particle excitations in the wavelet approach is independent of the energy scale. Our algorithms require a routine for generating one-dimensional Gaussian (1DG) states. We replace the standard method for 1DG-state generation, which requires the quantum computer to perform lots of costly arithmetic, with a novel method based on inequality testing that significantly reduces the need for arithmetic. Our method for 1DG-state generation is generic and could be extended to preparing states whose amplitudes can be computed on the fly by a quantum computer.
Baharvand, S & Pradhan, B 2022, 'Erosion and flood susceptibility evaluation in a catchment of Kopet-Dagh mountains using EPM and RFM in GIS', Environmental Earth Sciences, vol. 81, no. 20, p. 490.
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Erosion and flood events can damage soils, water, quality, and sediment transportation, causing many cumulative hazards. In developing countries, such as Iran, the empirical models, which are low-cost procedures to mitigate environmental hazards, are necessary to plan the watersheds. Hence, the main aim of this study is to evaluate erosion and flood susceptibility using empirical models of erosion potential method (EPM) and rational flood model (RFM) to prioritize the GIS-based prone zones in a catchment of the Kopet-Dagh Mountains. The results revealed that the heavy classes of erosion and flood susceptibility include 40.4–58.2% of the total study area, dominantly in the upstream catchments. The correlation test revealed a strong, significant, and direct association (R equal to 0.705) between W and Qp at the 99% confidence level. Consequently, the results of our research indicated the prioritization of the three sub-catchments based on their slight sensitivity and susceptibility to occurrences of soil erosion and flood events through future spatial developments. Ultimately, the model validity explained the AUC (area under the curve) values averagely equal to 0.898 and 0.917 for erosion and flood susceptibility evaluations (i.e., EPM and RFM), explaining the very good performance of the models and excellent sensitivities.
Bahrami, N, Reza Nikoo, M, Al-Rawas, G, Al-Wardy, M & Gandomi, AH 2022, 'Reservoir optimal operation with an integrated approach for managing floods and droughts using NSGA-III and prospect behavioral theory', Journal of Hydrology, vol. 610, pp. 127961-127961.
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Establishing a holistic approach to manage floods and droughts is essential considering the different hydrological conditions. This work aimed to demonstrate how the integrated management of floods and droughts (IMFD) can increase sustainability and decrease the vulnerability of reservoirs against water-related disasters. To do so, a non-integrated management of floods and droughts (NMFD) is compared to an IMFD approach for the optimal operation of a reservoir (Doroudzan dam) to evaluate the sustainability of each scenario, especially the environmental water demand for Bakhtegan lake. Doroudzan dam provides water for different users, especially the environmental water demand. The IMFD and NMFD are differentiated through the development of industrial activities and the construction of a new regulatory dam, respectively. Each management scenario is tested under three hydrological conditions, i.e., dry, normal, and wet, which are determined by the standardized runoff index (SRI). Additionally, in the IMFD scenario, optimal cropping patterns are proposed to farmers by policymakers to increase water use efficiency. The heterogeneity in farmers’ response to adopting these cropping patterns is simulated through a well-known behavioral theory, prospect theory (PT). A novel optimization algorithm, NSGA-III, is utilized to determine the optimal operation of the reservoir and a bankruptcy scenario is utilized to share released water from the reservoir between the involved stakeholders. Results show the superiority of the IMFD scenario against the NMFD scenario in terms of decreasing water shortage as well as agricultural and environmental vulnerability, especially in dry conditions due to implementing more sustainable solutions. The average annual financial wealth generated by the IMFD scenario is greater than that of the NMFD scenario by about 68% through development in the industrial activities as a more profitable sector. Also, the utilization of PT shows how d...
Bai, K, Zhu, X, Wen, S, Zhang, R & Zhang, W 2022, 'Broad Learning Based Dynamic Fuzzy Inference System With Adaptive Structure and Interpretable Fuzzy Rules', IEEE Transactions on Fuzzy Systems, vol. 30, no. 8, pp. 3270-3283.
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This article investigates the feasibility of applying the broad learning system (BLS) to realize a novel Takagi-Sugeno-Kang (TSK) neuro-fuzzy model, namely a broad learning based dynamic fuzzy inference system (BL-DFIS). It not only improves the accuracy and interpretability of neuro-fuzzy models but also solves the challenging problem that models are incapable of determining the optimal architecture autonomously. BL-DFIS first accomplishes a TSK fuzzy system under the framework of BLS, in which an extreme learning machine auto-encoder is employed to obtain feature representation in a fast and analytical way, and an interpretable linguistic fuzzy rule is integrated into the enhancement node to ensure the high interpretability of the system. Meanwhile, the extended-enhancement unit is designed to achieve the first-order TSK fuzzy system. In addition, a dynamic incremental learning algorithm with internal pruning and updating mechanism is developed for the learning of BL-DFIS, which enables the system to automatically assemble the optimal structure to obtain a compact rule base and an excellent classification performance. Experiments on benchmark datasets demonstrate that the proposed BL-DFIS can achieve a better classification performance than some state-of-the-art nonfuzzy and neuro-fuzzy methods, simultaneously using the most parsimonious model structure.
Ball, JE 2022, 'Modelling accuracy for urban design flood estimation', Urban Water Journal, vol. 19, no. 1, pp. 87-96.
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Management of flood risk remains a major problem in many urban environments. To generate the data needed for estimation of the flood risk, catchment models have been used with the reliability of the predicted catchment response for design flood estimation dependent upon the model calibration. However, the level of calibration required to achieve reliable design flood estimation remains unspecified. The purpose of this paper is to assess the event modelling accuracy needed if data from the calibrated model are to be used for continuous simulation of data for flood frequency analysis. For this purpose, a SWMM-based catchment model was investigated using 25 monitored events, while the assessment of the calibration was based on a normalised peak flow error. Alternative sets of parameter values were used to obtain estimates of the peak flow for each of the selected events. The best performing sets of these sets of parameter values were used with SWMM in a continuous simulation mode to predict flow sequences for extraction of Annual Maxima Series for an At-Site Flood Frequency Analysis. From the analysis of these At-Site Flood Frequency Analyses, it was concluded that the normalised peak flow error needed to be less than 10% if reliable design flood quantile estimates were to be obtained.
Balogun, A-L, Sheng, TY, Sallehuddin, MH, Aina, YA, Dano, UL, Pradhan, B, Yekeen, S & Tella, A 2022, 'Assessment of data mining, multi-criteria decision making and fuzzy-computing techniques for spatial flood susceptibility mapping: a comparative study', Geocarto International, vol. 37, no. 26, pp. 12989-13015.
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This study develops an Adaboost-GIS model for flood susceptibility mapping and evaluates its relative performance by undertaking a comparative assessment of the machine learning model with Multi-Criteria Decision Making (MCDM) and soft computing models integrated with GIS. An Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Fuzzy-AHP, Fuzzy-ANP and AdaBoost machine learning models were developed and integrated with GIS to classify the susceptibility of the study area. Out of 70 sample validation locations, Adaboost’s performance was the best with a 95.72% similarity match with very high and high susceptibility locations followed by F-ANP, ANP, F-AHP and AHP with 95.65%, 92.75%, 81.42% and 77.14% similarity matches, respectively. It also had the highest AUC (0.864). Thus, the Adaboost machine learning, Fuzzy computing and conventional MCDM models can be adopted by stakeholders for accurately assessing flood susceptibility, thereby fostering safe and resilient cities.
Banerjee, S, Lyu, J, Huang, Z, Leung, FHF, Lee, T, Yang, D, Su, S, Zheng, Y & Ling, SH 2022, 'Ultrasound spine image segmentation using multi-scale feature fusion Skip-Inception U-Net (SIU-Net)', Biocybernetics and Biomedical Engineering, vol. 42, no. 1, pp. 341-361.
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Scoliosis is a 3D spinal deformation where the spine takes a lateral curvature, forming an angle in the coronal plane. Diagnosis of scoliosis requires periodic detection, and frequent exposure to radiative imaging may cause cancer. A safer and more economical alternative imaging, i.e., 3D ultrasound imaging modality, is being explored. However, unlike other radiative modalities, an ultrasound image is noisy, which often suppresses the image's useful information. Through this research, a novel hybridized CNN architecture, multi-scale feature fusion Skip-Inception U-Net (SIU-Net), is proposed for a fully automatic bony feature detection, which can be further used to assess the severity of scoliosis safely and automatically. The proposed architecture, SIU-Net, incorporates two novel features into the basic U-Net architecture: (a) an improvised Inception block and (b) newly designed decoder-side dense skip pathways. The proposed model is tested on 109 spine ultrasound image datasets. The architecture is evaluated using the popular (i) Jaccard Index (ii) Dice Coefficient and (iii) Euclidean distance, and compared with (a) the basic U-net segmentation model, (b) a more evolved UNet++ model, and (c) a newly developed MultiResUNet model. The results show that SIU-Net gives the clearest segmentation output, especially in the important regions of interest such as thoracic and lumbar bony features. The method also gives the highest average Jaccard score of 0.781 and Dice score of 0.883 and the lowest histogram Euclidean distance of 0.011 than the other three models. SIU-Net looks promising to meet the objectives of a fully automatic scoliosis detection system.
Bao, G, Wang, K, Yang, L, He, J, He, B, Xu, X & Zheng, Y 2022, 'Feasibility evaluation of a Zn-Cu alloy for intrauterine devices: In vitro and in vivo studies', Acta Biomaterialia, vol. 142, pp. 374-387.
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The comprehensively adopted copper-containing intrauterine devices (Cu-IUDs) present typical adverse effects such as bleeding and pain at the initial stage of post-implantation. The replacement of Cu material is demanded. Zinc and its alloys, the emerging biodegradable materials, exhibited contraceptive effects since 1969. In this work, we evaluated the feasibility of bulk Zn alloys as IUD active material. Using pure Cu and pure Zn as control groups, we investigated the contraceptive performance of Zn-0.5Cu and Zn-1Cu alloys via in vitro and in vivo tests. The results showed that the main corrosion product of Zn-Cu alloys is ZnO from both in vitro and in vivo studies. CaZn2(PO4)2·2H2O is formed atop after long-term immersion in simulated uterine fluid, whereas CaCO3 is generally formed atop after implantation in the rat uterine environment. The cytocompatibility of the Zn-1Cu alloy was significantly higher than that of the pure Zn and pure Cu to the human endometrial epithelial cell lines. Furthermore, the in vivo results showed that the Zn-1Cu alloy presented much improved histocompatibility, least damage and the fastest recovery on endometrium structure in comparison to pure Zn, Zn-0.5Cu and pure Cu. The systematic and comparing studies suggest that Zn-1Cu alloy can be considered as a possible candidate for IUD with great biochemical and biocompatible properties as well as high contraceptive effectiveness. STATEMENT OF SIGNIFICANCE: The existing adverse effects with the intrinsic properties of copper materials for copper-containing intrauterine devices (Cu-IUD) are of concerns in their employment. Such as burst release of cupric ions (Cu2+) at the initial stage of the Cu-IUD. Zinc and its alloys which have been emerging as a potential biodegradable material exhibited contraceptive effects since 1969. In this study, Zn-1Cu alloys displayed significantly improved biocompatibility with human uterus cells and a decreased inflammatory response within the u...
Bao, L, Qi, B, Wang, Y, Dong, D & Wu, R 2022, 'Multi-channel quantum parameter estimation', Science China Information Sciences, vol. 65, no. 10.
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Bao, W, Yang, C, Wen, S, Zeng, M, Guo, J, Zhong, J & Xu, X 2022, 'A Novel Adaptive Deskewing Algorithm for Document Images', Sensors, vol. 22, no. 20, pp. 7944-7944.
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Document scanning often suffers from skewing, which may seriously influence the efficiency of Optical Character Recognition (OCR). Therefore, it is necessary to correct the skewed document before document image information analysis. In this article, we propose a novel adaptive deskewing algorithm for document images, which mainly includes Skeleton Line Detection (SKLD), Piecewise Projection Profile (PPP), Morphological Clustering (MC), and the image classification method. The image type is determined firstly based on the image’s layout feature. Thus, adaptive correcting is applied to deskew the image according to its type. Our method maintains high accuracy on the Document Image Skew Estimation Contest (DISEC’2013) and PubLayNet datasets, which achieved 97.6% and 80.1% accuracy, respectively. Meanwhile, extensive experiments show the superiority of the proposed algorithm.
Bardhan, A, GuhaRay, A, Gupta, S, Pradhan, B & Gokceoglu, C 2022, 'A novel integrated approach of ELM and modified equilibrium optimizer for predicting soil compression index of subgrade layer of Dedicated Freight Corridor', Transportation Geotechnics, vol. 32, pp. 100678-100678.
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This study proposes a high-performance machine learning model to sidestep the time of conducting actual laboratory tests of soil compression index (Cc), one of the important criteria for determining the settlement of subgrade layers of roadways, railways, and airport runways. The suggested method combines the modified equilibrium optimizer (MEO) and the extreme learning machine (ELM) in a novel way. In this study, Gaussian mutation with an exploratory search mechanism was incorporated to construct the MEO and used to enhance the performance of conventional ELM by optimizing its learning parameters. PCA (Principal component analysis)-based results exhibit that the developed ELM-MEO attained the most precise prediction with R2 = 0.9746, MAE = 0.0184, and RMSE = 0.0284 in training, and R2 = 0.9599, MAE = 0.0232, and RMSE = 0.0357 in the testing phase. The results showed that the proposed ELM-MEO model outperformed the other developed models, confirming the ELM-MEO model's superiority over the other models, such as random forest, gradient boosting machine, genetic programming, including the ELM and artificial neural network (ANN)-based models optimized with equilibrium optimizer, particle swarm optimization, Harris hawks optimization, slime mould algorithm, and marine predators algorithm. Based on the experimental results, the proposed ELM-MEO can be used as a promising alternative to predict soil Cc in civil engineering projects, including rail and road projects.
Bardhan, A, Kardani, N, Alzo’ubi, AK, Samui, P, Gandomi, AH & Gokceoglu, C 2022, 'A Comparative Analysis of Hybrid Computational Models Constructed with Swarm Intelligence Algorithms for Estimating Soil Compression Index', Archives of Computational Methods in Engineering, vol. 29, no. 7, pp. 4735-4773.
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Bardhan, A, Kardani, N, Alzo'ubi, AK, Roy, B, Samui, P & Gandomi, AH 2022, 'Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil consolidation parameter', Journal of Rock Mechanics and Geotechnical Engineering, vol. 14, no. 5, pp. 1588-1608.
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The study proposes an improved Harris hawks optimization (IHHO) algorithm by integrating the standard Harris hawks optimization (HHO) algorithm and mutation-based search mechanism for developing a high-performance machine learning solution for predicting soil compression index. HHO is a newly introduced meta-heuristic optimization algorithm (MOA) used to solve continuous search problems. Compared to the original HHO, the proposed IHHO can evade trapping in local optima, which in turn raises the search capabilities and enhances the search mechanism relying on mutation. Subsequently, a novel meta-heuristic-based soft computing technique called ELM-IHHO was established by integrating IHHO and extreme learning machine (ELM) to estimate soil compression index. A sum of 688 consolidation test data was collected for this purpose from an ongoing dedicated freight corridor railway project. To evaluate the generalization capability of the proposed ELM-IHHO model, a detailed comparison between ELM-IHHO and other well-established MOAs, such as particle swarm optimization, genetic algorithm, and biogeography-based optimization integrated with ELM, was performed. Based on the outcomes, the ELM-IHHO model exhibits superior performance over the other MOAs in predicting soil compression index.
Bardhan, A, Subbiah, S, Mohanty, K, Ibrar, I & Altaee, A 2022, 'Feasibility of Poly (Vinyl Alcohol)/Poly (Diallyldimethylammonium Chloride) Polymeric Network Hydrogel as Draw Solute for Forward Osmosis Process', Membranes, vol. 12, no. 11, pp. 1097-1097.
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Forward osmosis (FO) has been identified as an emerging technology for the concentration and crystallization of aqueous solutions at low temperatures. However, the application of the FO process has been limited due to the unavailability of a suitable draw solute. An ideal draw solute should be able to generate high osmotic pressure and must be easily regenerated with less reverse solute flux (RSF). Recently, hydrogels have attracted attention as a draw solution due to their high capacity to absorb water and low RSF. This study explores a poly (vinyl alcohol)/poly (diallyldimethylammonium chloride) (PVA-polyDADMAC) polymeric network hydrogel as a draw solute in forward osmosis. A low-pressure reverse osmosis (RO) membrane was used in the FO process to study the performance of the hydrogel prepared in this study as a draw solution. The robust and straightforward gel synthesis method provides an extensive-scale application. The results indicate that incorporating cationic polyelectrolyte poly (diallyldimethylammonium chloride) into the polymeric network increases swelling capacity and osmotic pressure, thereby resulting in an average water flux of the PVA-polyDADMAC hydrogel (0.97 L m−2 h−1) that was 7.47 times higher than the PVA hydrogel during a 6 h FO process against a 5000 mg L−1 NaCl solution (as a feed solution). The effect of polymer and cross-linker composition on swelling capacity was studied to optimize the synthesized hydrogel composition. At 50 °C, the hydrogel releases nearly >70% of the water absorbed during the FO process at room temperatures, and water flux can be recovered by up to 86.6% of the initial flux after 12 hydrogel (draw solute) regenerations. Furthermore, this study suggests that incorporating cationic polyelectrolytes into the polymeric network enhances FO performances and lowers the actual energy requirements for (draw solute) regeneration. This study represents a significant step toward the commercial implementati...
Bargshady, G, Zhou, X, Barua, PD, Gururajan, R, Li, Y & Acharya, UR 2022, 'Application of CycleGAN and transfer learning techniques for automated detection of COVID-19 using X-ray images', Pattern Recognition Letters, vol. 153, pp. 67-74.
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Barkhordari, MS, Armaghani, DJ & Fakharian, P 2022, 'Ensemble machine learning models for prediction of flyrock due to quarry blasting', International Journal of Environmental Science and Technology, vol. 19, no. 9, pp. 8661-8676.
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Barua, PD, Baygin, N, Dogan, S, Baygin, M, Arunkumar, N, Fujita, H, Tuncer, T, Tan, R-S, Palmer, E, Azizan, MMB, Kadri, NA & Acharya, UR 2022, 'Automated detection of pain levels using deep feature extraction from shutter blinds-based dynamic-sized horizontal patches with facial images', Scientific Reports, vol. 12, no. 1, p. 17297.
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AbstractPain intensity classification using facial images is a challenging problem in computer vision research. This work proposed a patch and transfer learning-based model to classify various pain intensities using facial images. The input facial images were segmented into dynamic-sized horizontal patches or “shutter blinds”. A lightweight deep network DarkNet19 pre-trained on ImageNet1K was used to generate deep features from the shutter blinds and the undivided resized segmented input facial image. The most discriminative features were selected from these deep features using iterative neighborhood component analysis, which were then fed to a standard shallow fine k-nearest neighbor classifier for classification using tenfold cross-validation. The proposed shutter blinds-based model was trained and tested on datasets derived from two public databases—University of Northern British Columbia-McMaster Shoulder Pain Expression Archive Database and Denver Intensity of Spontaneous Facial Action Database—which both comprised four pain intensity classes that had been labeled by human experts using validated facial action coding system methodology. Our shutter blinds-based classification model attained more than 95% overall accuracy rates on both datasets. The excellent performance suggests that the automated pain intensity classification model can be deployed to assist doctors in the non-verbal detection of pain using facial images in various situations (e.g., non-communicative patients or during surgery). This system can facilitate timely detection and management of pain.
Barua, PD, Karasu, M, Kobat, MA, Balık, Y, Kivrak, T, Baygin, M, Dogan, S, Demir, FB, Tuncer, T, Tan, R-S & Acharya, UR 2022, 'An accurate valvular heart disorders detection model based on a new dual symmetric tree pattern using stethoscope sounds', Computers in Biology and Medicine, vol. 146, pp. 105599-105599.
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Barua, PD, Tuncer, I, Aydemir, E, Faust, O, Chakraborty, S, Subbhuraam, V, Tuncer, T, Dogan, S & Acharya, UR 2022, 'L-Tetrolet Pattern-Based Sleep Stage Classification Model Using Balanced EEG Datasets', Diagnostics, vol. 12, no. 10, pp. 2510-2510.
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Background: Sleep stage classification is a crucial process for the diagnosis of sleep or sleep-related diseases. Currently, this process is based on manual electroencephalogram (EEG) analysis, which is resource-intensive and error-prone. Various machine learning models have been recommended to standardize and automate the analysis process to address these problems. Materials and methods: The well-known cyclic alternating pattern (CAP) sleep dataset is used to train and test an L-tetrolet pattern-based sleep stage classification model in this research. By using this dataset, the following three cases are created, and they are: Insomnia, Normal, and Fused cases. For each of these cases, the machine learning model is tasked with identifying six sleep stages. The model is structured in terms of feature generation, feature selection, and classification. Feature generation is established with a new L-tetrolet (Tetris letter) function and multiple pooling decomposition for level creation. We fuse ReliefF and iterative neighborhood component analysis (INCA) feature selection using a threshold value. The hybrid and iterative feature selectors are named threshold selection-based ReliefF and INCA (TSRFINCA). The selected features are classified using a cubic support vector machine. Results: The presented L-tetrolet pattern and TSRFINCA-based sleep stage classification model yield 95.43%, 91.05%, and 92.31% accuracies for Insomnia, Normal dataset, and Fused cases, respectively. Conclusion: The recommended L-tetrolet pattern and TSRFINCA-based model push the envelope of current knowledge engineering by accurately classifying sleep stages even in the presence of sleep disorders.
Barua, PD, Vicnesh, J, Gururajan, R, Oh, SL, Palmer, E, Azizan, MM, Kadri, NA & Acharya, UR 2022, 'Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review', International Journal of Environmental Research and Public Health, vol. 19, no. 3, pp. 1192-1192.
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Mental disorders (MDs) with onset in childhood or adolescence include neurodevelopmental disorders (NDDs) (intellectual disability and specific learning disabilities, such as dyslexia, attention deficit disorder (ADHD), and autism spectrum disorders (ASD)), as well as a broad range of mental health disorders (MHDs), including anxiety, depressive, stress-related and psychotic disorders. There is a high co-morbidity of NDDs and MHDs. Globally, there have been dramatic increases in the diagnosis of childhood-onset mental disorders, with a 2- to 3-fold rise in prevalence for several MHDs in the US over the past 20 years. Depending on the type of MD, children often grapple with social and communication deficits and difficulties adapting to changes in their environment, which can impact their ability to learn effectively. To improve outcomes for children, it is important to provide timely and effective interventions. This review summarises the range and effectiveness of AI-assisted tools, developed using machine learning models, which have been applied to address learning challenges in students with a range of NDDs. Our review summarises the evidence that AI tools can be successfully used to improve social interaction and supportive education. Based on the limitations of existing AI tools, we provide recommendations for the development of future AI tools with a focus on providing personalised learning for individuals with NDDs.
Barzegarkhoo, R, Farhangi, M, Aguilera, RP, Lee, SS, Blaabjerg, F & Siwakoti, YP 2022, 'Common-Ground Grid-Connected Five-Level Transformerless Inverter With Integrated Dynamic Voltage Boosting Feature', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 6, pp. 6661-6672.
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A single-phase common-ground five-level (5L) inverter with a dynamic voltage conversion gain and capability of operating in a wide input voltage range and a single-stage energy conversion configuration is presented in this article. The proposed topology requires nine active power switches and is comprised of an integrated switched-boost (SB) module connected in series to a switched-flying-capacitor (SFC) cell. Two self-balanced capacitors with a single boost inductor in the integrated SB module are employed to generate a 5L output voltage waveform with a dynamic voltage conversion gain. The current stress profile of all the active and passive elements is kept within a permissible input current range. By adopting an extra diode-capacitor-inductor network into the integrated SB module and with the utilization of the same SFC cell, the proposed topology is extended to achieve a quadratic voltage conversion gain while retaining the quality of ac voltage waveform. Theoretical analysis, closed-loop control/modulation principles, design guidance, comparative study, and relevant experimental results obtained from a 1.5-kW laboratory-built prototype are presented to ascertain the operation and feasibility of the proposed system.
Barzegarkhoo, R, Farhangi, M, Lee, SS, Aguilera, RP, Siwakoti, YP & Pou, J 2022, 'Nine-Level Nine-Switch Common-Ground Switched-Capacitor Inverter Suitable for High-Frequency AC-Microgrid Applications', IEEE Transactions on Power Electronics, vol. 37, no. 5, pp. 6132-6143.
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Voltage source multilevel inverters with reduced leakage current, single-stage voltage step-up feature, compact design, and an efficient performance are a promising technology for high-frequency ac (HFac) microgrids feeding through renewable energy sources. This article proposes a novel single-source common-grounded (CG) step-up nine-level (9L) inverter, which can be applied in HFac microgrid applications. The proposed CG-based boost inverter is comprised of only nine switches (9S) and three self-balanced capacitors. Using the switched-capacitor (SC) technique, a double voltage boosting feature within a single power processing stage is achieved, while the leakage current concern is eliminated due to a CG-based configuration between the input dc source and the null of the grid. With the help of an LC input filter, the input current profile is free from large discontinuous inrush spikes. The working principles of the proposed 9L9S-CGSC inverter are discussed in this article. The modulation and closed-loop control strategy, as well as a comparative study, are presented. Finally, the open and closed-loop grid-tied performances of the proposed topology are evaluated by both simulation and experimental results obtained from a 1.2-kW laboratory-built prototype.
Barzegarkhoo, R, Forouzesh, M, Lee, SS, Blaabjerg, F & Siwakoti, YP 2022, 'Switched-Capacitor Multilevel Inverters: A Comprehensive Review', IEEE Transactions on Power Electronics, vol. 37, no. 9, pp. 11209-11243.
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Multilevel inverters (MLIs) with switched-capacitor (SC) units have been a widely rehearsed research topic in power electronics since the last decade. Inductorless/transformerless operation with voltage-boosting feature and inherent capacitor self-voltage balancing performance with a reduced electromagnetic interference make the SC-MLI an attractive converter over the other available counterparts for various applications. There have been many developed SC-MLI structures recently put forward, where different basic switching techniques are used to generate multiple (discrete) output voltage levels. In general, the priority of the topological development is motivated by the number of output voltage levels, overall voltage gain, and full dc-link voltage utilization, while reducing the component counts and stress on devices for better efficiency and power density. To facilitate the direction of future research in SC-MLIs, this article presents a comprehensive review, critical analysis, and categorization of the existing topologies. Common fundamental units are generalized and summarized with their merits and demerits. Ultimately, major challenges and research directions are outlined leading to the future technology roadmap for more practical applications.
Barzegarkhoo, R, Khan, SA, Siwakoti, YP, Aguilera, RP, Lee, SS & Khan, MNH 2022, 'Implementation and Analysis of a Novel Switched-Boost Common-Ground Five-Level Inverter Modulated With Model Predictive Control Strategy', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 1, pp. 731-744.
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Common-ground (CG) string inverters with a transformerless (TL) circuit configuration have been broadly popular in grid-connected photovoltaic (PV) applications. The most important feature of a PV string TL inverter with a CG circuit connection is the elimination of leakage current concerns. Having the voltage boosting property within a single power processing stage can also be propitious to further facilitate the integration of low-scale PV string panels with higher efficiency. An efficient topology of such converters is presented in this study, which is able to produce a five-level stair-case output voltage waveform using an integrated switched-boost (SB) cell with only seven power switches. The proposed SB common grounded five-level (SBCG5L)-TL inverter can also be extended for the three-phase CG-based applications with the contribution of the same integrated SB cell. As for the single-phase configuration, it needs two dc-link capacitors and two power diodes along with two small inductors. A quasi-soft charging operation of the involved capacitors is also achieved. To control the injected current under the grid-connected condition, a single-step model predictive control (MPC) technique with a fixed switching frequency operation has also been presented. The proposed circuit description, the theoretical analysis of the applied MPC principles, and the comparative study with associated experimental results are also presented to ascertain the correctness and feasibility of the proposed SBCG5L-TL inverter.
Basack, S, Goswami, G, Khabbaz, H & Karakouzian, M 2022, 'Flow Characteristics through Granular Soil Influenced by Saline Water Intrusion: A Laboratory Investigation', Civil Engineering Journal, vol. 8, no. 5, pp. 863-878.
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The coastal geoenvironment initiates saline water intrusion into the freshwater aquifers, producing a geohydraulic problem. Such intrusion not only contaminates the fresh groundwater resources, making them unsuitable for human use, but also alters the hydraulic conductivity of the aquifer materials, which affects the coastal groundwater flow, influencing the water resources planning and management. Past investigations reveal that the groundwater flow can be linear or nonlinear depending upon the hydraulic gradient. Thus, the coefficients of nonlinear hydraulic conductivities are affected by saltwater intrusion. The present study focuses on an in-depth laboratory investigation into the influence of saltwater submergence on the nonlinear flow characteristics through granular soil. The fine sand samples have been submerged under saline water of specified concentrations for a specific duration, and the alteration in their nonlinear geohydraulic properties has been studied. It is observed that the flow characteristics through fine sand are significantly affected by the period of submergence and saline concentration. Appropriate analyses of the test results are performed to interpret the experimental data, and relevant conclusions are drawn therefrom. The novelty of this study is an in-depth analysis of nonlinear flow characterization affected by saline water intrusion. Doi: 10.28991/CEJ-2022-08-05-02 Full Text: PDF
Basack, S, Loganathan, MK, Goswami, G & Khabbaz, H 2022, 'Saltwater Intrusion into Coastal Aquifers and Associated Risk Management: Critical Review and Research Directives', Journal of Coastal Research, vol. 38, no. 3, pp. 654-672.
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Coastal regions mainly rely on sources of local fresh groundwater for domestic, irrigational, and industrial usages, which are vulnerable to high-risk of getting intruded by saltwater. Excessive pumping of fresh groundwater initiates advances of saltwater-freshwater interface inward due to hydraulic equilibrium and continuity. This introduces saline water intrusion into coastal aquifers. This is also caused by natural hazards like sea-level rise and storm-surge. The saltwater intrusion in coastal aquifers contaminates the freshwater storage, thereby emerging as a major environmental issue. To incorporate adequate coastal groundwater control and management techniques that are effective and conveniently implementable, understanding the phenomenon of saline water intrusion and the risk assessment is of utmost importance. Several scientific contributions including theoretical (analytical and numerical) solutions, experimental (laboratory and field) results, design recommendations, and risk analysis are available, indicating remarkable advances in the research area. The authors have attempted to summarize the significant contributions over the last few decades in each of these study aspects through extensive literature survey and critical analysis of the existing knowledge. It is observed that risk prevention and control methodologies such as qanat-well structure, shallow and deep wells might not be effective in many coastal areas as the complex intrusion process is yet to be understood clearly. Moreover, the high intensity coastal hazards that often occur due to climate change continue to make aquifers more vulnerable, adversely affecting the coastal groundwater management. The paper presents a critical overview of existing studies on saline water intrusion into coastal aquifers and associated risks and management techniques. Furthermore, adequate research directives with recommendations for future development are also provided.
Basack, S, Nimbalkar, S, Karakouzian, M, Bharadwaj, S, Xie, Z & Krause, N 2022, 'Field Installation Effects of Stone Columns on Load Settlement Characteristics of Reinforced Soft Ground', International Journal of Geomechanics, vol. 22, no. 4.
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Bashir, MR, Gill, AQ & Beydoun, G 2022, 'A Reference Architecture for IoT-Enabled Smart Buildings.', SN Comput. Sci., vol. 3, pp. 493-493.
Bashir, MR, Gill, AQ & Beydoun, G 2022, 'A Reference Architecture for IoT-Enabled Smart Buildings', SN Computer Science, vol. 3, no. 6, p. 493.
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AbstractThe management and analytics of big data generated from IoT sensors deployed in smart buildings pose a real challenge in today’s world. Hence, there is a clear need for an IoT focused Integrated Big Data Management and Analytics framework to enable the near real-time autonomous control and management of smart buildings. The focus of this paper is on the development and evaluation of the reference architecture required to support such a framework. The applicability of the reference architecture is evaluated by taking into account various example scenarios for a smart building involving the management and analysis of near real-time IoT data from 1000 sensors. The results demonstrate that the reference architecture can guide the complex integration and orchestration of real-time IoT data management, analytics, and autonomous control of smart buildings, and that the architecture can be scaled up to address challenges for other smart environments.
Baygin, M, Barua, PD, Dogan, S, Tuncer, T, Key, S, Acharya, UR & Cheong, KH 2022, 'A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal', Sensors, vol. 22, no. 5, pp. 2007-2007.
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Recently, deep models have been very popular because they achieve excellent performance with many classification problems. Deep networks have high computational complexities and require specific hardware. To overcome this problem (without decreasing classification ability), a hand-modeled feature selection method is proposed in this paper. A new shape-based local feature extractor is presented which uses the geometric shape of the frustum. By using a frustum pattern, textural features are generated. Moreover, statistical features have been extracted in this model. Textures and statistics features are fused, and a hybrid feature extraction phase is obtained; these features are low-level. To generate high level features, tunable Q factor wavelet transform (TQWT) is used. The presented hybrid feature generator creates 154 feature vectors; hence, it is named Frustum154. In the multilevel feature creation phase, this model can select the appropriate feature vectors automatically and create the final feature vector by merging the appropriate feature vectors. Iterative neighborhood component analysis (INCA) chooses the best feature vector, and shallow classifiers are then used. Frustum154 has been tested on three basic hand-movement sEMG datasets. Hand-movement sEMG datasets are commonly used in biomedical engineering, but there are some problems in this area. The presented models generally required one dataset to achieve high classification ability. In this work, three sEMG datasets have been used to test the performance of Frustum154. The presented model is self-organized and selects the most informative subbands and features automatically. It achieved 98.89%, 94.94%, and 95.30% classification accuracies using shallow classifiers, indicating that Frustum154 can improve classification accuracy.
Baygin, M, Yaman, O, Barua, PD, Dogan, S, Tuncer, T & Acharya, UR 2022, 'Exemplar Darknet19 feature generation technique for automated kidney stone detection with coronal CT images', Artificial Intelligence in Medicine, vol. 127, pp. 102274-102274.
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Kidney stone is a commonly seen ailment and is usually detected by urologists using computed tomography (CT) images. It is difficult and time-consuming to detect small stones in CT images. Hence, an automated system can help clinicians to detect kidney stones accurately. In this work, a novel transfer learning-based image classification method (ExDark19) has been proposed to detect kidney stones using CT images. The iterative neighborhood component analysis (INCA) is employed to select the most informative feature vectors and these selected features vectors are fed to the k nearest neighbor (kNN) classifier to detect kidney stones with a ten-fold cross-validation (CV) strategy. The proposed ExDark19 model yielded an accuracy of 99.22% with 10-fold CV and 99.71% using the hold-out validation method. Our results demonstrate that the proposed ExDark19 detect kidney stones over 99% accuracies for two validation techniques. This developed automated system can assist the urologists to validate their manual screening of kidney stones and hence reduce the possible human error.
Bayl-Smith, P, Taib, R, Yu, K & Wiggins, M 2022, 'Response to a phishing attack: persuasion and protection motivation in an organizational context', Information & Computer Security, vol. 30, no. 1, pp. 63-78.
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PurposeThis study aims to examine the effect of cybersecurity threat and efficacy upon click-through, response to a phishing attack: persuasion and protection motivation in an organizational context.Design/methodology/approachIn a simulated field trial conducted in a financial institute, via PhishMe, employees were randomly sent one of five possible emails using a set persuasion strategy. Participants were then invited to complete an online survey to identify possible protective factors associated with clicking and reporting behavior (N = 2,918). The items of interest included perceived threat severity, threat susceptibility, response efficacy and personal efficacy.FindingsThe results indicate that response behaviors vary significantly across different persuasion strategies. Perceptions of threat susceptibility increased the likelihood of reporting behavior beyond clicking behavior. Threat susceptibility and organizational response efficacy were also associated with increased odds of not responding to the simulated phishing email attack.Practical implicationsThis study again highlights human susceptibility to phishing attacks in the presence of social engineering strategies. The results suggest heightened awareness of phishing threats and responsibility to personal cybersecurity are key to ensuring secure business environments.Originality/valueThe authors extend existing phishing literature by investigating not only click-through behavior, but also...
Begum, H, Qian, J & Lee, JE-Y 2022, 'Effect of crystal orientation on liquid phase performance of piezoelectric-on-silicon elliptical plate resonators', Sensors and Actuators A: Physical, vol. 340, pp. 113548-113548.
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Various microelectromechanical (MEM) resonator topologies have been proposed for liquid phase sensing applications. Low liquid phase motional resistance (Rm) and moderately high liquid phase quality factor (Q) are critical to the performance of oscillators based on these resonators for real-time frequency tracking in sensing applications. We recently described a new topology we call the elliptical plate resonator EPR that delivers the lowest Rm after normalizing for area (which impacts mass sensitivity as a tradeoff for lower Rm). In this work, we show that further significant gains in performance can be made by choice of device alignment to the silicon crystal axis (< 110 > direction vs. < 100 > direction). We compare the liquid phase performance between the two orientations for a range of geometrical ratios defining the ellipse of the device. We show that the orientation makes a notable difference on trends in liquid phase Q and Rm. By aligning the EPR to the < 110 > direction, we demonstrate a liquid phase Q of 310 and Rm of 2.5 kΩ. Normalizing for area (Rm×A) to express the tradeoff between mass sensitivity and electrical performance in relation to device area, we report an Rm×A of 0.25 kΩ.mm2. We also show that these gains in liquid phase Rm and Q translate into significant lowering of the Allan deviation when these devices are embedded in close loop to track their frequency in real time with water loaded on the device as expected in liquid phase sensing applications.
Bekhit, M, Negm, NA, Abd El-Rahman, NR & Fekry, M 2022, 'Synthesis and evaluation of Gemini cationic surfactant based on 4-(4-nitrobenzyl)pyridine: surface and biological activities', Desalination and Water Treatment, vol. 274, pp. 150-158.
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Bem, NFSD, Ruppert, MG, Fleming, AJ & Yong, YK 2022, 'Simultaneous tip force and displacement sensing for AFM cantilevers with on-chip actuation: Design and characterization for off-resonance tapping mode', Sensors and Actuators A: Physical, vol. 338, pp. 113496-113496.
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The use of integrated on-chip actuation simplifies the identification of a cantilever resonance, can improve imaging speed, and enables the use of smaller cantilevers, which is required for low-force imaging at high speed. This article describes a cantilever with on-chip actuation and novel dual-sensing capabilities for AFM. The dual-sensing configuration allows for tip displacement and tip force to be measured simultaneously. A mathematical model is developed and validated with finite element analysis. A physical prototype is presented, and its calibration and validation are presented. The cantilever is optimized for use in off-resonance tapping modes. Experimental results demonstrate an agreement between the on-chip sensors and external force and displacement measurements.
Bendoy, AP, Zeweldi, HG, Park, MJ, Shon, HK, Kim, H, Chung, W-J & Nisola, GM 2022, 'Silicene nanosheets as support fillers for thin film composite forward osmosis membranes', Desalination, vol. 536, pp. 115817-115817.
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Development of membranes with enhanced separation and transport properties remains crucial for the advancement of forward osmosis (FO). Herein, a novel thin film composite (TFC) FO membrane with silicene-loaded polysulfone support (SN) is reported. Silicene loading was varied to obtain different SNs grown with polyamide (PA) layers to afford TFC-SN FO membranes. Characterization results reveal that optimal silicene loading (0.25 wt%) produced the most porous and most hydrophilic SN0.25 with finger-like pore structures. Low silicene loading showed minimal impact, whereas, excessive addition resulted in aggregation which diminished its effect in SN. Meanwhile, silicene had no influence on PA layer formation as all TFC-SNs registered similar solute permeability coefficient B = 0.14–0.16 LMH. On the other hand, TFC-SN0.25 achieved the highest water permeability coefficient A = 1.56 LMH bar−1 attributable to the favorable properties of SN0.25. TFC-SN0.25 also exhibited the lowest structural parameter S = 334 μm, which explains its superior FO performance relative to other TFC-SNs. Results from FO runs indicate that internal concentration polarization was reduced by 27.5–33% in TFC-SN0.25 compared with the control (TFC-SN0). FO runs in simulated low salinity water and seawater feed highlight the potential of TFC-SN0.25 for desalination. The developed TFC-SN0.25 can be repeatedly used and deliver consistent Jv values. Overall findings demonstrate the benefits of silicene for improved performance of TFC FO membranes.
Bendoy, AP, Zeweldi, HG, Park, MJ, Shon, HK, Kim, H, Chung, W-J & Nisola, GM 2022, 'Thermo-responsive hydrogel with deep eutectic mixture co-monomer as drawing agent for forward osmosis', Desalination, vol. 542, pp. 116067-116067.
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Deep eutectic mixture (DEM) and N-isopropylacrylamide (NIPAM) were co-polymerized as thermo-responsive P(NIPAM-co-DEM) hydrogel drawing agents for forward osmosis (FO). N-hexyl-N,N-dihydroxyethyl-N-methylammonium chloride–acrylic acid ([DHEA]Cl-AA) DEM is non-toxic and highly conductive due to its ionic (R4N+, Cl−) and hydrophilic (-OH, -C=O) groups. Addition of DEM at different contents (0–7.5 wt%) afforded P(NIPAM-co-DEM) with wide open pores as excellent water channels during water absorption. Their critical temperatures ranged Tc = 34.7–51.4 °C. At T < Tc, P(NIPAM-co-DEM) attained equilibrium swelling ratios = 32–43 (vs. 19 for PNIPAM), highlighting the advantage of DEM for enhanced water absorption. Heating the hydrogels at T > Tc resulted in 87.6–96 % dewatering efficiencies. Among the fabricated hydrogels, P(NIPAM-co-DEM) with 5 wt% DEM exhibited the highest water uptake and dewatering efficiency at moderate Tc. It achieved the highest FO water flux (initial Jv = 2.38 LMH in DI water feed). P(NIPAM-co-DEM) with 5 wt% DEM effectively and consistently desalinated low salinity water (2000 mg L−1 NaCl, Jv = 1.81 LMH) and treated domestic wastewater (Jv = 1.90 LMH) at T = 25 °C in cycled operations via efficient water recovery T = 45 °C and hydrogel drying via solar irradiation (1 kW m−2 for 1.5 h). Overall results demonstrate the potential of deep eutectic mixtures for the development of hydrogels as effective FO drawing agents.
Benedict, G & Gill, AQ 2022, 'A regulatory control framework for decentrally governed DLT systems: Action design research', Information & Management, vol. 59, no. 7, pp. 103555-103555.
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Benhelal, E, Rashid, MI, Rayson, MS, Brent, GF, Oliver, T, Stockenhuber, M & Kennedy, EM 2022, 'Corrigendum to “Direct aqueous carbonation of heat activated serpentine: Discovery of undesirable side reactions reducing process efficiency” [Appl. Energy 242 (2019) 1369–1382]', Applied Energy, vol. 325, pp. 119886-119886.
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Beni, HM, Mortazavi, H & Islam, MS 2022, 'Biomedical and biophysical limits to mathematical modeling of pulmonary system mechanics: a scoping review on aerosol and drug delivery', Biomechanics and Modeling in Mechanobiology, vol. 21, no. 1, pp. 79-87.
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Bennett, N, Erdogan, M, Karkada, M, Kureshi, N & Green, RS 2022, 'Mandatory gunshot wound reporting in Nova Scotia: a pre–post-evaluation of firearm-related injury rates', Canadian Journal of Emergency Medicine, vol. 24, no. 4, pp. 439-443.
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Bernardo, LS, Damaševičius, R, Ling, SH, de Albuquerque, VHC & Tavares, JMRS 2022, 'Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data', Biomedicines, vol. 10, no. 11, pp. 2746-2746.
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Parkinson’s disease (PD) is the most common form of Parkinsonism, which is a group of neurological disorders with PD-like motor impairments. The disease affects over 6 million people worldwide and is characterized by motor and non-motor symptoms. The affected person has trouble in controlling movements, which may affect simple daily-life tasks, such as typing on a computer. We propose the application of a modified SqueezeNet convolutional neural network (CNN) for detecting PD based on the subject’s key-typing patterns. First, the data are pre-processed using data standardization and the Synthetic Minority Oversampling Technique (SMOTE), and then a Continuous Wavelet Transformation is applied to generate spectrograms used for training and testing a modified SqueezeNet model. The modified SqueezeNet model achieved an accuracy of 90%, representing a noticeable improvement in comparison to other approaches.
Bersenev, EY, Berseneva, АP, Prysyazhnyuk, A, McGregor, C, Berseneva, IА, Funtova, II & Chernikova, AG 2022, 'Cybernetic Approach to Health Assessment', CARDIOMETRY, no. 23, pp. 31-40.
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The exploration of orbital space served as a prerequisite for the creation of a new direction of medical science in relation to the very extreme conditions of life of spacecraft crews. Space medicine, relying on the most modern research methods and approaches, thanks to the development of new medical devices and the use of unique data analysis algorithms, has made a significant contribution to the development of telemedicine, medical cybernetics, and prenosological principles for assessing the state of human health. The review reflects the main stages in the development of medical cybernetics and prenosological diagnostics based on the assessment of the regulatory components of the cardiovascular system. Discussed the aspects of the application of the method of mathematical analysis of the heart rhythm in relation to the assessment and forecast of the working capacity of cosmonauts, at the simulating model of microgravity and confinement. Shown the useful methodically apply for the healthcare of manufacture teams at the plants, passenger bus driver’s employments. As the part of appliance of the new advance tools of children and adolescents public health during the educating process at schools. The created system for analyzing the current functional state of human health and mathematical models that make it possible to predict its negative changes make it possible to predetermine the vector of development of medicine in the future. The foundations of knowledge gained over the period of more than 70 years of scientific activity of Professor R.M. Bavsky are reflected in promising areas of cardiology research using computer technologies - such as Cardiometry technologies.
Beyrampour-Basmenj, H, Rahmati, M, Moghamddam, MP, Kalan, ME, Alivand, M, Aliyari-Serej, Z, Nastarin, P, Omrani, M, Khodakarimi, S & Ebrahimi-Kalan, A 2022, 'Association between miRNAs expression and multiple sclerosis pathogenesis: A novel therapeutic approach', Gene Reports, vol. 26, pp. 101457-101457.
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Bhatnagar, P, Singh, AK, Gupta, KK & Siwakoti, YP 2022, 'A Switched-Capacitors-Based 13-Level Inverter', IEEE Transactions on Power Electronics, vol. 37, no. 1, pp. 644-658.
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Bhol, P, Swain, S, Altaee, A, Saxena, M & Samal, AK 2022, 'Cobalt–iron decorated tellurium nanotubes for high energy density supercapacitor', Materials Today Chemistry, vol. 24, pp. 100871-100871.
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We report the synthesis of cobalt-iron (Co-Fe) decorated tellurium nanotubes (Te NTs) using semiconductive Te NTs as a sacrificial template using the wet chemical method. The Co and Fe precursor concentration incorporated into Te NT plays a significant role in obtaining various bimetallic telluride structures. The one-dimensional (1-D) structure of Co-Fe decorated Te NTs with Te NTs in the backbone provides superior conductivity and exhibits high electrochemical performance with battery type electrode behaviour. The Co-Fe decorated Te NTs electrode is combined with the electric double-layer capacitors (EDLC) type electrode activated carbon (AC) to tune the energy density performance. The asymmetric assembly shows an excellent specific capacitance of 179.2 F g-1 (48.7 mAh g-1) at a current density of 0.9 A g-1 in 4 M KOH electrolyte. More importantly, it exhibits a maximum energy density of 62.1 Wh Kg-1 at a power density of 1138.2 W Kg-1 under a potential window of 1.58 V. This potential finding shows the significant applicability of Te NTs as a template for the synthesis of bimetallic tellurides with unique morphologies. The synergistic effect from multimetals and anisotropic morphology is beneficial for energy storage applications.
Bhowmick, S, Xu, F, Molla, MM & Saha, SC 2022, 'Chaotic phenomena of natural convection for water in a V-shaped enclosure', International Journal of Thermal Sciences, vol. 176, pp. 107526-107526.
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Bi, G, Man, H, Jiang, J, Li, Y, Chen, T, Zhang, X, Fang, D & Ding, X 2022, 'Effects of Zn addition on microstructure and tensile properties of Mg–Y–Co alloy', Journal of Materials Research and Technology, vol. 20, pp. 590-605.
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Bi, S, Cui, J, Ni, W, Jiang, Y, Yu, S & Wang, X 2022, 'Three-Dimensional Cooperative Positioning for Internet of Things Provenance', IEEE Internet of Things Journal, vol. 9, no. 20, pp. 19945-19958.
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A large number of Internet of Things (IoT) devices have been interconnected for information collection and exchange. The data are only meaningful if it is captured at the expected location (i.e., the IoT devices or sensors are not removed accidentally or intentionally). This article presents a new algorithm, which cooperatively locates multiple IoT devices deployed in a 3-D space based on pairwise Euclidean distance measurements. When the distance measurement noises are negligible, a new feasibility problem of rank-3 variables is formulated. We solve the problem using the difference-of-convex (DC) programming to preserve the rank-3 constraints, rather than relaxing the constraints, using semidefinite relaxation (SDR). When the distance measurements are corrupted by additive noises and nonlight-of-sight (NLOS) propagation, a maximum-likelihood estimation (MLE) problem is formulated and transformed to a DC program solved with the rank-3 constraints preserved. Simulation results indicate that the proposed approach can achieve satisfactory accuracy results with a low complexity and strong robustness to the irregular topology, poor connectivity, and measurement errors, as compared to existing SDR-based alternatives.
Bin Sawad, A, Narayan, B, Alnefaie, A, Maqbool, A, Mckie, I, Smith, J, Yuksel, B, Puthal, D, Prasad, M & Kocaballi, AB 2022, 'A Systematic Review on Healthcare Artificial Intelligent Conversational Agents for Chronic Conditions', Sensors, vol. 22, no. 7, pp. 2625-2625.
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This paper reviews different types of conversational agents used in health care for chronic conditions, examining their underlying communication technology, evaluation measures, and AI methods. A systematic search was performed in February 2021 on PubMed Medline, EMBASE, PsycINFO, CINAHL, Web of Science, and ACM Digital Library. Studies were included if they focused on consumers, caregivers, or healthcare professionals in the prevention, treatment, or rehabilitation of chronic diseases, involved conversational agents, and tested the system with human users. The search retrieved 1087 articles. Twenty-six studies met the inclusion criteria. Out of 26 conversational agents (CAs), 16 were chatbots, seven were embodied conversational agents (ECA), one was a conversational agent in a robot, and another was a relational agent. One agent was not specified. Based on this review, the overall acceptance of CAs by users for the self-management of their chronic conditions is promising. Users’ feedback shows helpfulness, satisfaction, and ease of use in more than half of included studies. Although many users in the studies appear to feel more comfortable with CAs, there is still a lack of reliable and comparable evidence to determine the efficacy of AI-enabled CAs for chronic health conditions due to the insufficient reporting of technical implementation details.
Binh, NTM, Ngoc, NH, Binh, HTT, Van, NK & Yu, S 2022, 'A family system based evolutionary algorithm for obstacle-evasion minimal exposure path problem in Internet of Things', Expert Systems with Applications, vol. 200, pp. 116943-116943.
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Barrier coverage in wireless sensor networks (WSNs) is a well-known model for military security applications in IoTs, in which sensors are deployed to detect every movement over the predefined border. The fundamental sub-problem of barrier coverage in WSNs is the minimal exposure path (MEP) problem. The MEP refers to the worst-case coverage path where an intruder can move through the sensing field with the lowest capability to be detected. Knowledge about MEP is useful for network designers to identify the worst coverage in WSNs. Most prior research focused on this problem with the assumption that the WSN has an ideal deployment environment without obstacles, causing existing gaps between theoretical and practical WSNs systems. To overcome this drawback, we investigate a systematic and generic MEP problem under real-world environment networks by presenting obstacles called Obstacle-Evasion-MEP (hereinafter OE-MEP). We propose an algorithm to create several types of arbitrary-shaped obstacles inside the deployment area of WSNs. The OE-MEP problem is an NP-Hard with high dimension, non-differentiation, non-linearity, and constraints. Based upon its characteristics, we then devise an elite algorithm namely Family System based Evolutionary Algorithm (FEA) with our newly-proposed concepts of Family System, tailored to efficiently solve the OE-MEP. We also build an extension to a custom-made simulation environment to integrate a variety of network topologies as well as obstacles. Experimental results on numerous instances indicate that the proposed algorithm is suitable for the converted OE-MEP problem and performs better in solution accuracy than existing approaches.
Binsawad, M, Abbasi, GA & Sohaib, O 2022, 'People’s expectations and experiences of big data collection in the Saudi context', PeerJ Computer Science, vol. 8, pp. e926-e926.
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Big data and machine learning technologies facilitate various business intelligence activities for businesses. However, personal data collection can generate adverse effects on consumers. Big data collection can compromise people’s sense of autonomy, harming digital privacy, transparency and trust. This research investigates personal data collection, control, awareness, and privacy regulation on people’s autonomy in Saudi. This study used a hybrid analytical model that incorporates symmetrical and asymmetrical analysisviafuzzy set qualitative comparative analysis (fsQCA) to analyze consumer sense of autonomy regarding big data collection. The symmetrical shows that ‘Control’ had the most significant influence on people’s autonomy, followed by ‘Big data collection’ and ‘Awareness’. The fsQCA shows 84% of the variation, explaining the people’s autonomy.
Blaker, K, Wijewardene, A, White, E, Stokes, G, Chong, S, Ganda, K, Ridley, L, Brown, S, White, C, Clifton-Bligh, R & Seibel, MJ 2022, 'Electronic search programs are effective in identifying patients with minimal trauma fractures', Osteoporosis International, vol. 33, no. 2, pp. 435-441.
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Blamires, SJ, Nobbs, M, Wolff, JO & Heu, C 2022, 'Nutritionally induced nanoscale variations in spider silk structural and mechanical properties', Journal of the Mechanical Behavior of Biomedical Materials, vol. 125, pp. 104873-104873.
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Bo, L, Li, Q, Tian, Y, Wu, D, Yu, Y, Chen, X & Gao, W 2022, 'Nonlinear dynamic investigation of the perovskite solar cell with GPLR-FGP stiffeners under blast impact', International Journal of Mechanical Sciences, vol. 213, pp. 106866-106866.
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The perovskite solar cell (PSC), as a potential disruptive space market entrant, has fascinated both the scientific community and aerospace industry due to the high specific power, flexibility, and low fabrication cost. With the aim of reducing structure weight while strengthening the blast-carrying capacity of the PSC, the novel graphene platelets (GPL) reinforced functionally graded porous (GPLR-FGP) stiffeners have been incorporated as enhancements against impact. This research explores the nonlinear dynamic characteristics of the PSC with GPLR-FGP stiffeners under blast load. Integrating the von-Kármán geometric nonlinearity and the first-order shear deformation theory, the governing motion equations are derived by utilizing Airy's stress function and the Galerkin method. The fourth-order Runge-Kutta approach is employed to capture the solutions of dynamic equations effectively. Diverse influences of the stiffener material, boundary condition, plate theory, porosity distribution, GPL dispersion, porosity coefficient, GPL weight fraction, GPL geometry, damping, nonlinear elastic foundation, and initial imperfection are investigated in the numerical study. Besides, the optimal parameters of the PSC with GPLR-FGP stiffeners are discovered, facilitating the following paces of space design and practical implementation in extraterrestrial circumstances.
Booth, E & Lim, R 2022, 'The Picture of Privilege: Examining the Lack of Diverse Characters in 2018 Australian Children’s Picture Books', Jeunesse, vol. 14, no. 1, pp. 65-83.
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This article explores the findings from the first “diversity count” of Australian children’s picture books, conducted in 2019 in partnership with advocacy group Voices from the Intersection (VFTI). Specifically, this article explores the eighty-three percent of 2018 Australian children’s picture books that did not feature a marginalized protagonist: namely, those that featured human characters who could not be identified as marginalized in any way, animals, and inhuman protagonists. We propose that the Australian publishing industry, rather than suffering from a “diversity deficit,” instead overrepresents a narrow demographic of human experiences and non-human protagonists. We suggest that the oversaturation of the local children’s picture book market with such similar stories disadvantages all children, who are denied a rich and diverse reading experience, as well as the opportunity to see themselves and their peers depicted. This article provides greater insight into the current debates about diversity and inclusion in children’s media.
Bordbar, M, Neshat, A, Javadi, S, Pradhan, B, Dixon, B & Paryani, S 2022, 'Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches', Natural Hazards, vol. 110, no. 3, pp. 1799-1820.
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The main objective of this study is to integrate adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural network (ANN) to design an integrated supervised committee machine artificial intelligence (SCMAI) model to spatially predict the groundwater vulnerability to seawater intrusion in Gharesoo-Gorgan Rood coastal aquifer placed in the northern part of Iran. Six hydrological GALDIT parameters (i.e., G groundwater occurrence, A aquifer hydraulic conductivity, L level of groundwater above sea level, D distance from the shore, I impact of the existing status of seawater intrusion in the region, and T thickness of the aquifer) were considered as inputs for each model. In the training step, the values of GALDIT’s vulnerability index were conditioned by using the values of TDS concentration in order to obtain the conditioned vulnerability index (CVI). The CVI was considered as the target for each model. After training the models, each model was tested using a separate TDS dataset. The results indicated that the ANN and ANFIS algorithms performed better than the SVM algorithm. The values of correlation were obtained as 88, 87, and 80% for ANN, ANFIS, and SVM models, respectively. In the testing step of the SCMAI model, the values of RMSE, R2, and r were obtained as 6.4, 0.95, and 97%, respectively. Overall, SCMAI model outperformed other models to spatially predicting vulnerable zones. The result of the SCMAI model confirmed that the western zones along the shoreline had the highest vulnerability to seawater intrusion; therefore, it seems critical to consider emergency protection plans for study area. Graphic abstract: [Figure not available: see fulltext.]
Bordhan, P, Razavi Bazaz, S, Jin, D & Ebrahimi Warkiani, M 2022, 'Advances and enabling technologies for phase-specific cell cycle synchronisation', Lab on a Chip, vol. 22, no. 3, pp. 445-462.
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Schematic illustration of conventional (left) and microfluidics-based (right) phase-specific cell cycle synchronization strategies.
Botín-Sanabria, DM, Mihaita, A-S, Peimbert-García, RE, Ramírez-Moreno, MA, Ramírez-Mendoza, RA & Lozoya-Santos, JDJ 2022, 'Digital Twin Technology Challenges and Applications: A Comprehensive Review', Remote Sensing, vol. 14, no. 6, pp. 1335-1335.
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A digital twin is a virtual representation of a physical object or process capable of collecting information from the real environment to represent, validate and simulate the physical twin’s present and future behavior. It is a key enabler of data-driven decision making, complex systems monitoring, product validation and simulation and object lifecycle management. As an emergent technology, its widespread implementation is increasing in several domains such as industrial, automotive, medicine, smart cities, etc. The objective of this systematic literature review is to present a comprehensive view on the DT technology and its implementation challenges and limits in the most relevant domains and applications in engineering and beyond.
Bour, H, Abolhasan, M, Jafarizadeh, S, Lipman, J & Makhdoom, I 2022, 'A multi-layered intrusion detection system for software defined networking', Computers and Electrical Engineering, vol. 101, pp. 108042-108042.
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The majority of existing DDoS defense mechanisms in SDN impose a significant computational burden on the controller and employ limited flow statistics and packet features. Tackling these issues, this paper presents a multi-layer defense mechanism that detects and mitigates three distinct types of flooding DDoS attacks. In the proposed framework, the detection process consists of flow-based and packet-based attack detection mechanisms employing Extreme Learning Machine-based Single-hidden Layer Feedforward Networks (ELM-SLFNs) and Case-based Information Entropy (C-IE), respectively. Moreover, the affected switches are avoided in the optimal path determined by the Floyd-Warshall algorithm, where the switches are classified based on the Hidden Markov Model (HMM) using the extracted packet features. Our simulation demonstrates the improved performance of our framework compared to similar schemes proposed in the literature in terms of different metrics, including attack detection rate, detection accuracy, false-positive rate, switch failure ratio, packet loss rate, response time, and CPU utilization.
Bourahmoune, K, Ishac, K & Amagasa, T 2022, 'Intelligent Posture Training: Machine-Learning-Powered Human Sitting Posture Recognition Based on a Pressure-Sensing IoT Cushion', Sensors, vol. 22, no. 14, pp. 5337-5337.
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We present a solution for intelligent posture training based on accurate, real-time sitting posture monitoring using the LifeChair IoT cushion and supervised machine learning from pressure sensing and user body data. We demonstrate our system’s performance in sitting posture and seated stretch recognition tasks with over 98.82% accuracy in recognizing 15 different sitting postures and 97.94% in recognizing six seated stretches. We also show that user BMI divergence significantly affects posture recognition accuracy using machine learning. We validate our method’s performance in five different real-world workplace environments and discuss training strategies for the machine learning models. Finally, we propose the first smart posture data-driven stretch recommendation system in alignment with physiotherapy standards.
Boyd-Moss, M, Firipis, K, Quigley, A, Rifai, A, Cichocki, A, Whitty, S, Ngan, C, Dekiwadia, C, Long, B, Nisbet, DR, Kapsa, R & Williams, RJ 2022, 'Hybrid Self‐Assembling Peptide/Gelatin Methacrylate (GelMA) Bioink Blend for Improved Bioprintability and Primary Myoblast Response', Advanced NanoBiomed Research, vol. 2, no. 2.
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Organ fabrication as the solution to renewable donor demands requires the ability to spatially deposit viable cells into biologically relevant constructs; necessitating reliable and effective cell deposition through bioprinting and the subsequent ability to mature. However, effective bioink development demands advances in both printability and control of cellular response. Effective bioinks are designed to retain shape fidelity, influence cellular behavior, having bioactive morphologies stiffness and highly hydrated environment. Hybrid hydrogels are promising candidates as they reduce the need to re‐engineer materials for tissue‐specific properties, with each component offering beneficial properties. Herein, a multicomponent bioink is developed whereby gelatin methacrylate (GelMA) and fluorenylmethoxycarbonyprotected self‐assembling peptides (Fmoc‐SAPs) undergo coassembly to yield a tuneable bioink. This study shows that the reported fibronectin‐inspired fmoc‐SAPs present cell attachment epitopes RGD and PHSRN in the form of bioactive nanofibers; and that the GelMA enables superior printability, stability in media, and controlled mechanical properties. Importantly, when in the hybrid format, no disruption to either the methacrylate crosslinking of GelMA, or self‐assembled peptide fibril formation is observed. Finally, studies with primary myoblasts show over 98% viability at 72 h and differentiation into fused myotubes at one and two weeks demonstrate the utility of the material as a functional bioink for muscle engineering.
Braytee, A, Naji, M & Kennedy, PJ 2022, 'Unsupervised Domain-Adaptation-Based Tensor Feature Learning With Structure Preservation', IEEE Transactions on Artificial Intelligence, vol. 3, no. 3, pp. 370-380.
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Domain adaptation (DA) is widely used in computer vision and pattern recognition applications. It is an effective process where a model is trained on objects from the source domain to predict the categories of the objects in the target domain. The aim of feature extraction in domain adaptation is to learn the best representation of the data in a certain domain and use it in other domains. However, the main challenge here is the difference between the data distributions of the source and target domains. Also, in computer vision, the data are represented as tensor objects such as 3-D images and video sequences. Most of the existing methods in DA apply vectorization to the data, which leads to information loss due to failure to preserve the natural tensor structure in a low-dimensional space. Thus, in this article, we propose unsupervised DA-based tensor feature learning (UDA-TFL) as a novel adapted feature extraction method that aims to avoid vectorization during transfer knowledge simultaneously; retain the structure of the tensor objects; reduce the data discrepancy between source and target domains; and represent the original tensor object in a lower dimensional space that is resistant to noise. Therefore, multilinear projections are determined to learn the tensor subspace without vectorizing the original tensor objects via an alternating optimization strategy. We integrate maximum mean discrepancy in the objective function to reduce the difference between source and target distributions. Extensive experiments are conducted on 39 cross-domain datasets from different fields, including images and videos. The promising results indicate that UDA-TFL significantly outperforms the state-of-the-art.
Breda, A, Saco, PM, Rodríguez, JF, Sandi, SG & Riccardi, G 2022, 'Assessing the effects of sediment and tidal level variability on coastal wetland evolution', Journal of Hydrology, vol. 613, pp. 128387-128387.
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Bridgeman, JC, Brown, BJ & Elman, SJ 2022, 'Boundary Topological Entanglement Entropy in Two and Three Dimensions', Communications in Mathematical Physics, vol. 389, no. 2, pp. 1241-1276.
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AbstractThe topological entanglement entropy is used to measure long-range quantum correlations in the ground space of topological phases. Here we obtain closed form expressions for the topological entropy of (2+1)- and (3+1)-dimensional loop gas models, both in the bulk and at their boundaries, in terms of the data of their input fusion categories and algebra objects. Central to the formulation of our results are generalized $${\mathcal {S}}$$ S -matrices. We conjecture a general property of these $${\mathcal {S}}$$ S -matrices, with proofs provided in many special cases. This includes constructive proofs for categories up to rank 5.
Brindha, GR, Rishiikeshwer, BS, Santhi, B, Nakendraprasath, K, Manikandan, R & Gandomi, AH 2022, 'Precise prediction of multiple anticancer drug efficacy using multi target regression and support vector regression analysis', Computer Methods and Programs in Biomedicine, vol. 224, no. Sci. Rep. 8 1 2018, pp. 107027-107027.
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BACKGROUND AND OBJECTIVES: The prediction of multiple drug efficacies using machine learning prediction techniques based on clinical and molecular attributes of tumors is a new approach in the field of precision medicine of oncology. The selection of suitable and effective therapeutic drugs among the potential drugs is performed computationally considering the tumor features. In this study, we developed and validated machine learning models to predict the efficacy of five anti-cancer drugs according to the clinical and molecular attributes of 30 oral squamous cell carcinoma (OSCC) cohorts. This sounds a bit odd - consider: Ranking of the drugs was achieved using their apoptotic priming. METHODS: We developed multiple drug efficacy prediction models based on three types of tumor characteristics by applying machine learning methods, including multi-target regression (MTR) and support vector regression (SVR). The prediction accuracy of existing machine learning methods was enhanced by introducing novel pre-processing techniques to develop Enhanced MTR (E_MTR), Enhanced Log-based MTR (EL_MTR), Enhanced Multi-target SVR (EM_SVR), and Enhanced Log-based Multi-target SVR (ELM_SVR). As a unique capability, ELM_SVR and EL_MTR rank the drugs based on their predicted efficacy. All the drug efficacy prediction models were built using OSCC real samples and theoretical samples. The best model was selected was based on dataset size and evaluation metrics, such as error terms, residuals and parameter tuning, and cross-validated (CV) using 30 real samples and 340 theoretical samples. RESULTS: When 30 real tumor samples were used for the train-test and CV methods, MTR models predicted the efficacy with less error than SVR models. Comparatively, using 340 theoretical samples for the train-test and CV methods, though MTR improved the performance, SVR predicted the efficacy with zero error. We found that, for small samples, the proposed MTR provided a 0.01 difference betwee...
Bui, HT, Hussain, OK, Prior, D, Hussain, FK & Saberi, M 2022, 'Proof by Earnestness (PoE) to determine the authenticity of subjective information in blockchains - application in supply chain risk management', Knowledge-Based Systems, vol. 250, pp. 108972-108972.
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Blockchain is being used in various global supply chains with its ever-increasing maturity and popularity. However, in the presence of subjective information that does not have a digital footprint, blockchain application is a grey area. This is due to the difficulty in confirming the authenticity or legitimacy of information before achieving consensus on it using existing mechanisms such as Proof of Work (PoW), Proof of Authority (PoA) or Proof of Stake (PoS). In this paper, we attempt to address this issue. Specifically, we propose the Proof by Earnestness (PoE) consensus mechanism that determines the subjective information's truthfulness before further processing and formalising in blockchains. We consider supply chain risk management (SCRM) as our application area due to the vast amount of available subjective information.
Bui, P, Ngo, T & Huynh, T 2022, 'Effect of ground rice husk ash on engineering properties and hydration products of SRC eco‐cement', Environmental Progress & Sustainable Energy, vol. 41, no. 2.
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AbstractThe effect of ground rice husk ash (GRHA) (R) on engineering properties and hydration products of eco‐cements containing ground granulated blast furnace slag (GGBFS) (S) and circulating fluidized bed combustion ash (CFA) (C) was studied. Four mixture proportions of SRC eco‐cements with GRHA replacement at levels of 0%, 15%, 30%, and 45% by mass of binder were investigated. A reference mixture proportion of paste with 100% ordinary Portland cement (OPC) was prepared for comparison purposes. A series of laboratory tests including setting time, compressive strength, water absorption, porosity, thermal conductivity, scanning electron microscope coupled with energy dispersive spectroscopy, X‐ray diffraction, and Fourier‐transform infrared spectroscopy analysis was carried out. Measured results showed that the GRHA increased setting time and porosity in the SRC eco‐cements having a water‐to‐powder (w/p) of 0.4, leading to the decrease in compressive strength and thermal conductivity while the increase in water absorption. The GRHA increased the cristobalite amount and decreased the portlandite amount in the SRC eco‐cements at the age of 28 days, resulting in the more significant long‐term compressive strength development when compared with the reference paste with 100% OPC. Consequently, the GRHA could be used at a level of 15% by mass of binder to produce the SRC eco‐cement with the compressive strength at 28 days of higher than 30 MPa and the thermal conductivity of 0.713 W/mK, resulting from the formations of AFt, C–S–H, and C–A–S–H gels.
Bui, VG, Tu Bui, TM, Ong, HC, Nižetić, S, Bui, VH, Xuan Nguyen, TT, Atabani, AE, Štěpanec, L, Phu Pham, LH & Hoang, AT 2022, 'Optimizing operation parameters of a spark-ignition engine fueled with biogas-hydrogen blend integrated into biomass-solar hybrid renewable energy system', Energy, vol. 252, pp. 124052-124052.
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The smart control of the biogas-hydrogen engine is needed to improve the overall energy efficiency of the hybrid renewable energy system. The paper presents some simulation results of the optimal control parameters of the engine aiming to achieve the compromise between performance and pollutant emissions of the biogas-hydrogen engine. In neat biogas fueling mode, the optimal equivalence ratio changes from 1.05 to 1.01 as the CH4 composition in biogas increases from 60% to 80%. By adding 20% hydrogen into biogas, the optimal equivalence ratio practically reaches the stoichiometric value, despite the variation of CH4 concentration. At the same operating condition and hydrogen content, an increase of 10% CH4 in biogas leads to a decrease of 2°CA in the optimal advanced ignition angle. However, at a given engine speed and biogas composition, the optimal advanced ignition angle decreased by 3°CA when adding 10% hydrogen into biogas. The optimal ignition angle is independent of the load regime. Under optimal operating conditions, the addition of 20% hydrogen content into biogas is found to improve the indicated engine cycle work by 6%, to reduce CO and HC emissions by 5–10 times; however, it increases NOx emission by 10–15% compared to neat biogas fueling mode.
Bukhari, A, Hussain, FK & Hussain, OK 2022, 'Fog node discovery and selection: A Systematic literature review', Future Generation Computer Systems, vol. 135, pp. 114-128.
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Bukhari, AA, Hussain, FK & Hussain, OK 2022, 'Intelligent context-aware fog node discovery', Internet of Things, vol. 20, pp. 100607-100607.
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Burden, AG, Caldwell, GA & Guertler, MR 2022, 'Towards human–robot collaboration in construction: current cobot trends and forecasts', Construction Robotics, vol. 6, no. 3-4, pp. 209-220.
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Collaborative robots, or cobots, provide opportunities for their use in a range of complex scenarios in different industries,including construction. As a variant of industrial robots commonly used in automation, cobots incorporate inbuilt safetymeasures, lower costs, and easier operator programming. This article questions the state of recent peer-reviewed researchregarding the uptake and implementation of collaborative robotics in the construction industry. A ‘horizon scanning’ reviewof literature is presented in this article to uncover recent trends and forecasts in cobotics research specific to the constructionindustry. The horizon scan targets examples of human–robot collaboration (HRC) and other human–robot interactions (HRI)focussed on construction tasks. By examining where HRC has been applied in construction, we identify which drivers, ena-blers, and barriers that influence the future of construction cobots. Human-readable task models coupled with vision systems,such as augmented reality or haptic feedback and wearable interaction devices are strong enablers in how HRC can be betteradopted. Most existing research into producing diversity in robot interaction methods for HRC prescribes to overcomingstatic approaches, which is well suited to answering the ever-changing nature of construction sites. On the other hand, thedynamic nature of construction sites and worker perceptions impact the uptake of new technologies in industry where cobotsare often mistaken for highly automated industrial arms. Based on these findings, the need to build trust through successfuluse cases and case studies that demonstrate successful outcomes and productivity evaluations are necessary to overcome thebarriers to cobot adoption in the construction industry.
Bykerk, L & Valls Miro, J 2022, 'Detection of Water Leaks in Suburban Distribution Mains with Lift and Shift Vibro-Acoustic Sensors', Vibration, vol. 5, no. 2, pp. 370-382.
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Leaks in Water Distribution Networks (WDNs) account for a large proportion of Non-Revenue Water (NRW) for utilities worldwide. Typically, a leak is only confirmed once water surfaces, allowing the leak to be traced; however, a high percentage of leaks may never surface, incurring large water losses and costs for utilities. Active Leak Detection (ALD) methods can be used to detect hidden leaks; however, the success of such methods is highly dependent on the available detection instrumentation and the experience of the operator. To aid in the detection of both hidden and surfacing leaks, deployment of vibro-acoustic sensors is being increasingly explored by water utilities for temporary structural health monitoring. In this paper, data were collected and curated from a range of temporary Lift and Shift (L&S) vibro-acoustic sensor deployments across suburban Sydney. Time-frequency and frequency-domain features were generated to assess the performance and suitability of two state-of-the-art binary classification models for water leak detection. The results drawn from the extensive field data sets are shown to provide reliable leak detection outcomes, with accuracies of at least 97% and low false positive rates. Through the use of such a reliable leak detection system, utilities can streamline their leak detection and repair processes, effectively mitigating NRW and reducing customer disruptions.
Bykerk, L & Valls Miro, J 2022, 'Vibro-Acoustic Distributed Sensing for Large-Scale Data-Driven Leak Detection on Urban Distribution Mains', Sensors, vol. 22, no. 18, pp. 6897-6897.
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Non-surfacing leaks constitute the dominant source of water losses for utilities worldwide. This paper presents advanced data-driven analysis methods for leak monitoring using commercial field-deployable semi-permanent vibro-acoustic sensors, evaluated on live data collected from extensive multi-sensor deployments across a sprawling metropolitan city. This necessarily includes a wide variety of pipeline sizes, materials and surrounding soils, as well as leak sources and rates brought about by external factors. The novel proposition for structural pipe health monitoring shows that excellent leak/no-leak classification results (>94% accuracy) can be observed using Convolutional Neural Networks (CNNs) trained with Short-Time Fourier Transforms (STFTs) of the raw audio files. Most notably, it is shown how this can be achieved irrespective of the sensor used, with four models from different manufactures being part of the investigation, and over time across extended densely populated areas.
C, M, Y, MS, S, S, Alhebaishi, N, Mosli, RH & Alhelou, HH 2022, 'Three‐phase service level agreements and trust management model for monitoring and managing the services by trusted cloud broker', IET Communications, vol. 16, no. 19, pp. 2309-2320.
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AbstractCloud computing is an environment where everything is provided as a service based on demand. It follows pay as per the used model in which the service consumer needs to pay for what they have consumed. Due to the increased dependence on digitalization, the number of consumers and providers tends to grow tremendously. The consumer who needs the service from the provider is not sure about the specified service outcome, and it is too hard for them to monitor and manage the service. Hence, a trusted third party called a trusted cloud broker (TCB) is introduced for managing the services. The service level agreements (SLA) management and reputation estimation framework is proposed, which includes three phases such as (i) SLA establishment between the three parties, (ii) violation detection by comparing the observed value of the TCB and (iii) the reputation and penalty estimation of the service. The novel TCB is created to monitor the deployed services, ensuring the achievement of SLA. The TCB observes the values and estimates the reputation value for each service. It is compared with the provider log‐based reputation value and found that the proposed model provides a more precise reputation value for the service providers.
Cagno, E, Accordini, D, Trianni, A, Katic, M, Ferrari, N & Gambaro, F 2022, 'Understanding the impacts of energy efficiency measures on a Company’s operational performance: A new framework', Applied Energy, vol. 328, pp. 120118-120118.
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Energy efficiency has long been considered a key component of an industrial company's competitive repertoire. However, despite the potential benefits of adopting so-called energy efficiency measures, their uptake in such companies remains low. In response, this study proposes a framework aimed at supporting key decision-makers in undertaking a thorough assessment of energy efficiency measures. This involves, on the one hand, providing a complete characterization of a general industrial energy efficiency measure and, on the other, identifying the multiple impacts stemming from its adoption based on a novel performance measurement system that encompasses sustainability features and is defined at the shop floor level. Once theoretically validated through literature, the framework is empirically tested with a heterogeneous sample of Italian companies. The preliminary results demonstrate the framework's ability to thoroughly assess energy efficiency measures, highlighting characteristics and impacts that are sometimes considered more critical than energy saving by industrial decision-makers and therefore able to guide the outcome of the adoption decision.
Cagno, E, Franzò, S, Storoni, E & Trianni, A 2022, 'A characterisation framework of energy services offered by energy service companies', Applied Energy, vol. 324, no. United Nations Industrial Development Organization 1991, pp. 119674-119674.
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Energy Service Companies (ESCOs) are key players in the provision of energy services, hence they are considered strategic means to foster energy efficiency across countries. Nonetheless, despite the undergoing radical transformation within the ESCOs industry - especially the increasing tendency to bundle services, recent research capable to capture such evolution and development, in terms of services, technologies, and markets targeted by ESCOs is scarce. The present study aims at fulfilling the outlined research gaps by developing a novel framework for the classification and characterisation of services offered by ESCOs, as well as technologies and markets addressed. Services are characterised into nine categories, namely: preliminary analysis; project assessment; project contracting; project financing; project technology management and project supervision; energy procurement, management of incentives and regulations, and other services. The developed framework is then preliminary tested by applying it to a sample of eight Italian ESCOs to assess its completeness and usefulness. Results show the capability of the framework to adequately detail and map clusters of services-technologies-markets addressed by ESCOs, also identifying areas for future business opportunities for ESCOs themselves. The study concludes with recommendations for industry and policymaking on future efforts to promote energy services, as well as future research in this area.
Cai, B, Li, X, Kong, W, Yuan, J & Yu, S 2022, 'A Reliable and Lightweight Trust Inference Model for Service Recommendation in SIoT', IEEE Internet of Things Journal, vol. 9, no. 13, pp. 10988-11003.
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In the era of Internet of Things (IoT), millions of heterogeneous IoT devices generate an explosion of data and services waiting to be discovered. The convergence of IoT with social networks (SIoT) interconnects multiple IoT applications and alleviates the common data sparsity and cold start problems in traditional recommendation systems. However, the social trust relationships may also be very sparse, which affects the accuracy of trust-based recommendation systems. Meanwhile, mobile devices have limited resources and are more vulnerable to malicious attacks in the IoT environment. In order to complete the trust relationship and further improve the trust-based recommendation performance, we propose a reliable and lightweight trust inference model for service recommendation in SIoT, called TIRec. Firstly, we obtain a comprehensive weighted centrality metric (LGWC) considering both local and global contexts. Based on this, we propose a corresponding lightweight trust path selection algorithm. Then, we present a reliable trust inference calculation algorithm consist of trust propagation and aggregation strategy, which can efficiently resist two common malicious attacks. Finally, we incorporate the rating, direct trust and indirect trust together into the matrix factorization model, and integrate the influence of truster and trustee to obtain the synthetic model for rating predication. To the best of our knowledge, this paper is the first to integrate trust inference algorithm into the trust-based recommendation systems. Extensive experiments are conducted on three real-world datasets, and the results show that our TIRec model performs better than other advanced recommendation models in both “all users” view and “cold start users” view.
Cai, G, Wang, C, Li, J, Xu, Z, He, X & Zhao, C 2022, 'Study on Tensile Properties of Unsaturated Soil Based on Three Dimensional Discrete Element Method', Yingyong Jichu yu Gongcheng Kexue Xuebao/Journal of Basic Science and Engineering, vol. 30, no. 5, pp. 1228-1244.
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Based on the discrete element method for unsaturated materials proposed by the author, the PFC3D (Particle Flow Code in Three Dimensions) particle flow discrete element analysis program is improved, and a discrete element model suitable for both clay and sand under uniaxial tension is established. The relationship between uniaxial tensile stress and displacement and uniaxial tensile strength are studied. The influence of different microstructure parameters on the tensile failure of soil is explored, and the relationship between saturation and cohesive strength between particles is established by taking uniaxial tensile strength as a bridge. The uniaxial tensile test of clay and sand with different initial void ratio and saturation is studied, and the tensile properties of unsaturated soil and the applicability of discrete element model and program to simulate unsaturated soil are deeply studied. The results show that:among the five microstructure parameters of normal bond strength, shear bond strength, Young's modulus, stiffness ratio and friction coefficient, the influence of normal bond strength on uniaxial tensile simulation is the largest, followed by shear bond strength, Young's modulus and stiffness ratio, and the friction coefficient has the least influence; the uniaxial tensile strength of clay increases at first and then decreases with the increase of saturation. The results show that the increase rate of uniaxial tensile strength on the left side (dry side) is greater than that on the right side (wet side); the uniaxial tensile strength of sand shows a 'increase-decrease-increase' rule with the increase of saturation; the simulation results are in good agreement with the experimental results, which verifies the applicability of the discrete element model and the numerical analysis program in the simulation of uniaxial tensile properties of unsaturated materials.
Cai, Y, Zhu, M, Meng, X, Zhou, JL, Zhang, H & Shen, X 2022, 'The role of biochar on alleviating ammonia toxicity in anaerobic digestion of nitrogen-rich wastes: A review', Bioresource Technology, vol. 351, pp. 126924-126924.
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This paper reviewed the mechanisms of biochar in relieving ammonia inhibition. Biochar affects nitrogen-rich waste's anaerobic digestion (AD) performance through four ways: promotion of direct interspecies electron transfer (DIET) and microbial growth, adsorption, pH buffering, and provision of nutrients. Biochar enhances the DIET pathway by acting as an electron carrier. The role of DIET in relieving ammonia nitrogen may be exaggerated because many related studies don't provide definite evidence. Therefore, some bioinformatics technology should be used to assist in investigating DIET. Biochar absorbs ammonia nitrogen by chemical adsorption (electrostatic attraction, ion exchange, and complexation) and physical adsorption. The absorption efficiency, mainly affected by the properties of biochar, pH and temperature of AD, can reach 50 mg g-1 on average. The biochar addition can buffer pH by reducing the concentrations of VFAs, alleviating ammonia inhibition. In addition, biochar can release trace elements and increase the bioavailability of trace elements.
Cao, L 2022, 'A New Age of AI: Features and Futures', IEEE Intelligent Systems, vol. 37, no. 1, pp. 25-37.
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Cao, L 2022, 'AI in Combating the COVID-19 Pandemic', IEEE Intelligent Systems, vol. 37, no. 2, pp. 3-13.
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The SARS-CoV-2 virus, the COVID-19 disease, and the resulting pandemic have reshaped the entire world in an unprecedented manner. Massive efforts have been made by AI communities to combat the pandemic. What roles has AI played in tackling COVID-19? How has AI performed in the battle against COVID-19? Where are the gaps and opportunities? What lessons can we learn to enhance the ability of AI to battle future pandemics? These questions, despite being fundamental, are yet to be answered in full or systematically. They need to be addressed by AI communities as a priority despite the easing of the omicron infectiousness and threat. This article reviews these issues with reflections on global AI research and the literature on tackling COVID-19. It is envisaged that the demand and priority of developing 'pandemic AI' will increase over time, with smart global epidemic early warning systems to be developed by a global collaborative AI effort.
Cao, L 2022, 'AI Science and Engineering', IEEE Intelligent Systems, vol. 37, no. 1, pp. 14-15.
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Cao, L 2022, 'AI Science and Engineering: A New Field', IEEE Intelligent Systems, vol. 37, no. 1, pp. 3-13.
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Cao, L 2022, 'AutoAI: Autonomous AI', IEEE Intelligent Systems, vol. 37, no. 5, pp. 3-5.
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In the AI evolution, a significant and lasting vision and mission has been on designing autonomous AI systems (AutoAI). AutoAI differs significantly from another set of movements on automated machine learning (AutoML) and automated data science (AutoDS), which are often deemed interchangeable with automated AI. AutoML and AutoDS aim to automate some of the analytical and learning tasks, processes, and pipelines. This issue highlights the theme on AutoAI: Autonomous AI with six feature articles. My editorial further clarifies various misconceptions, myths, and pitfalls about the three related and often confused areas: AutoAI, AutoML, and AutoDS. This issue also includes an article on parallel population and human in the column AI Expert, expert-machine collaboration in the column AI Focus, and another article on intelligent mobile spaces and metaverses for the AI and Cyber-Physical-Social Systems (AI-CPSS) department.
Cao, L 2022, 'Beyond AutoML: Mindful and Actionable AI and AutoAI With Mind and Action', IEEE Intelligent Systems, vol. 37, no. 5, pp. 6-18.
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Automated machine learning (AutoML), in particular, neural architecture search (NAS) for deep learning, has ignited the fast-paced development of automating data science (AutoDS) and artificial intelligence. However, in the existing literature and practice, AutoML, AutoDS, and autonomous AI (AutoAI) are highly interchangeable and primarily centered on the automation engineering of data-driven analytics and learning pipelines. This challenges the realization of the full spectrum of AI paradigms and human-like to human-level intelligent and autonomous systems. Going beyond the state-of-the-art paradigm of AutoML and their automation engineering, there is an expectation that the new age of AI and autonomous AI (or AutoAI+) will incorporate mind-to-action intelligence and integrate them with autonomy. We pave the way for this new AI and AutoAI integrating mindful AI and AutoAI with AI mind and mindfulness and actionable AI and AutoAI with AI actions and actionability and translating AI mind to AI action for autonomous, all-around AI systems.
Cao, L 2022, 'Beyond i.i.d.: Non-IID Thinking, Informatics, and Learning', IEEE Intelligent Systems, vol. 37, no. 4, pp. 5-17.
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Cao, L 2022, 'Decentralized AI: Edge Intelligence and Smart Blockchain, Metaverse, Web3, and DeSci', IEEE Intelligent Systems, vol. 37, no. 3, pp. 6-19.
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Centralization has dominated classic scientific, social, and economic developments. Decentralization has also received increasing attention in management, decision, governance, and economics, despite its incomparability in AI. Going beyond centralized and distributed AI, this article reviews and delineates the conceptual map, research issues, and technical opportunities of decentralized AI and edge intelligence. The complementarity and metasynthesis between centralized and decentralized AI are also elaborated. We further assess where decentralized AI and edge intelligence can enable and promote smart blockchain, Web3, metaverse and decentralized science disciplinarily, technically, practically, and more broadly.
Cao, L 2022, 'Deep Learning Applications', IEEE Intelligent Systems, vol. 37, no. 3, pp. 3-5.
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This issue highlights the technical theme on 'Deep Learning Applications,'one of the most active areas in this new age of AI and machine learning. Eight articles demonstrate new progress made in deep representation learning, deep neural network architectures, and their multidomain applications. Three column articles debate on decentralized AI, autonomous racing, and big AI.
Cao, L 2022, 'Non-IID Federated Learning', IEEE Intelligent Systems, vol. 37, no. 2, pp. 14-15.
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Cao, L 2022, 'Non-IID Learning', IEEE Intelligent Systems, vol. 37, no. 4, pp. 3-4.
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Cao, L & Zhu, C 2022, 'Personalized next-best action recommendation with multi-party interaction learning for automated decision-making', PLOS ONE, vol. 17, no. 1, pp. e0263010-e0263010.
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Automated next-best action recommendation for each customer in a sequential, dynamic and interactive context has been widely needed in natural, social and business decision-making. Personalized next-best action recommendation must involve past, current and future customer demographics and circumstances (states) and behaviors, long-range sequential interactions between customers and decision-makers, multi-sequence interactions between states, behaviors and actions, and their reactions to their counterpart’s actions. No existing modeling theories and tools, including Markovian decision processes, user and behavior modeling, deep sequential modeling, and personalized sequential recommendation, can quantify such complex decision-making on a personal level. We take a data-driven approach to learn the next-best actions for personalized decision-making by a reinforced coupled recurrent neural network (CRN). CRN represents multiple coupled dynamic sequences of a customer’s historical and current states, responses to decision-makers’ actions, decision rewards to actions, and learns long-term multi-sequence interactions between parties (customer and decision-maker). Next-best actions are then recommended on each customer at a time point to change their state for an optimal decision-making objective. Our study demonstrates the potential of personalized deep learning of multi-sequence interactions and automated dynamic intervention for personalized decision-making in complex systems.
Cao, TN-D, Bui, X-T, Le, L-T, Dang, B-T, Tran, DP-H, Vo, T-K-Q, Tran, H-T, Nguyen, T-B, Mukhtar, H, Pan, S-Y, Varjani, S, Ngo, HH & Vo, T-D-H 2022, 'An overview of deploying membrane bioreactors in saline wastewater treatment from perspectives of microbial and treatment performance', Bioresource Technology, vol. 363, pp. 127831-127831.
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Cao, X & Tsang, IW 2022, 'Distribution Disagreement via Lorentzian Focal Representation', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 10, pp. 6872-6889.
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Error disagreement-based active learning (AL) selects the data that maximally update the error of a classification hypothesis. However, poor human supervision (e.g. few labels, improper classifier parameters) may weaken or clutter this update; moreover, the computational cost of performing a greedy search to estimate the errors using a deep neural network is intolerable. In this paper, a novel disagreement coefficient based on distribution, not error, provides a tighter bound on label complexity, which further guarantees its generalization in hyperbolic space. The focal points derived from the squared Lorentzian distance, present more effective hyperbolic representations on aspherical distribution from geometry, replacing the typical Euclidean, kernelized, and Poincar centroids. Experiments on different deep AL tasks show that, the focal representation adopted in a tree-likeliness splitting, significantly perform better than typical baselines of geometric centroids and error disagreement, and state-of-the-art neural network architectures-based AL, dramatically accelerating the learning process.
Cao, X & Tsang, IW 2022, 'Shattering Distribution for Active Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 1, pp. 215-228.
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Active learning (AL) aims to maximize the learning performance of the current hypothesis by drawing as few labels as possible from an input distribution. Generally, most existing AL algorithms prune the hypothesis set via querying labels of unlabeled samples and could be deemed as a hypothesis-pruning strategy. However, this process critically depends on the initial hypothesis and its subsequent updates. This article presents a distribution-shattering strategy without an estimation of hypotheses by shattering the number density of the input distribution. For any hypothesis class, we halve the number density of an input distribution to obtain a shattered distribution, which characterizes any hypothesis with a lower bound on VC dimension. Our analysis shows that sampling in a shattered distribution reduces label complexity and error disagreement. With this paradigm guarantee, in an input distribution, a Shattered Distribution-based AL (SDAL) algorithm is derived to continuously split the shattered distribution into a number of representative samples. An empirical evaluation of benchmark data sets further verifies the effectiveness of the halving and querying abilities of SDAL in real-world AL tasks with limited labels. Experiments on active querying with adversarial examples and noisy labels further verify our theoretical insights on the performance disagreement of the hypothesis-pruning and distribution-shattering strategies. Our code is available at https://github.com/XiaofengCao-MachineLearning/Shattering-Distribution-for-Active-Learning.
Cao, X, Tsang, IW & Xu, J 2022, 'Cold-Start Active Sampling Via γ-Tube', IEEE Transactions on Cybernetics, vol. 52, no. 7, pp. 6034-6045.
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Active learning (AL) improves the generalization performance for the current classification hypothesis by querying labels from a pool of unlabeled data. The sampling process is typically assessed by an informative, representative, or diverse evaluation policy. However, the policy, which needs an initial labeled set to start, may degenerate its performance in a cold-start hypothesis. In this article, we first show that typical AL sampling can be equivalently formulated as geometric sampling over minimum enclosing balls1 (MEBs) of clusters. Following the ɣ-tube structure in geometric clustering, we then divide one MEB covering a cluster into two parts: 1) a ɣ-tube and 2) a ɣ-ball. By estimating the error disagreement between sampling in MEB and ɣ-ball, our theoretical insight reveals that ɣ-tube can effectively measure the disagreement of hypotheses in original space over MEB and sampling space over ɣ-ball. To tighten our insight, we present generalization analysis, and the results show that sampling in ɣ-tube can derive higher probability bound to achieve a nearly zero generalization error. With these analyses, we finally apply the informative sampling policy of AL over ɣ-tube to present a tube AL (TAL) algorithm against the cold-start sampling issue. As a result, the dependency between the querying process and the evaluation policy of active sampling can be alleviated. Experimental results show that by using the ɣ-tube structure to deal with cold-start sampling, TAL achieves the superior performance than standard AL evaluation baselines by presenting substantial accuracy improvements. Image edge recognition extends our theoretical results.
Cao, Y, Li, B, Wen, S & Huang, T 2022, 'Consensus tracking of stochastic multi-agent system with actuator faults and switching topologies', Information Sciences, vol. 607, pp. 921-930.
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This article studies the consensus tracking control of stochastic multi-agent system (MAS) under randomly switched topology with faulty actuators. The system is considered as a stochastic one, under which the followers can track with a virtual leader while the leader's trajectory can only be used for a small subset of followers. The topology conversion is controlled by a continuous-time Markov process. A fault-tolerant control strategy is designed for each agent with its neighbors’ information, then the proposed control protocol is proved with Lyapunov stability theory. Simulation part verifies the mentioned controller.
Cao, Y, Xiao, Y, Pan, Z, Zhou, L & Chen, W 2022, 'Direct generation of 2D arrays of random numbers for high-fidelity optical ghost diffraction and information transmission through scattering media', Optics and Lasers in Engineering, vol. 158, pp. 107141-107141.
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Cao, Y, Xiao, Y, Pan, Z, Zhou, L & Chen, W 2022, 'High-fidelity temporally-corrected transmission through dynamic smoke via pixel-to-plane data encoding', Optics Express, vol. 30, no. 20, pp. 36464-36464.
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We propose a new approach for high-fidelity free-space optical data transmission through dynamic smoke using a series of 2D arrays of random numbers as information carriers. Data to be transmitted in dynamic smoke environment is first encoded into a series of 2D arrays of random numbers. Then, the generated 2D arrays of random numbers and the fixed reference pattern are alternately embedded into amplitude-only spatial light modulator, and are illuminated to propagate through dynamic smoke in free space. Real-time optical thickness (OT) is calculated to describe temporal change of the properties of optical wave in dynamic smoke environment, and transmission noise and errors caused by dynamic smoke are temporally suppressed or corrected. Optical experiments are conducted to analyze the proposed method using different experimental parameters in various scenarios. Experimental results fully verify feasibility and effectiveness of the proposed method. It is experimentally demonstrated that irregular analog signals can always be retrieved with high fidelity at the receiving end by using the proposed method, when average optical thickness (AOT) is lower than 2.5. The proposed method also shows high robustness against dynamic smoke with different concentrations. The proposed method could open up an avenue for high-fidelity free-space optical data transmission through dynamic smoke.
Cao, Y, Xiao, Y, Pan, Z, Zhou, L & Chen, W 2022, 'Physically-Secured Ghost Diffraction and Transmission', IEEE Photonics Technology Letters, vol. 34, no. 22, pp. 1238-1241.
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Cao, Y, Zhao, L, Wen, S & Huang, T 2022, 'Lag H∞ synchronization of coupled neural networks with multiple state couplings and multiple delayed state couplings', Neural Networks, vol. 151, pp. 143-155.
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This paper mainly focuses on the lag H∞ synchronization problem of coupled neural networks with multiple state or delayed state couplings. On one hand, by exploiting state feedback controller and Lyapunov functional, a criterion of lag H∞ synchronization for coupled neural networks with multiple state couplings (CNNMSCs) is insured, and lag H∞ synchronization problem in CNNMSCs is also coped with based on the adaptive state feedback controller. On the other hand, we explore the lag H∞ synchronization for coupled neural networks with multiple delayed state couplings (CNNMDSCs) by utilizing similar control strategies. At last, two numerical examples are presented to verify the effectiveness and correctness of lag H∞ synchronization for CNNMSCs and CNNMDSCs.
Catchpoole, DR, Gao, D & Mullins, P 2022, 'The ISBER 2022 Awards', Biopreservation and Biobanking, vol. 20, no. 3, pp. 306-307.
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Cavaleri, L, Barkhordari, MS, Repapis, CC, Armaghani, DJ, Ulrikh, DV & Asteris, PG 2022, 'Convolution-based ensemble learning algorithms to estimate the bond strength of the corroded reinforced concrete', Construction and Building Materials, vol. 359, pp. 129504-129504.
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Cetindamar, D, Shdifat, B & Erfani, E 2022, 'Understanding Big Data Analytics Capability and Sustainable Supply Chains', Information Systems Management, vol. 39, no. 1, pp. 19-33.
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This paper presents the knowledge available in the literature regarding big data analytics capability (BDAC) and sustainable supply chain performance (SSCP). A detailed analysis of systematic literature reviews points out the lack of studies bridging these two separate streams of work. The paper puts forward a research agenda for researchers interested in understanding the impact of big data on sustainability.
Chacon, A, Kielly, M, Rutherford, H, Franklin, DR, Caracciolo, A, Buonanno, L, D’Adda, I, Rosenfeld, A, Guatelli, S, Carminati, M, Fiorini, C & Safavi-Naeini, M 2022, 'Detection and discrimination of neutron capture events for NCEPT dose quantification', Scientific Reports, vol. 12, no. 1.
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AbstractNeutron Capture Enhanced Particle Therapy (NCEPT) boosts the effectiveness of particle therapy by capturing thermal neutrons produced by beam-target nuclear interactions in and around the treatment site, using tumour-specific $$^{10}$$ 10 B or $$^{157}$$ 157 Gd-based neutron capture agents. Neutron captures release high-LET secondary particles together with gamma photons with energies of 478 keV or one of several energies up to 7.94 MeV, for $$^{10}$$ 10 B and $$^{157}$$ 157 Gd, respectively. A key requirement for NCEPT’s translation is the development of in vivo dosimetry techniques which can measure both the direct ion dose and the dose...
Chai, J & Tsang, IW 2022, 'Learning With Label Proportions by Incorporating Unmarked Data', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 10, pp. 5898-5912.
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Learning with label proportions (LLP) deals with the problem that the training data are provided as bags, where the label proportions of training bags rather than the labels of individual training instances are accessible. Existing LLP studies assume that the label proportions of all training bags are accessible. However, in many applications, it is time-consuming to mark all training bags with label proportions, which leads to the problem of learning with both marked and unmarked bags, namely, semisupervised LLP (SLLP). In this work, we propose semisupervised proportional support vector machine (SS-∝SVM), which extends the proportional SVM (∝SVM) model to its semisupervised version. To the best of our knowledge, SS-∝SVM is the first attempt to cope with the SLLP problem. Two realizations, alter-SS-∝SVM and conv-SS-∝SVM, which are based on alternating optimization and convex relaxation, respectively, are developed to solve the proposed SS-∝SVM model. Moreover, we design a cutting plane (CP) method to optimize conv-SS-∝SVM with a guaranteed convergence rate and present a fast accelerated proximal gradient method to solve the multiple kernel learning subproblem in conv-SS-∝SVM efficiently. Empirical experiments not only justify the superiority of SS-∝SVM over its supervised counterpart in classification accuracy but also demonstrate the high competitive computational efficiency of the CP optimization of conv-SS-∝SVM.
Chakraborty, S 2022, 'TOPSIS and Modified TOPSIS: A comparative analysis', Decision Analytics Journal, vol. 2, pp. 100021-100021.
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Chakraborty, S, Milner, LE, Zhu, X, Parker, A & Heimlich, M 2022, 'Analysis and Comparison of Marchand and Transformer Baluns Applied in GaAs', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 11, pp. 4278-4282.
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Chakraborty, SC, Qamruzzaman, M, Zaman, MWU, Alam, MM, Hossain, MD, Pramanik, BK, Nguyen, LN, Nghiem, LD, Ahmed, MF, Zhou, JL, Mondal, MIH, Hossain, MA, Johir, MAH, Ahmed, MB, Sithi, JA, Zargar, M & Moni, MA 2022, 'Metals in e-waste: Occurrence, fate, impacts and remediation technologies', Process Safety and Environmental Protection, vol. 162, pp. 230-252.
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Electronic waste (e-waste) is generated from the discarded electronic products. The generation of e-waste has increased significantly in the recent decades. Globally, the increased rate of e-waste generation is almost 2 metric tonnes (Mt) per year. It is estimated that about 74 Mt of e-waste will be produced in 2030. Therefore, e-waste can be a significant threat to the environment. Toxic metals (e.g., lead, mercury, nickel, and cadmium) are released to the environment from the e-waste and eventually enter into soil, sediment, groundwater, and surface water. The release of toxic metals in the environment causes adverse effects on human health, aquatic animals, and plants. Therefore, the proper management of e-waste is essential and becomes a major concern in the world. In this regard, this review provides a comprehensive summary of the occurrence, fate, and remediation of metals generated from e-waste. The literature survey revealed that household electrical appliances are the primary source of e-waste, comprising approximately 50% of the overall production of e-waste. Among different remediation technologies, the combination of biological, physical, and chemical processes shows relatively high removal efficiency; and they possess multiple advantages over other remediation technologies. Finally, this review also includes future outlook on e-waste management and remediation technologies.
Chakraborty, SC, Zaman, MWU, Hoque, M, Qamruzzaman, M, Zaman, JU, Hossain, D, Pramanik, BK, Nguyen, LN, Nghiem, LD, Mofijur, M, Mondal, MIH, Sithi, JA, Shahriar, SMS, Johir, MAH & Ahmed, MB 2022, 'Metals extraction processes from electronic waste: constraints and opportunities', Environmental Science and Pollution Research, vol. 29, no. 22, pp. 32651-32669.
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The skyrocketing demand and progressive technology have increased our dependency on electrical and electronic devices. However, the life span of these devices has been shortened because of rapid scientific expansions. Hence, massive volumes of electronic waste (e-waste) is generating day by day. Nevertheless, the ongoing management of e-waste has emerged as a major threat to sustainable economic development worldwide. In general, e-waste contains several toxic substances such as metals, plastics, and refractory oxides. Metals, particularly lead, mercury, nickel, cadmium, and copper along with some valuable metals such as rare earth metals, platinum group elements, alkaline and radioactive metal are very common; which can be extracted before disposing of the e-waste for reuse. In addition, many of these metals are hazardous. Therefore, e-waste management is an essential issue. In this study, we critically have reviewed the existing extraction processes and compared among different processes such as physical, biological, supercritical fluid technologies, pyro and hydrometallurgical, and hybrid methods used for metals extraction from e-waste. The review indicates that although each method has particular merits but hybrid methods are eco-friendlier with extraction efficiency > 90%. This study also provides insight into the technical challenges to the practical realization of metals extraction from e-waste sources.
Chalmers, T, Eaves, S, Lees, T, Lin, C, Newton, PJ, Clifton‐Bligh, R, McLachlan, CS, Gustin, SM & Lal, S 2022, 'The relationship between neurocognitive performance and HRV parameters in nurses and non‐healthcare participants', Brain and Behavior, vol. 12, no. 3, p. e2481.
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AbstractNurses represent the largest sector of the healthcare workforce, and it is established that they are faced with ongoing physical and mental demands that leave many continuously stressed. In turn, this chronic stress may affect cardiac autonomic activity, which can be non‐invasively evaluated using heart rate variability (HRV). The association between neurocognitive parameters during acute stress situations and HRV has not been previously explored in nurses compared to non‐nurses and such, our study aimed to assess these differences. Neurocognitive data were obtained using the Mini‐Mental State Examination and Cognistat psychometric questionnaires. ECG‐derived HRV parameters were acquired during the Trier Social Stress Test. Between‐group differences were found in domain‐specific cognitive performance for the similarities (p = .03), and judgment (p = .002) domains and in the following HRV parameters: SDNNbaseline, (p = .004), LFpreparation (p = .002), SDNNpreparation (p = .002), HFpreparation (p = .02), and TPpreparation (p = .003). Negative correlations were found between HF power and domain‐specific cognitive performance in nurses. In contrast, both negative and positive correlations were found between HRV and domain‐specific cognitive performance in the non‐nurse group. The current findings highlight the prospective use of autonomic HRV markers in relation to cognitive performance while building a relationship between autonomic dysfunction and cognition.
Chalmers, T, Hickey, BA, Newton, P, Lin, C-T, Sibbritt, D, McLachlan, CS, Clifton-Bligh, R, Morley, J & Lal, S 2022, 'Stress Watch: The Use of Heart Rate and Heart Rate Variability to Detect Stress: A Pilot Study Using Smart Watch Wearables', Sensors, vol. 22, no. 1, pp. 151-151.
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Stress is an inherent part of the normal human experience. Although, for the most part, this stress response is advantageous, chronic, heightened, or inappropriate stress responses can have deleterious effects on the human body. It has been suggested that individuals who experience repeated or prolonged stress exhibit blunted biological stress responses when compared to the general population. Thus, when assessing whether a ubiquitous stress response exists, it is important to stratify based on resting levels in the absence of stress. Research has shown that stress that causes symptomatic responses requires early intervention in order to mitigate possible associated mental health decline and personal risks. Given this, real-time monitoring of stress may provide immediate biofeedback to the individual and allow for early self-intervention. This study aimed to determine if the change in heart rate variability could predict, in two different cohorts, the quality of response to acute stress when exposed to an acute stressor and, in turn, contribute to the development of a physiological algorithm for stress which could be utilized in future smartwatch technologies. This study also aimed to assess whether baseline stress levels may affect the changes seen in heart rate variability at baseline and following stress tasks. A total of 30 student doctor participants and 30 participants from the general population were recruited for the study. The Trier Stress Test was utilized to induce stress, with resting and stress phase ECGs recorded, as well as inter-second heart rate (recorded using a FitBit). Although the present study failed to identify ubiquitous patterns of HRV and HR changes during stress, it did identify novel changes in these parameters between resting and stress states. This study has shown that the utilization of HRV as a measure of stress should be calculated with consideration of resting (baseline) anxiety and stress states in order to ens...
Chalmers, T, Hickey, BA, Newton, P, Lin, C-T, Sibbritt, D, McLachlan, CS, Clifton-Bligh, R, Morley, JW & Lal, S 2022, 'Associations between Sleep Quality and Heart Rate Variability: Implications for a Biological Model of Stress Detection Using Wearable Technology', International Journal of Environmental Research and Public Health, vol. 19, no. 9, pp. 5770-5770.
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Introduction: The autonomic nervous system plays a vital role in the modulation of many vital bodily functions, one of which is sleep and wakefulness. Many studies have investigated the link between autonomic dysfunction and sleep cycles; however, few studies have investigated the links between short-term sleep health, as determined by the Pittsburgh Quality of Sleep Index (PSQI), such as subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction, and autonomic functioning in healthy individuals. Aim: In this cross-sectional study, the aim was to investigate the links between short-term sleep quality and duration, and heart rate variability in 60 healthy individuals, in order to provide useful information about the effects of stress and sleep on heart rate variability (HRV) indices, which in turn could be integrated into biological models for wearable devices. Methods: Sleep parameters were collected from participants on commencement of the study, and HRV was derived using an electrocardiogram (ECG) during a resting and stress task (Trier Stress Test). Result: Low-frequency to high-frequency (LF:HF) ratio was significantly higher during the stress task than during the baseline resting phase, and very-low-frequency and high-frequency HRV were inversely related to impaired sleep during stress tasks. Conclusion: Given the ubiquitous nature of wearable technologies for monitoring health states, in particular HRV, it is important to consider the impacts of sleep states when using these technologies to interpret data. Very-low-frequency HRV during the stress task was found to be inversely related to three negative sleep indices: sleep quality, daytime dysfunction, and global sleep score.
Chandra Adhikari, S, Kumar Chanda, R, Bhowmick, S, Nath Mondal, R & Chandra Saha, S 2022, 'Pressure-Induced Instability Characteristics of a Transient Flow and Energy Distribution through a Loosely Bent Square Duct', Energy Engineering, vol. 119, no. 1, pp. 429-451.
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Chandra Shit, R, Sharma, S, Watters, P, Yelamarthi, K, Pradhan, B, Davison, R, Morgan, G & Puthal, D 2022, 'Privacy‐preserving cooperative localization in vehicular edge computing infrastructure', Concurrency and Computation: Practice and Experience, vol. 34, no. 14.
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SummaryAdvancement of computing and communication techniques transforms the traditional transport system into the intelligent transportation system (ITS). The development of distributed computing in a vehicular network platform also called Vehicular Edge Computing (VEC) promise to address most of the challenges faced by the ITS. Localization is important in these vehicular networks because of its key contribution in autonomous driving, smart traffic monitoring, and collision avoidance services. For localization, current GPS and hybrid methods are in‐efficient because of GPS outage in urban infrastructure and dynamic nature of the vehicular networks. The cooperative localization approaches, on the other hand, use dedicated short range communication to broadcast messages and estimate location. However, these messages are un‐encrypted and periodic which gives a privacy risk for vehicles. This article presents a privacy‐preserving cooperative localization in vehicular network based upon dynamic pseudonym changing strategy. First, the localization delay is addressed with the implementation of dynamic vehicular edge assignment for computational task management. In the next step, the localization is estimated from the neighbor and road side unit ranging measurement followed by a real‐time prediction of the vehicle. The performance of the proposed algorithms is analyzed in terms of localization accuracy and privacy preservation strength. Furthermore, the proposed method is simulated in a real city scenario followed by localization accuracy and privacy analysis. Finally, the localization accuracy and privacy strength of the proposed approach are compared with the state‐of‐the‐art methods.
Chandrakanthan, V, Rorimpandey, P, Zanini, F, Chacon, D, Olivier, J, Joshi, S, Kang, YC, Knezevic, K, Huang, Y, Qiao, Q, Oliver, RA, Unnikrishnan, A, Carter, DR, Lee, B, Brownlee, C, Power, C, Brink, R, Mendez-Ferrer, S, Enikolopov, G, Walsh, W, Göttgens, B, Taoudi, S, Beck, D & Pimanda, JE 2022, 'Mesoderm-derived PDGFRA+ cells regulate the emergence of hematopoietic stem cells in the dorsal aorta', Nature Cell Biology, vol. 24, no. 8, pp. 1211-1225.
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AbstractMouse haematopoietic stem cells (HSCs) first emerge at embryonic day 10.5 (E10.5), on the ventral surface of the dorsal aorta, by endothelial-to-haematopoietic transition. We investigated whether mesenchymal stem cells, which provide an essential niche for long-term HSCs (LT-HSCs) in the bone marrow, reside in the aorta–gonad–mesonephros and contribute to the development of the dorsal aorta and endothelial-to-haematopoietic transition. Here we show that mesoderm-derived PDGFRA+stromal cells (Mesp1derPSCs) contribute to the haemogenic endothelium of the dorsal aorta and populate the E10.5–E11.5 aorta–gonad–mesonephros but by E13.5 were replaced by neural-crest-derived PSCs (Wnt1derPSCs). Co-aggregating non-haemogenic endothelial cells withMesp1derPSCs but notWnt1derPSCs resulted in activation of a haematopoietic transcriptional programme in endothelial cells and generation of LT-HSCs. Dose-dependent inhibition of PDGFRA or BMP, WNT and NOTCH signalling interrupted this reprogramming event. Together, aorta–gonad–mesonephrosMesp1derPSCs could potentially be harnessed to manufacture LT-HSCs from endothelium.
Chandrasekar, T, Raju, SK, Ramachandran, M, Patan, R & Gandomi, AH 2022, 'Lung cancer disease detection using service-oriented architectures and multivariate boosting classifier', Applied Soft Computing, vol. 122, pp. 108820-108820.
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Big data analytics in healthcare is emerging as a promising field to extract valuable information from large databases and enhance results with fewer costs. Although numerous methods have been proposed for big data analytics in the medical field, an authorized entity is required to access data, inhibiting diagnosis accuracy and efficiency. Particularly, the detection of lung cancer is critical as it is the third most common type of cancer occurring in both males and females in the US and a leading cause of cancer-related deaths worldwide, the detection of lung cancer. Therefore, this study introduces the Multivariate Ruzicka Regressed eXtreme Gradient Boosting Data Classification (MRRXGBDC) technique and service-oriented architecture (SOA) to improve the prediction accuracy and reduce the prediction time of lung cancer in big data analytics. Service-oriented architectures (SOAs) provide a set of healthcare services, where patient data are stored in the database of a physician or other certified entity. After receiving the patient data as input, several multivariate Ruzicka logistic regression trees are constructed by the physician to calculate the relationship between the dependent and independent variables. With this regression analysis, the presence or absence of disease is discovered. The experimental results reveal that the MRRXGBDC technique performs better with 10% improvement in prediction accuracy, 50% reduction of false positives, and 11% faster prediction time for lung cancer detection compared to existing works.
Chang, L, Feng, X, Yao, K, Qin, L & Zhang, W 2022, 'Accelerating Graph Similarity Search via Efficient GED Computation', IEEE Transactions on Knowledge and Data Engineering, vol. PP, no. 99, pp. 1-1.
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Chang, W, Shi, Y, Tuan, HD & Wang, J 2022, 'Unified Optimal Transport Framework for Universal Domain Adaptation', Advances in Neural Information Processing Systems, vol. 35.
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Universal Domain Adaptation (UniDA) aims to transfer knowledge from a source domain to a target domain without any constraints on label sets. Since both domains may hold private classes, identifying target common samples for domain alignment is an essential issue in UniDA. Most existing methods require manually specified or hand-tuned threshold values to detect common samples thus they are hard to extend to more realistic UniDA because of the diverse ratios of common classes. Moreover, they cannot recognize different categories among target-private samples as these private samples are treated as a whole. In this paper, we propose to use Optimal Transport (OT) to handle these issues under a unified framework, namely UniOT. First, an OT-based partial alignment with adaptive filling is designed to detect common classes without any predefined threshold values for realistic UniDA. It can automatically discover the intrinsic difference between common and private classes based on the statistical information of the assignment matrix obtained from OT. Second, we propose an OT-based target representation learning that encourages both global discrimination and local consistency of samples to avoid the over-reliance on the source. Notably, UniOT is the first method with the capability to automatically discover and recognize private categories in the target domain for UniDA. Accordingly, we introduce a new metric H3-score to evaluate the performance in terms of both accuracy of common samples and clustering performance of private ones. Extensive experiments clearly demonstrate the advantages of UniOT over a wide range of state-of-the-art methods in UniDA.
Chang, Z, Long, G, Xie, Y & Zhou, JL 2022, 'Chemical effect of sewage sludge ash on early-age hydration of cement used as supplementary cementitious material', Construction and Building Materials, vol. 322, pp. 126116-126116.
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Sewage sludge ash has potential to be used as supplementary cementitious material in the production of building materials. The chemical effect of sewage sludge ash on early-age hydration of cement was investigated in this study. Two types of sludge ash calcined at 600 °C (S600) and 800 °C (S800) were blended with cement for analysis of hydration heat evolution, solid phase assemblage and aqueous composition. The aluminate dissolution of S600 ash promoted the formation of ettringite and consumption of gypsum, resulting in a high initial hydration heat. However, high concentrations of Al and Si caused by continuous dissolution of aluminate and silicate in S600 ash inhibited significantly the C3S dissolution. Interestingly, S800 ash had slight effect on early cement hydration since higher calcination temperature decreased the activity of aluminate. As compared with the reference, cumulative hydration heat of blended paste with 30% S800 ash at 7 days was increased by 18.72% indicating the occurrence of reaction between sludge ash and cement. Further study is recommended to focus on the long-term performance of cement-based materials blended with sludge ash.
Chang, Z, Long, G, Xie, Y & Zhou, JL 2022, 'Pozzolanic reactivity of aluminum-rich sewage sludge ash: Influence of calcination process and effect of calcination products on cement hydration', Construction and Building Materials, vol. 318, pp. 126096-126096.
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The application of aluminum-based flocculant in wastewater treatment results in a large amount of aluminum-rich sewage sludge. This work investigated the influence of calcination process on physicochemical characteristics and pozzolanic activity of aluminum (Al)-rich sludge ash and studied the effect of sludge ash on cement hydration. The results showed that higher calcination temperature from 600 ℃ to 900 ℃ increased the amorphous content in sludge ash. The pozzolanic activity of sludge ash calcined at 800 ℃ and 900 ℃ was confirmed by Frattini test. In view of strength activity index of blended mortar and energy conservation, the optimal calcination condition of sewage sludge ash was calcined at 800 ℃ with air-cooling. The addition of sludge ash promoted the transformation of ettringite to monosulfate phase in cement paste. However, the high Al concentration dissolved from S6 and S7 ash inhibited significantly the cement hydration and resulted in low compressive strength values of the blended mortars. The pozzolanic reaction of S8 and S9 ash produced more hydration heat and additional Al-bearing products such as katoite and monosulfate which contributed to the strength development of mortars. Furthermore, the heavy metals in sewage sludge can be immobilized in ash structure during calcination process and the structure of hydration products, which ensures the environmental security of sludge ash utilization in construction materials.
Chang, Z, Long, G, Xie, Y & Zhou, JL 2022, 'Recycling sewage sludge ash and limestone for sustainable cementitious material production', Journal of Building Engineering, vol. 49, pp. 104035-104035.
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Supplementary cementitious materials have significant potential to reduce greenhouse gas emissions in the production of construction materials. This work investigated the synergistic effect of partially replacing cement by sewage sludge ash and limestone for sustainable cementitious material production. The hydration phases and pore structure characteristics were determined by X-ray diffraction and the BET nitrogen sorption method, respectively. A central composite rotational design (CCRD) was used to study the effect of the water binder ratio (w/b), sludge ash and limestone content on the compressive strength. The results of microstructure tests showed that the addition of limestone enhanced the formation of carboaluminate hydrates. The additional hydration products filled in large pores of paste, resulting in a well-refined microstructure of the ternary mixture. Thus, the 90-day strength activity index (SAI) of mortar with 15% sludge ash and 7.5% limestone was 100.6% compared to the reference. Despite the adverse effect of limestone on the compressive strength, the synergistic effect of sludge ash and limestone contributed to the reduction of economic cost and greenhouse gas emission in the production of sustainable cementitious materials. For the same compressive strength level, the ternary mixture composed of 15% sludge ash and 7.5% ground limestone reduced Portland cement consumption by 23.13% and CO2-eq emission intensity by 13.52%.
Che, X, Zuo, H, Lu, J & Chen, D 2022, 'Fuzzy Multioutput Transfer Learning for Regression', IEEE Transactions on Fuzzy Systems, vol. 30, no. 7, pp. 2438-2451.
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Multi-output regression aims to predict multiple continuous outputs simultaneously using the common set of input variables. The significant challenge arises from modeling relevance between inputs and outputs. Moreover, the shortage of labeled multi-output data and the divergence of data are other factors that impede the development of multi-output regression problems. The recent emergence of transfer learning techniques, which have the ability of leveraging previously acquired knowl- edge from a similar domain, provide a solution to the above issues. In this paper, a novel fuzzy transfer learning method is proposed to tackle the multi-output regression problems in ho- mogeneous and heterogeneous scenarios. By considering output- input dependencies and inter-output correlations, fuzzy rules are extracted to reflect the shared characteristics of different outputs and capture their uniqueness. For a homogeneous scenario, fuzzy rules are first accumulated in a related domain (called the source domain), which has a sufficient amount of training data. Based on different transform strategies, the fuzzy rules are then transferred to improve the new but similar regression tasks in the current domain (called the target domain), where only a few data have multiple responses. On this basis, we handle a more complex heterogeneous scenario by learning a latent input space to reduce the disagreement of variables between domains. The experiment results on thirteen real-world datasets with multiple outputs illustrate the effectiveness of our method. The impact of core coefficients on performance is also analyzed.
Chen, C, Ding, L, Liu, B, Du, Z, Liu, Y, Di, X, Shan, X, Lin, C, Zhang, M, Xu, X, Zhong, X, Wang, J, Chang, L, Halkon, B, Chen, X, Cheng, F & Wang, F 2022, 'Exploiting Dynamic Nonlinearity in Upconversion Nanoparticles for Super-Resolution Imaging', Nano Letters, vol. 22, no. 17, pp. 7136-7143.
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Single-beam super-resolution microscopy, also known as superlinear microscopy, exploits the nonlinear response of fluorescent probes in confocal microscopy. The technique requires no complex purpose-built system, light field modulation, or beam shaping. Here, we present a strategy to enhance this technique's spatial resolution by modulating excitation intensity during image acquisition. This modulation induces dynamic optical nonlinearity in upconversion nanoparticles (UCNPs), resulting in variations of nonlinear fluorescence response in the obtained images. The higher orders of fluorescence response can be extracted with a proposed weighted finite difference imaging algorithm from raw fluorescence images to generate an image with higher resolution than superlinear microscopy images. We apply this approach to resolve single nanoparticles in a large area, improving the resolution to 132 nm. This work suggests a new scope for the development of dynamic nonlinear fluorescent probes in super-resolution nanoscopy.
Chen, D, Liu, Y, Li, M, Guo, P, Zeng, Z, Hu, J & Guo, YJ 2022, 'A Polarization Programmable Antenna Array', Engineering, vol. 16, pp. 100-114.
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Chen, D, Wu, C, Li, J & Liao, K 2022, 'A numerical study of gas explosion with progressive venting in a utility tunnel', Process Safety and Environmental Protection, vol. 162, pp. 1124-1138.
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A numerical model of a progressive vented gas explosion is presented. A CFD tool in combination with correlation analysis and an artificial neural network (ANN) were utilized to establish and refine the numerical model. The experimental results of 44 fixed vented gas explosions and one progressive vented gas explosion with moving obstacles were used to validate the numerical accuracy. The results indicated that the method to estimate the activation pressure of the pressure relief panels for a fixed vented gas explosion achieved a lower overpressure prediction compared to that for a progressive vented gas explosion. The progressive venting procedure was modelled by two-layer pressure relief panels with the upper layer having activation pressures with a linear ascent trend. The vents on the tunnel had an insignificant impact on the explosion load after being lifted over the tunnel top, and their falling process was unnecessary to be modelled. A non-negligible impact of the obstacles inside the tunnel on the flow field upon being pushed away from their initial positions was demonstrated. By employing an ANN, the critical parameters in the numerical model were determined, which were used to accurately replicate the experimental results. The findings clarified a revenue for the modeling of a progressive vented gas explosion as well as some shortcomings of the CFD tool.
Chen, H, Demerdash, NAO, EL-Refaie, AM, Guo, Y, Hua, W & Lee, CHT 2022, 'Investigation of a 3D-Magnetic Flux PMSM With High Torque Density for Electric Vehicles', IEEE Transactions on Energy Conversion, vol. 37, no. 2, pp. 1442-1454.
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This paper presents an investigation of a 3D-magnetic flux permanent magnet synchronous motor (3D-MF PMSM) used for electric vehicle applications. The investigated 3D-MF PMSM consists of an integrated radial-flux and axial-flux structure. It has two radial-flux air-gaps and two axial-flux air-gaps, as well as a toroidal winding wound stator. The integrated structure helps to concentrate all the flux within the motor to maximize torque production. Moreover, there are no end-windings in this motor and all the stator windings effectively are used in torque production. A comprehensive performance evaluation, in terms of the back-electromotive force, average output torque, cogging torque, torque ripple, flux-weakening capability, etc., of the investigated 3D-MF PMSM is conducted. An interior PMSM is purposely included as a benchmark for comparison. The results show that compared to the benchmark interior PMSM, the original 3D-MF PMSM exhibits significantly improved torque density, higher power factor, and higher efficiency, but suffers from serious cogging torque and torque ripple. Accordingly, an unaligned arrangement is introduced to the 3D-MF PMSM. As a result, the cogging torque and torque ripple are significantly reduced.
Chen, J, Vinod, JS, Indraratna, B, Ngo, NT, Gao, R & Liu, Y 2022, 'A discrete element study on the deformation and degradation of coal-fouled ballast', Acta Geotechnica, vol. 17, no. 9, pp. 3977-3993.
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AbstractThis paper presents the results of Discrete Element Modelling (DEM) which quantitively examine the effect of coal fouling on the deformation and degradation of ballast upon cyclic loading. The degradation model described herein considers the Weibull distribution effects in tandem with a granular medium hardening law that incorporates the maximum contact criterion to capture surface abrasion and corner breakage of angular ballast. The DEM model had been calibrated initially with laboratory data obtained from large-scale direct shear testing. Subsequently, a series of cubical shear test simulations have been carried out using DEM to understand the behaviour of fouled ballast whereby the numerical particle degradation modelling could simulate the experimental response of the ballast assembly at various fouling levels. The results show that the increased level of fouling exacerbates the sleeper settlement, while decreasing the resilient modulus and the particle breakage. Ballast beneath the sleeper experiences significant breakage compared to the crib ballast, and not surprisingly, the extent of damage decreases with depth. Rigorous microscopic analysis is also presented in relation to inter-particle contacts, particle velocity and anisotropy of the ballast assembly. This micromechanical examination highlights that the decrease in ballast breakage for fouled assemblies is predominantly attributed to the inevitable decrease in inter-particle contact pressures as effected by the coating of ballast aggregates by the coal fines.
Chen, J, Wu, Y, Yang, Y, Wen, S, Shi, K, Bermak, A & Huang, T 2022, 'An Efficient Memristor-Based Circuit Implementation of Squeeze-and-Excitation Fully Convolutional Neural Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 4, pp. 1779-1790.
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Recently, there has been a surge of interest in applying memristors to hardware implementations of deep neural networks due to various desirable properties of the memristor, such as nonvolativity, multivalue, and nanosize. Most existing neural network circuit designs, however, are based on generic frameworks that are not optimized for memristors. Furthermore, to the best of our knowledge, there are no existing efficient memristor-based implementations of complex neural network operators, such as deconvolutions and squeeze-and-excitation (SE) blocks, which are critical for achieving high accuracy in common medical image analysis applications, such as semantic segmentation. This article proposes convolution-kernel first (CKF), an efficient scheme for designing memristor-based fully convolutional neural networks (FCNs). Compared with existing neural network circuits, CKF enables effective parameter pruning, which significantly reduces circuit power consumption. Furthermore, CKF includes the novel, memristor-optimized implementations of deconvolution layers and SE blocks. Simulation results on real medical image segmentation tasks confirm that CKF obtains up to 56.2% reduction in terms of computations and 33.62-W reduction in terms of power consumption in the circuit after weight pruning while retaining high accuracy on the test set. Moreover, the pruning results can be applied directly to existing circuits without any modification for the corresponding system.
Chen, L, Armaghani, DJ, Fakharian, P, Bhatawdekar, RM, Samui, P, Khandelwal, M & Khedher, KM 2022, 'Correction to: A study on environmental issues of blasting using advanced support vector machine algorithms', International Journal of Environmental Science and Technology, vol. 19, no. 7, pp. 6241-6241.
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Chen, L, Armaghani, DJ, Fakharuab, P, Bhatawdekar, RM, Samui, P, Khandelwal, M & Khedher, KM 2022, 'A study on environmental issues of blasting using advanced support vector machine algorithms', International Journal of Environmental Science and Technology, vol. 19, no. 7, pp. 6221-6240.
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Chen, L, Chen, L, Ge, Z, Sun, Y, Hamilton, T & Zhu, X 2022, 'A W-Band SPDT Switch With 15-dBm P1dB in 55-nm Bulk CMOS', IEEE Microwave and Wireless Components Letters, vol. 32, no. 7, pp. 879-882.
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Power-handling capability of bulk CMOS-based single-pole double-throw (SPDT) switch operating in millimeter-wave (mm-wave) and subterahertz region is significantly limited by the reduced threshold voltage of deeply scaled transistors. A unique design technique based on impedance transformation network (ITN) is presented in this work, which improves 1-dB compression point, namely P1dB, without deteriorating other performance. To prove the presented solution is valid, a 70-100-GHz switch is designed and implemented in a 55-nm bulk CMOS technology. At 90 GHz, it achieves a measured P1dB of 15 dBm, an insertion loss (IL) of 3.5 dB, and an isolation (ISO) of 18 dB. The total area of the chip is only 0.14 mm2.
Chen, L, Liu, Y, Ren, Y, Zhu, C, Yang, S & Guo, YJ 2022, 'Synthesizing Wideband Frequency-Invariant Shaped Patterns by Linear Phase Response-Based Iterative Spatiotemporal Fourier Transform', IEEE Transactions on Antennas and Propagation, vol. 70, no. 11, pp. 10378-10390.
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Chen, L, Zhu, H, Gomez-Garcia, R & Zhu, X 2022, 'Miniaturized On-Chip Notch Filter With Sharp Selectivity and >35-dB Attenuation in 0.13-μm Bulk CMOS Technology', IEEE Electron Device Letters, vol. 43, no. 8, pp. 1175-1178.
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Chen, Q, Guo, D, Ke, W, Xu, C & Nimbalkar, S 2022, 'Novel Open Trench Techniques in Mitigating Ground-Borne Vibrations due to Traffic under a Wide Range of Ground Conditions', International Journal of Geomechanics, vol. 22, no. 6.
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Chen, S, Eager, D & Zhao, L 2022, 'Enhanced frequency synchronization for concurrent aeroelastic and base vibratory energy harvesting using a softening nonlinear galloping energy harvester', Journal of Intelligent Material Systems and Structures, vol. 33, no. 5, pp. 687-702.
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This paper proposes a softening nonlinear aeroelastic galloping energy harvester for enhanced energy harvesting from concurrent wind flow and base vibration. Traditional linear aeroelastic energy harvesters have poor performance with quasi-periodic oscillations when the base vibration frequency deviates from the aeroelastic frequency. The softening nonlinearity in the proposed harvester alters the self-excited galloping frequency and simultaneously extends the large-amplitude base-excited oscillation to a wider frequency range, achieving frequency synchronization over a remarkably broadened bandwidth with periodic oscillations for efficient energy conversion from dual sources. A fully coupled aero-electro-mechanical model is built and validated with measurements on a devised prototype. At a wind speed of 5.5 m/s and base acceleration of 0.1 g, the proposed harvester improves the performance by widening the effective bandwidth by 300% compared to the linear counterpart without sacrificing the voltage level. The influences of nonlinearity configuration, excitation magnitude, and electromechanical coupling strength on the mechanical and electrical behavior are examined. The results of this paper form a baseline for future efficiency enhancement of energy harvesting from concurrent wind and base vibration utilizing monostable stiffness nonlinearities.
Chen, S, Jin, Y, Xu, M, Zhang, P, Zhou, Y, Qian, X, Song, Q, Bu, S, Sun, J & Li, L 2022, 'Relationship Between Thyroid Hormone and Liver Steatosis Analysis Parameter in Obese Participants: A Case-Control Study', Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, vol. Volume 15, pp. 887-896.
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OBJECTIVE: The thyroid hormone has been demonstrated to be associated with nonalcoholic fatty liver disease (NAFLD) in different populations. However, the relationship between thyroid hormone and the degree of liver steatosis in overweight/obese subjects is still unclear. Liver ultra-sound attenuation (LiSA) is a newly developed ultrasound attenuation parameter for the analysis of hepatic steatosis. The study aimed to characterize the relationship between thyroid hormone and LiSA in overweight/obese participants. METHODS: This case-control study was performed in Ningbo First Hospital, China. A total of 24 lean, 66 overweight and 49 obese participants were consecutively recruited from January 2021 to May 2021. Thyroid hormone and other clinical features were measured. LiSA was acquired by using a Hepatus ultrasound machine. Multiple linear regression analyses were performed to examine associations of LiSA and clinic indices. RESULTS: Obese subjects had higher LiSA, fT3 and TSH levels than lean participants of similar age and sex (P < 0.05). LiSA was positively associated with the fT3 level. The multiple linear regression analyses showed that fT3 (ß = 0.353, P < 0.001) was independently associated with LiSA in overweight/obese participants. CONCLUSION: The fT3 level was independently associated with the degree of liver steatosis among the overweight/obese participants.
Chen, S-L, Liu, Y, Zhu, H, Chen, D & Guo, YJ 2022, 'Millimeter-Wave Cavity-Backed Multi-Linear Polarization Reconfigurable Antenna', IEEE Transactions on Antennas and Propagation, vol. 70, no. 4, pp. 2531-2542.
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Chen, S-L, Liu, Y, Ziolkowski, RW, Li, Z & Guo, YJ 2022, 'High-Gain Single-Feed Overmoded Cavity Antenna With Closely Spaced Phased Patch Surface', IEEE Transactions on Antennas and Propagation, vol. 70, no. 1, pp. 229-239.
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Chen, S-L, Wu, G-B, Wong, H, Chen, B-J, Chan, CH & Guo, YJ 2022, 'Millimeter-Wave Slot-Based Cavity Antennas With Flexibly-Chosen Linear Polarization', IEEE Transactions on Antennas and Propagation, vol. 70, no. 8, pp. 6604-6616.
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Slot-based cavity antennas are hailed as promising candidates for millimeter-wave applications. Nevertheless, the linear-polarization (LP) angle of their broadside main beam is limited by the slots etched on the cavity’s top surface. In this work, an innovative technique is developed to significantly improve the selection flexibility of their LP inclination angle. It is attained by an integration of a single-layer, closely-spaced C-shaped patch surface. A TE710-mode slot-based cavity antenna is employed as the base configuration, which radiates a broadside beam with its LP along ϕ=90°. To effectively predict and monitor the polarization conversion of the surface-integrated TE710-mode cavity antenna, an analysis method using a unit cavity extracted from its original cavity antenna is presented. A subsequent surface-integrated system with the specified 45°-LP was then simulated, fabricated, and measured. The measured results validate that a 45°-LP state is achieved with an operating bandwidth from 33.3 to 36.5 GHz. Further investigation is conducted to flexibly choose the LP direction from ϕ=15° to 165°. Two more examples with the fabricated antenna prototypes successfully radiate the specified ϕ=15° and 75° LP beam, respectively. This near-field polarization conversion surface can be generalized to cavities with different resonant modes.
Chen, S-L, Ziolkowski, RW, Jones, B & Guo, YJ 2022, 'Analysis, Design, and Measurement of Directed-Beam Toroidal Waveguide-Based Leaky-Wave Antennas', IEEE Transactions on Antennas and Propagation, vol. 70, no. 11, pp. 10141-10155.
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Chen, W-H, Chen, K-H, Chein, R-Y, Ong, HC & Arunachalam, KD 2022, 'Optimization of hydrogen enrichment via palladium membrane in vacuum environments using Taguchi method and normalized regression analysis', International Journal of Hydrogen Energy, vol. 47, no. 100, pp. 42280-42292.
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Chen, W-H, Hoang, AT, Nižetić, S, Pandey, A, Cheng, CK, Luque, R, Ong, HC, Thomas, S & Nguyen, XP 2022, 'Biomass-derived biochar: From production to application in removing heavy metal-contaminated water', Process Safety and Environmental Protection, vol. 160, pp. 704-733.
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Chen, X, Chen, S, Yao, J, Zheng, H, Zhang, Y & Tsang, IW 2022, 'Learning on Attribute-Missing Graphs', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 2, pp. 740-757.
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Graphs with complete node attributes have been widely explored recently. While in practice, there is a graph where attributes of only partial nodes could be available and those of the others might be entirely missing. This attribute-missing graph is related to numerous real-world applications and there are limited studies investigating the corresponding learning problems. Existing graph learning methods including the popular GNN cannot provide satisfied learning performance since they are not specified for attribute-missing graphs. Thereby, designing a new GNN for these graphs is a burning issue to the graph learning community. In this article, we make a shared-latent space assumption on graphs and develop a novel distribution matching-based GNN called structure-attribute transformer (SAT) for attribute-missing graphs. SAT leverages structures and attributes in a decoupled scheme and achieves the joint distribution modeling of structures and attributes by distribution matching techniques. It could not only perform the link prediction task but also the newly introduced node attribute completion task. Furthermore, practical measures are introduced to quantify the performance of node attribute completion. Extensive experiments on seven real-world datasets indicate SAT shows better performance than other methods on both link prediction and node attribute completion tasks.
Chen, X, Huo, P, Yang, L, Wei, W & Ni, B-J 2022, 'A Comprehensive Analysis of Evolution and Underlying Connections of Water Research Themes in the 21st Century', Sci Total Environ, vol. 835, pp. 155411-155411.
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This work aimed to reflect the advancements in water-related science, technology, and policy and shed light on future research opportunities related to water through a systematic overview of Water Research articles published in the first 21.5 years of the 21st century. Specific bibliometric analyses were performed to i) reveal the temporal and spatial trends of water-related research themes and ii) identify the underlying connections between research topics. The results showed that while top topics including wastewater (treatment), drinking water, adsorption, model, biofilm, and bioremediation remained constantly researched, there were clear shifts in topics over the years, leading to the identification of trending-up and emerging research topics. Compared to the first decade of the 21st century, the second decade not only experienced significant uptrends of disinfection by-products, anaerobic digestion, membrane bioreactor, advanced oxidation processes, and pharmaceuticals but also witnessed the emerging popularity of PFAS, anammox, micropollutants, emerging contaminants, desalination, waste activated sludge, microbial community, forward osmosis, antibiotic resistance genes, resource recovery, and transformation products. On top of the temporal evolution, distinct spatial evolution existed in water-related research topics. Microplastics and Covid-19 causing global concerns were hot topics detected, while metagenomics and machine learning were two technical approaches emerging in recent years. These consistently popular, trending-up and emerging research topics would most likely attract continuous/increasing research input and therefore constitute a major part of the prospective water-related research publications.
Chen, X, Huo, P, Yang, L, Wei, W, Yang, L, Wei, W & Ni, B-J 2022, 'Influences of Granule Properties on the Performance of Autotrophic Nitrogen Removal Granular Reactor: A Model-Based Evaluation', Bioresour Technol, vol. 356, pp. 127307-127307.
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This work studied the impacts of key granule properties on the granular reactor performing partial nitritation/anammox from the modeling perspective. The results could guide not only future reliable modeling but also practical startup/operation of the reactor. To achieve high total nitrogen (TN) removal whilst avoiding significant N2O production, inoculated granules should be big and anammox-enriched. The optimum boundary layer thickness for maximum TN removal increased with the decreasing diffusivity of soluble components in the granule structure. Even though a thick boundary layer could protect anammox bacteria from elevated dissolved oxygen (DO) (e.g., 0.5 g-O2/m3) and obtain high TN removal (>90.0%) and low N2O production (<1.8%), even complete removal of the boundary layer would fail to provide sufficient substrate for anammox and therefore couldn't increase TN removal to 90.0% and decrease N2O production to <2.4% at insufficient DO (e.g., 0.3 g-O2/m3 in the presence of lifted influent NH4+ concentration).
Chen, X, Li, F, Huo, P, Liu, J, Yang, L, Li, X, Wei, W & Ni, B-J 2022, 'Influences of longitudinal gradients on methane-driven membrane biofilm reactor for complete nitrogen removal: A model-based investigation', Water Research, vol. 220, pp. 118665-118665.
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Integrating anammox with denitrifying anaerobic methane oxidation (DAMO) in the membrane biofilm reactor (MBfR) is a promising technology capable of achieving complete nitrogen removal from wastewater. However, it remains unknown whether reactor configurations featuring longitudinal gradients parallel to the membrane surface would affect the performance of the CH4-driven MBfR. To this end, this work aims to study the impacts of longitudinal heterogeneity potentially present in the gas and liquid phases on a representative CH4-driven MBfR performing anammox/DAMO by applying the reported modified compartmental modeling approach. Through comparing the modeling results of different reactor configurations, this work not only offered important guidance for better design, operation and monitoring of the CH4-driven MBfR, but also revealed important implications for prospective related modeling research. The total nitrogen removal efficiency of the MBfR at non-excessive CH4 supply (e.g., surface loading of ≤0.064 g-COD m-2 d-1 in this work) was found to be insensitive to both longitudinal gradients in the liquid and gas phases. Comparatively, the longitudinal gradient in the liquid phase led to distinct longitudinal biomass stratification and therefore played an influential role in the effective CH4 utilization efficiency, which was also related to the extent of reactor compartmentation considered in modeling. When supplied with non-excessive CH4, the MBfR is recommended to be designed/operated with both the biofilm reactor and the membrane lumen as plug flow reactors (PFRs) with co-current flow of wastewater and CH4, which could mitigate dissolved CH4 discharge in the effluent. For the reactor configurations with the biofilm reactor designed/operated as a PFR, multi-spot sampling in the longitudinal direction is needed to obtain a correct representation of the microbial composition of the MBfR.
Chen, X, Li, Y, Yao, L, Adeli, E, Zhang, Y & Wang, X 2022, 'Generative adversarial U-Net for domain-free few-shot medical diagnosis', Pattern Recognition Letters, vol. 157, pp. 112-118.
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Chen, X, Wen, H, Ni, W, Zhang, S, Wang, X, Xu, S & Pei, Q 2022, 'Distributed Online Optimization of Edge Computing With Mixed Power Supply of Renewable Energy and Smart Grid', IEEE Transactions on Communications, vol. 70, no. 1, pp. 389-403.
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Chen, X, Yi, X, Khan, MIW, Li, X, Chen, W, Zhu, J, Yang, Y, Kolodziej, KE, Monroe, NM & Han, R 2022, 'A 140-GHz FMCW TX/RX-Antenna-Sharing Transceiver With Low-Inherent-Loss Duplexing and Adaptive Self-Interference Cancellation', IEEE Journal of Solid-State Circuits, vol. 57, no. 12, pp. 3631-3645.
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This article presents a 140-GHz frequency-modulated continuous-wave (FMCW) radar transceiver featuring transmit/receive (TX/RX) antenna sharing that address a TX/RX beam misalignment problem when large-aperture lenses/mirrors/reflectarrays are used for pencil beam forming. A full-duplexing technique based on circular polarization and geometrical symmetry is applied to mitigate the 3 dB + 3 dB insertion loss inherent to conventionally adopted directional couplers, while still maintaining high TX-to-RX isolation. In addition, a self-adaptive self-interference cancellation (SIC) is implemented to suppress extra leakage due to antenna mismatch from a desired frontside radiation scheme. The TX/RX antenna sharing enables the pairing with a large 3-D printed planar lens and boosts the measured effective isotropic radiated power (EIRP) to 25.2 dBm. The measured total radiated power and minimum single-sideband noise figure (SSB NF) including antenna and duplexer losses are 6.2 dBm and 20.2 dB, respectively. The measured total TX-RX isolation is 33.3 dB under 14-GHz wide FMCW chirps. Based on a 65-nm complimentary metal-oxide-semiconductor (CMOS) technology, the chip has a die area of 3.1 mm2 and consumes 405 mW of dc power. Among all reported sub-THz transceivers with TX/RX antenna sharing, this work demonstrates the highest total radiated power and is the only work that has >30 dB of TX-RX isolation while mitigating the inherent 6 dB coupler loss.
Chen, XC, Hellmann, A & Sood, S 2022, 'A framework for analyst economic incentives and cognitive biases: Origination of the walk-down in earnings forecasts', Journal of Behavioral and Experimental Finance, vol. 36, pp. 100759-100759.
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Chen, Y, Ding, C, Jia, Y & Liu, Y 2022, 'Antenna/Propagation Domain Self-Interference Cancellation (SIC) for In-Band Full-Duplex Wireless Communication Systems', Sensors, vol. 22, no. 5, pp. 1699-1699.
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In-band full duplex (IBFD) is regarded as one of the most significant technologies for addressing the issue of spectrum scarcity in 5G and beyond systems. In the realization of practical IBFD systems, self-interference, i.e., the interference that the transmitter causes to the collocated receiver, poses a major challenge to antenna designers; it is a prerequisite for applying other self-interference cancellation (SIC) techniques in the analog and digital domains. In this paper, a comprehensive survey on SIC techniques in the antenna/propagation (AP) domain is provided and the pros and cons of each technique are studied. Opportunities and challenges of employing IBFD antennas in future wireless communications networks are discussed.
Chen, Y, Lin, S, Liang, Z, Surawski, NC & Huang, X 2022, 'Smouldering organic waste removal technology with smoke emissions cleaned by self-sustained flame', Journal of Cleaner Production, vol. 362, pp. 132363-132363.
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Smouldering is slow, low-temperature and flameless, and has been potentially regarded as an alternative for organic waste removal technology. However, as an incomplete combustion process, toxic smoke and pollution from the smouldering are significant concerns that limit its popularization. This work applies a newly developed smouldering-based waste removal technology to investigate the removal of coffee waste, wood waste, and organic soil (simulated sludge) via using a flame to clean smouldering emissions at different airflow velocities (3–24 mm/s). Once ignited from the top, the smouldering front first propagates downwards where a stable flame situated above could be piloted and sustained to purify the smouldering emissions until the smouldering front reached the bottom of the fuel bed. The efficiency of pollution mitigation was demonstrated by significantly lower CO and VOCs emission after purification by self-sustained flame. The equivalent critical mass flux of flammable gases required for igniting the smouldering emissions is 0.5 g/m2∙s, regardless of the fuel type. The smouldering temperature, propagation rate and burning flux all increase with the airflow velocity but are also slightly sensitive to the type of waste. This work enriches strategies for the clean treatment of smouldering emissions and promotes an energy efficient and environmentally friendly method for organic waste removal.
Chen, Y, Shimoni, O, Huang, G, Wen, S, Liao, J, Duong, HTT, Maddahfar, M, Su, QP, Ortega, DG, Lu, Y, Campbell, DH, Walsh, BJ & Jin, D 2022, 'Upconversion nanoparticle‐assisted single‐molecule assay for detecting circulating antigens of aggressive prostate cancer', Cytometry Part A, vol. 101, no. 5, pp. 400-410.
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AbstractSensitive and quantitative detection of molecular biomarkers is crucial for the early diagnosis of diseases like metabolic syndrome and cancer. Here we present a single‐molecule sandwich immunoassay by imaging the number of single nanoparticles to diagnose aggressive prostate cancer. Our assay employed the photo‐stable upconversion nanoparticles (UCNPs) as labels to detect the four types of circulating antigens in blood circulation, including glypican‐1 (GPC‐1), leptin, osteopontin (OPN), and vascular endothelial growth factor (VEGF), as their serum concentrations indicate aggressive prostate cancer. Under a wide‐field microscope, a single UCNP doped with thousands of lanthanide ions can emit sufficiently bright anti‐Stokes' luminescence to become quantitatively detectable. By counting every single streptavidin‐functionalized UCNP which specifically labeled on each sandwich immune complex across multiple fields of views, we achieved the Limit of Detection (LOD) of 0.0123 ng/ml, 0.2711 ng/ml, 0.1238 ng/ml, and 0.0158 ng/ml for GPC‐1, leptin, OPN and VEGF, respectively. The serum circulating level of GPC‐1, leptin, OPN, and VEGF in a mixture of 10 healthy normal human serum was 25.17 ng/ml, 18.04 ng/ml, 11.34 ng/ml, and 1.55 ng/ml, which was within the assay dynamic detection range for each analyte. Moreover, a 20% increase of GPC‐1 and OPN was observed by spiking the normal human serum with recombinant antigens to confirm the accuracy of the assay. We observed no cross‐reactivity among the four biomarker analytes, which eliminates the false positives and enhances the detection accuracy. The developed single upconversion nanoparticle‐assisted single‐molecule assay suggests its potential in clinical usage for prostate cancer detection by monitoring tiny concentration differences in a panel of serum biomarkers.
Chen, Y, Su, Y, Zhang, M, Chai, H, Wei, Y & Yu, S 2022, 'FedTor: An Anonymous Framework of Federated Learning in Internet of Things', IEEE Internet of Things Journal, vol. 9, no. 19, pp. 18620-18631.
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With a large number of devices and a wealth of user data sets, the Internet of Things (IoT) has become a great host for federated learning (FL). At the same time, the massive amount of user data in IoT results in desperate demand for privacy preserving. The onion router (Tor) is a promising method to solve the privacy issue in IoT-based FL by user anonymity. However, IoT devices' resource is too limited to execute the cryptographic operations in Tor. Moreover, network traffics in Tor can be easily controlled by malicious routers with a fake high self-reported bandwidth. In this article, taking advantage of the Tor, we will introduce an anonymous FL framework in IoT called FedTor. To decrease the cryptographic cost in conventional Tor, we propose a lightweight shared key generation scheme for resource-limited IoT devices. Furthermore, we use the difference between the self-reported bandwidth and the bandwidth observed from others to measure the reputation of onion routers. A reputation-based router selection (RBRS) scheme is then brought up to defend traffic control from malicious routers. We conducted extensive simulations to compare FedTor with related works. The results show that the RBRS scheme can decrease the malicious rate of onion routers and the lightweight shared key has a cost advantage over other schemes.
Chen, Y, Sun, X, Wei, W, Dong, Y & Liang, CJ 2022, 'A Prediction and Visual Analysis Method for Graduation Destination of Undergraduates Based on LambdaMART Model', International Journal of Information and Communication Technology Education, vol. 18, no. 2, pp. 1-19.
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Predicting graduation destination can help students determine their learning goals in advance, help faculty optimize curriculum and provide career guidance for students. In this paper, the authors first propose a prediction algorithm for graduation destination of undergraduates based on LambdaMART, called PGDU_LM, which uses Spearman correlation coefficient to analyze the correlation between subjects and graduate destinations and extract characteristic subjects, and uses LambdaMART ranking model to calculate students' propensity scores in different graduate destinations. Second, a visual analysis method for students' course grades and graduation destinations is designed to support users to analyze student data from multiple dimensions. Finally, a prediction and visual analysis system for graduation destination of undergraduates, PGDUvis, is designed and implemented. A case study and user evaluation on this system was conducted using the academic data of students from five majors who graduated from a university during 2016-2020, and the results illustrate the effectiveness of this method.
Chen, Y, Wu, D, Dai, K & Gao, W 2022, 'A numerically efficient framework in failure mode evaluation of a wind turbine tower under cyclones', Marine Structures, vol. 86, pp. 103303-103303.
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With more wind turbines being constructed in cyclone-prone regions, tower failure cases are increasingly reported in recent years. To simulate the genuine tower performance under extreme events, nonlinear dynamic analyses on a highly refined finite element model were adopted in recent research. However, existing research based on such practice has certain deficiencies in terms of computational cost reduction, aero-structure interaction characterization and failure mode classification. This article proposed a numerically efficient framework in the failure mode identification and evaluation of a wind turbine tower under cyclones (hurricanes/typhoons) to cope with the forenamed deficiencies. The aero-structure interaction, geometric and material nonlinearity of a turbine structure are realized and validated before involved into that framework, in which three different failure modes can be classified and analyzed with high numerical efficiency. At last, the effects of external environmental and internal structural parameters, e.g., wind velocity, turbine scale, parking status, section slenderness and material model are considered for both onshore and offshore wind turbines (OWTs), so as to reflect the wide applicability of this proposed framework and provide instructive reference for wind turbine design under extreme conditions like cyclones.
Chen, Y, Wu, D, Li, H & Gao, W 2022, 'Quantifying the fatigue life of wind turbines in cyclone-prone regions', Applied Mathematical Modelling, vol. 110, pp. 455-474.
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Wind turbines are designed to harvest kinetic energy of the wind, which are more susceptible to the impact of tropical cyclones compared with other high-rise structures. Most of the existing research of cyclone (hurricane/typhoon) impacts on wind turbine merely concentrated on its short-term failure (strength or stability failure). However in this paper, it is evidently demonstrated that cyclones can also have a significant impact on the long-term failure (fatigue failure) of a wind turbine. In this study, a novel framework is developed in the fatigue life evaluation of a wind turbine, in which two external factors, i.e., the progressive change of cyclone direction and intensity observed at a specific site, combined with one internal factor, i.e., a parked wind turbine with feathered or unfeathered blades are considered. Subsequently, the effect of cyclone-normal-wind direction misalignment and cyclone average recurrence intervals are included to have a synthetic assessment on the damage potential of cyclones. The proposed fatigue analysis framework can be also extended to other structures, e.g., the hybrid wind-tidal energy conversion system in cyclone-prone regions.
Chen, Y, Ye, L, Kinloch, AJ & Zhang, YX 2022, '3D printed carbon-fibre reinforced composite lattice structures with good thermal-dimensional stability', Composites Science and Technology, vol. 227, pp. 109599-109599.
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Chen, Y, Ye, L, Zhang, YX & Yang, C 2022, 'A multi-material topology optimization with temperature-dependent thermoelastic properties', Engineering Optimization, vol. 54, no. 12, pp. 2140-2155.
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Chen, Y, Zhao, L, Zhang, Y, Huang, S & Dissanayake, G 2022, 'Anchor Selection for SLAM Based on Graph Topology and Submodular Optimization', IEEE Transactions on Robotics, vol. 38, no. 1, pp. 329-350.
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This article considers simultaneous localization and mapping (SLAM) problem for robots in situations where accurate estimates for some of the robot poses, termed anchors, are available. These may be acquired through external means, for example, by either stopping the robot at some previously known locations or pausing for a sufficient period of time to measure the robot poses with an external measurement system. The main contribution is an efficient algorithm for selecting a fixed number of anchors from a set of potential poses that minimizes estimated error in the SLAM solution. Based on a graph-topological connection between the D-optimality design metric and the tree-connectivity of the pose-graph, the anchor selection problem can be formulated approximately as a submatrix selection problem for reduced weighted Laplacian matrix, leading to a cardinality-constrained submodular maximization problem. Two greedy methods are presented to solve this submodular optimization problem with a performance guarantee. These methods are complemented by Cholesky decomposition, approximate minimum degree permutation, order reuse, and rank-1 update that exploit the sparseness of the weighted Laplacian matrix. We demonstrate the efficiency and effectiveness of the proposed techniques on public-domain datasets, Gazebo simulations, and real-world experiments.
Chen, Z, Fang, J, Wei, W, Ngo, HH, Guo, W & Ni, B-J 2022, 'Emerging adsorbents for micro/nanoplastics removal from contaminated water: Advances and perspectives', Journal of Cleaner Production, vol. 371, pp. 133676-133676.
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Micro/nanoplastics (MPs/NPs) are emerging pollutants in the water environment. MPs/NPs' high buoyant and persistent properties and potential toxic effects on living organisms make them priority pollutants in water. To overcome plastic pollution, great efforts have been made to remove MPs/NPs from contaminated water. Recently, adsorption has been proved as an efficient strategy, and emerging adsorbents have shown promising removal performance. In this review, we provide a comprehensive review of recent advancements in adsorbents for the eradication of MPs/NPs from water. Engineered adsorbents (e.g., carbon materials, sponge/aerogel/fiber materials, metal (hydr)oxides, and metal-organic frameworks (MOFs)) are first summarized, and the adsorbents' structure-performance correlation is emphasized. Afterward, critical experimental factors (e.g., pH value, metal ions, anions, dissolved organic matters (DOM)) are analyzed. At last, challenges and prospects in this field are highlighted to guide the development of novel high-performance adsorbents for MPs/NPs pollution control.
Chen, Z, Liu, X, Wei, W, Chen, H & Ni, B-J 2022, 'Removal of microplastics and nanoplastics from urban waters: Separation and degradation', Water Research, vol. 221, pp. 118820-118820.
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The omnipresent micro/nanoplastics (MPs/NPs) in urban waters arouse great public concern. To build a MP/NP-free urban water system, enormous efforts have been made to meet this goal via separating and degrading MPs/NPs in urban waters. Herein, we comprehensively review the recent developments in the separation and degradation of MPs/NPs in urban waters. Efficient MP/NP separation techniques, such as adsorption, coagulation/flocculation, flotation, filtration, and magnetic separation are first summarized. The influence of functional materials/reagents, properties of MPs/NPs, and aquatic chemistry on the separation efficiency is analyzed. Then, MP/NP degradation methods, including electrochemical degradation, advanced oxidation processes (AOPs), photodegradation, photocatalytic degradation, and biological degradation are detailed. Also, the effects of critical functional materials/organisms and operational parameters on degradation performance are discussed. At last, the current challenges and prospects in the separation, degradation, and further upcycling of MPs/NPs in urban waters are outlined. This review will potentially guide the development of next-generation technologies for MP/NP pollution control in urban waters.
Chen, Z, Ren, Z, Zheng, R, Gao, H & Ni, B-J 2022, 'Migration behavior of impurities during the purification of waste graphite powders', Journal of Environmental Management, vol. 315, pp. 115150-115150.
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Metal-laden solid wastes (e.g., waste graphite powders) have attracted great attention owing to their hazardous effects on the surrounding soil and water. Additionally, the metal-bearing impurities also hinder the reutilization of waste graphite powders. Thus, it is necessary to remove these inorganic impurities and figure out the removal mechanism of impurities in the purification process. In this study, an alkaline roasting-water washing-acid leaching (AWA) method was used to upgrade the waste graphite powders, and the migration behavior of diverse impurities has been qualitatively and quantitatively investigated. A graphite product with high impurity removal efficiencies is attained under optimal conditions. The removal of impurities mainly follows three routes: (1) V-, P-, and S-bearing impurities were complete removed (some formed soluble salts during alkaline roasting, and the remainder was dissolved in acid); (2) most Al-, K-, and Si-bearing impurities were removed by alkaline roasting, with the remainder was dissolved in the acid-leaching process; and (3) Fe-, Mg-, Ti-, Ca-, and Zn-bearing impurities were decomposed at high temperature and reacted with alkali to form hydroxides or oxides, which was subsequently dissolved in acid. In addition, the treatment of the generated wastewater is also discussed. The uncovered migration mechanisms of diverse impurities would guide the purification and reutilization process of other metal-bearing solid wastes efficiently.
Chen, Z, Wang, S, Fu, A, Gao, Y, Yu, S & Deng, RH 2022, 'LinkBreaker: Breaking the Backdoor-Trigger Link in DNNs via Neurons Consistency Check', IEEE Transactions on Information Forensics and Security, vol. 17, pp. 2000-2014.
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Backdoor attacks cause model misbehaving by first implanting backdoors in deep neural networks (DNNs) during training and then activating the backdoor via samples with triggers during inference. The compromised models could pose serious security risks to artificial intelligence systems, such as misidentifying 'stop' traffic sign into '80km/h'. In this paper, we investigate the connection characteristic between the backdoor and the trigger in DNNs and observe the fact that the backdoor is implanted via establishing a link between a cluster of neurons, representing the backdoor, and the triggers. Based on this observation, we design LinkBreaker, a new generic scheme for defending against backdoor attacks. In particular, LinkBreaker deploys a neuron consistency check mechanism for identifying compromised neuron set related to the trigger. Then, the LinkBreaker regulates the model to make predictions based on benign neuron set only and thus breaks the link between the backdoor and the trigger. Compared to previous defenses, LinkBreaker offers a more general backdoor countermeasure that is not only effective against input-agnostic backdoors but also source-specific backdoors, which the later can not be defeated by majority of state-of-the-arts. Besides, LinkBreaker is robust against adversarial examples, which, to a large extent, provides a holistic defense against adversarial example attacks on DNNs, while almost all current backdoor defenses do not have such consideration and capability. Extensive experimental evaluations on real datasets demonstrate that LinkBreaker is with high efficacy of suppressing trigger inputs while incurring no noticeable accuracy deterioration on benign inputs.
Chen, Z, Wei, W & Ni, B-J 2022, 'Transition metal chalcogenides as emerging electrocatalysts for urea electrolysis', Current Opinion in Electrochemistry, vol. 31, pp. 100888-100888.
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Urea electrolysis is an up-and-coming approach to realize sustainable energy-saving hydrogen fuel production and purification of urea-bearing wastes (e.g. urine, industrial wastewater). To attain a high urea electrolysis efficiency, high-performance electrocatalysts are highly required. Of late, transition metal (TM) chalcogenides-based materials are emerging as promising candidates for urea electrolysis. The catalytic performance of TM chalcogenides-based catalysts is optimized by tuning the internal/external characteristics, including nanostructure control, composition optimization, and heterostructuring. In this review, recent achievements in high-efficiency electrocatalysts based on TM chalcogenides for urea electrolysis are critically discussed. First, the electrochemistry of urea electrolysis is analyzed. Next, recent progress in TM chalcogenides-based electrocatalysts for urea electrolysis is detailed. The electrocatalyst design strategies are particularly elucidated, as well as the catalyst structure–performance correlation. Ultimately, perspectives on crucial scientific issues in this booming field are highlighted.
Chen, Z, Wei, W, Chen, H & Ni, B-J 2022, 'Recent advances in waste-derived functional materials for wastewater remediation', Eco-Environment & Health, vol. 1, no. 2, pp. 86-104.
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Chen, Z, Wei, W, Liu, X & Ni, B-J 2022, 'Emerging electrochemical techniques for identifying and removing micro/nanoplastics in urban waters', Water Research, vol. 221, pp. 118846-118846.
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The ubiquitous micro/nanoplastics (MPs/NPs) in urban waters are priority pollutants due to their toxic effects on living organisms. Currently, great efforts have been made to realize a plastic-free urban water system, and the identification and removal of MPs/NPs are two primary issues. Among diverse methods, emerging electrochemical techniques have gained growing interests owing to their facile implementation, high efficiency, eco-compatibility, onsite operation, etc. Herein, recent progress in the electrochemical identification and removal of MPs/NPs in urban waters are comprehensively reviewed. The electrochemical sensing of MPs/NPs and their released pollutants (e.g., bisphenol A (BPA)) has been analyzed, and the sensing principles and the featured electrochemical devices/electrodes are examined. Afterwards, recent applications of electrochemical methods (i.e., electrocoagulation, electroadsorption, electrokinetic separation and electrochemical degradation) in MPs/NPs removal are discussed in detail. The influences of critical parameters (e.g., plastics' property, current density and electrolyte) in the electrochemical identification and removal of MPs/NPs are also analyzed. Finally, the current challenges and prospects in electrochemical sensing and removal of MPs/NPs in urban waters are elaborated. This review would advance efficient electrochemical technologies for future MPs/NPs pollutions management in urban waters.
Chen, Z, Wei, W, Ni, B-J & Chen, H 2022, 'Plastic wastes derived carbon materials for green energy and sustainable environmental applications', Environmental Functional Materials, vol. 1, no. 1, pp. 34-48.
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Chen, Z, Wei, W, Song, L & Ni, B-J 2022, 'Hybrid Water Electrolysis: A New Sustainable Avenue for Energy-Saving Hydrogen Production', Sustainable Horizons, vol. 1, pp. 100002-100002.
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Chen, Z, Wei, W, Zou, W, Li, J, Zheng, R, Wei, W, Ni, B-J & Chen, H 2022, 'Integrating electrodeposition with electrolysis for closed-loop resource utilization of battery industrial wastewater', Green Chemistry, vol. 24, no. 8, pp. 3208-3217.
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Closed-loop reutilization of battery industrial wastewater by converting wastewater pollutants into highly efficient electrocatalysts for wastewater electrolysis.
Chen, Z, Yuan, L, Lin, X, Qin, L & Zhang, W 2022, 'Balanced Clique Computation in Signed Networks: Concepts and Algorithms.', CoRR, vol. abs/2204.00515, no. 99, pp. 1-14.
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Clique is one of the most fundamental models for cohesive subgraph mining in network analysis. Existing clique model mainly focuses on unsigned networks. However, in real world, many applications are modeled as signed networks with positive and negative edges. As the signed networks hold their own properties different from the unsigned networks, the existing clique model is inapplicable for the signed networks. Motivated by this, we propose the balanced clique model that considers the most fundamental and dominant theory, structural balance theory, for signed networks. Following the balanced clique model, we study the maximal balanced clique enumeration problem (MBCE) which computes all the maximal balanced cliques in a given signed network. Moreover, in some applications, users prefer a unique and representative balanced clique with maximum size rather than all balanced cliques. Thus, we also study the maximum balanced clique search problem (MBCS) which computes the balanced clique with maximum size. We show that MBCE problem and MBCS problem are both NP-Hard. For the MBCE problem, a straightforward solution is to treat the signed network as two unsigned networks and leverage the off-the-shelf techniques for unsigned networks. However, such a solution is inefficient for large signed networks. To address this problem, in this paper, we first propose a new maximal balanced clique enumeration algorithm by exploiting the unique properties of signed networks. Based on the new proposed algorithm, we devise two optimization strategies to further improve the efficiency of the enumeration. For the MBCS problem, we first propose a baseline solution. To overcome the huge search space problem of the baseline solution, we propose a new search framework based on search space partition....
Chen, Z, Zheng, R, Li, S, Wang, R, Wei, W, Wei, W, Ni, B-J & Chen, H 2022, 'Dual-anion etching induced in situ interfacial engineering for high-efficiency oxygen evolution', Chemical Engineering Journal, vol. 431, pp. 134304-134304.
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Designing novel catalysts for oxygen evolution reaction (OER) with high cost-effectiveness plays a central role in sustainably driving renewable energy conversion and storage. Here we demonstrate the in situ interfacial engineering for constructing efficient OER catalysts based on the electrochemical dual-anion etching of natural arsenopyrite. The OER catalyst (FeAsS) prepared from natural arsenopyrite via an environment-friendly ball milling approach achieves a current density of 10 mA cm−2 at an overpotential of 200 mV, outperforming many state-of-the-art catalysts. The in-depth study indicates that the co-etching of lattice As and S under the OER conditions triggers the in situ surface self-reconstruction, and a self-optimized catalytic active and stable FeAsS/α-FeOOH interface has been developed. Computational studies further confirm that the strong electronic coupling effect between α-FeOOH and FeAsS significantly tunes the binding energy between reaction intermediates and active sites, finally leading to an enhanced OER activity. The dual-anion etching of precatalysts induced in situ interfacial engineering demonstrated here expands the way of exploring other multiple nonmetallic elements involved nanomaterials as efficient OER precatalysts. This study also stimulates further study on the eco-design of electroactive materials for advanced energy conversion/storage applications from earth-abundance natural resources.
Chen, Z, Zheng, R, Wei, W, Wei, W, Ni, B-J & Chen, H 2022, 'Unlocking the electrocatalytic activity of natural chalcopyrite using mechanochemistry', Journal of Energy Chemistry, vol. 68, pp. 275-283.
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Manipulating the structure self-reconstruction of transition metal sulfide-based (pre)catalysts during the oxygen evolution reaction (OER) process is of great interest for developing cost-effective OER catalysts, which remains a central challenge. Here we realize a deep structure self-reconstruction of natural chalcopyrite to unlock its OER performance via mechanochemical activation. Compared with the manually milled counterpart (CuFeS2-HM), the mechanically milled catalyst (CuFeS2-BM) with a reduced crystallinity exhibits a 7.11 times higher OER activity at 1.53 V vs. RHE. In addition, the CuFeS2-BM requires a low overpotential of 243 mV for generating 10 mA cm−2 and exhibits good stability over 24 h. Further investigations suggest that the excellent OER performance of CuFeS2-BM mainly originates from the decreased crystallinity induced the in situ deep structure self-reconstruction of the originally sulfides into the electroactive and stable metal (oxy)hydroxide phase (e.g., α-FeOOH) via S etching under OER conditions. This study demonstrates that regulating the crystallinity of catalysts is a promising design strategy for developing highly efficient OER catalysts via managing the structure self-reconstruction process, which can be further extended to the design of efficient catalysts for other advanced energy conversion devices. In addition, this study unveils the great potentials of engineering abundant natural minerals as cost-effective catalysts for diverse applications.
Chen, Z, Zheng, R, Wei, W, Wei, W, Zou, W, Li, J, Ni, B-J & Chen, H 2022, 'Recycling spent water treatment adsorbents for efficient electrocatalytic water oxidation reaction', Resources, Conservation and Recycling, vol. 178, pp. 106037-106037.
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Heavy metal contaminated spent adsorbents are of great environmental concern due to their hazardous effects and large-scale accumulation in the natural environment. Converting massive spent adsorbents into efficient electrocatalysts with a facile strategy can address the challenge of growing energy demand and achieving carbon neutral goal. Herein, we demonstrated a 'spent adsorbents to heterostructured electrocatalysts' conversion strategy based on the 'waste-to-wealth' principle. Via a facile boriding process, the metal ions laden biochar-based spent adsorbents (SA) have been totally transformed into magnetic metal borides/biochar heterostructures, which exhibit excellent activities towards oxygen evolution reaction. The optimized NiCuFeB/SA catalyst takes a low overpotential of 251 mV to drive a current density of 10 mA cm−2, outperforming many Ni/Fe-based catalysts synthesized from commercial material resources. Comprehensive analyses suggest the high catalytic efficiency mainly attributes to the porous biochar confined well-dispersed nano-sized metallic borides, the in-situ evolved active metal (oxy)hydroxides, favourable charge-transfer kinetics, as well as the heterostructure and amorphous feature. This work offers a general strategy to efficiently reutilize the spent metal-bearing biochar-based adsorbents, which can be extended to advanced energy applications-oriented reutilization of other metal-contaminated solid wastes in an economically and environmental-benign manner.
Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Bui, XT, Wei, W, Ni, B, Varjani, S & Hoang, NB 2022, 'Enhanced photo-fermentative biohydrogen production from biowastes: An overview', Bioresource Technology, vol. 357, pp. 127341-127341.
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Clean energy like hydrogen can be a promising strategy to solve problems of global warming. Photo-fermentation (PF) is an attractive technology for producing biohydrogen from various biowastes cost-effectively and environmentally friendly. However, challenges of low light conversion efficiency and small yields of biohydrogen production still limit its application. Thus, advanced strategies have been investigated to enhance photo-fermentative biohydrogen production. This review discusses advanced technologies that show positive outcomes in improving biohydrogen production by PF, including the following. Firstly, genetic engineering enhances light transfer efficiency, change the activity of enzymes, and improves the content of ATP, ammonium and antibiotic tolerance of photosynthetic bacteria. Secondly, immobilization technology is refined. Thirdly, nanotechnology makes great strides as a scientific technique and fourthly, integration of dark and photo-fermentation technology is possible. Some suggestions for further studies to achieve high levels of efficiency of photo-fermentative biohydrogen production are mentioned in this paper.
Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Deng, L, Chen, Z, Ye, Y, Bui, XT & Hoang, NB 2022, 'Advanced strategies for enhancing dark fermentative biohydrogen production from biowaste towards sustainable environment', Bioresource Technology, vol. 351, pp. 127045-127045.
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Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Zhang, S, Deng, S, An, D & Hoang, NB 2022, 'Impact factors and novel strategies for improving biohydrogen production in microbial electrolysis cells', Bioresource Technology, vol. 346, pp. 126588-126588.
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Cheng, D, Wang, X, Zhang, Y & Zhang, L 2022, 'Graph Neural Network for Fraud Detection via Spatial-Temporal Attention', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 8, pp. 3800-3813.
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Card fraud is an important issue and incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based approaches to detect fraudulent behavior from transaction records. But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant fraudulent behavior patterns in the online detection system. Therefore, in this work, we propose a spatial-temporal attention-based graph network (STAGN) for credit card fraud detection. In particular, we learn the temporal and location-based transaction graph features by a graph neural network first. Afterwards, we employ the spatial-temporal attention on top of learned tensor representations, which are then fed into a 3D convolution network. The attentional weights are jointly learned in an end-to-end manner with 3D convolution and detection networks. After that, we conduct extensive experiments on the real-word card transaction dataset. The result shows that STAGN performs better than other state-of-the-art baselines in both AUC and precision-recall curves. Moreover, we conduct empirical studies with domain experts on the proposed method for fraud detection and knowledge discovery; the result demonstrates its superiority in detecting suspicious transactions, mining spatial and temporal fraud hotspots, and uncover fraud patterns. The effectiveness of the proposed method in other user behavior-based tasks is also demonstrated. Finally, in order to tackle the challenges of big data, we integrate our proposed STAGN into the fraud detection system as the predictive model and present the implementation detail of each module in the system.
Cheng, J, You, H, Tian, M, Kuang, S, Liu, S, Chen, H, Li, X, Liu, H & Liu, T 2022, 'Occurrence of nitrite-dependent anaerobic methane oxidation bacteria in the continental shelf sediments', Process Safety and Environmental Protection, vol. 168, pp. 626-632.
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Nitrite-dependent anaerobic methane oxidation (N-damo) is a key bioprocess coupling global carbon and nitrogen cycles and is mediated by NC10 bacteria. So far, the distribution of N-damo bacteria in marine sediments has rarely been reported. In this study, the sediments from the Bohai Sea, Yellow Sea and East China Sea were taken as the research objects, and the ecological distribution of N-damo bacteria was investigated by quantitative PCR and amplicon sequencing. Quantitative PCR results demonstrated that the highest average copy number of N-damo bacterial 16S rRNA gene was in the Bohai Sea, followed by the East China Sea, while the lowest was observed in the Yellow Sea. Based on the OTU numbers, the N-damo bacterial diversity was highest in East China Sea, followed by the Bohai Sea, while lowest in the Yellow Sea. The N-damo bacterial community structure exhibited an obvious spatial distribution among the three seas. Sediment nitrite nitrogen content is the key environmental factor affecting the abundance and diversity of N-damo bacteria, and sediment ammonia nitrogen content is the key environmental factor affecting the community structure of N-damo bacteria.
Cheng, Z, Ye, D, Zhu, T, Zhou, W, Yu, PS & Zhu, C 2022, 'Multi‐agent reinforcement learning via knowledge transfer with differentially private noise', International Journal of Intelligent Systems, vol. 37, no. 1, pp. 799-828.
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In multi-agent reinforcement learning, transfer learning is one of the key techniques used to speed up learning performance through the exchange of knowledge among agents. However, there are three challenges associated with applying this technique to real-world problems. First, most real-world domains are partially rather than fully observable. Second, it is difficult to pre-collect knowledge in unknown domains. Third, negative transfer impedes the learning progress. We observe that differentially private mechanisms can overcome these challenges due to their randomization property. Therefore, we propose a novel differential transfer learning method for multi-agent reinforcement learning problems, characterized by the following three key features. First, our method allows agents to implement real-time knowledge transfers between each other in partially observable domains. Second, our method eliminates the constraints on the relevance of transferred knowledge, which expands the knowledge set to a large extent. Third, our method improves robustness to negative transfers by applying differentially exponential noise and relevance weights to transferred knowledge. The proposed method is the first to use the randomization property of differential privacy to stimulate the learning performance in multi-agent reinforcement learning system. We further implement extensive experiments to demonstrate the effectiveness of our proposed method.
Cherukuri, SK, Kumar, BP, Kaniganti, KR, Muthubalaji, S, Devadasu, G, Babu, TS & Alhelou, HH 2022, 'A Novel Array Configuration Technique for Improving the Power Output of the Partial Shaded Photovoltaic System', IEEE Access, vol. 10, pp. 15056-15067.
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Chi, F, Zhang, G, Ren, N, Zhang, J, Du, F, Zheng, X, Zhang, C, Lin, Z, Li, R, Shi, X & Zhu, Y 2022, 'The anti-alcoholism drug disulfiram effectively ameliorates ulcerative colitis through suppressing oxidative stresses-associated pyroptotic cell death and cellular inflammation in colonic cells', International Immunopharmacology, vol. 111, pp. 109117-109117.
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Chilvers, J, Yang, L, Lin, X & Zhang, YX 2022, 'Experimental and numerical investigations of hybrid-fibre engineered cementitious composite panels under contact explosions', Engineering Structures, vol. 266, pp. 114582-114582.
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Chiniforush, AA, Gharehchaei, M, Akbar Nezhad, A, Castel, A, Moghaddam, F, Keyte, L, Hocking, D & Foster, S 2022, 'Numerical simulation of risk mitigation strategies for early-age thermal cracking and DEF in concrete', Construction and Building Materials, vol. 322, pp. 126478-126478.
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Choi, PJ, Lim, S, Shon, H & An, AK 2022, 'Incorporation of negatively charged silver nanoparticles in outer-selective hollow fiber forward osmosis (OSHF-FO) membrane for wastewater dewatering', Desalination, vol. 522, pp. 115402-115402.
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Thin film nanocomposite (TFN) outer selective hollow fiber forward osmosis (OSHF FO) membranes incorporated with negatively charged silver nanoparticles (NPs) were fabricated for wastewater dewatering. The performances of five silver loading concentrations, namely 0% (pristine), 0.0002%, 0.0005%, 0.0010%, and 0.0015%, were compared. Among the four non-zero loading concentrations, 0.0010% showed the best performance. The pristine membrane had a better flux performance than the silver-loaded membranes; however, the silver-loaded membranes lasted longer (over 30 days) and had a higher flux recovery after salt cleaning. The pristine membrane also concentrated the feed solution 4 h faster than the silver-loaded membranes (68 h); however, the performance was not stable, and the water flux continuously decreased. In contrast, the performance of the silver-load membranes was more stable and plateaued between 2 LMH and 3 LMH. Due to the negatively charged silver NPs, the TFN OSHF FO membranes showed a stronger fouling resistance and stable performance, and thus a longer life expectancy. Therefore, the use of TFN OSHF FO membranes with embedded silver NPs can be an alternative strategy for wastewater treatment and dewatering.
Chou, Y-L, Moreira, C, Bruza, P, Ouyang, C & Jorge, J 2022, 'Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications', Information Fusion, vol. 81, pp. 59-83.
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Choukimath, MC, Banapurmath, NR, Riaz, F, Patil, AY, Jalawadi, AR, Mujtaba, MA, Shahapurkar, K, Khan, TMY, Alsehli, M, Soudagar, MEM & Fattah, IMR 2022, 'Experimental and Computational Study of Mechanical and Thermal Characteristics of h-BN and GNP Infused Polymer Composites for Elevated Temperature Applications', Materials, vol. 15, no. 15, pp. 5397-5397.
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Polymer-based nanocomposites are being considered as replacements for conventional materials in medium to high-temperature applications. This article aims to discover the synergistic effects of reinforcements on the developed polymer-based nanocomposite. An epoxy-based polymer composite was manufactured by reinforcing graphene nanoplatelets (GNP) and h-boron nitride (h-BN) nanofillers. The composites were prepared by varying the reinforcements with the step of 0.1 from 0.1 to 0.6%. Ultrasonication was carried out to ensure the homogenous dispersion of reinforcements. Mechanical, thermal, functional, and scanning electron microscopy (SEM) analysis was carried out on the novel manufactured composites. The evaluation revealed that the polymer composite with GNP 0.2 by wt % has shown an increase in load-bearing capacity by 265% and flexural strength by 165% compared with the pristine form, and the polymer composite with GNP and h-BN 0.6 by wt % showed an increase in load-bearing capacity by 219% and flexural strength by 114% when compared with the pristine form. Furthermore, the evaluation showed that the novel prepared nanocomposite reinforced with GNP and h-BN withstands a higher temperature, around 340 °C, which is validated by thermogravimetric analysis (TGA) trials. The numerical simulation model is implemented to gather the synthesised nanocomposite’s best composition and mechanical properties. The minor error between the simulation and experimental data endorses the model’s validity. To demonstrate the industrial applicability of the presented material, a case study is proposed to predict the temperature range for compressor blades of gas turbine engines containing nanocomposite material as the substrate and graphene/h-BN as reinforcement particles.
Chowdhury, RR, Chattopadhyay, S & Adak, C 2022, 'CAHPHF: Context-Aware Hierarchical QoS Prediction With Hybrid Filtering', IEEE Transactions on Services Computing, vol. 15, no. 4, pp. 2232-2247.
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IEEE With the proliferation of Internet-of-Things and continuous growth in the number of web-services at the Internet-scale, service-recommendation is becoming a challenge nowadays. One of the prime aspects influencing the service-recommendation is the Quality-of-Service(QoS) parameter, which depicts the performance of a web-service. In general, the service provider furnishes the QoS values before service deployment. In reality, the QoS values of service vary across different users, time, locations, etc. Therefore, estimating the QoS value of service before its execution is an important task. Thus, QoS-prediction has gained significant attention. Multiple approaches are available in the literature for predicting QoS. However, these approaches are yet to reach the desired accuracy level. Here, we study the QoS-prediction problem across different users and propose a novel solution by considering the contextual information of both services and users. Our proposal includes two key-steps: (a)hybrid-filtering, (b)hierarchical-prediction-mechanism. On one hand, the hybrid-filtering aims to obtain a set of similar users and services, given a target user and a service. On the other hand, the goal of the hierarchical-prediction-mechanism is to estimate the QoS value accurately by leveraging hierarchical-neural-regression. We evaluated our framework on WS-DREAM datasets. The experimental results show our framework outperformed the major state-of-the-art approaches.
Chu, X, Flerchinger, GN, Ma, L, Fang, Q, Malone, RW, Yu, Q, He, J, Wang, N, Feng, H & Zou, Y 2022, 'Development of RZ-SHAW for simulating plastic mulch effects on soil water, soil temperature, and surface energy balance in a maize field', Agricultural Water Management, vol. 269, pp. 107666-107666.
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Chu, Y, Zhao, S, He, L & Niu, F 2022, 'Wind noise suppression in filtered-x least mean squares-based active noise control systems', The Journal of the Acoustical Society of America, vol. 152, no. 6, pp. 3340-3345.
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Wind noise is notorious for its detrimental impacts on audio devices. This letter evaluates the influence of wind noise on the active noise control performance of headphones in a wind tunnel, and the noise reduction is found to decrease with wind speeds. To improve the performance of noise control systems in windy environments, the filtered-x least mean squares algorithm is modified based on the total least squares technique, taking the characteristics of wind noise into account. Computer simulations with real-recorded data demonstrate that the proposed algorithm could improve the noise reduction by approximately 3 dB in windy conditions.
Clarke, L, Bukhari, W, O'Gorman, CM, Khalilidehkordi, E, Arnett, S, Woodhall, M, Prain, KM, Parratt, JDE, Barnett, MH, Marriott, MP, McCombe, PA, Sutton, I, Boggild, M, Brownlee, W, Carroll, WM, Hodgkinson, S, Macdonell, RAL, Mason, DF, Pereira, J, Slee, M, Das, C, Henderson, APD, Kermode, AG, Lechner-Scott, J, Waters, P, Sun, J & Broadley, SA 2022, 'Response to treatment in NMOSD: the Australasian experience', Multiple Sclerosis and Related Disorders, vol. 58, pp. 103408-103408.
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Clemon, LM 2022, 'Rapid estimation of viral emission source location via genetic algorithm', Computational Mechanics, vol. 69, no. 5, pp. 1213-1224.
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AbstractIndoor spread of infectious diseases is well-studied as a common transmission route. For highly infectious diseases, like Sars-CoV-2, considering poorly or semi ventilated areas outdoors is increasingly important. This is important in communities with high proportions of infected people, highly infectious variants, or where spread is difficult to manage. This work develops a simulation framework based on probabilistic distributions of viral particles, decay, and infection. The methodology reduces the computational cost of generating rapid estimations of a wide variety of scenarios compared to other simulation methods with high computational cost and more fidelity. Outdoor predictions are provided in example applications for a gathering of five people with oscillating wind and a public speaking event. The results indicate that infection is sensitive to population density and outdoor transmission is plausible and likely locations of a virtual super-spreader are identified. Outdoor gatherings should consider precautions to reduce infection spread.
Costa, PCS, An, D, Sanders, YR, Su, Y, Babbush, R & Berry, DW 2022, 'Optimal Scaling Quantum Linear-Systems Solver via Discrete Adiabatic Theorem', PRX Quantum, vol. 3, no. 4, p. 040303.
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Recently, several approaches to solving linear systems on a quantum computer have been formulated in terms of the quantum adiabatic theorem for a continuously varying Hamiltonian. Such approaches have enabled near-linear scaling in the condition number κ of the linear system, without requiring a complicated variable-time amplitude amplification procedure. However, the most efficient of those procedures is still asymptotically suboptimal by a factor of log(κ). Here, we prove a rigorous form of the adiabatic theorem that bounds the error in terms of the spectral gap for intrinsically discrete-time evolutions. In combination with the qubitized quantum walk, our discrete adiabatic theorem gives a speed-up for all adiabatic algorithms. Here, we use this combination to develop a quantum algorithm for solving linear systems that is asymptotically optimal, in the sense that the complexity is strictly linear in κ, matching a known lower bound on the complexity. Our O[κlog(1/ µ)] complexity is also optimal in terms of the combined scaling in κ and the precision µ. Compared to existing suboptimal methods, our algorithm is simpler and easier to implement. Moreover, we determine the constant factors in the algorithm, which would be suitable for determining the complexity in terms of gate counts for specific applications.
Cousin, E, Duncan, BB, Stein, C, Ong, KL, Vos, T, Abbafati, C, Abbasi-Kangevari, M, Abdelmasseh, M, Abdoli, A, Abd-Rabu, R, Abolhassani, H, Abu-Gharbieh, E, Accrombessi, MMK, Adnani, QES, Afzal, MS, Agarwal, G, Agrawaal, KK, Agudelo-Botero, M, Ahinkorah, BO, Ahmad, S, Ahmad, T, Ahmadi, K, Ahmadi, S, Ahmadi, A, Ahmed, A, Ahmed Salih, Y, Akande-Sholabi, W, Akram, T, Al Hamad, H, Al-Aly, Z, Alcalde-Rabanal, JE, Alipour, V, Aljunid, SM, Al-Raddadi, RM, Alvis-Guzman, N, Amini, S, Ancuceanu, R, Andrei, T, Andrei, CL, Anjana, RM, Ansar, A, Antonazzo, IC, Antony, B, Anyasodor, AE, Arabloo, J, Arizmendi, D, Armocida, B, Artamonov, AA, Arulappan, J, Aryan, Z, Asgari, S, Ashraf, T, Astell-Burt, T, Atorkey, P, Atout, MMW, Ayanore, MA, Badiye, AD, Baig, AA, Bairwa, M, Baker, JL, Baltatu, OC, Banik, PC, Barnett, A, Barone, MTU, Barone-Adesi, F, Barrow, A, Bedi, N, Belete, R, Belgaumi, UI, Bell, AW, Bennett, DA, Bensenor, IM, Beran, D, Bhagavathula, AS, Bhaskar, S, Bhattacharyya, K, Bhojaraja, VS, Bijani, A, Bikbov, B, Birara, S, Bodolica, V, Bonny, A, Brenner, H, Briko, NI, Butt, ZA, Caetano dos Santos, FL, Cámera, LA, Campos-Nonato, IR, Cao, Y, Cao, C, Cerin, E, Chakraborty, PA, Chandan, JS, Chattu, VK, Chen, S, Choi, J-YJ, Choudhari, SG, Chowdhury, EK, Chu, D-T, Corso, B, Dadras, O, Dai, X, Damasceno, AAM, Dandona, L, Dandona, R, Dávila-Cervantes, CA, De Neve, J-W, Denova-Gutiérrez, E, Dhamnetiya, D, Diaz, D, Ebtehaj, S, Edinur, HA, Eftekharzadeh, S, El Sayed, I, Elgendy, IY, Elhadi, M, Elmonem, MA, Faisaluddin, M, Farooque, U, Feng, X, Fernandes, E, Fischer, F, Flood, D, Freitas, M, Gaal, PA, Gad, MM, Gaewkhiew, P, Getacher, L, Ghafourifard, M, Ghanei Gheshlagh, R, Ghashghaee, A, Ghith, N, Ghozali, G, Gill, PS, Ginawi, IA, Glushkova, EV, Golechha, M, Gopalani, SV, Guimarães, RA, Gupta, RD, Gupta, R, Gupta, VK, Gupta, VB, Gupta, S, Habtewold, TD, Hafezi-Nejad, N, Halwani, R, Hanif, A, Hankey, GJ, Haque, S, Hasaballah, AI, Hasan, SS, Hashi, A, Hassanipour, S, Hay, SI, Hayat, K, Heidari, M, Hossain, MBH, Hossain, S, Hosseini, M, Hoveidamanesh, S, Huang, J, Humayun, A, Hussain, R, Hwang, B-F, Ibitoye, SE, Ikuta, KS, Inbaraj, LR, Iqbal, U, Islam, MS, Islam, SMS, Islam, RM, Ismail, NE, Isola, G, Itumalla, R, Iwagami, M, Iyamu, IO, Jahani, MA, Jakovljevic, M, Jayawardena, R, Jha, RP, John, O, Jonas, JB, Joo, T, Kabir, A, Kalhor, R & et al. 2022, 'Diabetes mortality and trends before 25 years of age: an analysis of the Global Burden of Disease Study 2019', The Lancet Diabetes & Endocrinology, vol. 10, no. 3, pp. 177-192.
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BACKGROUND: Diabetes, particularly type 1 diabetes, at younger ages can be a largely preventable cause of death with the correct health care and services. We aimed to evaluate diabetes mortality and trends at ages younger than 25 years globally using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019. METHODS: We used estimates of GBD 2019 to calculate international diabetes mortality at ages younger than 25 years in 1990 and 2019. Data sources for causes of death were obtained from vital registration systems, verbal autopsies, and other surveillance systems for 1990-2019. We estimated death rates for each location using the GBD Cause of Death Ensemble model. We analysed the association of age-standardised death rates per 100 000 population with the Socio-demographic Index (SDI) and a measure of universal health coverage (UHC) and described the variability within SDI quintiles. We present estimates with their 95% uncertainty intervals. FINDINGS: In 2019, 16 300 (95% uncertainty interval 14 200 to 18 900) global deaths due to diabetes (type 1 and 2 combined) occurred in people younger than 25 years and 73·7% (68·3 to 77·4) were classified as due to type 1 diabetes. The age-standardised death rate was 0·50 (0·44 to 0·58) per 100 000 population, and 15 900 (97·5%) of these deaths occurred in low to high-middle SDI countries. The rate was 0·13 (0·12 to 0·14) per 100 000 population in the high SDI quintile, 0·60 (0·51 to 0·70) per 100 000 population in the low-middle SDI quintile, and 0·71 (0·60 to 0·86) per 100 000 population in the low SDI quintile. Within SDI quintiles, we observed large variability in rates across countries, in part explained by the extent of UHC (r2=0·62). From 1990 to 2019, age-standardised death rates decreased globally by 17·0% (-28·4 to -2·9) for all diabetes, and by 21·0% (-33·0 to -5·9) when considering only type 1 diabetes. However, the low SDI quintile had the lowest decline for both all diabete...
Cu Thi, P, Ball, JE & Dao, NH 2022, 'Early stopping technique using a genetic algorithm for calibration of an urban runoff model', International Journal of River Basin Management, vol. 20, no. 4, pp. 545-554.
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Identifying suitable parameter sets for use in catchment modelling remains a critical issue in hydrology. This paper describes an early stopping technique (EST) for use during calibration of a multi-parameter urban catchment modelling system. The proposed method takes advantage of MODE and lower confidence limit (LCL) functions in statistical analysis of spanning set of objective function values. The paper also introduces a monitoring process and regularization techniques to avoid under/overfitting during the calibration and to enhance generalisation performance. The methodology is assessed using SWMM and linked with a Genetic Algorithm for calibration of a Powells Creek catchment model in Sydney, Australia. Results demonstrate that the statistical spanning set analysis approach overcomes issues of poor interpretation and deterioration in the model’s generalisation properties. By stopping early, the calibration process avoided overfitting; this was indicated by too closely fitting to the calibration dataset and a failure to fit to the monitoring dataset.
Cui, H, Wang, W, Xu, F, Saha, S & Liu, Q 2022, 'Transitional free convection flow and heat transfer within attics in cold climate', Thermal Science, vol. 26, no. 6 Part A, pp. 4699-4709.
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The transitional free convection flow and heat transfer within attics in cold climate are investigated using 3-D numerical simulations for a range of Rayleigh numbers from 103 to 106 and height-length ratios from 0.1 to 1.5. The development process of free convection in the attic could be classified into three-stages: an initial stage, a transitional stage, and a fully developed stage. Flow structures in different stages including transverse and longitudinal rolls are critically analyzed in terms of the location and strength of convection rolls and their impacts on the heat transfer. The transition unsteady flow and asymmetry flow in the fully developed stage is discussed for the fixed height-length ratio 0.5. Various flow regimes are given in a bifurcation diagram in the parameter space of Rayleigh numbers (102 < Ra < 107) for height-length ratios (0.1 < A < 1.5). The time series of heat transfer rate through the bottom wall is quantified for different height-length ratios. The overall heat transfer rate for the low Prandtl fluid (Pr = 0.7) could be enhanced based on 3-D flow structure.
Cui, L, Guo, L, Gao, L, Cai, B, Qu, Y, Zhou, Y & Yu, S 2022, 'A Covert Electricity-Theft Cyberattack Against Machine Learning-Based Detection Models', IEEE Transactions on Industrial Informatics, vol. 18, no. 11, pp. 7824-7833.
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The advanced metering infrastructure (AMI) in modern networked smart homes brings various advantages. However, smart homes are vulnerable to many cyberattacks, and the most striking one is energy theft. Researchers have developed many countermeasures, fostered by advanced machine learning (ML) techniques. Nevertheless, recent advances are not robust enough in practice, partially due to the vulnerabilities of ML algorithms. In this paper, we present a covert electricity theft strategy through mimicking normal consumption patterns. Such attack is almost impossible to be detected by existing solutions as the manipulated data have little deviation against honest usage records. To address this threat, we initially identify and define two levels of consumption deviations: home-level and interpersonal-level, respectively. Then, we propose a feature extraction method and develop a novel detection model based on deep learning. Extensive experiments show that the presented attack could evade existing mainstream detectors and the proposed countermeasure outperforms existing leading methods.
Cui, L, Qu, Y, Xie, G, Zeng, D, Li, R, Shen, S & Yu, S 2022, 'Security and Privacy-Enhanced Federated Learning for Anomaly Detection in IoT Infrastructures', IEEE Transactions on Industrial Informatics, vol. 18, no. 5, pp. 3492-3500.
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Internet of Things (IoT) anomaly detection is significant due to its fundamental roles of securing modern critical infrastructures, such as falsified data injection detection and transmission line faults diagnostic in smart grids. Researchers have proposed various detection methods fostered by machine learning (ML) techniques. Federated learning (FL), as a promising distributed ML paradigm, has been employed recently to improve detection performance due to its advantages of privacy-preserving and lower latency. However, existing FL-based methods still suffer from efficiency, robustness, and security challenges. To address these problems, in this article, we initially introduce a blockchain-empowered decentralized and asynchronous FL framework for anomaly detection in IoT systems, which ensures data integrity and prevents single-point failure while improving the efficiency. Further, we design an improved differentially private FL based on generative adversarial nets, aiming to optimize data utility throughout the training process. To the best of our knowledge, it is the first system to employ a decentralized FL approach with privacy-preserving for IoT anomaly detection. Simulation results on the real-world dataset demonstrate the superior performance from aspects of robustness, accuracy, and fast convergence while maintaining high level of privacy and security protection.
Cui, Q, Hu, X, Ni, W, Tao, X, Zhang, P, Chen, T, Chen, K-C & Haenggi, M 2022, 'Vehicular mobility patterns and their applications to Internet-of-Vehicles: a comprehensive survey', Science China Information Sciences, vol. 65, no. 11.
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AbstractWith the growing popularity of the Internet-of-Vehicles (IoV), it is of pressing necessity to understand transportation traffic patterns and their impact on wireless network designs and operations. Vehicular mobility patterns and traffic models are the keys to assisting a wide range of analyses and simulations in these applications. This study surveys the status quo of vehicular mobility models, with a focus on recent advances in the last decade. To provide a comprehensive and systematic review, the study first puts forth a requirement-model-application framework in the IoV or general communication and transportation networks. Existing vehicular mobility models are categorized into vehicular distribution, vehicular traffic, and driving behavior models. Such categorization has a particular emphasis on the random patterns of vehicles in space, traffic flow models aligned to road maps, and individuals’ driving behaviors (e.g., lane-changing and car-following). The different categories of the models are applied to various application scenarios, including underlying network connectivity analysis, off-line network optimization, online network functionality, and real-time autonomous driving. Finally, several important research opportunities arise and deserve continuing research efforts, such as holistic designs of deep learning platforms which take the model parameters of vehicular mobility as input features, qualification of vehicular mobility models in terms of representativeness and completeness, and new hybrid models incorporating different categories of vehicular mobility models to improve the representativeness and completeness.
Cui, Q, Zhang, Z, Yanpeng, S, Ni, W, Zeng, M & Zhou, M 2022, 'Dynamic Multichannel Access Based on Deep Reinforcement Learning in Distributed Wireless Networks', IEEE Systems Journal, vol. 16, no. 4, pp. 5831-5834.
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Cui, Z, Chen, H, Cui, L, Liu, S, Liu, X, Xu, G & Yin, H 2022, 'Reinforced KGs reasoning for explainable sequential recommendation', World Wide Web, vol. 25, no. 2, pp. 631-654.
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We explore the semantic-rich structured information derived from the knowledge graphs (KGs) associated with the user-item interactions and aim to reason out the motivations behind each successful purchase behavior. Existing works on KGs-based explainable recommendations focus purely on path reasoning based on current user-item interactions, which generally result in the incapability of conjecturing users’ subsequence preferences. Considering this, we attempt to model the KGs-based explainable recommendation in sequential settings. Specifically, we propose a novel architecture called Reinforced Sequential Learning with Gated Recurrent Unit (RSL-GRU), which is composed of a Reinforced Path Reasoning Network (RPRN) component and a GRU component. RSL-GRU takes users’ sequential behaviors and their associated KGs in chronological order as input and outputs potential top-N items for each user with appropriate reasoning paths from a global perspective. Our RPRN features a remarkable path reasoning capacity, which is regulated by a user-conditioned derivatively action pruning strategy, a soft reward strategy based on an improved multi-hop scoring function, and a policy-guided sequential path reasoning algorithm. Experimental results on four of Amazon’s large-scale datasets show that our method achieves excellent results compared with several state-of-the-art alternatives.
Cui, Z, Wang, X, Ngo, H & Zhu, G 2022, 'In-situ monitoring of membrane fouling migration and compression mechanism with improved ultraviolet technique in membrane bioreactors', Bioresource Technology, vol. 347, pp. 126684-126684.
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da Rocha, CG, Saldanha, RB, Tonini de Araújo, M & Consoli, NC 2022, 'Social and environmental assessments of Eco-friendly Pavement alternatives', Construction and Building Materials, vol. 325, pp. 126736-126736.
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Dadgar, S & Neshat, M 2022, 'A Novel Hybrid Multi-Modal Deep Learning for Detecting Hashtag Incongruity on Social Media', Sensors, vol. 22, no. 24, pp. 9870-9870.
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Hashtags have been an integral element of social media platforms over the years and are widely used by users to promote, organize and connect users. Despite the intensive use of hashtags, there is no basis for using congruous tags, which causes the creation of many unrelated contents in hashtag searches. The presence of mismatched content in the hashtag creates many problems for individuals and brands. Although several methods have been presented to solve the problem by recommending hashtags based on the users’ interest, the detection and analysis of the characteristics of these repetitive contents with irrelevant hashtags have rarely been addressed. To this end, we propose a novel hybrid deep learning hashtag incongruity detection by fusing visual and textual modality. We fine-tune BERT and ResNet50 pre-trained models to encode textual and visual information to encode textual and visual data simultaneously. We further attempt to show the capability of logo detection and face recognition in discriminating images. To extract faces, we introduce a pipeline that ranks faces based on the number of times they appear on Instagram accounts using face clustering. Moreover, we conduct our analysis and experiments on a dataset of Instagram posts that we collect from hashtags related to brands and celebrities. Unlike the existing works, we analyze these contents from both content and user perspectives and show a significant difference between data. In light of our results, we show that our multimodal model outperforms other models and the effectiveness of object detection in detecting mismatched information.
Dai, J, Yang, C, Xu, D, Wen, S, Jian, M & Yang, D 2022, 'Leaderless Consensus of Semilinear Hyperbolic Multiagent Systems with Semipositive or Seminegative Definite Convection', Discrete Dynamics in Nature and Society, vol. 2022, no. 1, pp. 1-8.
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This paper deals with a leaderless consensus of semilinear first‐order hyperbolic partial differential equation‐based multiagent systems (HPDEMASs). A consensus controller under an undirected graph is designed. Dealing with different convection assumptions, two different boundary conditions are presented, one right endpoint and the other left endpoint. Two sufficient conditions for leaderless consensus of HPDEMAS are presented by giving the gain range in the case of the symmetric seminegative definite convection coefficient and the semipositive definite convection coefficient, respectively. Two examples are presented to show the effectiveness of the control methods.
Dai, P, Hassan, M, Sun, X, Zhang, M, Bian, Z & Liu, D 2022, 'A framework for multi-robot coverage analysis of large and complex structures', Journal of Intelligent Manufacturing, vol. 33, no. 5, pp. 1545-1560.
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Dai, W, Mu, J, Chen, Z, Zhang, J, Pei, X, Luo, W & Ni, B-J 2022, 'Design of Few-Layer Carbon Nitride/Bifeo3 Composites for Efficient Organic Pollutant Photodegradation', Environ Res, vol. 215, no. Pt 1, pp. 114190-114190.
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Heterojunction-driven photocatalysis can degrade various organic pollutants, and developing carbon nitride-based composite photocatalysts is of great significance and gains growing interest. In this study, a two-dimensional graphitic carbon nitride nanosheets/BiFeO3 (GCNNs/BiFeO3) Z-scheme heterojunction has been synthesized through the electrostatic spinning and post-calcination The obtained GCNNs/BiFeO3 nanofibers show large surface contact between GCNNs the and BiFeO3 nanostructures. The Z-scheme heterojunction shows a remarkably enhanced photocatalytic performance, which could degrade 94% of tetracycline (TC) and 88% of Rhodamine B (RhB) under LED visible light irradiation in 150 min. Radical trapping experiments demonstrate the effective construction of Z-scheme heterojunctions, and •O2- and h+ are the main active species in the photocatalytic degradation process. This study realizes a novel nanostructured GCNNs/BiFeO3 heterojunction for photodegradation applications, which would guide the design of next-generation efficient photocatalysts.
Dai, Y, Zhang, X, Liu, Y, Yu, H, Su, W, Zhou, J, Ye, Q & Huang, Z 2022, '1,6;2,3-Bis-BN Cyclohexane: Synthesis, Structure, and Hydrogen Release', Journal of the American Chemical Society, vol. 144, no. 19, pp. 8434-8438.
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BN/CC isosterism has been widely investigated as a strategy to expand carbon-based compounds. The introduction of BN units in organic molecules always results in novel properties. In this work, we reported the first synthesis and characterization of 1,6;2,3-bis-BN cyclohexane, an isostere of cyclohexane with two adjacent BN pairs. Its ring flipping barrier is similar to that of cyclohexane. Protic hydrogens on N in 1,6;2,3-bis-BN cyclohexane show higher reactivity than its isomeric bis-BN cyclohexane. This compound exhibits an appealing hydrogen storage capability of >9.0 wt %, nearly twice as much as the 1,2;4,5-bis-BN cyclohexane.
Dang, B-T, Bui, X-T, Tran, DPH, Hao Ngo, H, Nghiem, LD, Hoang, T-K-D, Nguyen, P-T, Nguyen, HH, Vo, T-K-Q, Lin, C, Yi Andrew Lin, K & Varjani, S 2022, 'Current application of algae derivatives for bioplastic production: A review', Bioresource Technology, vol. 347, pp. 126698-126698.
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Improper use of conventional plastics poses challenges for sustainable energy and environmental protection. Algal derivatives have been considered as a potential renewable biomass source for bioplastic production. Algae derivatives include a multitude of valuable substances, especially starch from microalgae, short-chain length polyhydroxyalkanoates (PHAs) from cyanobacteria, polysaccharides from marine and freshwater macroalgae. The algae derivatives have the potential to be used as key ingredients for bioplastic production, such as starch and PHAs or only as an additive such as sulfated polysaccharides. The presence of distinctive functional groups in algae, such as carboxyl, hydroxyl, and sulfate, can be manipulated or tailored to provide desirable bioplastic quality, especially for food, pharmaceutical, and medical packaging. Standardizing strains, growing conditions, harvesting and extracting algae in an environmentally friendly manner would be a promising strategy for pollution control and bioplastic production.
Dang, B-T, Nguyen, T-T, Bui, X-T, Hao Ngo, H, Andrew Lin, K-Y, Tomoaki, I, Saunders, T, Huynh, T-N, Ngoc-Dan Cao, T, Visvanathan, C, Varjani, S & Rene, ER 2022, 'Non-submerged attached growth process for domestic wastewater treatment: Influence of media types and internal recirculation ratios', Bioresource Technology, vol. 343, pp. 126125-126125.
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Dang, B-T, Nguyen, T-T, Ngo, HH, Pham, M-D-T, Le, LT, Nguyen, N-K-Q, Vo, T-D-H, Varjani, S, You, S-J, Lin, KA, Huynh, K-P-H & Bui, X-T 2022, 'Influence of C/N ratios on treatment performance and biomass production during co-culture of microalgae and activated sludge', Science of The Total Environment, vol. 837, pp. 155832-155832.
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Dang, B-T, Tran, DPH, Nguyen, N-K-Q, Cao, HTN, Tomoaki, I, Huynh, K-P-H, Pham, T-T, Varjani, S, Hao Ngo, H, Wang, Y-F, You, S-J & Bui, X-T 2022, 'Comparison of degradation kinetics of tannery wastewater treatment using a nonlinear model by salt-tolerant Nitrosomonas sp. and Nitrobacter sp.', Bioresource Technology, vol. 351, pp. 127000-127000.
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Dang, TD, Hoang, D & Nguyen, DN 2022, 'Trust-Based Scheduling Framework for Big Data Processing with MapReduce', IEEE Transactions on Services Computing, vol. 15, no. 1, pp. 279-293.
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Security and privacy have become a great concern in cloud computing platforms in which users risk the leakage of their private data. The leakage can happen while the data is at rest (in storage), in processing, or on moving within a cloud or between different cloud infrastructures, e.g., from private to public clouds. This paper focuses on protecting data "in processing". For big data applications, the MapReduce framework has been proven as an efficient solution and has been widely deployed, e.g., in healthcare and business data analysis. In this article, we propose a trust-based framework for MapReduce in big data processing tasks. Specifically, we first quantify and propose to assign the sensitive values for data and trust values for map and reduce slots. We then compute the trust value of each resource employed in the big data processing tasks. Depending on the data's sensitivity level of a task, the task requires a given level of trust (i.e., higher sensitive data requires servers/slots with higher trust level). The MapReduce scheduling problem is then formulated as the maximum weighted matching problem of a bipartite graph that aims to maximize the total trust value over all possible assignments subject to various trust requirement of different tasks. The problem is known to be NP-hard. To tackle it, we observe that within a computing node (VM), slots share the same trust value granted from the secured transformation phase. This helps reduce the number of slot nodes of a weight bipartite graph. Leveraging this fact, we propose an efficient heuristic algorithm that achieves 94.7% of the optimal solution obtained via exhaustive search. Extensive simulations show that the trust-based scheduling scheme provides much higher protection for data sensitivity while ensuring good performance for big data applications.
Dang, VM, Nguyen, VD, Van, HT, Nguyen, VQ, Nguyen, TN & Nghiem, LD 2022, 'Removal of Cr(VI) and Pb(II) from aqueous solution using Mg/Al layered double hydroxides-mordenite composite', Separation Science and Technology, vol. 57, no. 15, pp. 2432-2445.
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Dang-Ngoc, H, Nguyen, DN, Ho-Van, K, Hoang, DT, Dutkiewicz, E, Pham, Q-V & Hwang, W-J 2022, 'Secure Swarm UAV-Assisted Communications With Cooperative Friendly Jamming', IEEE Internet of Things Journal, vol. 9, no. 24, pp. 25596-25611.
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This article proposes a cooperative friendly jamming framework for swarm unmanned aerial vehicle (UAV)-assisted amplify-and-forward (AF) relaying networks with wireless energy harvesting. In particular, we consider a swarm of hovering UAVs that relays information from a terrestrial base station to a distant mobile user and simultaneously generates friendly jamming signals to interfere/obfuscate an eavesdropper. Due to the limited energy of the UAVs, we develop a collaborative time-switching relaying protocol that allows the UAVs to collaborate in harvesting wireless energy, relay information, and jam the eavesdropper. To evaluate the performance, we derive the secrecy outage probability (SOP) for two popular detection techniques at the eavesdropper, i.e., selection combining and maximum-ratio combining. Monte Carlo simulations are then used to validate the theoretical SOP derivation. Using the derived SOP, one can obtain engineering insights to optimize the energy harvesting time and the number of UAVs in the swarm to achieve a given secrecy protection level. Furthermore, simulations show the effectiveness of the proposed framework in terms of SOP compared to the conventional AF relaying system. The analytical SOP derived in this work can also be helpful in future UAV secure-communications optimizations (e.g., trajectory, locations of UAVs). As an example, we present a case study to find the optimal corridor to locate the swarm so as to minimize the system SOP. Our proposed framework helps secure communications for various applications that require large coverage, e.g., industrial IoT, smart city, intelligent transportation systems, and critical IoT infrastructures like energy and water.
Daniel, S 2022, 'A phenomenographic outcome space for ways of experiencing lecturing', Higher Education Research & Development, vol. 41, no. 3, pp. 681-698.
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After decades of increasing evidence in favour of active learning, lecturing remains the dominant face-to-face teaching mode. Just as a rigorous research approach is required to understand how to improve student learning outcomes, we also need research about how to reform teaching practice. Some initial steps in this direction have shown that successful pedagogical reforms are long-term, contextualised, and address teachers’ beliefs about teaching. It is not enough to put in place overarching policy directives about active learning, nor to simply share best practice, because these strategies do not engage with the particular teaching contexts and beliefs of individual academics. Professional development programs to shift academics away from the traditional lecture must incorporate academics’ conceptions of lecturing. Although there has been some research into conceptions of university teaching in general, there is a dearth of literature focusing on conceptions of lecturing in particular. This article addresses that gap, by using a phenomenographic approach to interview 30 academics about their lecturing experiences. From analysing the transcripts, a hierarchy of five ways of experiencing lecturing was identified: (1) Lecturing as soliloquy, (2) Lecturing as connecting meaning, (3) Lecturing as cultivating individuals, (4) Lecturing as transformatively co-creating, (5) Lecturing as enacting research. Three themes of expanding awareness framed this hierarchy: interaction, student diversity, and lecture purpose. By extrapolating these themes downwards, a zeroth category was conjectured: Lecturing as reading. Implications for educators are discussed, along with potentially fruitful avenues of future research.
Darwish, A, Halkon, B & Oberst, S 2022, 'Non-Contact Vibro-Acoustic Object Recognition Using Laser Doppler Vibrometry and Convolutional Neural Networks', Sensors, vol. 22, no. 23, pp. 9360-9360.
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Laser Doppler vibrometers (LDVs) have been widely adopted due to their large number of benefits in comparison to traditional contacting vibration transducers. Their high sensitivity, among other unique characteristics, has also led to their use as optical microphones, where the measurement of object vibration in the vicinity of a sound source can act as a microphone. Recent work enabling full correction of LDV measurement in the presence of sensor head vibration unlocks new potential applications, including integration within autonomous vehicles (AVs). In this paper, the common AV challenge of object classification is addressed by presenting and evaluating a novel, non-contact vibro-acoustic object recognition technique. This technique utilises a custom set-up involving a synchronised loudspeaker and scanning LDV to simultaneously remotely solicit and record responses to a periodic chirp excitation in various objects. The 864 recorded signals per object were pre-processed into spectrograms of various forms, which were used to train a ResNet-18 neural network via transfer learning to accurately recognise the objects based only on their vibro-acoustic characteristics. A five-fold cross-validation optimisation approach is described, through which the effects of data set size and pre-processing type on classification accuracy are assessed. A further assessment of the ability of the CNN to classify never-before-seen objects belonging to groups of similar objects on which it has been trained is then described. In both scenarios, the CNN was able to obtain excellent classification accuracy of over 99.7%. The work described here demonstrates the significant promise of such an approach as a viable non-contact object recognition technique suitable for various machine automation tasks, for example, defect detection in production lines or even loose rock identification in underground mines.
Darwish, A, Halkon, B, Rothberg, S, Oberst, S & Fitch, R 2022, 'A comparison of time and frequency domain-based approaches to laser Doppler vibrometer instrument vibration correction', Journal of Sound and Vibration, vol. 520, pp. 116607-116607.
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Das, D, Hossain, MJ, Mishra, S & Singh, B 2022, 'Bidirectional Power Sharing of Modular DABs to Improve Voltage Stability in DC Microgrids', IEEE Transactions on Industry Applications, vol. 58, no. 2, pp. 2369-2377.
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Das, SS, Dawn, S, Gope, S & Alhelou, HH 2022, 'Social welfare improvement using market clearing mechanism of a wind farm‐CAES hybrid congested transmission system', IET Renewable Power Generation, vol. 16, no. 11, pp. 2279-2291.
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AbstractThe goal of any well‐established deregulated power sector is to satisfy their customer by delivering the power with maintaining the best quality at a reasonable price. This paper presents the impression of the wind farm (WF) and compressed air energy storage (CAES) hybrid systems in the congested deregulated power system. The main motto of this work is to enhance social welfare by using a market‐clearing mechanism of the WFCAES hybrid system. To determine the effect of the hybrid system, initially the market‐clearing power (MCPO) problem is measured without considering the WF‐CAES hybrid system, and then the same work has been carried out with considering the WF‐CAES hybrid system. The bus sensitivity factor (BSF) decides the optimal position of the WF‐CAES hybrid system in the congested day‐ahead electrical system. The study is followed out through generator sensitivity factor (GSF) with the application of artificial bee colony algorithm (ABC) and slime mould algorithm (SMA) to calculate optimal market clearing price (MCP) and market clearing volume (MCV). The market‐clearing power problem has been solved in a modified IEEE‐30 bus test system. The SMA algorithm is been implemented in the market‐clearing mechanism for the first time which is the novelty of this paper.
Dayarathne, N, Angove, MJ, Paudel, SR, Ngo, HH, Guo, W & Mainali, B 2022, 'Optimisation of dual coagulation process for the removal of turbidity in source water using streaming potential', Groundwater for Sustainable Development, vol. 16, pp. 100714-100714.
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De Carvalho Gomes, S, Zhou, JL, Zeng, X & Long, G 2022, 'Water treatment sludge conversion to biochar as cementitious material in cement composite', Journal of Environmental Management, vol. 306, pp. 114463-114463.
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Water treatment sludge was successfully thermally converted to obtain biochar as a stable material with resource potential. This research explored the application of sludge biochar as a supplementary cementitious material. The cement paste samples incorporating different amounts of sludge biochar were prepared, hardened, and analyzed for performance. The results show an improvement in hydration kinetics and mechanical properties of cement paste incorporating biochar, compared to raw sewage sludge. The mineralogical, thermal and microscopic analyses show evidence of pozzolanic activity of the biochar. The samples with 2% and 5% biochar showed higher heat release than the reference material. Specimens with 1%, 2% and 5% biochar showed a slightly higher compressive strength at 28 days compared to the reference material. Sludge conversion to biochar will incur an estimated cost of US$398.23/ton, which is likely to be offset by the substantial benefits from avoiding landfill and saving valuable cementitious materials. Therefore, this research has demonstrated that through conversion to biochar, water treatment sludge can be promoted as a sustainable and alternative cementitious material for cement with minimum environmental impacts, hence contributing to circular economy.
Deady, M, Glozier, N, Calvo, R, Johnston, D, Mackinnon, A, Milne, D, Choi, I, Gayed, A, Peters, D, Bryant, R, Christensen, H & Harvey, SB 2022, 'Preventing depression using a smartphone app: a randomized controlled trial', Psychological Medicine, vol. 52, no. 3, pp. 457-466.
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AbstractBackgroundThere is evidence that depression can be prevented; however, traditional approaches face significant scalability issues. Digital technologies provide a potential solution, although this has not been adequately tested. The aim of this study was to evaluate the effectiveness of a new smartphone app designed to reduce depression symptoms and subsequent incident depression amongst a large group of Australian workers.MethodsA randomized controlled trial was conducted with follow-up assessments at 5 weeks and 3 and 12 months post-baseline. Participants were employed Australians reporting no clinically significant depression. The intervention group (N = 1128) was allocated to use HeadGear, a smartphone app which included a 30-day behavioural activation and mindfulness intervention. The attention-control group (N = 1143) used an app which included a 30-day mood monitoring component. The primary outcome was the level of depressive symptomatology (PHQ-9) at 3-month follow-up. Analyses were conducted within an intention-to-treat framework using mixed modelling.ResultsThose assigned to the HeadGear arm had fewer depressive symptoms over the course of the trial compared to those assigned to the control (F3,734.7 = 2.98, p = 0.031). Prevalence of depression over the 12-month period was 8.0% and 3.5% for controls and HeadGear recipients, respectively, with odds of depression caseness amongst the intervention group of 0.43 (
Dehghani, M, Ghiasi, M, Niknam, T, Rouzbehi, K, Wang, Z, Siano, P & Alhelou, HH 2022, 'Control of LPV Modeled AC-Microgrid Based on Mixed H2/H∞ Time-Varying Linear State Feedback and Robust Predictive Algorithm', IEEE Access, vol. 10, pp. 3738-3755.
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This paper presents a robust model predictive control (RMPC) method with a new mixed H2/ H linear time-varying state feedback design. In addition, we propose a linear parameter-varying model for inverters in a microgrid (MG), in which disturbances and uncertainty are considered, where the inverters connect in parallel to renewable energy sources (RES). The proposed RMPC can use the gain-scheduled control law and satisfy both the H2 and H proficiency requirements under various conditions, such as disturbance and load variation. A multistep control method is proposed to reduce the conservativeness caused by the unique feedback control law, enhance the control proficiency, and strengthen the RMPC feasible area. Furthermore, a practical and efficient RMPC is designed to reduce the online computational burden. The presented controller can implement load sharing among distributed generators (DGs) to stabilize the frequency and voltage of an entire smart island. The proposed strategy is implemented and studied in a MG with two DG types and various load types. Specifically, through converters, one type of DGs is used to control frequency and voltage, and the other type is used to control current. These two types of DGs operate in a parallel mode. Simulation results show that the proposed RMPCs are input-to-state practically stable (ISpS). Compared with other controllers in the literature, the proposed strategy can lead to minor total harmonic distortion (THD), lower steady-state error, and faster response to system disturbance and load variation.
Dehghanimadvar, M, Shirmohammadi, R, Ahmadi, F, Aslani, A & Khalilpour, KR 2022, 'Mapping the development of various solar thermal technologies with hype cycle analysis', Sustainable Energy Technologies and Assessments, vol. 53, pp. 102615-102615.
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Deng, J, Chen, X, Jiang, R, Song, X & Tsang, IW 2022, 'A Multi-View Multi-Task Learning Framework for Multi-Variate Time Series Forecasting', IEEE Transactions on Knowledge and Data Engineering, vol. PP, no. 99, pp. 1-16.
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Multi-variate time series (MTS) data is a ubiquitous class of data abstraction in the real world. Any instance of MTS is generated from a hybrid dynamical system and their specific dynamics are usually unknown. The hybrid nature of such a dynamical system is a result of complex external attributes, such as geographic location and time of day, each of which can be categorized into either spatial attributes or temporal attributes. Therefore, there are two fundamental views which can be used to analyze MTS data, namely the spatial view and the temporal view. Moreover, from each of these two views, we can partition the set of data samples of MTS into disjoint forecasting tasks in accordance with their associated attribute values. Then, samples of the same task will manifest similar forthcoming pattern, which is less sophisticated to be predicted in comparison with the original single-view setting. Considering this insight, we propose a novel multi-view multi-task (MVMT) learning framework for MTS forecasting. Instead of being explicitly presented in most scenarios, MVMT information is deeply concealed in the MTS data, which severely hinders the model from capturing it naturally. To this end, we develop two kinds of basic operations, namely task-wise affine transformation and task-wise normalization, respectively. Applying these two operations with prior knowledge on the spatial and temporal view allows the model to adaptively extract MVMT information while predicting. Extensive experiments on three datasets are conducted to illustrate that canonical architectures can be greatly enhanced by the MVMT learning framework in terms of both effectiveness and efficiency. In addition, we design rich case studies to reveal the properties of representations produced at different phases in the entire prediction procedure.
Deng, K, Zhu, S, Dai, W, Yang, C & Wen, S 2022, 'New Criteria on Stability of Dynamic Memristor Delayed Cellular Neural Networks', IEEE Transactions on Cybernetics, vol. 52, no. 6, pp. 5367-5379.
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Dynamic memristor (DM)-cellular neural networks (CNNs), which replace a linear resistor with flux-controlled memristor in the architecture of each cell of traditional CNNs, have attracted researchers' attention. Compared with common neural networks, the DM-CNNs have an outstanding merit: when a steady state is reached, all voltages, currents, and power consumption of DM-CNNs disappeared, in the meantime, the memristor can store the computation results by serving as nonvolatile memories. The previous study on stability of DM-CNNs rarely considered time delay, while delay is quite common and highly impacts the stability of the system. Thus, taking the time delay effect into consideration, we extend the original system to DM-D(delay)CNNs model. By using the Lyapunov method and the matrix theory, some new sufficient conditions for the global asymptotic stability and global exponential stability with a known convergence rate of DM-DCNNs are obtained. These criteria generalized some known conclusions and are easily verified. Moreover, we find DM-DCNNs have 3ⁿ equilibrium points (EPs) and 2ⁿ of them are locally asymptotically stable. These results are obtained via a given constitutive relation of memristor and the appropriate division of state space. Combine with these theoretical results, the applications of DM-DCNNs can be extended to other fields, such as associative memory, and its advantage can be used in a better way. Finally, numerical simulations are offered to illustrate the effectiveness of our theoretical results.
Deng, L, Guo, W, Ngo, HH, Zhang, X, Chen, C, Chen, Z, Cheng, D, Ni, S-Q & Wang, Q 2022, 'Recent advances in attached growth membrane bioreactor systems for wastewater treatment', Science of The Total Environment, vol. 808, pp. 152123-152123.
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Deng, L, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Pandey, A, Varjani, S & Hoang, NB 2022, 'Recent advances in circular bioeconomy based clean technologies for sustainable environment', Journal of Water Process Engineering, vol. 46, pp. 102534-102534.
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Deng, S, Ji, J, Wen, G & Xu, H 2022, 'Two-parameter dynamics of an autonomous mechanical governor system with time delay', Nonlinear Dynamics, vol. 107, no. 1, pp. 641-663.
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A deep understanding of the dynamical behavior in the parameter-state space plays a vital role in both the optimal design and motion control of mechanical systems. By combining the GPU parallel computing technique with two determinate indicators, namely the Lyapunov exponents and Poincaré section, this paper presents a detailed study on the two-parameter dynamics of a mechanical governor system with different time delays. By identifying different responses in the two-parameter plane, the effect of time delay on the complexity of the evolutionary process is fully revealed. The path-following calculation scheme and time domain collocation method are used to explore the detailed bifurcation mechanisms. An interesting phenomenon that the number of intersection points of some periodic responses on the specified Poincaré section differs from the actual period characteristics is found in classifying the dynamic behavior. For example, the commonly exhibited period-one orbit may have two or more intersection points on the Poincaré section rather than one point. The variations of the basins of attraction are also discussed in the plane of initial history conditions to demonstrate the multistability phenomena and chaotic transitions.
Deng, S, Peng, S, Ngo, HH, Oh, SJ-A, Hu, Z, Yao, H & Li, D 2022, 'Characterization of nitrous oxide and nitrite accumulation during iron (Fe(0))- and ferrous iron (Fe(II))-driven autotrophic denitrification: mechanisms, environmental impact factors and molecular microbial characterization', Chemical Engineering Journal, vol. 438, pp. 135627-135627.
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The iron (Fe(0))-/ferrous iron (Fe(II))-driven autotrophic denitrification processes have been alternative methods for nitrogen removal from low organic carbon (OC) wastewater, but the accumulation of nitrous oxide (N2O) and nitrite (NO2−) along with these processes remains unclear. This research aimed to systematically characterize the N2O/NO2− accumulation in Fe(0)-/Fe(II)-ADN processes through investigating the mechanisms, impact factors, and molecular biological characteristics. Results showed that Fe(II)-ADN was effective in NO3− reduction but was less efficient in N2O reduction (k = 0.50 h−1) than Fe(0)-ADN (k = 1.82 h−1). NO2−/N2O accumulation in Fe(II)-ADN (28.6%/30.7%) was much higher than that in Fe(0)-ADN (12.6%/1.5%). Introducing hydrogenotrophic denitrification (H-ADN) into Fe(II)-ADN system significantly (p < 0.05) reduced NO2−/N2O accumulation. Fe(0)-ADN was proved a coupled process of Fe(II)- and H-ADN by in-situ generating Fe(II)/H2, and Fe(II)-ADN and H-ADN mainly contributed to NO3− and NO2−/N2O reduction, respectively. Optimum pH (7.5) and temperature (30–35 °C) were confirmed with controlled NO2–/N2O accumulation and effective denitrification. Dosing inorganic carbon (IC) and OC enhanced denitrification and reduced NO2–/N2O accumulation, where OC was more efficient with an optimum dosage of 0.25 mmol C/mmol N. 16S rRNA high-throughput sequencing and Pearson Correlation Coefficients verified that Thiobacillus was the main contributor to NO3− reduction, whereas Thauera and Acidovorax possessed high NO2−/N2O reduction capability. Real-time quantitative polymerase chain reaction and enzyme activity assay demonstrated that the nitrite reductase encoded by gene nirK and the nitrous oxide reductase encoded by gene nosZ were efficient in catalyzing the further reduction of NO2− and N2O, respectively. This study could provide an in-depth understanding of NO2−/N2O accumulation in Fe(II)-/Fe(0)-ADN processes and contribute to their application, optimiza...
Deng, Z, Mu, H, Jiang, L, Xi, W, Xu, X & Zheng, W 2022, 'Preparation and characterization of electrospun PLGA-SF nanofibers as a potential drug delivery system', Materials Chemistry and Physics, vol. 289, pp. 126452-126452.
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Currently drug-controlled release technology has gained a great deal of attention on the field of biomedicine. Nevertheless, most drug-controlled release systems usually require complex modification. Herein, the monolayer and multilayer structure nanofibers for drug delivery of acetylsalicylic acid (ASA), as a model drug, were fabricated using the one-step electrospinning technique for the first time, which was composed with the poly(lactic-co-glycolic acid) (PLGA) and silk fibroin (SF). Meanwhile, the effects of the mass radio of PLGA to SF on the properties of the electrospun monolayer nanofiber membrane was further investigated. The results of the electrospun monolayer PLGA-SF nanofiber membrane with mass radio of 2:1 displayed significant improvement in hydrophilicity as well as mechanical properties compared to the other mass radio of nanofibers membranes. Moreover, drug release studies revealed that nanofiber membrane with multilayer structure was able to promote a much more sustained release of ASA in comparison with monolayer PLGA-SF nanofibers membranes. More remarkable, the nanofiber membrane with multilayer structure presented good biocompatibility. Overall, these results demonstrate the potential of using PLGA-SF nanofibers with multilayer structure as a scaffold for drug-controlled release.
Deng, Z, Zhao, L, Mu, H, Jiang, L, Xi, W, Xu, X & Zheng, W 2022, 'High selective property of gelatin/MWCNTs functionalized carbon fiber microelectrode: Toward real-time monitoring of ascorbate', Journal of Electroanalytical Chemistry, vol. 914, pp. 116315-116315.
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Deng, Z, Zhao, L, Zhou, H, Xu, X & Zheng, W 2022, 'Recent advances in electrochemical analysis of hydrogen peroxide towards in vivo detection', Process Biochemistry, vol. 115, pp. 57-69.
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des Ligneris, E, Merenda, A, Chen, X, Wang, J, Johannessen, B, Bedford, NM, Callahan, DL, Dumée, LF & Kong, L 2022, 'In Situ Growth of Cu/CuO/Cu2O Nanocrystals within Hybrid Nanofibers for Adsorptive Arsenic Removal', ACS Applied Nano Materials, vol. 5, no. 10, pp. 14437-14446.
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Deuse, J, West, N & Syberg, M 2022, 'Rediscovering scientific management. The evolution from industrial engineering to industrial data science', International Journal of Production Management and Engineering, vol. 10, no. 1, pp. 1-12.
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Industrial Engineering, through its role as design, planning and organizational body of the industrial production, has been crucial for the success of manufacturing companies for decades. The potential, expected over the course of Industry 4.0 and through the application of Data Analytic tools and methods, requires a coupling to established methods. This creates the necessity to extend the traditional job description of Industrial Engineering by new tools from the field of Data Analytics, namely Industrial Data Science. Originating from the historic pioneers of Industrial Engineering, it is evident that the basic principles will remain valuable. However, further development in view of the data analytic possibilities is already taking place. This paper reviews the origins of Industrial Engineering with reference to four pioneers, draws a connection to current day usage, and considers possibilities for future applications of Industrial Data Science.
Deutsch, F, Regina Bullen, I, Nguyen, K, Tran, N-H, Elliott, M & Tran, N 2022, 'Current state of play for HPV-positive oropharyngeal cancers', Cancer Treatment Reviews, vol. 110, pp. 102439-102439.
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Clinically, HPV-positive oropharyngeal cancers (OPCs) have been shown to have a distinct prognosis, compared to HPV-negative tumours, particularly in survival rates and responses to treatment. These patients have better survival chances and improved prognosis, indicating that a more exhaustive knowledge of these distinctions would aid in the discovery of clinical approaches for both HPV-positive and negative tumours. Furthermore, there is increasing evidence that HPV-related oropharyngeal cancers constitute an epidemiological, molecular, and clinical distinct form as compared to non-HPV related ones therefore, the treatment of these specific subtype of oropharyngeal cancers should adopt a distinct clinical treatment pipeline. Our review will examine the current approaches for the diagnosis and treatment of OPC and discuss the relevance of de-escalation clinical trials in progress.
Deveci, O & Shannon, AG 2022, 'On The Complex-Type Catalan Transform of the k-Fibonacci Numbers', Journal of Integer Sequences, vol. 25, no. 4.
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We define a type of complex Catalan number and find some its properties. We also produce a complex Catalan transform and its inverse, together with associated generating functions and related matrices. These lead to connections with complex Catalan transforms of the k-Fibonacci numbers and the determinants of their Hankel matrices. The paper finishes with a conjecture.
Deveci, Ö, Shannon, AG & Karaduman, E 2022, 'The complex-type Fibonacci p-Sequences', Annals of the University of Craiova - Mathematics and Computer Science Series, vol. 49, no. 2, pp. 260-269.
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In this paper, we define a new sequence which is called the complex-type Fibonacci p-sequence and we obtain the generating matrix of this complex-type Fibonacci p-sequence. We also derive the determinantal and the permanental representations. Then, using the roots of the characteristic polynomial of the complex-type Fibonacci p-sequence, we produce the Binet formula for this defined sequence. In addition, we give the combinatorial representations, the generating function, the exponential representation and the sums of the complex-type Fibonacci p-numbers.
Devitt, SJ 2022, 'Blueprinting quantum computing systems', Journal and proceedings of the Royal Society of New South Wales, vol. 155, no. 1, pp. 5-39.
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The development of quantum computing systems has been a staple of academic research since the mid-1990s when the first proposal for physical platforms were proposed using Nuclear Magnetic Resonance and Ion-Trap hardware. These first proposals were very basic, essentially consisting of identifying a physical qubit (two-level quantum system) that could be isolated and controlled to achieve universal quantum computation. Over the past thirty years, the nature of quantum architecture design has changed significantly and the scale of investment, groups and companies involved in building quantum computers has increased exponentially. Architectural design for quantum computers examines systems at scale: fully error-corrected machines, potentially consisting of millions if not billions of physical qubits. These designs increasingly act as blueprints for academic groups and companies and are becoming increasingly more detailed, taking into account both the nature and operation of the physical qubits themselves and also peripheral environmental and control infrastructure that is required for each physical system. In this paper, several architectural structures that I have worked on will be reviewed, each of which has been adopted by either a national quantum computing program or a quantum startup. This paper was written in the context of an award with the Royal Society of New South Wales, focused on my personal contributions and impact to quantum computing development, and should be read with that in mind.1
DeWitt, D, Chan, SF & Loban, R 2022, 'Virtual reality for developing intercultural communication competence in Mandarin as a Foreign language', Educational technology research and development, vol. 70, no. 2, pp. 615-638.
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Dhana Raju, V, Nair, JN, Venu, H, Subramani, L, M. Soudagar, ME, Mujtaba, MA, Khan, TMY, Ismail, KA, Elfasakhany, A, Yusuf, AA, Mohamed, BA & Fattah, IMR 2022, 'Combined assessment of injection timing and exhaust gas recirculation strategy on the performance, emission and combustion characteristics of algae biodiesel powered diesel engine', Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 44, no. 4, pp. 8554-8571.
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Currently, the vehicle industry is confronted with issues such as the depletion of fossil resources, an increase in crude oil costs, and stricter emission regulatory standards. In this scenario, the use of viable alternatives to diesel as a fuel is necessary. This study discusses the combined effects of injection time and exhaust gas recirculation (EGR) on neat algal biodiesel-powered diesel engines. The transesterification technique was used to extract algal oil methyl ester (AOME), and the majority of the fuel qualities of AOME were quite comparable to diesel. The practicality of neat AOME for diesel engines operating at varied injection timings such as 19º BTDC, 23º BTDC, and 27º BTDC was investigated. The results of the tests revealed that advanced injection timing has a 3.02% higher BTE than standard fuel injection timing at maximum load for the AOME. Compared to other injection timings at full load, the neat AOME at 27º BTDC has better combustion characteristics and lower exhaust emissions. At full load, however, NOx emissions were higher. NOx emission was reduced by 35.24% when AOME was burned at 27º BTDC combined with 10% exhaust gas recirculation (EGR) compared to 27º BTDC without EGR.
Dhananjaya, M, Ponuru, D, Babu, TS, Aljafari, B & Alhelou, HH 2022, 'A New Multi-Output DC-DC Converter for Electric Vehicle Application', IEEE Access, vol. 10, pp. 19072-19082.
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Dhananjaya, M, Potnuru, D, Manoharan, P & Alhelou, HH 2022, 'Design and Implementation of Single-Input-Multi-Output DC-DC Converter Topology for Auxiliary Power Modules of Electric Vehicle', IEEE Access, vol. 10, pp. 76975-76989.
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Dhandapani, Y, Joseph, S, Bishnoi, S, Kunther, W, Kanavaris, F, Kim, T, Irassar, E, Castel, A, Zunino, F, Machner, A, Talakokula, V, Thienel, K-C, Wilson, W, Elsen, J, Martirena, F & Santhanam, M 2022, 'Durability performance of binary and ternary blended cementitious systems with calcined clay: a RILEM TC 282-CCL, review', Materials and Structures, vol. 55, no. 5.
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Dhandapani, Y, Joseph, S, Geddes, DA, Zhao, Z, Boustingorry, P, Bishnoi, S, Vieira, M, Martirena, F, Castel, A, Kanavaris, F & Riding, KA 2022, 'Fresh properties of concrete containing calcined clays: a review by RILEM TC-282 CCL', Materials and Structures, vol. 55, no. 6, p. 151.
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This state of the art presents an overview on the effects of calcined clay inclusion on the fresh properties of concrete under the framework of RILEM TC-282 CCL. Progress in recent literature was reviewed to determine the effects of calcined clay, particularly metakaolin and lower grade kaolinite clays, on fresh concrete properties and how to control them using admixtures, particle packing, and mixture proportioning. A summary of recent studies on the use of superplasticizers in modified (or combined form) to improve compatibility have shown promising outcomes to control the rheological properties of calcined clay binders. Superplasticizer demand required to achieve workable concrete increases with increasing dosage of calcined clay and increases substantially for concrete produced with calcined clay at water-to-cementitious material ratios below 0.40. A comparative analysis of data from several literature shows that the addition of calcined clay could reduce setting time when used without superplasticizers. Addition of superplasticizers could help to control and increase the setting time significantly. Calcined clay can be used to make concrete with similar workability and setting times as concrete containing Portland cement through the use of polycarboxylate-based superplasticizers. However, more studies in future should focus on retention of workability by suitable methodologies for various construction activities. Care should be exercised to avoid long setting times with high dosages of superplasticizers.
Dhivagar, R, Deepanraj, B, Mohanraj, M & Chyuan Ong, H 2022, 'Second law based thermodynamic analysis of crushed gravel sand and biomass evaporator assisted solar still', Sustainable Energy Technologies and Assessments, vol. 52, pp. 102160-102160.
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Dhull, P, Guevara, AP, Ansari, M, Pollin, S, Shariati, N & Schreurs, D 2022, 'Internet of Things Networks: Enabling Simultaneous Wireless Information and Power Transfer', IEEE Microwave Magazine, vol. 23, no. 3, pp. 39-54.
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Di, X, Wang, D, Su, QP, Liu, Y, Liao, J, Maddahfar, M, Zhou, J & Jin, D 2022, 'Spatiotemporally mapping temperature dynamics of lysosomes and mitochondria using cascade organelle-targeting upconversion nanoparticles', Proceedings of the National Academy of Sciences, vol. 119, no. 45, p. e2207402119.
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The intracellular metabolism of organelles, like lysosomes and mitochondria, is highly coordinated spatiotemporally and functionally. The activities of lysosomal enzymes significantly rely on the cytoplasmic temperature, and heat is constantly released by mitochondria as the byproduct of adenosine triphosphate (ATP) generation during active metabolism. Here, we developed temperature-sensitive LysoDots and MitoDots to monitor the in situ thermal dynamics of lysosomes and mitochondria. The design is based on upconversion nanoparticles (UCNPs) with high-density surface modifications to achieve the exceptionally high sensitivity of 2.7% K −1 and low uncertainty of 0.8 K for nanothermometry to be used in living cells. We show the measurement is independent of the ion concentrations and pH values. With Ca 2+ ion shock, the temperatures of both lysosomes and mitochondria increased by ∼2 to 4 °C. Intriguingly, with chloroquine (CQ) treatment, the lysosomal temperature was observed to decrease by up to ∼3 °C, while mitochondria remained relatively stable. Lastly, with oxidative phosphorylation inhibitor treatment, we observed an ∼3 to 7 °C temperature increase and a thermal transition from mitochondria to lysosomes. These observations indicate different metabolic pathways and thermal transitions between lysosomes and mitochondria inside HeLa cells. The nanothermometry probes provide a powerful tool for multimodality functional imaging of subcellular organelles and interactions with high spatial, temporal, and thermal dynamics resolutions.
Diao, K, Sun, X, Bramerdorfer, G, Cai, Y, Lei, G & Chen, L 2022, 'Design optimization of switched reluctance machines for performance and reliability enhancements: A review', Renewable and Sustainable Energy Reviews, vol. 168, pp. 112785-112785.
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Dietrich, H, Elder, M, Piggott, A, Qiao, Y & Weiß, A 2022, 'The Isomorphism Problem for Plain Groups Is in ΣP3', Leibniz International Proceedings in Informatics, LIPIcs, vol. 219, pp. 26:1-26:14.
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Testing isomorphism of infinite groups is a classical topic, but from the complexity theory viewpoint, few results are known. Sénizergues and the fifth author (ICALP2018) proved that the isomorphism problem for virtually free groups is decidable in PSPACE when the input is given in terms of so-called virtually free presentations. Here we consider the isomorphism problem for the class of plain groups, that is, groups that are isomorphic to a free product of finitely many finite groups and finitely many copies of the infinite cyclic group. Every plain group is naturally and efficiently presented via an inverse-closed finite convergent length-reducing rewriting system. We prove that the isomorphism problem for plain groups given in this form lies in the polynomial time hierarchy, more precisely, in ΣP3. This result is achieved by combining new geometric and algebraic characterisations of groups presented by inverse-closed finite convergent length-reducing rewriting systems developed in recent work of the second and third authors (2021) with classical finite group isomorphism results of Babai and Szemerédi (1984).
Dikshit, A, Pradhan, B & Santosh, M 2022, 'Artificial neural networks in drought prediction in the 21st century–A scientometric analysis', Applied Soft Computing, vol. 114, pp. 108080-108080.
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Droughts are the most spatially complex geohazard, which often lasts for years, thereby severely impacting socio-economic sectors. One of the critical aspects of drought studies is developing a reliable and robust forecasting model, which could immensely help drought management planners in adopting adequate measures. Further, the prediction of drought events are extremely challenging due to the involvement of several hydro-meteorological factors, which are further aggravated by the effect of climate change. Among the several techniques such as statistical, physical and data-driven that are used to forecast droughts, artificial neural networks provide one of the most robust approach. As droughts are inherently non-linear and multivariate in nature, the capability of neural networks to capture the dynamic relationship easily and efficiently has seen a rise in its use. Here we evaluate the most used architectures in the last two decades, using scientometric analysis. A general framework used in drought prediction studies is explained and examples from various continents are provided, thus exploring the topic in a global context. The findings show that using sophisticated input representation, the artificial intelligence-based solutions applied to drought prediction of hydro-meteorological variables have promising success, particularly in complex geographical scenarios. The future works need to focus on interpretable models, use of deep learning architectures for long lead time forecasting and use of neural networks to predict different drought characteristics like drought propagation and flash droughts. We also summarize the most widely used neural network approaches in spatial drought prediction, which would serve as a foundation for future research in drought prediction studies.
Dikshit, A, Pradhan, B, Assiri, ME, Almazroui, M & Park, H-J 2022, 'Solving transparency in drought forecasting using attention models', Science of The Total Environment, vol. 837, pp. 155856-155856.
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Droughts are one of the most devastating and recurring natural disaster due to a multitude of reasons. Among the different drought studies, drought forecasting is one of the key aspects of effective drought management. The occurrence of droughts is related to a multitude of factors which is a combination of hydro-meteorological and climatic factors. These variables are non-linear in nature, and neural networks have been found to effectively forecast drought. However, classical neural nets often succumb to over-fitting due to various lag components among the variables and therefore, the emergence of new deep learning and explainable models can effectively solve this problem. The present study uses an Attention-based model to forecast meteorological droughts (Standard Precipitation Index) at short-term forecast range (1-3 months) for five sites situated in Eastern Australia. The main aim of the work is to interpret the model outcomes and examine how a deep neural network achieves the forecasting results. The plots show the importance of the variables along with its short-term and long-term dependencies at different lead times. The results indicate the importance of large-scale climatic indices at different sequence dependencies specific to the study site, thus providing an example of the necessity to build a spatio-temporal explainable AI model for drought forecasting. The use of such interpretable models would help the decision-makers and planners to use data-driven models as an effective measure to forecast droughts as they provide transparency and trust while using these models.
Dikshit, A, Pradhan, B, Huete, A & Park, H-J 2022, 'Spatial based drought assessment: Where are we heading? A review on the current status and future', Science of The Total Environment, vol. 844, pp. 157239-157239.
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Droughts are the most spatially complex natural hazards that exert global impacts and are further aggravated by climate change. The investigation of drought events is challenging as it involves numerous factors ranging from detection and assessment to modelling, management and mitigation. The analysis of these factors and their quantitative assessments have significantly evolved in recent times. In this paper, we review recent methods used to examine and model droughts from a spatial viewpoint. Our analysis was conducted at three spatial scales (point-wise, regional and global) and we evaluated how recent spatial methods have advanced our understanding of drought through case study examples. Further, we also examine and provide a broad overview of relevant case studies related to future drought occurrences under climate change. This study is a comprehensive synthesis of the various quantitative techniques used to assess the spatial characteristics of droughts at different spatial scales, and not an exhaustive review of all drought aspects. However, this serves as a basis for understanding the key milestones and advances accomplished through new spatial concepts relative to the traditional approaches to study drought. This work also aims to address the gaps in knowledge that are in need of further attention and provides recommendations to improve our understanding of droughts.
Ding, A, Lin, W, Chen, R, Ngo, HH, Zhang, R, He, X, Nan, J, Li, G & Ma, J 2022, 'Improvement of sludge dewaterability by energy uncoupling combined with chemical re-flocculation: Reconstruction of floc, distribution of extracellular polymeric substances, and structure change of proteins', Science of The Total Environment, vol. 816, pp. 151646-151646.
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Ding, A, Ren, Z, Hu, L, Zhang, R, Ngo, HH, Lv, D, Nan, J, Li, G & Ma, J 2022, 'Oxidation and coagulation/adsorption dual effects of ferrate (VI) pretreatment on organics removal and membrane fouling alleviation in UF process during secondary effluent treatment', Science of The Total Environment, vol. 850, pp. 157986-157986.
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Ding, L, Razavi Bazaz, S, Asadniaye Fardjahromi, M, McKinnirey, F, Saputro, B, Banerjee, B, Vesey, G & Ebrahimi Warkiani, M 2022, 'A modular 3D printed microfluidic system: a potential solution for continuous cell harvesting in large-scale bioprocessing', Bioresources and Bioprocessing, vol. 9, no. 1.
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AbstractMicrofluidic devices have shown promising applications in the bioprocessing industry. However, the lack of modularity and high cost of testing and error limit their implementation in the industry. Advances in 3D printing technologies have facilitated the conversion of microfluidic devices from research output to applicable industrial systems. Here, for the first time, we presented a 3D printed modular microfluidic system consisting of two micromixers, one spiral microfluidic separator, and one microfluidic concentrator. We showed that this system can detach and separate mesenchymal stem cells (MSCs) from microcarriers (MCs) in a short time while maintaining the cell’s viability and functionality. The system can be multiplexed and scaled up to process large volumes of the industry. Importantly, this system is a closed system with no human intervention and is promising for current good manufacturing practices. Graphical Abstract
Ding, L, Razavi Bazaz, S, Hall, T, Vesey, G & Ebrahimi Warkiani, M 2022, 'Giardia purification from fecal samples using rigid spiral inertial microfluidics', Biomicrofluidics, vol. 16, no. 1, pp. 014105-014105.
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Giardia is one of the most common waterborne pathogens causing around 200 × 106 diarrheal infections annually. It is of great interest to microbiological research as it is among the oldest known eukaryotic cells. Purifying Giardia from fecal samples for both research and diagnostic purposes presents one of the most difficult challenges. Traditional purification methods rely on density gradient centrifugation, membrane-based filtration, and sedimentation methods, which suffer from low recovery rates, high costs, and poor efficiency. Here, we report on the use of microfluidics to purify Giardia cysts from mouse feces. We propose a rigid spiral microfluidic device with a trapezoidal cross section to effectively separate Giardia from surrounding debris. Our characterizations reveal that the recovery rate is concentration-dependent, and our proposed device can achieve recovery rates as high as 75% with 0.75 ml/min throughput. Moreover, this device can purify Giardia from extremely turbid samples to a level where cysts are visually distinguishable with just one round of purification. This highly scalable and versatile 3D printed microfluidic device is then capable of further purifying or enhancing the recovery rate of the samples by recirculation. This device also has the potential to purify other gastrointestinal pathogens of similar size, and throughput can be significantly increased by parallelization.
Ding, L, Razavi Bazaz, S, Shrestha, J, A. Amiri, H, Mas-hafi, S, Banerjee, B, Vesey, G, Miansari, M & Ebrahimi Warkiani, M 2022, 'Rapid and Continuous Cryopreservation of Stem Cells with a 3D Micromixer', Micromachines, vol. 13, no. 9, pp. 1516-1516.
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Cryopreservation is the final step of stem cell production before the cryostorage of the product. Conventional methods of adding cryoprotecting agents (CPA) into the cells can be manual or automated with robotic arms. However, challenging issues with these methods at industrial-scale production are the insufficient mixing of cells and CPA, leading to damage of cells, discontinuous feeding, the batch-to-batch difference in products, and, occasionally, cross-contamination. Therefore, the current study proposes an alternative way to overcome the abovementioned challenges; a highly efficient micromixer for low-cost, continuous, labour-free, and automated mixing of stem cells with CPA solutions. Our results show that our micromixer provides a more homogenous mixing of cells and CPA compared to the manual mixing method, while the cell properties, including surface markers, differentiation potential, proliferation, morphology, and therapeutic potential, are well preserved.
Ding, L, Shan, X, Wang, D, Liu, B, Du, Z, Di, X, Chen, C, Maddahfar, M, Zhang, L, Shi, Y, Reece, P, Halkon, B, Aharonovich, I, Xu, X & Wang, F 2022, 'Lanthanide Ion Resonance‐Driven Rayleigh Scattering of Nanoparticles for Dual‐Modality Interferometric Scattering Microscopy', Advanced Science, vol. 9, no. 32, pp. e2203354-2203354.
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AbstractLight scattering from nanoparticles is significant in nanoscale imaging, photon confinement. and biosensing. However, engineering the scattering spectrum, traditionally by modifying the geometric feature of particles, requires synthesis and fabrication with nanometre accuracy. Here it is reported that doping lanthanide ions can engineer the scattering properties of low‐refractive‐index nanoparticles. When the excitation wavelength matches the ion resonance frequency of lanthanide ions, the polarizability and the resulted scattering cross‐section of nanoparticles are dramatically enhanced. It is demonstrated that these purposely engineered nanoparticles can be used for interferometric scattering (iSCAT) microscopy. Conceptually, a dual‐modality iSCAT microscopy is further developed to identify different nanoparticle types in living HeLa cells. The work provides insight into engineering the scattering features by doping elements in nanomaterials, further inspiring exploration of the geometry‐independent scattering modulation strategy.
Ding, L, Shan, X, Wang, D, Liu, B, Du, Z, Di, X, Chen, C, Maddahfar, M, Zhang, L, Shi, Y, Reece, P, Halkon, B, Aharonovich, I, Xu, X & Wang, F 2022, 'Lanthanide Ion Resonance‐Driven Rayleigh Scattering of Nanoparticles for Dual‐Modality Interferometric Scattering Microscopy (Adv. Sci. 32/2022)', Advanced Science, vol. 9, no. 32.
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Ding, W, Ming, Y, Cao, Z & Lin, C-T 2022, 'A Generalized Deep Neural Network Approach for Digital Watermarking Analysis', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 3, pp. 613-627.
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Technology advancement has facilitated digital content, such as images, being acquired in large volumes. However, requirement from the privacy or legislation perspective still demands the need for intellectual content protection. In this paper, we propose a deep neural network (DNN) based watermarking method to achieve this goal. Instead of training a neural network for protecting a specific image, we train the network on an image dataset and generalize the trained model to protect distinct test images in a bulk manner. Respective evaluations from both the subjective and objective aspects confirm the generality and practicality of our proposed method. To demonstrate the robustness of this general neural watermarking approach, commonly used attacks are applied to the watermarked images to examine the corresponding extracted watermarks, which still retain sufficient recognizable traits for some occasions. Testing on distinctive dataset shows the satisfying generalization of our proposed method, and practice such as loss function adjustment can cater to the capacity requirement of complicated watermark. We also discuss some traits of the trained model, which incur the vulnerability to JPEG compression attack. However, remedy seeking for this can potentially open a window to understand the underlying working principle of DNN in future work. Considering its performance and economy, it is concluded that subsequent studies that generalize our work on utilizing DNN for intellectual content protection might be a promising research trend.
Ding, W, Zhou, X, Jin, W, Zhao, Z, Gao, S, Chen, Y, Han, W, Liu, H & Wang, Q 2022, 'A novel aquatic worm (Limnodrilus hoffmeisteri) conditioning method for enhancing sludge dewaterability by decreasing filamentous bacteria', Science of The Total Environment, vol. 849, pp. 157949-157949.
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In this study, a novel aquatic worm conditioning method was proposed to enhance sludge dewaterability by reducing filamentous bacteria. The optimal treatment time was 4 days and the optimal sludge concentration was 5000 mg/L. Under these conditions, the sludge dewaterability was improved with CST of 16.69 s, reduction in sludge SRF of 48.95 %, and reduction in LfA of 58.23 %. After bio-conditioning, sludge flocs broke up by the aquatic worm predation. The absolute zeta potential decreased to -8.27 mV, and the particle size increased from 36.64 μm to 48.05 μm. Proteins, polysaccharides and other organic substances in sludge EPS and microbial cells were released, with the viscosity reduced to 1.16 mPa·s and the bound water converted into free water. Besides, the number and abundance of representative filamentous Chloroflexi decreased, resulting in the enhancement of sludge dewatering performance. Overall, the aquatic worm conditioning process can be divided into two steps: Sludge destruction by the aquatic worm predation and sludge re-coagulation by filamentous bacteria as a skeleton.
Ding, Z, Chen, C, Wen, S, Li, S & Wang, L 2022, 'Lag projective synchronization of nonidentical fractional delayed memristive neural networks', Neurocomputing, vol. 469, pp. 138-150.
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In this paper, lag projective synchronization of nonidentical fractional delayed memristive neural networks (NFDMNN) is investigated. Due to the existence of memristor, the analysis is based on the theory of differential equations with discontinuous right-hand side proposed by Filippov. A novel controller with fractional integral sliding-mode surface is devised firstly. Successively, some sufficient criteria ensuring lag projective synchronization of NFDMNN are obtained, depending on the fractional calculus inequalities and Lyapunov direct method. Moreover, the related results improve and enrich previous synchronization works. Lastly, the validity of conclusions is verified through a simulation example.
Dinh, TH, Singh, AK, Linh Trung, N, Nguyen, DN & Lin, C-T 2022, 'EEG Peak Detection in Cognitive Conflict Processing Using Summit Navigator and Clustering-Based Ranking', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 1548-1556.
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Correct detection of peaks in electroencephalogram (EEG) signals is of essence due to the significant correlation of those potentials with cognitive performance and disorders. This paper proposes a novel and non-parametric approach to detect prediction error negativity (PEN) in cognitive conflict processing. The PEN candidates are first located from the input signal via an adaptation of a recent effective method for local maxima extraction, processed in a multi-scale manner. The found candidates are then fused and ranked based on their shape and location-based features. False positives caused by candidates' magnitude are eliminated by rotating the sorted candidate list where the one with the second-best ranking score will be identified as PEN. The EEG data collected from a 3D object selection task have been used to verify the efficacy of the proposed approach. Compared with the state-of-the-art peak detection techniques, the proposed method shows an improvement of at least 2.67% in accuracy and 6.27% in sensitivity while requires only about 4 ms to process an epoch. The accuracy and computational efficiency of the proposed technique in the detection of PEN in cognitive conflict processing would lead to promising applications in performance improvement of brain-computer interfaces (BCIs).
Ditta, A, Tabish, AN, Mujtaba, MA, Amjad, M, Yusuf, AA, Chaudhary, GQ, Razzaq, L, Abdelrahman, A & Kalam, MA 2022, 'Experimental investigation of a hybrid configuration of solar thermal collectors and desiccant indirect evaporative cooling system', Frontiers in Energy Research, vol. 10.
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This paper presents the integrated performance of a solar-assisted desiccant dehumidifier along with Maisotsenko cycle (M-cycle) counter flow heat and mass exchanger. This system handles latent load and sensible load separately. The hybrid configuration of solar thermal collectors was analyzed for efficiency of solar collectors and solar fraction. High consumption of fossil fuels, which are already present in a limited amount, is also associated with environmental problems and climate change issues, as these increase the chances of global warming. These issues demand of us to shift towards renewable energy resources. Increase in world energy use results in a number of environmental problems, such as climate change, in addition to global warming and ozone depletion. In building services, HVAC systems are major concerns. To overcome the requirement, conventional air conditioning and vapor compression systems are mainly used for air conditioning, although these also have some environmental problems. Solar thermal applications in combination with other renewable-energy-dependent cooling practices have generated a huge interest towards sustainable solutions, keeping in view several techno-economical, environmental, and climatic advantages. The experimental investigation reveals that the maximum outlet temperature and efficiency of solar thermal collectors was 87°C and 56% respectively. The maximum cooling capacity of the system is evaluated at 4.6 kW.
Do, MH, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Liu, Q, Nghiem, DL, Thanh, BX, Zhang, X & Hoang, NB 2022, 'Performance of a dual-chamber microbial fuel cell as a biosensor for in situ monitoring Bisphenol A in wastewater', Science of The Total Environment, vol. 845, pp. 157125-157125.
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This research explores the possibilities of a dual-chamber microbial fuel cell as a biosensor to measure Bisphenol A (BPA) in wastewater. BPA is an organic compound and is considered to be an endocrine disruptor, affecting exposed organisms, the environment, and human health. The performance of the microbial fuel cells (MFCs) was first controlled with specific operational conditions (pH, temperature, fuel feeding rate, and organic loading rate) to obtain the best accuracy of the sensor signal. After that, BPA concentrations varying from 50 to 1000 μg L-1 were examined under the biosensor's cell voltage generation. The outcome illustrates that MFC generates the most power under the best possible conditions of neutral pH, 300 mg L-1 of COD, R 1000 Ω, and ambient temperature. In general, adding BPA improved the biosensor's cell voltage generation. A slight linear trend between voltage output generation and BPA concentration was observed with R2 0.96, which indicated that BPA in this particular concentration range did not real harm to the MFC's electrogenic bacteria. Scanning electron microscope (SEM) images revealed a better cover biofilm after BPA injection on the surface electrode compared to it without BPA. These results confirmed that electroactive biofilm-based MFCs can serve to detect BPA found in wastewaters.
Do, MH, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Pandey, A, Sharma, P, Varjani, S, Nguyen, TAH & Hoang, NB 2022, 'A dual chamber microbial fuel cell based biosensor for monitoring copper and arsenic in municipal wastewater', Science of The Total Environment, vol. 811, pp. 152261-152261.
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Dogan, S, Barua, PD, Baygin, M, Chakraborty, S, Ciaccio, EJ, Tuncer, T, Abd Kadir, KA, Md Shah, MN, Azman, RR, Lee, CC, Ng, KH & Acharya, UR 2022, 'Novel multiple pooling and local phase quantization stable feature extraction techniques for automated classification of brain infarcts', Biocybernetics and Biomedical Engineering, vol. 42, no. 3, pp. 815-828.
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Dogan, S, Datta Barua, P, Kutlu, H, Baygin, M, Fujita, H, Tuncer, T & Acharya, UR 2022, 'Automated accurate fire detection system using ensemble pretrained residual network', Expert Systems with Applications, vol. 203, pp. 117407-117407.
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Nowadays, fires have been commonly seen worldwide and especially forest fires are big disasters for humanity. The prime objective of this work is to develop an accurate fire warning model by using images. In this work, two new deep feature engineering models are proposed to detect the fire accurately using images. To create deep features, residual networks (ResNet) are chosen since these networks are one of the highly accurate convolutional neural networks. In this work, four pretrained ResNets: ResNet18, ResNet50, ResNet101, and InceptionResNetV2 are used. These networks were trained using a cluster of ImageNet dataset and features were extracted using the last pooling and fully connected layers of these networks. Hence, eight feature vectors are chosen using these networks and the top 256 features of these networks are chosen using neighborhood component analysis (NCA). Support vector machine (SVM) classifier has been used for classification. Moreover, by using the eight feature vectors generated, two ensemble models have been presented. In the first ensemble model, generated all features are concatenated and the top 1000 features are chosen using a feature selector used (NCA), and these features are classified using SVM. In the second ensemble model, iterative hard majority voting (IHMV) has been applied to the generated results. The developed ensemble ResNet models attained 98.91% and 99.15% classification accuracies using an SVM classifier with a 10-fold cross-validation strategy. Our results obtained demonstrate the high classification accuracy of our presented ensemble pretrained ResNet-based deep feature extraction models. These developed models are ready to be tested with higher databases before actual real-world application.
Dolmark, T, Sohaib, O, Beydoun, G, Wu, K & Taghikhah, F 2022, 'The Effect of Technology Readiness on Individual Absorptive Capacity Toward Learning Behavior in Australian Universities.', J. Glob. Inf. Manag., vol. 30, no. 1, pp. 1-21.
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Recipient's absorptive capacity (ACAP) is a barrier to knowledge transfer in organizations. The technology readiness (TR) dimensions measure an individual's technological beliefs and aligns with the individual's ACAP. The purpose of this research is to study if technological beliefs have a causal effect onto individual learning capability and behaviour. University's knowledge transfer makes them an ideal context for this research. Through surveying individuals and conducting statistical analysis, the authors provide empirical evidence that there is a causal effect from the TR dimensions to individuals ACAP and their technological learning behaviour at the individual level. The findings could potentially help leverage technology to address said recipient's ACAP. It would also benefit the development of new technologies, in particular in e-learning and tailoring pedagogy.
Dong, D & Petersen, IR 2022, 'Quantum estimation, control and learning: Opportunities and challenges', Annual Reviews in Control, vol. 54, pp. 243-251.
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Dong, F, Lu, J, Song, Y, Liu, F & Zhang, G 2022, 'A Drift Region-Based Data Sample Filtering Method', IEEE Transactions on Cybernetics, vol. 52, no. 9, pp. 9377-9390.
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Concept drift refers to changes in the underlying data distribution of data streams over time. A well-trained model will be outdated if concept drift occurs. Once concept drift is detected, it is necessary to understand where the drift occurs to support the drift adaptation strategy and effectively update the outdated models. This process, called drift understanding, has rarely been studied in this area. To fill this gap, this article develops a drift region-based data sample filtering method to update the obsolete model and track the new data pattern accurately. The proposed method can effectively identify the drift region and utilize information on the drift region to filter the data sample for training models. The theoretical proof guarantees the identified drift region converges uniformly to the real drift region as the sample size increases. Experimental evaluations based on four synthetic datasets and two real-world datasets demonstrate our method improves the learning accuracy when dealing with data streams involving concept drift.
Dong, L, Yang, Y, Liu, Z, Ren, Q, Li, J, Zhang, Y & Wu, C 2022, 'Microstructure and mechanical behaviour of 3D printed ultra-high performance concrete after elevated temperatures', Additive Manufacturing, vol. 58, pp. 103032-103032.
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This study investigated the characteristics of 3D printed ultra-high performance concrete (3DP-UHPC) after elevated temperatures. The effects of the bonding strip, steel fibre, specimen preparation method, loading direction and temperature on the fire resistance of 3DP-UHPC were analysed. The variations in microstructure and mineral composition of 3DP-UHPC after different temperatures were examined using scanning electron microscopy (SEM) and energy spectrum analyser (EDS). The strength degradation mechanism of 3DP-UHPC after the elevated temperatures was revealed in terms of the macro and micro levels. Meanwhile, the compressive strength of 3DP-UHPC after the elevated temperatures was measured, and its corresponding compressive constitutive model was proposed. The experimental results indicated that 3DP-UHPC had certain fire resistance, and the addition of steel fibre and the preparation method improved its fire resistance. The expansion of the crack at the junction of the steel fibre and matrix, as well as the oxidation and decarburization of steel fibre, affected the compressive strength of 3DP-UHPC after 400 ℃. During heating, water vapour escaped from the weak interface of the bonding strip endowed 3DP-UHPC with slightly better elevated-temperature burst resistance as compared to mould-casting ultra-high performance concrete (MC-UHPC). The compressive strength of 3DP-UHPC was the highest after 300 ℃ for the target temperatures set in this study, but the temperature had little effect on the strength difference between each direction of 3DP-UHPC. The compressive constitutive model of 3DP-UHPC after the elevated temperatures was developed, facilitating its engineering application in the field of fire safety.
Dong, M, Yuan, F, Yao, L, Wang, X, Xu, X & Zhu, L 2022, 'A survey for trust-aware recommender systems: A deep learning perspective', Knowledge-Based Systems, vol. 249, pp. 108954-108954.
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A significant remaining challenge for existing recommender systems is that users may not trust recommender systems for either inaccurate recommendation or lack of explanation. Thus, it becomes critical to embrace a trustworthy recommender system. This survey provides a systematic summary of three categories of trust issues in recommender systems: social-aware recommender systems, which leverage users’ social trust relationships; robust recommender systems, which filter untruthful information, noises and enhance attack resistance; and explainable recommender systems, which provide explanations of the recommended items. We focus on the work based on deep learning techniques, which is an emerging area in the recommendation research.
Dong, W, Li, W, Guo, Y, Qu, F, Wang, K & Sheng, D 2022, 'Piezoresistive performance of hydrophobic cement-based sensors under moisture and chloride-rich environments', Cement and Concrete Composites, vol. 126, pp. 104379-104379.
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Silicone hydrophobic powder (SHP) and crystalline waterproofing admixture (CWA) were used to improve the impermeability of carbon black (CB)/cement-based sensors. The mechanical, electrical and piezoresistive properties, waterproofing and chloride resistance of CB/cementitious composites were investigated in this study. The piezoresistivity before or after different durations of immersion in freshwater and 3% sodium chloride solution and the stability in freshwater and marine environment were studied and compared. The results show that compressive strength increased with the additions of CWA and SHP, while the tensile strength slightly decreased with CWA, due to the formation of crystalline. Moreover, cementitious composites with SHP exhibited the best water impermeability, while the counterpart containing CWA presented the optimal chloride resistance. Although cementitious composites with SHP exhibited the highest electrical resistivity, the most stable piezoresistivity occurred after 90 days of immersion in freshwater. On the other hand, cementitious composites incorporating CWA presented the lowest electrical resistivity, but the piezoresistivity continually decreased with the immersion duration. Because of the free ions, piezoresistivity increased as a result of the immersion in sodium chloride solution. The related results will provide an insight into the piezoresistivity of hydrophobic cement-based sensors under moisture and chloride environments for future structural health monitoring.
Dong, W, Li, W, Guo, Y, Sun, Z, Qu, F, Liang, R & Shah, SP 2022, 'Application of intrinsic self-sensing cement-based sensor for traffic detection of human motion and vehicle speed', Construction and Building Materials, vol. 355, pp. 129130-129130.
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To develop smart concrete pavement for intelligent infrastructure, the self-sensing performance of smart pavement with embedded cement-based sensors was experimentally investigated in this study. The self-sensing behaviors of mortar pavement is evaluated by the self-sensing of compression force, human motion detection, and vehicle speed monitoring. Because of the well-dispersed carbon nanofiber (CNF), the developed cement-based sensors intrinsically showed excellent piezoresistivity. The cement-based sensors connected in series were well bonded within the mortar slab, which indicates effective force transmission from the mortar slab to the cement-based sensors. The results showed that the smart mortar slab exhibited linear and repeatable fractional changes of resistivity (FCR) in response to cyclic compression force. With the cement-based sensors embedded, the smart mortar slab could monitor the human motions, such as ‘up-down’ feet or jumping movements. Moreover, the smart mortar slab could detect the exact vehicle speed with high accuracy for the traffic detection. The characterization on the interfaces between cement-based sensors and mortar slab demonstrated the excellent connections, which confirmed the smooth force transmission from the mortar slab to the cement-based sensors due to the excellent interfacial bonding between them. Moreover, the FCR value presented a firm relationship to the vehicle speed, with a decreasing trend with the increase of vehicle speed. The results will promote the practical applications of cement-based sensors, especially in the field of concrete pavement or road, to achieve smart concrete infrastructures.
Dong, W, Li, W, Guo, Y, Wang, K & Sheng, D 2022, 'Mechanical properties and piezoresistive performances of intrinsic graphene nanoplate/cement-based sensors subjected to impact load', Construction and Building Materials, vol. 327, pp. 126978-126978.
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The electrical, mechanical properties, and piezoresistive performances of intrinsic graphene nanoplate (GNP)/cementitious composites were investigated after subjected to impact load in this paper. The stabilized electrical resistivity before/after exposure to impact load and real-time electrical response under dynamic load were simultaneously studied. The cement hydration and microstructures of (GNP)/cementitious composites were characterized by thermal gravity analysis (TGA) and scanning electron microscope. The nearly identical hydration degree of 1.0% GNP filled cement mortar (1GNPCM) and mortar with 2% GNP (2GNPCM) indicates the physical interactions between the GNP and cement matrix. The excellent intrinsic physical properties of GNP played an important role in the enhancements of GNP/cementitious composites. After exposed to impact, the stabilized electrical resistivity, mechanical performance, and piezoresistivity of 1GNPCM were greatly changed, whereas the counterpart of 2GNPCM was well-maintained and nearly unaffected. Therefore, the severe microstructural deteriorations in 1GNPCM could be responsible for the variations, which damaged the conductive passages. The almost unchanged mechanical, electrical and piezoresistive properties enable 2GNPCM as a promising cement-based senor to provide stable piezoresistivity even after exposure to impact load. The related outcomes provide an insight into the development of impact-resistant cement-based sensors and promote the applications of cement-based sensors under extreme loading conditions.
Dong, W, Li, W, Sun, Z, Ibrahim, I & Sheng, D 2022, 'Intrinsic graphene/cement-based sensors with piezoresistivity and superhydrophobicity capacities for smart concrete infrastructure', Automation in Construction, vol. 133, pp. 103983-103983.
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Dong, W, Li, W, Wang, K, Shah, SP & Sheng, D 2022, 'Multifunctional cementitious composites with integrated self-sensing and self-healing capacities using carbon black and slaked lime', Ceramics International, vol. 48, no. 14, pp. 19851-19863.
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This study aims to develop multifunctionality of cementitious composites with the integrated self-sensing and self-healing capacities by incorporating conductive carbon black (CB) with CB-encapsulated slaked lime (SL). The microsized SL particles were premixed with a half of designed content of nanosized CB particles. When CB agglomerations coat around the SL surfaces, SL does not hydrate until the CB coating is removed. Another half of designed weight of CB is uniformly dispersed using ultrasonication with superplasticizer and added to obtain piezoresistivity. The results show that the stress sensing capacity of CB-SL-cementitious composite performs well with the compressive stress. Autogenous healing performances presented significantly can improve the self-healing capacity with the increase of SL. Furthermore, the healing efficiency is affected by the crack width and dispersion of SL, and the smaller cracks with SL are more easily healed. The size of CB agglomerations decreases with the added SL, and the main product of self-healing is calcium carbonate.
Dong, Y, Guo, S, Wang, Q, Yu, S & Yang, Y 2022, 'Content Caching-Enhanced Computation Offloading in Mobile Edge Service Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 1, pp. 872-886.
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Cache enhanced computation offloading as a novel offloading paradigm in mobile edge computing (MEC) can reduce more task execution latency than traditional computation offloading by reusing of computation offloading data. However, existing works only focus on the enhancement between computation offloading and data caching but ignore the competition for cache resources between them. To this end, in this paper, we propose a caching enhanced computation offloading algorithm in mobile edge service networks (MESN), by considering the cache resources competition. We formulate a joint optimization problem of content caching and cache-enhanced computation offloading. Furthermore, we give the optimal caching strategy to achieve the equilibrium between the resources competition. By our offloading algorithm caching strategy, the average response time of computation and content request tasks can get further reduction. In addition, we design two low time complexity algorithms, i.e., mixed caching algorithm and enhanced offloading algorithm, to solve the sub-problems, i.e., smart base station (SBS) caching sub-problem and computation offloading sub-problem, transformed by the original optimization problem. The simulation results show that our algorithms can quickly converge and our scheme can reduce 20.52% average response time of all tasks at most compared with other schemes.
Dorji, P, Phuntsho, S, Kim, DI, Lim, S, Park, MJ, Hong, S & Shon, HK 2022, 'Electrode for selective bromide removal in membrane capacitive deionisation', Chemosphere, vol. 287, no. Pt 2, pp. 132169-132169.
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Due to the shortage of freshwater around the world, seawater is becoming an important water source. However, seawater contains a high concentration of bromide that can form harmful disinfection by-products during water disinfection. Therefore, the current seawater reverse osmosis (SWRO) has to adopt two-pass reverse osmosis (RO) configuration for effective bromide removal, increasing the overall desalination cost. In this study, a bromide selective composite electrode was developed for membrane capacitive deionisation (MCDI). The composite electrode was developed by coating a mixture of bromide selective resin and anion exchange polymer on the surface of the commercial activated carbon electrode, and its performance was compared to that of conventional carbon electrode. The results demonstrated that the composite electrode has ten times better bromide selectivity than the conventional carbon electrode. The study shows the potential application of MCDI for the selective removal of target ions from water sources and the potential for resource recovery through basic modification of commercial electrode.
Dorji, U, Dorji, P, Shon, H, Badeti, U, Dorji, C, Wangmo, C, Tijing, L, Kandasamy, J, Vigneswaran, S, Chanan, A & Phuntsho, S 2022, 'On-site domestic wastewater treatment system using shredded waste plastic bottles as biofilter media: Pilot-scale study on effluent standards in Bhutan', Chemosphere, vol. 286, no. Pt 2, pp. 131729-131729.
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In this study, a 1000 L/d capacity one-off on-site wastewater treatment system was operated for over a year as a pilot alternative to the conventional on-site treatment as currently used in urban Bhutan. An up-flow anaerobic sludge blanket (UASB) was used for blackwater treatment (to replace 'septic tank followed by an anaerobic biofilter (ABF) (to replace soak pits) for the treatment of a mixture of greywater and UASB effluent. Shredded waste plastic bottles were used as the novel biofilter media in the ABF. During a yearlong operation, the pilot system produced a final treated effluent from ABF with average BOD5 28 mg/L, COD 38 mg/L, TSS 85 mg/L and 5 log units of Escherichia coli. These effluents met three out of four of the national effluent discharge limits of Bhutan, but unsuccessful to meet the Escherichia coli standard over a yearlong operation. Further, process optimisation may enable more significant Escherichia coli removal. An economic analysis indicates that the total unit cost (capital and operating expenditures) of this alternative wastewater treatment system for more than 50 users range between USD 0.27-0.37/person/month comparable to USD 0.29-0.42/person/month for the current predominant on-site system, i.e., 'septic tanks'. This pilot study, therefore, indicates that this wastewater treatment system using shredded waste plastic biofilter media has high potential to replace the current conventional treatment, i.e., 'septic tanks', which are often overloaded with greywater and discharging effluents which does not meet the national standards.
Douglas, ANJ, Morgan, AL, Irga, PJ & Torpy, FR 2022, 'The need for multifaceted approaches when dealing with the differing impacts of natural disasters and anthropocentric events on air quality', Atmospheric Pollution Research, vol. 13, no. 11, pp. 101570-101570.
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Du, T, Chen, J, Qu, F, Li, C, Zhao, H, Xie, B, Yuan, M & Li, W 2022, 'Degradation prediction of recycled aggregate concrete under sulphate wetting–drying cycles using BP neural network', Structures, vol. 46, pp. 1837-1850.
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Recycling construction and demolition wastes (C&DW) and industrial by-products as construction materials is an effective way to solve the serious environmental problems caused by these wastes. In this paper, the performance degradation of recycled aggregate concrete (RAC) containing fly ash (FA) under sulphate wetting–drying cycles was investigated by considering the recycled coarse aggregate (RCA) incorporation ratio, water-to-binder (W/B) ratio, and FA incorporation ratio. The mass loss rate (Km) and corrosion resistance coefficient of compressive strength (Kf) were considered for the analysis. The results show that as the number of sulphate wetting–drying cycle increased, the increase in RCA incorporation ratio, W/B ratio and FA incorporation ratio led to a gradual decrease in the Km of RAC. A substantially linear relationship could be observed between Kf and the three influencing factors. In contract, fly ash incorporation ratio had a positive effect on Kf. Finally, based on the back propagation (BP) neural network, a novel model considering the three influencing factors is developed to predict the sulphate resistance of RCA under wetting–drying cycles. In conclusion, RAC containing fly ash can both promote the C&DW recycling and give excellent sulphate resistance.
Du, T, Li, C, Wang, X, Ma, L, Qu, F, Wang, B, Peng, J & Li, W 2022, 'Effects of pipe diameter, curing age and exposure temperature on chloride diffusion of concrete with embedded PVC pipe', Journal of Building Engineering, vol. 57, pp. 104957-104957.
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Concrete structures are often embedded with pipe opening for the installation of pipelines which tends to weaken the integrity and durability. The effects of pipe diameter, curing age, and exposure temperature, on the chloride ion resistance of concrete with embedded PVC pipe (CEPP) were investigated in this paper. The testing parameters include compressive strength, electric flux density, chloride ion diffusion coefficient, chloride ion penetration depth, and chloride ion content. The results showed that electric flux density and chloride ion diffusion coefficient of CEPP increased with the diameter of PVC pipes following a second-degree parabola and a linear relation respectively, while the chloride ion diffusion coefficient decreased with the prolonging curing age. The chloride ion resistance and compressive strength of CEPP were decreased with the increase of pipe diameters, because the weak areas formed in the transition interfaces between the PVC pipes and concrete matrices and expanded with increased diameter. The chloride ion penetration depth and chloride ion content were relatively higher in the testing points near the PVC pipes than the ones far away from the PVC pipes. In addition, the rate of chloride ion penetration of CEPP could be accelerated by the higher exposure temperatures, leading to severer chloride ion penetration of CEPP. Finally, a novel modified Fick's second law diffusion model considering the effects of pipe diameters and curing age was proposed to predict the chloride ion resistance of CEPP.
Du, Y, Ma, R, Wang, L, Qian, J & Wang, Q 2022, '2D/1D BiOI/g-C3N4 nanotubes heterostructure for photoelectrochemical overall water splitting', Science of The Total Environment, vol. 838, no. Pt 2, pp. 156166-156166.
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To boost the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) performances, the BiOI/graphitic carbon nitride nanotubes (g-C3N4 nanotubes) heterojunction was synthesized herein through the hydrothermal method. BiOI in-situ grew on the surface of g-C3N4 nanotubes derived from melamine. The rapid recombination between photoexcited electrons and holes of pristine semiconductors was prevented via building the stable heterojunction. The SEM results indicated that the BiOI was wrapped around the surface of g-C3N4 nanotubes, resulting in an optimized electronic transmission pathway. Much lower charge transfer resistance at the p-n heterojunction was demonstrated compared with pristine BiOI according to the EIS results, thus leading to the faster surface reaction rates. Moreover, the composite exhibited both outstanding OER and HER activities under illuminated conditions. This study may shed light upon establishing a bifunctional photoelectrocatalysis for photoelectrochemical water splitting based on stable 2D metal and 1D metal-free nanocomposite.
Duan, Y, Chen, N, Shen, S, Zhang, P, Qu, Y & Yu, S 2022, 'FDSA-STG: Fully Dynamic Self-Attention Spatio-Temporal Graph Networks for Intelligent Traffic Flow Prediction', IEEE Transactions on Vehicular Technology, vol. 71, no. 9, pp. 9250-9260.
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With the development of transportation and the ever-improving of vehicular technology, Artificial Intelligence (AI) has been popularized in Intelligent Transportation Systems (ITS), especially in Traffic Flow Prediction (TFP). TFP plays an increasingly important role in alleviating traffic pressure caused by regional emergencies and coordinating resource allocation in advance to deployment decisions. However, existing research can hardly model the original intricate structural relationships of the transportation network (TN) due to the lack of in-depth consideration of the dynamic relevance of spatial, temporal, and periodic characteristics. Motivated by this and combined with deep learning (DL), we propose a novel framework entitled Fully Dynamic Self-Attention Spatio-Temporal Graph Networks (FDSA-STG) by improving the attention mechanism using Graph Attention Networks (GATs). In particular, to dynamically integrate the correlations of spatial dimension, time dimension, and periodic characteristics for highly-accurate prediction, we correspondingly devised three components including the spatial graph attention component (SGAT), the temporal graph attention component (TGAT), and the fusion layer. In this case, three groups of similar structures are designed to extract the flow characteristics of recent periodicity, daily periodicity, and weekly periodicity. Extensive evaluation results show the superiority of FDSA-STG from perspectives of prediction accuracy and prediction stability improvements, which also testifies high model adaptability to the dynamic characteristics of the actual observed traffic flow (TF).
Duan, Y, Wang, Z, Wang, J, Wang, Y-K & Lin, C-T 2022, 'Position-aware image captioning with spatial relation', Neurocomputing, vol. 497, pp. 28-38.
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Image caption aims to generate a language description of a given image. The problem can be solved by learning semantic information of visual objects and generating descriptions based on extracted embedding. However, the spatial relationship between visual objects and their static position is not fully explored by existing methods. In this work, we propose a Position-Aware Transformer (PAT) model that extracts both regional and static global visual features and unify both the regional and global by incorporating spatial information aligned to each visual feature. To make a better representation of spatial information and correlation between extracted visual features, we propose and compare three subtle approaches to explore position embedding with spatial relation information explicitly. Moreover, we jointly consider the static global and regional embedding for spatial modeling. Experimental results illustrate that our proposed model achieves competitive performance on the COCO image captioning dataset, where the PAT model could respectively reach 38.7, 28.6, and 58.6 on BLEU-4, METEOR, and ROUGE-L respectively. Extensive experiments suggest that the proposed PAT model could also reach competitive performance on related visual-language tasks including visual question answering (VQA) and multi-modal retrieval. Detailed ablation studies are conducted to report how each part would contribute to the final performance, which could be a good reference for follow-up spatial information representation works.
Duong, HC, Nghiem, LD, Ansari, AJ, Vu, TD & Nguyen, KM 2022, 'Assessment of pilot direct contact membrane distillation regeneration of lithium chloride solution in liquid desiccant air-conditioning systems using computer simulation', Environmental Science and Pollution Research, vol. 29, no. 28, pp. 41941-41952.
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Membrane distillation (MD) has been increasingly explored for treatment of various hyper saline waters, including lithium chloride (LiCl) solutions used in liquid desiccant air-conditioning (LDAC) systems. In this study, the regeneration of liquid desiccant LiCl solution by a pilot direct contact membrane distillation (DCMD) process is assessed using computer simulation. Unlike previous experimental investigations, the simulation allows to incorporate both temperature and concentration polarisation effects in the analysis of heat and mass transfer through the membrane, thus enabling the systematic assessment of the pilot DCMD regeneration of the LiCl solution. The simulation results demonstrate distinctive profiles of water flux, thermal efficiency, and LiCl concentration along the membrane under cocurrent and counter-current flow modes, and the pilot DCMD process under counter-current flow is superior to that under cocurrent flow regarding the process thermal efficiency and LiCl concentration enrichment. Moreover, for the pilot DCMD regeneration of LiCl solution under the counter-current flow, the feed inlet temperature, LiCl concentration, and especially the membrane leaf length exert profound impacts on the process performance: the process water flux halves from 12 to 6 L/(m2·h) whilst thermal efficiency decreases by 20% from 0.46 to 0.37 when the membrane leaf length increases from 0.5 to 1.5 m.
Duong, TD, Li, Q & Xu, G 2022, 'Stochastic intervention for causal inference via reinforcement learning', Neurocomputing, vol. 482, pp. 40-49.
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Dwivedi, KA, Kumar, V, Wang, C-T, Chong, WT & Ong, HC 2022, 'Design and feasibility study of novel swirler incorporated microbial fuel cell for enhancing power generation and domestic wastewater treatment', Journal of Cleaner Production, vol. 337, pp. 130382-130382.
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Eager, D, Ishac, K, Zhou, S & Hossain, I 2022, 'Investigating the Knuckleball Effect in Soccer Using a Smart Ball and Training Machine', Sensors, vol. 22, no. 11, pp. 3984-3984.
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The term knuckleball in sporting jargon is used to describe a ball that has been launched with minimal spin, resulting in a trajectory that is erratic and unpredictable. This phenomenon was first observed in baseball (where the term originated) and has since been observed in other sports. While knuckleball has long fascinated the scientific community, the bulk of research has primarily focused on knuckleball as it occurs in baseball. Following the changes in the design of the soccer ball after the 2006 World Cup, knuckleball and ball aerodynamics were exploited by soccer players. This research examined the properties of a knuckleball in the sport of soccer. We designed and evaluated a system that could reproduce the knuckleball effect on soccer balls based on previous theories and characteristics outlined in our literature review. Our system is comprised of the Adidas miCoach Smart Ball, a companion smart phone app for data collection, a ball-launching machine with programmable functions, and a video-based tracking system and Tracker motion analysis software. The results from the testing showed that our system was successfully able to produce knuckleball behaviour on the football in a highly consistent manner. This verified the dynamic models of knuckleball that we outline. While a small portion of the data showed some lateral deviations (zig-zag trajectory), this erratic and unpredictable trajectory was much smaller in magnitude when compared to examples seen in professional games. The sensor data from the miCoach app and trajectory data from the Tracker motion analysis software, showed that the knuckleballs were consistently reproduced in-line with theoretical dynamics.
Eager, D, Zhou, S, Hossain, I, Ishac, K & Halkon, B 2022, 'Research on Impact Attenuation Characteristics of Greyhound Racing Track Padding for Injury Prevention', Vibration, vol. 5, no. 3, pp. 497-512.
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To reduce injuries to greyhounds caused by collisions with fixed racing track objects such as the outside fence or the catching pen structures, padding systems are widely adopted. However, there are currently neither recognised standards nor minimum performance thresholds for greyhound industry padding systems. This research is the first of its kind to investigate the impact attenuation characteristics of different padding systems for use within the greyhound racing industry for the enhanced safety and welfare of racing greyhounds. A standard head injury criterion (HIC) meter was used to examine padding impact attenuation performance based on the maximum g-force, HIC level and the HIC duration. Initially, greyhound racing speed was recorded and analysed with the IsoLynx system to understand the potential impact hazard to greyhounds during racing which indicates the necessity for injury prevention with padding. A laboratory test was subsequently conducted to compare the impact attenuation performance of different kinds of padding. Since padding impact attenuation characteristics are also affected by the installation and substrate, onsite testing was conducted to obtain the padding system impact attenuation performance in actual greyhound racing track applications. The test results confirm that the padding currently used within the greyhound industry is adequate for the fence but inadequate when used for rigid structural members such as the catching pen gate supports. Thus, increasing the padding thickness is strongly recommended if it is used at such locations. More importantly, it is also recommended that, after the installation of padding on the track, its impact attenuation characteristics be tested according to the methodology developed herein to verify the suitability for protecting greyhounds from injury.
Eager, D, Zhou, S, Ishac, K, Hossain, I, Richards, A & Sharwood, LN 2022, 'Investigation into the Trampoline Dynamic Characteristics and Analysis of Double Bounce Vibrations', Sensors, vol. 22, no. 8, pp. 2916-2916.
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Double bounce is an unusual and potentially very hazardous phenomenon that most trampoline users may have experienced, yet few would have really understood how and why it occurs. This paper provides an in-depth investigation into the double bounce. Firstly, the static and dynamic characteristics of a recreational trampoline are analysed theoretically and verified through experiments. Then, based on the developed trampoline dynamic model, double bounce simulation is conducted with two medicine balls released with different time delays. Through simulation, the process of double bounce is presented in detail, which comprehensively reveals how energy is transferred between users during double bounce. Furthermore, the effect of release time delay on double bounce is also presented. Finally, we conducted an experiment which produced similar results to the simulation and validated the reliability of the trampoline dynamic model and double bounce theoretical analysis.
Ejegwa, PA, Wen, S, Feng, Y, Zhang, W & Tang, N 2022, 'Novel Pythagorean Fuzzy Correlation Measures Via Pythagorean Fuzzy Deviation, Variance, and Covariance With Applications to Pattern Recognition and Career Placement', IEEE Transactions on Fuzzy Systems, vol. 30, no. 6, pp. 1660-1668.
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Pythagorean fuzzy set (PFS) is an importance soft computing tool for curbing embedded vagueness in decision-making. To enhance the applicability of PFSs in modelling practical problems, many computing methods have been studied among which, correlation coefficient is vital. This paper proposes some novel methods of computing correlation between PFSs via the three characteristic parameters of PFSs by incorporating the ideas of Pythagorean fuzzy deviation, variance and covariance. These novel methods evaluate the magnitude of relationship, show the potency of correlation between the PFSs, and also indicate whether the PFSs are related in either positive or negative sense. The proposed techniques are substantiated with some theoretical results, and numerically validated to be superior in terms of accuracy and reliability in contrast to some hitherto similar techniques. Decision-making processes involving pattern recognition and career placement problems are determined with the aid of the proposed techniques.
El‐Hawat, O, Fatahi, B & Taciroglu, E 2022, 'Novel post‐tensioned rocking piles for enhancing the seismic resilience of bridges', Earthquake Engineering & Structural Dynamics, vol. 51, no. 2, pp. 393-417.
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AbstractThe rocking pile foundation system is a relatively new design concept that can be implemented in bridges to improve their seismic performance. This type of foundation prevents plastic damage at the bridge piers and the foundation system, which are difficult to repair and can lead to collapse. However, lack of adequate energy dissipation in this type of foundation can result in large deck displacements and subsequent catastrophic failures of the bridge. The present study proposes a novel foundation system that integrates post‐tensioned piles with the rocking foundation to simultaneously prevent plastic hinging at the piers and reduce the deck displacements during severe earthquakes. The effectiveness of the proposed foundation system is investigated and compared against the rocking pile and conventional fixed‐base foundation systems using identical bridge configurations. Three‐dimensional finite element models of these bridges were developed to capture possible nonlinear behavior of the bridge as well as soil‐structure interaction effects. Six strong earthquakes with both horizontal components were selected and scaled to the appropriate seismic hazard level with a return period of 2475 years. Static pushover and nonlinear time‐history analyses were then performed to compare the dynamic response of the bridges, including deck displacements, pier and pile inertial forces, and other nonlinear behavior experienced by the structure. The results reveal that by integrating the post‐tensioned piles with the rocking foundation, the deck displacements were reduced to an acceptable limit without subjecting the bridge to any damage. In contrast, the bridge with the fixed base foundation experienced extensive damage at the piers, and the bridge with the rocking foundation experienced substantial deck displacements that ultimately led to unseating, resulting in the collapse of both bridges. It was therefore concluded that the p...
Elsemary, MT, Maritz, MF, Smith, LE, Warkiani, M, Bandara, V, Napoli, S, Barry, SC, Coombs, JT & Thierry, B 2022, 'Inertial Microfluidic Purification of CAR‐T‐Cell Products', Advanced Biology, vol. 6, no. 1, pp. e2101018-e2101018.
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AbstractChimeric antigen receptor T (CAR‐T) cell therapy is rapidly becoming a frontline cancer therapy. However, the manufacturing process is time‐, labor‐ and cost‐intensive, and it suffers from significant bottlenecks. Many CAR‐T products fail to reach the viability release criteria set by regulators for commercial cell therapy products. This results in non‐recoupable costs for the manufacturer and is detrimental to patients who may not receive their scheduled treatment or receive out‐of‐specification suboptimal formulation. It is demonstrated here that inertial microfluidics can, within minutes, efficiently deplete nonviable cells from low‐viability CAR‐T cell products. The percentage of viable cells increases from 40% (SD ± 0.12) to 71% (SD ± 0.09) for untransduced T cells and from 51% (SD ± 0.12) to 71% (SD ± 0.09) for CAR‐T cells, which meets the clinical trials’ release parameters. In addition, the processing of CAR‐T cells formulated in CryStor yields a 91% reduction in the amount of the cryoprotectant dimethyl sulfoxide. Inertial microfluidic processing has no detrimental effects on the proliferation and cytotoxicity of CAR‐T cells. Interestingly, ≈50% of T‐regulatory and T‐suppressor cells are depleted, suggesting the potential for inertial microfluidic processing to tune the phenotypical composition of T‐cell products.
Eshkevari, M, Jahangoshai Rezaee, M, Saberi, M & Hussain, OK 2022, 'An end-to-end ranking system based on customers reviews: Integrating semantic mining and MCDM techniques', Expert Systems with Applications, vol. 209, pp. 118294-118294.
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Eskandari, M, Savkin, AV, Alhelou, HH & Blaabjerg, F 2022, 'Explicit Impedance Modeling and Shaping of Grid-Connected Converters via an Enhanced PLL for Stabilizing the Weak Grid Connection', IEEE Access, vol. 10, pp. 128874-128889.
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Eslahi, H, Hamilton, TJ & Khandelwal, S 2022, 'Compact and Energy Efficient Neuron With Tunable Spiking Frequency in 22-nm FDSOI', IEEE Transactions on Nanotechnology, vol. 21, pp. 189-195.
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Esselle, K, Matekovits, L, Yang, Y, Thalakotuna, D, Afzal, M, Kovaleva, M & Singh, K 2022, 'Guest Editorial Disruptive Beam-Steering Antenna Technologies for Emerging and Future Satellite Services', IEEE Antennas and Wireless Propagation Letters, vol. 21, no. 11, pp. 2211-2218.
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Etaati, B, Dehkordi, AA, Sadollah, A, El-Abd, M & Neshat, M 2022, 'A Comparative State-of-the-Art Constrained Metaheuristics Framework for TRUSS Optimisation on Shape and Sizing', Mathematical Problems in Engineering, vol. 2022, pp. 1-13.
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In order to develop the dynamic effectiveness of the structures such as trusses, the application of optimisation methods plays a significant role in improving the shape and size of elements. However, conjoining two heterogeneous variables, nodal coordinates and cross-sectional elements, makes a challenging optimisation problem that is nonlinear, multimodal, large-scale with dynamic constraints. To handle these challenges, evolutionary and swarm optimisation algorithms can be robust and practical tools and show great potential to solve such complex problems. This paper proposed a comparative truss optimisation framework to solve two large-scale structures, including 314-bar and 260-bar trusses. The proposed framework consists of twelve state-of-the-art bio-inspired algorithms. The experimental results show that the marine predators algorithm (MPA) performed best compared with other algorithms in terms of convergence speed and the quality of the proposed designs of the trusses.
Ezugwu, AE, Agushaka, JO, Abualigah, L, Mirjalili, S & Gandomi, AH 2022, 'Prairie Dog Optimization Algorithm', Neural Computing and Applications, vol. 34, no. 22, pp. 20017-20065.
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This study proposes a new nature-inspired metaheuristic that mimics the behaviour of the prairie dogs in their natural habitat called the prairie dog optimization (PDO). The proposed algorithm uses four prairie dog activities to achieve the two common optimization phases, exploration and exploitation. The prairie dogs' foraging and burrow build activities are used to provide exploratory behaviour for PDO. The prairie dogs build their burrows around an abundant food source. As the food source gets depleted, they search for a new food source and build new burrows around it, exploring the whole colony or problem space to discover new food sources or solutions. The specific response of the prairie dogs to two unique communication or alert sound is used to accomplish exploitation. The prairie dogs have signals or sounds for different scenarios ranging from predator threats to food availability. Their communication skills play a significant role in satisfying the prairie dogs' nutritional needs and anti-predation abilities. These two specific behaviours result in the prairie dogs converging to a specific location or a promising location in the case of PDO implementation, where further search (exploitation) is carried out to find better or near-optimal solutions. The performance of PDO in carrying out optimization is tested on a set of twenty-two classical benchmark functions and ten CEC 2020 test functions. The experimental results demonstrate that PDO benefits from a good balance of exploration and exploitation. Compared with the results of other well-known population-based metaheuristic algorithms available in the literature, the PDO shows stronger performance and higher capabilities than the other algorithms. Furthermore, twelve benchmark engineering design problems are used to test the performance of PDO, and the results indicate that the proposed PDO is effective in estimating optimal solutions for real-world optimization problems with unknown global opt...
Faehrmann, PK, Steudtner, M, Kueng, R, Kieferova, M & Eisert, J 2022, 'Randomizing multi-product formulas for Hamiltonian simulation', Quantum, vol. 6, pp. 806-806.
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Quantum simulation, the simulation of quantum processes on quantum computers, suggests a path forward for the efficient simulation of problems in condensed-matter physics, quantum chemistry, and materials science. While the majority of quantum simulation algorithms are deterministic, a recent surge of ideas has shown that randomization can greatly benefit algorithmic performance. In this work, we introduce a scheme for quantum simulation that unites the advantages of randomized compiling on the one hand and higher-order multi-product formulas, as they are used for example in linear-combination-of-unitaries (LCU) algorithms or quantum error mitigation, on the other hand. In doing so, we propose a framework of randomized sampling that is expected to be useful for programmable quantum simulators and present two new multi-product formula algorithms tailored to it. Our framework reduces the circuit depth by circumventing the need for oblivious amplitude amplification required by the implementation of multi-product formulas using standard LCU methods, rendering it especially useful for early quantum computers used to estimate the dynamics of quantum systems instead of performing full-fledged quantum phase estimation. Our algorithms achieve a simulation error that shrinks exponentially with the circuit depth. To corroborate their functioning, we prove rigorous performance bounds as well as the concentration of the randomized sampling procedure. We demonstrate the functioning of the approach for several physically meaningful examples of Hamiltonians, including fermionic systems and the Sachdev–Ye–Kitaev model, for which the method provides a favorable scaling in the effort.
Fahmideh, M, Grundy, J, Beydoun, G, Zowghi, D, Susilo, W & Mougouei, D 2022, 'A model-driven approach to reengineering processes in cloud computing.', Inf. Softw. Technol., vol. 144, pp. 106795-106795.
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Context: The reengineering process of large data-intensive legacy software applications (“legacy applications” for brevity) to cloud platforms involves different interrelated activities. These activities are related to planning, architecture design, re-hosting/lift-shift, code refactoring, and other related ones. In this regard, the cloud computing literature has seen the emergence of different methods with a disparate point of view of the same underlying legacy application reengineering process to cloud platforms. As such, the effective interoperability and tailoring of these methods become problematic due to the lack of integrated and consistent standard models. Objective: We design, implement, and evaluate a novel framework called MLSAC (Migration of Legacy Software Applications to the Cloud). The core aim of MLSAC is to facilitate the sharing and tailoring of reengineering methods for migrating legacy applications to cloud platforms. MLSAC achieves this by using a collection of coherent and empirically tested cloud-specific method fragments from the literature and practice. A metamodel (or meta-method) together with corresponding instantiation guidelines is developed from this collection. The metamodel can also be used to create and maintain bespoke reengineering methods in a given scenario of reengineering to cloud platforms. Approach: MLSAC is underpinned by a metamodeling approach that acts as a representational layer to express reengineering methods. The design and evaluation of MLSAC are informed by the guidelines from the design science research approach. Results: Our framework is an accessible guide of what legacy-to-cloud reengineering methods can look like. The efficacy of the framework is demonstrated by modeling real-world reengineering scenarios and obtaining user feedback. Our findings show that the framework provides a fully-fledged domain-specific, yet platform-independent, foundation for the semi-automated representing, maintaining, ...
Faisal, SN & Iacopi, F 2022, 'Thin-Film Electrodes Based on Two-Dimensional Nanomaterials for Neural Interfaces', ACS Applied Nano Materials, vol. 5, no. 8, pp. 10137-10150.
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Fallahpoor, M, Chakraborty, S, Heshejin, MT, Chegeni, H, Horry, MJ & Pradhan, B 2022, 'Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection', Computers in Biology and Medicine, vol. 145, pp. 105464-105464.
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Fan, H, Yu, X, Yang, Y & Kankanhalli, M 2022, 'Deep Hierarchical Representation of Point Cloud Videos via Spatio-Temporal Decomposition', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 12, pp. 9918-9930.
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Fan, H, Zhuo, T, Yu, X, Yang, Y & Kankanhalli, M 2022, 'Understanding Atomic Hand-Object Interaction With Human Intention', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 1, pp. 275-285.
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Hand-object interaction plays a very important role when humans manipulate objects. While existing methods focus on improving hand-object recognition with fully automatic methods, human intention has been largely neglected in the recognition process, thus leading to undesirable interaction descriptions. To better interpret human-object interaction that is aligned to human intention, we argue that a reference specifying human intention should be taken into account. Thus, we propose a new approach to represent interactions while reflecting human purpose with three key factors, i.e., hand, object and reference. Specifically, we design a pattern of <hand-object, object-reference, hand, object, reference> (HOR) to recognize intention based atomic hand-object interactions. This pattern aims to model interactions with the states of hand, object, reference and their relationships. Furthermore, we design a simple yet effective Spatially Part-based (3+1)D convolutional neural network, namely SP(3+1)D, which leverages 3D and 1D convolutions to model visual dynamics and object position changes based on our HOR, respectively. With the help of our SP(3+1)D network, the recognition results are able to indicate human purposes accurately. To evaluate the proposed method, we annotate a Something-1.3k dataset, which contains 10 atomic hand-object interactions and about 130 videos for each interaction. Experimental results on Something-1.3k demonstrate the effectiveness of our SP(3+1)D network.
Fan, J, Yao, J, Yu, Y & Li, Y 2022, 'A macroscopic viscoelastic model of magnetorheological elastomer with different initial particle chain orientation angles based on fractional viscoelasticity', Smart Materials and Structures, vol. 31, no. 2, pp. 025025-025025.
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Abstract In this paper, a macroscopic viscoelastic modeling method for magnetorheological elastomer (MRE) based on fractional derivative model is presented to describe the dynamic viscoelastic properties of MRE with different initial particle chain orientation angles. The angle between the particle chain and the applied magnetic field is used as an indicator to describe the directionality of the particle chain. MRE samples with different initial inclination angles have been designed and fabricated. The dynamic viscoelastic properties of different MRE samples under shear working mode were measured using a parallel plate rheometer. The dynamic viscoelastic properties of MRE with different initial inclination angles are analyzed under the test conditions of different strain amplitude, frequency and magnetic flux density. The test results show that the initial inclination angle of the particle chain in the MRE has a significant effect on the dynamic viscoelastic properties of the MRE. A polynomial function is used to describe the relationship between the initial particle chain orientation angle and the magneto-induced modulus of MRE. A phenomenological model of magneto-induced modulus is established based on the fractional derivative model. The model parameters are identified using the nonlinear least square method. The predicted values of the model are in good agreement with the experimental results, indicating that the model can well describe the dynamic viscoelastic properties of MRE.
Fan, J, Zhang, J, Xu, H, Peng, Y, Sang, X, Zhou, Q & Wang, K 2022, 'Influence of the cathode position on beam current characteristics in the thermionic electron gun', Radiation Detection Technology and Methods, vol. 6, no. 3, pp. 401-408.
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Fan, S, Ni, W, Tian, H, Huang, Z & Zeng, R 2022, 'Carrier Phase-Based Synchronization and High-Accuracy Positioning in 5G New Radio Cellular Networks', IEEE Transactions on Communications, vol. 70, no. 1, pp. 564-577.
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Inspired by excellent precision of carrier phase positioning, this paper presents a new carrier phase positioning technique for 5G new radio cellular networks with a focus on clock synchronization and integer ambiguity resolution. A carrier-phase based clock offset estimation method is first proposed to achieve precise clock synchronization among base stations, and proved to achieve the Cramér-Rao Lower Bound (CRLB) asymptotically. A fusion method is developed to fuse the estimated positions of a mobile station (MS) based on time-difference-of-arrival, with the estimated position changes based on the temporal changes of carrier phase measurements. While circumventing the integer ambiguities of the carrier phase measurements, the fusion method provides quality interim estimates of the MS positions, at which the measurements can be linearized to resolve the integer ambiguities. As a result, precise MS positions can be obtained based on the disambiguated carrier phase measurements. Numerical simulations show that the proposed carrier phase positioning can achieve a centimeter-level accuracy in wireless cellular networks.
Fan, Y, Chen, D, Wang, H, Pan, Y, Peng, X, Liu, X & Liu, Y 2022, 'Automatic BASED scoring on scalp EEG in children with infantile spasms using convolutional neural network', Frontiers in Molecular Biosciences, vol. 9, p. 931688.
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In recent years, the Burden of Amplitudes and Epileptiform Discharges (BASED) score has been used as a reliable, accurate, and feasible electroencephalogram (EEG) grading scale for infantile spasms. However, manual EEG annotation is, in general, very time-consuming, and BASED scoring is no exception. Convolutional neural networks (CNNs) have proven their great potential in many EEG classification problems. However, very few research studies have focused on the use of CNNs for BASED scoring, a challenging but vital task in the diagnosis and treatment of infantile spasms. This study proposes an automatic BASED scoring framework using EEG and a deep CNN. The feasibility of using CNN for automatic BASED scoring was investigated in 36 patients with infantile spasms by annotating their long-term EEG data with four levels of the BASED score (scores 5, 4, 3, and ≤2). In the validation set, the accuracy was 96.9% by applying a multi-layer CNN to classify the EEG data as a 4-label problem. The extensive experiments have demonstrated that our proposed approach offers high accuracy and, hence, is an important step toward an automatic BASED scoring algorithm. To the best of our knowledge, this is the first attempt to use a CNN to construct a BASED-based scoring model.
Fan, Y, Liu, D & Ye, L 2022, 'A Novel Continuum Robot With Stiffness Variation Capability Using Layer Jamming: Design, Modeling, and Validation', IEEE Access, vol. 10, pp. 130253-130263.
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This paper presents a novel continuum robot (OctRobot-I) that has controllable stiffness variation capability in both the transverse and axial directions. Robot design, stiffness variation analysis and experimental testing are discussed in detail. Stiffness models based on the Euler-Bernoulli beam theory are developed, and then four static deflection cases are analysed. Experiments are conducted with two types of layer jamming sheaths (overlap numbers n =3, 5) and four different vacuum pressures (0kPa, 25kPa, 50kPa, 75kPa) at three different bending angles (0°, 90°, 180°). The results demonstrate that the stiffness changing tendency is in compliance with the derived models and show that the robot has a good stiffness variable capability. With the jamming sheath of n =3, the stiffness ranges (ratios) are 36.4 to 241.7 N/m (6.6) and 92.9 to 19.3×103 N/m (207.8) in the transverse and axial directions, respectively. With the jamming sheath of n =5, the stiffness ranges (ratios) are 65.7 to 398.3 N/m (6.1) and 106.7 to 20.8×103 N/m (194.9) in the transverse and axial directions, respectively. Additionally, the actuating and gripping experiments demonstrate that this robot has good performance in real-world applications.
Fang, C, Meng, X, Hu, Z, Xu, F, Zeng, D, Dong, M & Ni, W 2022, 'AI-Driven Energy-Efficient Content Task Offloading in Cloud-Edge-End Cooperation Networks', IEEE Open Journal of the Computer Society, vol. 3, pp. 162-171.
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To tackle a challenging energy efficiency problem caused by the growing mobile Internet traffic, this paper proposes a deep reinforcement learning (DRL)-based green content task offloading scheme in cloud-edge-end cooperation networks. Specifically, we formulate the problem as a power minimization model, where requests arriving at a node for the same content can be aggregated in its queue and in-network caching is widely deployed in heterogeneous environments. A novel DRL algorithm is designed to minimize the power consumption by making collaborative caching and task offloading decisions in each slot on the basis of content request information in previous slots and current network state. Numerical results show that our proposed content task offloading model achieves better power efficiency than the existing popular counterparts in cloud-edge-end collaboration networks, and fast converges to the stable state.
Fang, G, Lu, H, Al-Nakashli, R, Chapman, R, Zhang, Y, Ju, LA, Lin, G, Stenzel, MH & Jin, D 2022, 'Enabling peristalsis of human colon tumor organoids on microfluidic chips', Biofabrication, vol. 14, no. 1, pp. 015006-015006.
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Abstract Peristalsis in the digestive tract is crucial to maintain physiological functions. It remains challenging to mimic the peristaltic microenvironment in gastrointestinal organoid culture. Here, we present a method to model the peristalsis for human colon tumor organoids on a microfluidic chip. The chip contains hundreds of lateral microwells and a surrounding pressure channel. Human colon tumor organoids growing in the microwell were cyclically contracted by pressure channel, mimicking the in vivo mechano-stimulus by intestinal muscles. The chip allows the control of peristalsis amplitude and rhythm and the high throughput culture of organoids simultaneously. By applying 8% amplitude with 8 ∼ 10 times min−1, we observed the enhanced expression of Lgr5 and Ki67. Moreover, ellipticine-loaded polymeric micelles showed reduced uptake in the organoids under peristalsis and resulted in compromised anti-tumor efficacy. The results indicate the importance of mechanical stimuli mimicking the physiological environment when using in vitro models to evaluate nanoparticles. This work provides a method for attaining more reliable and representative organoids models in nanomedicine.
Fang, J, Ge, Y, Chen, Z, Xing, B, Bao, S, Yong, Q, Chi, R, Yang, S & Ni, B-J 2022, 'Flotation purification of waste high-silica phosphogypsum', Journal of Environmental Management, vol. 320, pp. 115824-115824.
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High-silica phosphogypsum (PG) is a kind of industrial by-product with great utilization potential. However, it is difficult to reuse PG directly due to the related gangue minerals (e.g., SiO2), and thus efficient purification is required to allow its further applications. Herein, a typical high-silica phosphogypsum waste was purified by a new 'reverse-direct flotation' method. The organic matters and fine slimes were removed by reverse flotation, and then, the silica impurity was removed by direct flotation. Via the closed-circuit flotation process, the whiteness of the PG concentrate is improved from 33.23 to 63.42, and the purity of gypsum in the PG concentrate increases from 83.90% to 96.70%, with a gypsum recovery of 85%. Additionally, the content of SiO2 is significantly reduced from 11.11% to 0.07%. In-depth investigations suggest that the difference in the floatability of gypsum and quartz is prominently intensified by flotation reagents at pH = 2-2.5, and thus leads to good desilication performance. Further characteristics of the PG concentrate prove that impurities have been well removed, and the PG concentrate meets the requirement of related standards for gypsum building materials. The flotation method reported here paves the way for the purification of high-silica phosphogypsum, which can be extended to the purification and value-added reutilization of other industrial solid wastes.
Fang, W, Ying, M & Wu, X 2022, 'Differentiable Quantum Programming with Unbounded Loops.', CoRR, vol. abs/2211.04507.
Fani, A, Golroo, A, Ali Mirhassani, S & Gandomi, AH 2022, 'Pavement maintenance and rehabilitation planning optimisation under budget and pavement deterioration uncertainty', International Journal of Pavement Engineering, vol. 23, no. 2, pp. 414-424.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. One of the key parts of a pavement management system is the maintenance and rehabilitation planning. The planning is usually developed under the assumption that all parameters are known with certainty. In practice, there are various parameters afflicted with large uncertainty. Ignoring the uncertainty may lead to a suboptimal plan adversely affecting the network conditions. The objective of this study is to develop an optimisation framework for network-level pavement maintenance and rehabilitation planning considering the uncertain nature of pavement deterioration and the budget with an applicable approach. A multistage stochastic mixed-integer programming model is proposed to find the optimal plan that is feasible for all possible scenarios of uncertainty and optimise the expectation of objective function. Two case studies of 4 and 21 pavement sections are presented to show the applicability of the proposed method. The value of stochastic solution and the expected value of perfect information which are the indices for evaluating the benefits of using the stochastic model are, respectively, 30% and 85% of the objective function of here and now model for the first case study and 26% and 42% of it regarding the second one. The indices are high indicating the effectiveness of the stochastic solution.
Fani, A, Naseri, H, Golroo, A, Mirhassani, SA & Gandomi, AH 2022, 'A progressive hedging approach for large-scale pavement maintenance scheduling under uncertainty', International Journal of Pavement Engineering, vol. 23, no. 7, pp. 2460-2472.
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This study approaches a multi-stage stochastic mixed-integer programming model for the high-level complexity of large-scale pavement maintenance scheduling problems. The substance of some parameters in the mentioned problems is uncertain. Ignoring the uncertainty of these parameters in the pavement maintenance scheduling problems may lead to suboptimal solutions and unstable pavement conditions. In this study, annual budget and pavement deterioration rate are considered uncertain parameters. On the other hand, pavement agencies generally face large-scale pavement networks. The complexity of the proposed stochastic model increases exponentially with the number of network sections and scenarios. The problem is solved using the Progressive Hedging Algorithm (PHA), which is suitable for large-scale stochastic programming problems, by achieving an effective decomposition over scenarios. A modified adaptive strategy for choosing the penalty parameter value is applied that aims to improve the solution process. A pavement network including 251 sections is considered the case study for this investigation, and the current study seeks optimal maintenance scheduling over a finite analysis period. The performance of the stochastic model is compared with that of the deterministic model. The results indicate that the introduced approach is competent to address uncertainty in maintenance and rehabilitation problems.
Fani, B, Shahgholian, G, Haes Alhelou, H & Siano, P 2022, 'Inverter-based islanded microgrid: A review on technologies and control', e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 2, pp. 100068-100068.
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Farah, N, Lei, G, Zhu, J & Guo, Y 2022, 'Two-vector Dimensionless Model Predictive Control of PMSM Drives Based on Fuzzy Decision Making', CES Transactions on Electrical Machines and Systems, vol. 6, no. 4, pp. 393-403.
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Farasat, M, Thalakotuna, D, Hu, Z & Yang, Y 2022, 'A Simple and Effective Approach for Scattering Suppression in Multiband Base Station Antennas', Electronics, vol. 11, no. 21, pp. 3423-3423.
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The high band pattern distortions in an 1810–2690 MHz frequency band, introduced due to low band radiators working in 690–960 MHz, are mitigated by a simple yet effective change to the low band-radiating elements. A novel horizontal and vertical radiating element is designed instead of a conventional slant polarized low band-radiating element to reduce the scattering. The slant polarization is achieved from the horizontal and vertical dipoles, using a 180° hybrid coupler. The vertical dipole length is optimized to improve the high band patterns. The experimental results verified that the proposed horizontal and vertical low band dipole result in the reduction of high band pattern distortions. The low band-radiating elements provide >12 dB return loss over the entire frequency band 690–960 MHz and provide comparable pattern performance to a conventional slant low band dipole.
Farhangi, M, Barzegarkhoo, R, Aguilera, RP, Lee, SS, Lu, DD-C & Siwakoti, YP 2022, 'A Single-Source Single-Stage Switched-Boost Multilevel Inverter: Operation, Topological Extensions, and Experimental Validation', IEEE Transactions on Power Electronics, vol. 37, no. 9, pp. 11258-11271.
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In this article, we present a family of multilevel converters with the single-stage dynamic voltage-boosting feature, reduced number of circuit components, modular structure, bidirectional operation, continuous input current, and acceptable overall efficiency. The proposed structure is based on a three-level single-stage boost integrated inverter with an embedded quasi-H-bridge (QHB) cell. It is comprised of five unidirectional power switches and a floating capacitor. By differential connection of two or three QHB cells and with the aim of a single inductor/input dc source, several derived topologies for both the single and three-phase applications with different multilevel output voltage performances have been achieved. The aforementioned advantages make this converter a suitable candidate for renewable energy applications. Theoretical analysis, design consideration, comparative study, and several experimental results for a 3-kW laboratory-built system are presented to validate the effectiveness and feasibility of this proposal.
Faro, B, Abedin, B & Cetindamar, D 2022, 'Hybrid organizational forms in public sector’s digital transformation: a technology enactment approach', Journal of Enterprise Information Management, vol. 35, no. 6, pp. 1742-1763.
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PurposeThe purpose of this paper is to examine how public sector organizations become nimbler while retaining their resilience during digital transformation.Design/methodology/approachThe study adopts a hermeneutic approach in conducting deep expert interviews with 22 senior executives and managers of multiple organizations. The method blends theory and expert views to study digital transformation in the context of enterprise information management.FindingsDrawing on technology enactment framework (TEF), this research poses that organizational form is critical in the enactment of technologies in digital transformation. By extending the TEF, the authors claim that organizations are not in pure bureaucratic or network organizational form during digital transformation; instead, they need a hybrid combination in order to support competing strategic needs for nimbleness and resilience simultaneously. The four hybrid organizational forms presented in this model (4R) allow for networks and bureaucracy to coexist, though at different levels depending on the level of resiliency and nimbleness required at each point in the continuous digital transformation journey.Research limitations/implicationsThe main theoretical contribution of this research is to extend the TEF to illustrate that the need for coexistence of nimbleness with stability in a digital transformation results in a hybrid of networks and bureaucratic organization forms. This research aims to guide public sector organizations' digital transformation with extended the TEF as a tool for building the required organizational forms to influ...
Farooq, MA & Nimbalkar, S 2022, 'Novel sustainable base material for concrete slab track', Construction and Building Materials, vol. 366.
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Fastier-Wooller, JW, Vu, T-H, Nguyen, H, Nguyen, H-Q, Rybachuk, M, Zhu, Y, Dao, DV & Dau, VT 2022, 'Multimodal Fibrous Static and Dynamic Tactile Sensor', ACS Applied Materials & Interfaces, vol. 14, no. 23, pp. 27317-27327.
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Fatemi, N, Tierling, S, Es, HA, Varkiani, M, Mojarad, EN, Aghdaei, HA, Walter, J & Totonchi, M 2022, 'DNA methylation biomarkers in colorectal cancer: Clinical applications for precision medicine', International Journal of Cancer, vol. 151, no. 12, pp. 2068-2081.
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AbstractColorectal cancer (CRC) is the second leading cause of cancer death worldwide that is attributed to gradual long‐term accumulation of both genetic and epigenetic changes. To reduce the mortality rate of CRC and to improve treatment efficacy, it will be important to develop accurate noninvasive diagnostic tests for screening, acute and personalized diagnosis. Epigenetic changes such as DNA methylation play an important role in the development and progression of CRC. Over the last decade, a panel of DNA methylation markers has been reported showing a high accuracy and reproducibility in various semi‐invasive or noninvasive biosamples. Research to obtain comprehensive panels of markers allowing a highly sensitive and differentiating diagnosis of CRC is ongoing. Moreover, the epigenetic alterations for cancer therapy, as a precision medicine strategy will increase their therapeutic potential over time. Here, we discuss the current state of DNA methylation‐based biomarkers and their impact on CRC diagnosis. We emphasize the need to further identify and stratify methylation‐biomarkers and to develop robust and effective detection methods that are applicable for a routine clinical setting of CRC diagnostics particularly at the early stage of the disease.
Fathipour, H, Payan, M, Jamshidi Chenari, R & Fatahi, B 2022, 'General failure envelope of eccentrically and obliquely loaded strip footings resting on an inherently anisotropic granular medium', Computers and Geotechnics, vol. 146, pp. 104734-104734.
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Faust, O, Hong, W, Loh, HW, Xu, S, Tan, R-S, Chakraborty, S, Barua, PD, Molinari, F & Acharya, UR 2022, 'Heart rate variability for medical decision support systems: A review', Computers in Biology and Medicine, vol. 145, pp. 105407-105407.
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Heart Rate Variability (HRV) is a good predictor of human health because the heart rhythm is modulated by a wide range of physiological processes. This statement embodies both challenges to and opportunities for HRV analysis. Opportunities arise from the wide-ranging applicability of HRV analysis for disease detection. The availability of modern high-quality sensors and the low data rate of heart rate signals make HRV easy to measure, communicate, store, and process. However, there are also significant obstacles that prevent a wider use of this technology. HRV signals are both nonstationary and nonlinear and, to the human eye, they appear noise-like. This makes them difficult to analyze and indeed the analysis findings are difficult to explain. Moreover, it is difficult to discriminate between the influences of different complex physiological processes on the HRV. These difficulties are compounded by the effects of aging and the presence of comorbidities. In this review, we have looked at scientific studies that have addressed these challenges with advanced signal processing and Artificial Intelligence (AI) methods.
Fayaz, H, Khan, SA, Saleel, CA, Shaik, S, Yusuf, AA, Veza, I, Fattah, IMR, Rawi, NFM, Asyraf, MRM & Alarifi, IM 2022, 'Developments in Nanoparticles Enhanced Biofuels and Solar Energy in Malaysian Perspective: A Review of State of the Art', Journal of Nanomaterials, vol. 2022, no. 1, pp. 1-22.
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The rapid rise in global oil prices, the scarcity of petroleum sources, and environmental concerns have all created severe issues. As a result of the country’s rapid expansion and financial affluence, Malaysia’s energy consumption has skyrocketed. Biodiesel and solar power are currently two of the most popular alternatives to fossil fuels in Malaysia. These two types of renewable energy sources appear to be viable options because of their abundant availability together with environmental and performance competence to highly polluting and fast depleting fossil fuels. The purpose of adopting renewable technology is to expand the nation’s accessibility to a reliable and secure power supply. The current review article investigates nonconventional energy sources added with nanosized metal particles called as nanomaterials including biodiesel and solar, as well as readily available renewable energy options. Concerning the nation’s energy policy agenda, the sources of energy demand are also investigated. The article evaluates Malaysia’s existing position in renewable energy industries, such as biodiesel and solar, as well as the impact of nanomaterials. This review article discusses biodiesel production, applications, and government policies in Malaysia, as well as biodiesel consumption and recent developments in the bioenergy sector, such as biodiesel property modifications utilizing nanoparticle additions. In addition, the current review study examines the scope of solar energy, different photovoltaic concentrators, types of solar energy harvesting systems, photovoltaic electricity potential in Malaysia, and the experimental setup of solar flat plate collectors (FPC) with nanotechnology.
Fazal, MAU, Ferguson, S & Saeed, Z 2022, 'Investigating cognitive workload in concurrent speech-based information communication', International Journal of Human-Computer Studies, vol. 157, pp. 102728-102728.
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Fazeli, A, Nguyen, HH, Tuan, HD & Poor, HV 2022, 'Non-Coherent Multi-Level Index Modulation', IEEE Transactions on Communications, vol. 70, no. 4, pp. 2240-2255.
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Feng, A, Akther, N, Duan, X, Peng, S, Onggowarsito, C, Mao, S, Fu, Q & Kolev, SD 2022, 'Recent Development of Atmospheric Water Harvesting Materials: A Review', ACS Materials Au, vol. 2, no. 5, pp. 576-595.
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Feng, B, Huang, Y, Tian, A, Wang, H, Zhou, H, Yu, S & Zhang, H 2022, 'DR-SDSN: An Elastic Differentiated Routing Framework for Software-Defined Satellite Networks', IEEE Wireless Communications, vol. 29, no. 6, pp. 80-86.
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Software-defined satellite networking (SDSN) has recently gained unprecedented attention due to its great controllability for traffic delivery. However, it is still facing several fundamental challenges in routing, which mainly involves effective network maintenance, heterogeneous network convergence, and customized flow steering. Hence, in this article, we propose an elastic routing framework for SDSN, aiming to first build a robust control path for signalling exchanges, then offer a transparent channel for IP services based on Loc/ID mappings and associated encapsulations, and finally enable hybrid packet forwarding manners to meet user various demands. Moreover, we define a new 8 B network header for DR-SDSN to further decrease overheads in packet encapsulations, where 16-bit addresses are used instead of 32-bit IPv4 and 128-bit IPv6 addresses. At last, we have implemented a corresponding proof-of-concept prototype with the modified Ryu and OvS, and the Linux socket application programming interface is also extended to handle our protocol with 16-bit addresses. Extensive evaluations are performed and associated results have confirmed the feasibility and advantages of the proposed DR-SDSN framework.
Feng, B, Tian, A, Yu, S, Li, J, Zhou, H & Zhang, H 2022, 'Efficient Cache Consistency Management for Transient IoT Data in Content-Centric Networking', IEEE Internet of Things Journal, vol. 9, no. 15, pp. 12931-12944.
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Since Internet of Things (IoT) communications can enjoy many advantages brought by content-centric networking (CCN) in nature, there is an increasing interest on their integration for better information retrieval and distribution. Nevertheless, different from the conventional multimedia traffic of which contents are hardly changed, IoT data are always transient and updated by their producers according to the actual situation. As a result, if without any effective countermeasures, outdated copies are inevitably stored by CCN routers and then distributed to the associated consumers, degrading both caching efficiency and user experience. In fact, most of related policies take little account of information freshness for cached contents, and how to tackle transient IoT data in CCN is still an ignored but crucial issue required for further explorations. Therefore, in this article, we propose an efficient popularity-based cache consistency management scheme, which aims to guarantee freshness of IoT data returned by on-path routers and avoid heavy signalling costs introduced at the same time. Extensive simulations were performed under both real-world scare-free and binary-tree topologies, and corresponding results have proved the efficiency of the proposed scheme in timely evictions of outdated IoT data stored by CCN in-network caching.
Feng, G, Fang, Z, Jie, L, Zhen, F & Guangquan, Z 2022, 'Neighbor-Searching Discrepancy-based Real Concept Drift', IEEE Transactions on Pattern Analysis and Machine Intelligence.
Feng, K, Ji, JC, Li, Y, Ni, Q, Wu, H & Zheng, J 2022, 'A novel cyclic-correntropy based indicator for gear wear monitoring', Tribology International, vol. 171, pp. 107528-107528.
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Gearbox is a vulnerable component of a turbine's drivetrain and plays a vital role in the power transmission in wind turbines. Wind turbines usually operate under harsh working environments, such as in deserts, oceans, and on hills. The adverse operating conditions (such as inevitable fluctuating wind loads and speeds) make the gearbox transmission prone to reliability degradation and premature failure. Gear wear is a common and unavoidable surface degradation phenomenon during the lifespan of the gear transmission system. The gear wear propagation can result in severe failures, such as gear surface spalling, gear root crack, and gear tooth breakage, all of which could lead to the failure of the drivetrain system of wind turbines and bring unexpected economic loss, even serious accidents. Thus, it is crucial to monitor the gear wear propagation progression in order to enable reliable and safe operation. To this end, this paper develops a novel vibration-based health indicator to monitor the gear surface degradation induced by gear wear progression. With the help of the novel indicator developed, the health status of the gearbox can be well evaluated and thus predictive maintenance-based decisions can be made to reduce maintenance costs and minimize gearbox failures in wind turbines. A series of endurance tests under different lubrication conditions and operational conditions are carried out to verify the effectiveness of the gear wear monitoring indicator.
Feng, K, Ji, JC, Wang, K, Wei, D, Zhou, C & Ni, Q 2022, 'A novel order spectrum-based Vold-Kalman filter bandwidth selection scheme for fault diagnosis of gearbox in offshore wind turbines', Ocean Engineering, vol. 266, pp. 112920-112920.
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Vold-Kalman order tracking filter is an effective technique for dealing with non-stationary vibrations which offshore wind turbines often encounter. It has a unique capability to extract and track the time waveforms of harmonics in short transients without phase bias, and this capability is beneficial to the condition monitoring of offshore wind turbines. In general, the accuracy of the tracking results of the Vold-Kalman filer for condition monitoring is heavily dependent on the selection of filter bandwidth. A fixed filter bandwidth becomes problematic when processing different types of signals under varying operating conditions. Significant errors may arise in the tracking, rendering the condition monitoring of offshore wind turbines unreliable. To address this issue, this paper proposes a novel scheme for Vold-Kalman filter bandwidth selection to guarantee the consistency and accuracy of the offshore wind turbine condition monitoring process, ensuring reliable fault diagnosis. A numerical model is used to evaluate the effectiveness of the proposed bandwidth selection scheme first. Then the proposed scheme is further validated through the offshore wind turbine planetary gearbox datasets, together with the demonstration of the fault diagnosis capability of the filtered results.
Feng, L, Huang, Y, Tsang, IW, Gupta, A, Tang, K, Tan, KC & Ong, Y-S 2022, 'Towards Faster Vehicle Routing by Transferring Knowledge From Customer Representation', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 2, pp. 952-965.
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Feng, S, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Liu, Y, Zhang, S, Phong Vo, HN, Bui, XT & Ngoc Hoang, B 2022, 'Volatile fatty acids production from waste streams by anaerobic digestion: A critical review of the roles and application of enzymes', Bioresource Technology, vol. 359, pp. 127420-127420.
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Volatile fatty acids (VFAs) produced from organic-rich wastewater by anaerobic digestion attract attention due to the increasing volatile fatty acids market, sustainability and environmentally friendly characteristics. This review aims to give an overview of the roles and applications of enzymes, a biocatalyst which plays a significant role in anaerobic digestion, to enhance volatile fatty acids production. This paper systematically overviewed: (i) the enzymatic pathways of VFAs formation, competition, and consumption; (ii) the applications of enzymes in VFAs production; and (iii) feasible measures to boost the enzymatic processes. Furthermore, this review presents a critical evaluation on the major obstacles and feasible future research directions for the better applications of enzymatic processes to promote VFAs production from wastewater.
Feng, S, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Liu, Y, Zhang, X, Bui, XT, Varjani, S & Hoang, BN 2022, 'Wastewater-derived biohydrogen: Critical analysis of related enzymatic processes at the research and large scales', Science of The Total Environment, vol. 851, no. Pt 2, pp. 158112-158112.
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Organic-rich wastewater is a feasible feedstock for biohydrogen production. Numerous review on the performance of microorganisms and the diversity of their communities during a biohydrogen process were published. However, there is still no in-depth overview of enzymes for biohydrogen production from wastewater and their scale-up applications. This review aims at providing an insightful exploration of critical discussion in terms of: (i) the roles and applications of enzymes in wastewater-based biohydrogen fermentation; (ii) systematical introduction to the enzymatic processes of photo fermentation and dark fermentation; (iii) parameters that affect enzymatic performances and measures for enzyme activity/ability enhancement; (iv) biohydrogen production bioreactors; as well as (v) enzymatic biohydrogen production systems and their larger scales application. Furthermore, to assess the best applications of enzymes in biohydrogen production from wastewater, existing problems and feasible future studies on the development of low-cost enzyme production methods and immobilized enzymes, the construction of multiple enzyme cooperation systems, the study of biohydrogen production mechanisms, more effective bioreactor exploration, larger scales enzymatic biohydrogen production, and the enhancement of enzyme activity or ability are also addressed.
Feng, Z-K, Huang, Q-Q, Niu, W-J, Yang, T, Wang, J-Y & Wen, S-P 2022, 'Multi-step-ahead solar output time series prediction with gate recurrent unit neural network using data decomposition and cooperation search algorithm', Energy, vol. 261, pp. 125217-125217.
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As the solar energy develops sharply in recent years, accurate solar output forecasting is becoming one of the most important and challenging problems in modern power system. For enhancing the prediction accuracy of solar output, this research proposes an effective forecasting method using the famous compete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), gated recurrent unit (GRU) and cooperation search algorithm (CSA). The proposed methodology is composed of three important stages: firstly, the solar output signal is divided into a set of relatively simple subcomponents with obvious frequency differences via the CEEMDAN method; secondary, the GRU model is used to individually forecast each subcomponent while the CSA method is used to optimize the GRU parameters and enhance the forecasting ability; finally, the simulation values of all constructed models are added to obtain the corresponding forecasting results. The developed model takes advantages of the data decomposition technique and advanced machine learning to identify the suitable dependence relationship and network topology structures. Extensive experiments indicate that the developed model can yield accurate forecasting results for solar outputs in comparison with several traditional forecasting methods with respect to different evaluation criteria. Thus, an effective framework combining the signal decomposition technique and evolutionary method into machine learning model is presented for solar output forecasting.
Ferdowsi, A, Mousavi, S-F, Mohamad Hoseini, S, Faramarzpour, M & Gandomi, AH 2022, 'A Survey of PSO Contributions to Water and Environmental Sciences', Studies in Computational Intelligence, vol. 1043, pp. 85-102.
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Particle Swarm Optimization (PSO) is a nature-inspired optimizer that has attracted a lot of attention since its inception in 1995 due to its ease of application and promising results. The inspiration of PSO is the collaborative swarm behavior of biological populations—a noted computational intelligence technique. There is no doubt that PSO has made outstanding contributions to vast water and environmental science problems in the real world thus far. The standard PSO has been improved many times over the past decades, leading to various hybrid, multi-objective, and improved versions. This chapter aims to present a comprehensive overview of PSO application in solving such problems, initially discussing how PSO works and obtains optimal solution. Subsequently, different versions of PSO are presented followed by their respective contributions to the water and environmental problems. Our survey revealed that PSO has been employed both solely and collaboratively with other approaches like machine learning techniques and simulation software to solve single- and multiple-objective problems of different sectors from surface water to renewable energy generation.
Ferguson, BM, Entezari, A, Fang, J & Li, Q 2022, 'Optimal placement of fixation system for scaffold-based mandibular reconstruction', Journal of the Mechanical Behavior of Biomedical Materials, vol. 126, pp. 104855-104855.
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A current challenge in bone tissue engineering is to create favourable biomechanical conditions conducive to tissue regeneration for a scaffold implanted in a segmental defect. This is particularly the case immediately following surgical implantation when a firm mechanical union between the scaffold and host bone is yet to be established via osseointegration. For mandibular reconstruction of a large segmental defect, the position of the fixation system is shown here to have a profound effect on the mechanical stimulus (for tissue regeneration within the scaffold), structural strength, and structural stiffness of the tissue scaffold-host bone construct under physiological load. This research combines computer tomography (CT)-based finite element (FE) modelling with multiobjective optimisation to determine the optimal height and angle to place a titanium fixation plate on a reconstructed mandible so as to enhance tissue ingrowth, structural strength and structural stiffness of the scaffold-host bone construct. To this end, the respective design criteria for fixation plate placement are to: (i) maximise the volume of the tissue scaffold experiencing levels of mechanical stimulus sufficient to initiate bone apposition, (ii) minimise peak stress in the scaffold so that it remains intact with a diminished risk of failure and, (iii) minimise scaffold ridge displacement so that the reconstructed jawbone resists deformation under physiological load. First, a CT-based FE model of a reconstructed human mandible implanted with a bioceramic tissue scaffold is developed to visualise and quantify changes in the biomechanical responses as the fixation plate's height and/or angle are varied. The volume of the scaffold experiencing appositional mechanical stimulus is observed to increase with the height of the fixation plate. Also, as the principal load-transfer mechanism to the scaffold is via the fixation system, there is a significant ingress of appositional stimulus ...
Figiela, M, Wysokowski, M, Stanisz, E, Hao, D & Ni, B 2022, 'Highly Sensitive, Fast Response and Selective Glucose Detection Based on CuO/Nitrogen‐doped Carbon Non‐enzymatic Sensor', Electroanalysis, vol. 34, no. 11, pp. 1725-1734.
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AbstractThis work presents development of an innovative CuO/nitrogen‐doped carbon composite (CuO−C) that may be effectively applied for the modification of a glassy carbon electrode (GCE) and creation of a non‐enzymatic sensor for glucose detection. The structure of the CuO−C nanostructured material was analyzed by scanning electron microscopy, Fourier transform infrared spectroscopy, X‐ray diffraction, and atomic absorption spectroscopy. The prepared electroactive material, based on CuO including carbon structures derived from chitosan, showed excellent performance in terms of electrocatalytic oxidation of glucose. Under optimal conditions, the modified electrode displays high sensitivity (1546 μA mM−1 cm−2), a low detection limit (1.95 μM) and short response time (4 s).
Figuerola-Wischke, A, Gil-Lafuente, AM & Merigó, JM 2022, 'The uncertain ordered weighted averaging adequacy coefficient operator', International Journal of Approximate Reasoning, vol. 148, pp. 68-79.
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Fleck, R, Gill, R, Pettit, TJ, Torpy, FR & Irga, PJ 2022, 'Bio-solar green roofs increase solar energy output: The sunny side of integrating sustainable technologies', Building and Environment, vol. 226, pp. 109703-109703.
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In urban spaces, localised energy generation through rooftop solar has become increasingly popular, and green roofs are often used for a range of services such as thermal insulation. In recent years, the adoption of Bio-solar green roofs (BSGR) for both thermal insulation and increased solar energy outputs has increased. Here we present two buildings of the same dimensions and location, similar age and construction material, where one hosts a BSGR, and the other a conventional solar roof (CSR) in Sydney, Australia. Each solar array hosted a range of environmental sensors, including ambient temperature and global horizontal irradiance (GHI). The modelled BSGR average hourly energy output was 4.5% higher than the CSR (seasonal trends observed Spring; 4.14%, Summer; 4.16%, and Autumn; 5.21%) with BSGR producing 14.26 MWh more than the CSR, valued at $4526.22 AUD, and equal to 11.55 t e-CO2 greenhouse gas mitigation. Further potential for up to 1.55 t of CO2 could be mitigated by the plant material on the roof, provided the trimming of plant material during maintenance is conducted responsibly. In this instance, the implementation of a BSGR increased the system's solar output by 23.88 kWh per m2 of panel coverage, as well as reducing the e-CO2 emissions by 0.019 t per m2 over the CSR. When compared to the results of previously reported pilot studies and some simulations, it is evident that the implementation of a BSGR is favourable for maximising energy production and the mitigation of GHGs.
Fleck, R, Gill, RL, Saadeh, S, Pettit, T, Wooster, E, Torpy, F & Irga, P 2022, 'Urban green roofs to manage rooftop microclimates: A case study from Sydney, Australia', Building and Environment, vol. 209, pp. 108673-108673.
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Urbanisation has led to a growing need for sustainable development leading to climate resilient cities. As the urban heat burden increases in severity, technologies to improve the thermal comfort of cities are increasingly required. Green roofs are one such technology that can provide increased building thermal performance. In this study, we investigate two identical buildings, except, one was equipped with a green roof, and the other without. We present the longest-term assessment conducted on an Australian green roof with in-situ thermal monitoring coupled with surface temperature assessments. Field measurements were utilised to calculate the thermal buffer potential of the green roof compared to a near-identical conventional roof, over three seasons. Our findings indicated a reduction in rooftop surface temperatures up to 20 °C when ambient temperatures exceeded 40 °C, as well as improvements to heat flow of up to 55.54%. These results indicate that green roofs may contribute to the much-needed reduction in ambient city temperature to alleviate overheating and the costs associated with the urban heat island effect.
Fleck, R, Westerhausen, MT, Killingsworth, N, Ball, J, Torpy, FR & Irga, PJ 2022, 'The hydrological performance of a green roof in Sydney, Australia: A tale of two towers', Building and Environment, vol. 221, pp. 109274-109274.
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This study describes the sister buildings Daramu house and International house in Barangaroo, Sydney (Australia's largest metropolitan city), with and without a green roof, respectively. Trace metal samples were collected from both roofs and analysed using ICP-MS to determine the bioretention potential of the green roof to remediate soluble and particulate stormwater trace metal contamination. Retention of ambient trace metal contamination by the green roof substrate was deemed significant for soluble copper and particulate zinc, chromium and copper. In addition, hydrological models (DRAINS and SWMM) were applied to predict the performance of the green roof to identify its ability to manage stormwater runoff and frequency, as well as to analyse the green roof's performance in complex surface flooding situations where storage or backwater effects occur in overflow routes and surface flows. Our results demonstrate a reduction in peak stormwater flow by 18.29 L/s (∼50%) for storms as infrequent as 1 in 5 years, and peak flow reductions up to 90% storms of lower intensities. These results are significant as it demonstrates that a green roof could remediating trace metals contamination, thus reducing the impact on aquatic environments through stormwater runoff. It also highlights their potential to reduce stormwater flow, and utilise this additional water for evapotranspiration, leading to cooler ambient temperatures. Future works should aim to quantify the remediation effect of various planted species on in-situ green roofs, as well as determine the specific retention capabilities of various substrate compositions.
Flores Terrazas, V, Sedehi, O, Papadimitriou, C & Katafygiotis, LS 2022, 'A Bayesian framework for calibration of multiaxial fatigue curves', International Journal of Fatigue, vol. 163, pp. 107105-107105.
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Flores Terrazas, V, Sedehi, O, Papadimitriou, C & Katafygiotis, LS 2022, 'A streamline approach to multiaxial fatigue monitoring using virtual sensing', Structural Control and Health Monitoring, vol. 29, no. 1.
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Flores‐Sosa, M, Avilés‐Ochoa, E & Merigó, JM 2022, 'Exchange rate and volatility: A bibliometric review', International Journal of Finance & Economics, vol. 27, no. 1, pp. 1419-1442.
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AbstractThe exchange rate is one of the most important prices in open economies. Exchange rate volatility (ERV) has been studied in terms of its measurement, forecast and impact and relationship with other variables. This article proposes a bibliometric analysis of ERV compared with two databases Web of Science and Scopus. The number of data obtained reflects the importance of the topic in scientific research. In addition, we identify authors, institutions and countries of great influence studying currency volatility. The evolution of the study through time shows the increase in attention on the topic. VOS viewer software has been used to create graphic maps and visualize the connections existing in the study.
Flores-Sosa, M, Avilés-Ochoa, E, Merigó, JM & Kacprzyk, J 2022, 'The OWA operator in multiple linear regression', Applied Soft Computing, vol. 124, pp. 108985-108985.
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Flores-Sosa, M, León-Castro, E, Merigó, JM & Yager, RR 2022, 'Forecasting the exchange rate with multiple linear regression and heavy ordered weighted average operators', Knowledge-Based Systems, vol. 248, pp. 108863-108863.
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Fonseka, C, Ryu, S, Naidu, G, Kandasamy, J & Vigneswaran, S 2022, 'Recovery of water and valuable metals using low pressure nanofiltration and sequential adsorption from acid mine drainage', Environmental Technology & Innovation, vol. 28, pp. 102753-102753.
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Acid mine drainage (AMD) contains an array of valuable resources such as Rare Earth Elements (REE) and Copper (Cu) which can be recovered along with fresh water. Low pressure nanofiltration with NF90 membrane was first studied to recover fresh water from synthetic AMD and concentration of dissolved metals for subsequent efficient selective recovery. Organic matter (OM) present in AMD was found to cause membrane fouling which resulted in significant flux decline. Powdered eggshell was investigated as a low-cost adsorbent for OM removal. The study showed that a 0.2 mg/l dose of powdered eggshell adsorbed 100% of OM and Fe with no significant loss of other dissolved metals. A steady permeate flux of 15.5 ± 0.2 L/m2h (LMH) was achieved for pre-treated AMD with a solute rejection rate of more than 98%. A chromium-based metal organic framework (MOF) modified with N- (phosphonomethyl) iminodiacetic acid (PMIDA) and an amine-grafted mesoporous silica (SBA15) material was synthesized for selective recovery of REE and Cu, respectively. The two adsorbents were used sequentially to selectively adsorb REE (91%) and Cu (90%) from pH adjusted concentrated feed. The formation of coordinating complexes with carboxylate and phosphonic groups on MOF was found to be the primary driving force for selective REE adsorption. Selective uptake of Cu onto amine-grafted SBA15 was due to the formation of strong chelating bonds between Cu and amine ligands. Both adsorbents remained structurally stable over 5 regeneration cycles. The findings here highlight the practical potential of membrane/adsorption hybrid systems for water and valuable metal (REE) recovery from AMD.
Fowler, K, Peel, M, Saft, M, Peterson, TJ, Western, A, Band, L, Petheram, C, Dharmadi, S, Tan, KS, Zhang, L, Lane, P, Kiem, A, Marshall, L, Griebel, A, Medlyn, BE, Ryu, D, Bonotto, G, Wasko, C, Ukkola, A, Stephens, C, Frost, A, Gardiya Weligamage, H, Saco, P, Zheng, H, Chiew, F, Daly, E, Walker, G, Vervoort, RW, Hughes, J, Trotter, L, Neal, B, Cartwright, I & Nathan, R 2022, 'Explaining changes in rainfall–runoff relationships during and after Australia's Millennium Drought: a community perspective', Hydrology and Earth System Sciences, vol. 26, no. 23, pp. 6073-6120.
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Abstract. The Millennium Drought lasted more than a decade and is notable for causing persistent shifts in the relationship between rainfall and runoff in many southeastern Australian catchments. Research to date has successfully characterised where and when shifts occurred and explored relationships with potential drivers, but a convincing physical explanation for observed changes in catchment behaviour is still lacking. Originating from a large multi-disciplinary workshop, this paper presents and evaluates a range of hypothesised process explanations of flow response to the Millennium Drought. The hypotheses consider climatic forcing, vegetation, soil moisture dynamics, groundwater, and anthropogenic influence. The hypotheses are assessed against evidence both temporally (e.g. why was the Millennium Drought different to previous droughts?) and spatially (e.g. why did rainfall–runoff relationships shift in some catchments but not in others?). Thus, the strength of this work is a large-scale assessment of hydrologic changes and potential drivers. Of 24 hypotheses, 3 are considered plausible, 10 are considered inconsistent with evidence, and 11 are in a category in between, whereby they are plausible yet with reservations (e.g. applicable in some catchments but not others). The results point to the unprecedented length of the drought as the primary climatic driver, paired with interrelated groundwater processes, including declines in groundwater storage, altered recharge associated with vadose zone expansion, and reduced connection between subsurface and surface waterprocesses. Other causes include increased evaporative demand and harvestingof runoff by small private dams. Finally, we discuss the need for long-termfield monitoring, particularly targeting internal catchment processes andsubsurface dynamics. We recommend continued investment in the understanding of hydrological shifts, particularly given their relevance to water planning under ...
Francis, I & Saha, S 2022, 'Surface tension effects on flow dynamics and alveolar mechanics in the acinar region of human lung'.
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Computational fluid dynamics (CFD) simulations, in-vitro setups, andexperimental ex-vivo approaches have been applied to numerous alveolargeometries over the past years. They aimed to study and examine airflowpatterns, particle transport, and particle-alveolar wall deposition fractions.These studies are imperative to both pharmaceutical and toxicological studies,especially nowadays with the escalation of the menacing COVID-19 virus.However, most of these studies ignored the surfactant layer that covers thealveoli and the effect of the air-surfactant surface tension on flow dynamicsand air-alveolar surface mechanics. The present study employs a realistic humanbreathing profile of 4.75 to emphasize the importance of the surfactant layerby numerically comparing airflow phenomena between a surfactant-enriched andsurfactant-deficient model. The acinar model exhibits physiologically accuratealveolar and duct dimensions extending from lung generations 18 to 23. Proximallung generations experience dominant recirculating flow while farthergenerations in the distal alveolar region exhibit dominant radial flows. In thesurfactant-enriched model, surface tension values alternate during inhalationand exhalation. In the surfactant-deficient model, only water coats thealveolar walls. Results showed that surfactant deficiency in the alveoliadversely alters airflow behavior and generates unsteady chaotic breathingthrough the production of vorticities, accompanied by higher vorticity andvelocity magnitudes. In addition, high air-water surface tension in thesurfactant-deficient case was found to induce higher shear stress values on thealveolar walls than that of the surfactant-enriched case. Overall, it wasconcluded that the presence of the surfactant improves respiratory mechanicsand allows for smooth breathing and normal respiration.
Francis, I & Saha, SC 2022, 'Computational fluid dynamics and machine learning algorithms analysis of striking particle velocity magnitude, particle diameter, and impact time inside an acinar region of the human lung', Physics of Fluids, vol. 34, no. 10, pp. 101904-101904.
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Complementing computational fluid dynamics (CFD) simulations with machine learning algorithms is becoming increasingly popular as the combination reduces the computational time of the CFD simulations required for classifying, predicting, or optimizing the impact of geometrical and physical variables of a specific study. The main target of drug delivery studies is indicating the optimum particle diameter for targeting particular locations in the lung to achieve a desired therapeutic effect. In addition, the main goal of molecular dynamics studies is to investigate particle–lung interaction through given particle properties. Therefore, this study combines the two by numerically determining the optimum particle diameter required to obtain an ideal striking velocity magnitude (velocity at the time of striking the alveoli, i.e., deposition by sedimentation/diffusion) and impact time (time from release until deposition) inside an acinar part of the lung. At first, the striking velocity magnitudes and time for impact (two independent properties) of three different particle diameters (0.5, 1.5, and 5 μm) are computed using CFD simulations. Then, machine learning classifiers determine the particle diameter corresponding to these two independent properties. In this study, two cases are compared: A healthy acinus where a surfactant layer covers the inner surface of the alveoli providing low air–liquid surface tension values (10 mN/m), and a diseased acinus where only a water layer covers the surface causing high surface tension values (70 mN/m). In this study, the airflow velocity throughout the breathing cycle corresponds to a person with a respiratory rate of 13 breaths per minute and a volume flow rate of 6 l/min. Accurate machine learning results showed that all three particle diameters attain larger velocities and smaller impact times in a diseased acinus compared to a healthy one. In both cases, the 0.5-μm particles acquire the smallest velocities an...
Francis, I & Saha, SC 2022, 'Surface tension effects on flow dynamics and alveolar mechanics in the acinar region of human lung', Heliyon, vol. 8, no. 10, pp. e11026-e11026.
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Francis, I, Shrestha, J, Paudel, KR, Hansbro, PM, Warkiani, ME & Saha, SC 2022, 'Recent advances in lung-on-a-chip models', Drug Discovery Today, vol. 27, no. 9, pp. 2593-2602.
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Franz, A, Oberst, S, Peters, H, Berger, R & Behrend, R 2022, 'How do medical students learn conceptual knowledge? High-, moderate- and low-utility learning techniques and perceived learning difficulties', BMC Medical Education, vol. 22, no. 1.
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Abstract Background Acquiring medical knowledge is a key competency for medical students and a lifelong requirement for physicians. Learning techniques can improve academic success and help students cope with stressors. To support students’ learning process medical faculties should know about learning techniques. The purpose of this study is to analyse the preferred learning techniques of female and male as well as junior and senior medical students and how these learning techniques are related to perceived learning difficulties. Methods In 2019, we conducted an online survey with students of the undergraduate, competency-based curriculum of medicine at Charité – Universitätsmedizin Berlin. We chose ten learning techniques of high, moderate and low utility according to Dunlosky et al. (2013) and we asked medical students to rate their preferred usage of those techniques using a 5-point Likert scale. We applied t-tests to show differences in usage between female and male as well as junior and senior learners. Additionally, we conducted a multiple regression analysis to explore the predictive power of learning techniques regarding perceived difficulties. Results A total of 730 medical students (488 women, 242 men, Mage = 24.85, SD = 4.49) use three techniques the most: ‘highlighting’ (low utility), ‘self-explanation’ (moderate utility) and ‘practice testing’ (high utility). Female students showed a significantly higher usage of low-utility learning techniques (t(404.24) = -7.13, p < .001) and a higher usage of high-utility learning techniques (t(728) = -2.50, p <...
Fu, A, Yu, S, Zhang, Y, Wang, H & Huang, C 2022, 'NPP: A New Privacy-Aware Public Auditing Scheme for Cloud Data Sharing with Group Users', IEEE Transactions on Big Data, vol. 8, no. 1, pp. 14-24.
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Today, cloud storage becomes one of the critical services, because users can easily modify and share data with others in cloud. However, the integrity of shared cloud data is vulnerable to inevitable hardware faults, software failures or human errors. To ensure the integrity of the shared data, some schemes have been designed to allow public verifiers (i.e., third party auditors) to efficiently audit data integrity without retrieving the entire users' data from cloud. Unfortunately, public auditing on the integrity of shared data may reveal data owners' sensitive information to the third party auditor. In this paper, we propose a new privacy-aware public auditing mechanism for shared cloud data by constructing a homomorphic verifiable group signature. Unlike the existing solutions, our scheme requires at least t group managers to recover a trace key cooperatively, which eliminates the abuse of single-authority power and provides non-frameability. Moreover, our scheme ensures that group users can trace data changes through designated binary tree; and can recover the latest correct data block when the current data block is damaged. In addition, the formal security analysis and experimental results indicate that our scheme is provably secure and efficient.
Fu, K, Cai, X, Yuan, B, Yang, Y & Yao, X 2022, 'An Efficient Surrogate Assisted Particle Swarm Optimization for Antenna Synthesis', IEEE Transactions on Antennas and Propagation, vol. 70, no. 7, pp. 4977-4984.
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By virtue of the prediction abilities of machine learning (ML) methods, the ML-assisted evolutionary algorithm has been treated as an efficient solution for antenna design automation. This article presents an efficient ML-based surrogate-assisted particle swarm optimization (SAPSO). The proposed algorithm closely combines the particle swarm optimization (PSO) with two ML-based approximation models. Then, a novel mixed prescreening (mixP) strategy is proposed to pick out promising individuals for full-wave electromagnetic (EM) simulations. As the optimization procedure progresses, the ML models are dynamically updated once new training data are obtained. Finally, the proposed algorithm is verified by three real-world antenna examples. The results show that the proposed SAPSO-mixP can find favorable results with a much smaller number of EM simulations than other methods.
Fu, L, Shi, B, Wen, S, Morsch, M, Wang, G, Zhou, Z, Mi, C, Sadraeian, M, Lin, G, Lu, Y, Jin, D & Chung, R 2022, 'Aspect Ratio of PEGylated Upconversion Nanocrystals Affects the Cellular Uptake In Vitro and In Vivo', Acta Biomaterialia, vol. 147, pp. 403-413.
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The central nervous system (CNS) is protected by the blood-brain barrier (BBB), which acts as a physical barrier to regulate and prevent the uptake of endogenous metabolites and xenobiotics. However, the BBB prevents most non-lipophilic drugs from reaching the CNS following systematic administration. Therefore, there is considerable interest in identifying drug carriers that can maintain the biostability of therapeutic molecules and target their transport across the BBB. In this regard, upconversion nanoparticles (UCNPs) have become popular as a nanoparticle-based solution to this problem, with the additional benefit that they display unique properties for in vivo visualization. The majority of studies to date have explored basic spherical UCNPs for drug delivery applications. However, the biophysical properties of UCNPs, cell uptake and BBB transport have not been thoroughly investigated. In this study, we described a one-pot seed-mediated approach to precisely control longitudinal growth to produce bright UCNPs with various aspect ratios. We have systematically evaluated the effects of the physical aspect ratios and PEGylation of UCNPs on cellular uptake in different cell lines and an in vivo zebrafish model. We found that PEGylated the original UCNPs can enhance their biostability and cell uptake capacity. We identify an optimal aspect ratio for UCNP uptake into several different types of cultured cells, finding that this is generally in the ratio of 2 (length/width). This data provides a crucial clue for further optimizing UCNPs as a drug carrier to deliver therapeutic agents into the CNS. STATEMENT OF SIGNIFICANCE: The central nervous system (CNS) is protected by the blood-brain barrier (BBB), which acts as a highly selective semipermeable barrier of endothelial cells to regulate and prevent the uptake of toxins and pathogens. However, the BBB prevents most non-lipophilic drugs from reaching the CNS following systematic administration. The proposed...
Fumanal-Idocin, J, Takac, Z, Fernandez, J, Sanz, JA, Goyena, H, Lin, C-T, Wang, Y-K & Bustince, H 2022, 'Interval-Valued Aggregation Functions Based on Moderate Deviations Applied to Motor-Imagery-Based Brain–Computer Interface', IEEE Transactions on Fuzzy Systems, vol. 30, no. 7, pp. 2706-2720.
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In this work we develop moderate deviation functions to measure similarity and dissimilarity among a set of given interval-valued data to construct interval-valued aggregation functions, and we apply these functions in two Motor-Imagery Brain Computer Interface (MI-BCI) systems to classify electroencephalography signals. To do so, we introduce the notion of interval-valued moderate deviation function and, in particular, we study those interval-valued moderate deviation functions which preserve the width of the input intervals. In order to apply them in a MI-BCI system, we first use fuzzy implication operators to measure the uncertainty linked to the output of each classifier in the ensemble of the system, and then we perform the decision making phase using the new interval-valued aggregation functions. We have tested the goodness of our proposal in two MI-BCI frameworks, obtaining better results than those obtained using other numerical aggregation and interval-valued OWA operators, and obtaining competitive results versus some non aggregation-based frameworks.
Fumanal-Idocin, J, Wang, Y-K, Lin, C-T, Fernandez, J, Sanz, JA & Bustince, H 2022, 'Motor-Imagery-Based Brain–Computer Interface Using Signal Derivation and Aggregation Functions', IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 7944-7955.
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Brain-computer interface (BCI) technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to BCI is motor imagery (MI). In BCI applications, the electroencephalography (EEG) is a very popular measurement for brain dynamics because of its noninvasive nature. Although there is a high interest in the BCI topic, the performance of existing systems is still far from ideal, due to the difficulty of performing pattern recognition tasks in EEG signals. This difficulty lies in the selection of the correct EEG channels, the signal-to-noise ratio of these signals, and how to discern the redundant information among them. BCI systems are composed of a wide range of components that perform signal preprocessing, feature extraction, and decision making. In this article, we define a new BCI framework, called enhanced fusion framework, where we propose three different ideas to improve the existing MI-based BCI frameworks. First, we include an additional preprocessing step of the signal: a differentiation of the EEG signal that makes it time invariant. Second, we add an additional frequency band as a feature for the system: the sensorimotor rhythm band, and we show its effect on the performance of the system. Finally, we make a profound study of how to make the final decision in the system. We propose the usage of both up to six types of different classifiers and a wide range of aggregation functions (including classical aggregations, Choquet and Sugeno integrals, and their extensions and overlap functions) to fuse the information given by the considered classifiers. We have tested this new system on a dataset of 20 volunteers performing MI-based brain-computer interface experiments. On this dataset, the new system achieved 88.80% accuracy. We also propose an optimized version of our system that is able to obtain up to 90.76%. Furthermore, we find that the pair Choquet/Sugeno integrals and overlap fun...
Gadipudi, N, Elamvazuthi, I, Lu, C-K, Paramasivam, S & Su, S 2022, 'Lightweight spatial attentive network for vehicular visual odometry estimation in urban environments', Neural Computing and Applications, vol. 34, no. 21, pp. 18823-18836.
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Galvão, N, Matos, JC, Hajdin, R, Ferreira, L & Stewart, MG 2022, 'Impact of construction errors on the structural safety of a post-tensioned reinforced concrete bridge', Engineering Structures, vol. 267, pp. 114650-114650.
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The ageing of bridge stock in developed countries worldwide and the increasing number of recorded bridge collapses have underlined the need for more sophisticated and comprehensive assessment procedures concerning the safety and serviceability of structures. In many recent failures, construction errors or deficiencies have contributed to the unfortunate outcome either by depleting the safety margin or speeding up the deterioration rate of structures. This research aims to quantify the impact of construction errors on the structural safety of a bridge considering corresponding models available in the literature that probabilistically characterise the occurrence rate and severity of some of these errors. The nominal probability of failure of structures, neglecting construction errors, is typically computed in numerous works in the literature. Therefore, the novelty of this paper lies in the consideration of an additional source of uncertainty (i.e., construction errors) combined with sophisticated numerical methods leading to a more refined estimation of the probability of failure of structures. Accordingly, some benchmark results focussing on error-free and error-included scenarios are established, providing useful information to close the gap between the nominal and the actual probability of failure of a railway bridge.
Gan, L, Teng, Z, Zhang, Y, Zhu, L, Wu, F & Yang, Y 2022, 'SemGloVe: Semantic Co-Occurrences for GloVe From BERT', IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 30, pp. 2696-2704.
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Ganaie, MA, Tanveer, M & Lin, C-T 2022, 'Large-Scale Fuzzy Least Squares Twin SVMs for Class Imbalance Learning', IEEE Transactions on Fuzzy Systems, vol. 30, no. 11, pp. 4815-4827.
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Twin support vector machines (TSVMs) have been successfully employed for binary classification problems. With the advent of machine learning algorithms, data have proliferated and there is a need to handle or process large-scale data. TSVMs are not successful in handling large-scale data due to the following: 1) the optimization problem solved in the TSVM needs to calculate large matrix inverses, which makes it an ineffective choice for large-scale problems; 2) the empirical risk minimization principle is employed in the TSVM and, hence, may suffer due to overfitting; and 3) the Wolfe dual of TSVM formulation involves positive-semidefinite matrices, and hence, singularity issues need to be resolved manually. Keeping in view the aforementioned shortcomings, in this article, we propose a novel large-scale fuzzy least squares TSVM for class imbalance learning (LS-FLSTSVM-CIL). We formulate the LS-FLSTSVM-CIL such that the proposed optimization problem ensures that: 1) no matrix inversion is involved in the proposed LS-FLSTSVM-CIL formulation, which makes it an efficient choice for large-scale problems; 2) the structural risk minimization principle is implemented, which avoids the issues of overfitting and results in better performance; and 3) the Wolfe dual formulation of the proposed LS-FLSTSVM-CIL model involves positive-definite matrices. In addition, to resolve the issues of class imbalance, we assign fuzzy weights in the proposed LS-FLSTSVM-CIL to avoid bias in dominating the samples of class imbalance problems. To make it more feasible for large-scale problems, we use an iterative procedure known as the sequential minimization principle to solve the objective function of the proposed LS-FLSTSVM-CIL model. From the experimental results, one can see that the proposed LS-FLSTSVM-CIL demonstrates superior performance in comparison to baseline classifiers. To demonstrate the feasibility of the proposed LS-FLSTSVM-CIL on large-scale classification problems...
Ganbat, N, Altaee, A, Zhou, JL, Lockwood, T, Al-Juboori, RA, Hamdi, FM, Karbassiyazdi, E, Samal, AK, Hawari, A & Khabbaz, H 2022, 'Investigation of the effect of surfactant on the electrokinetic treatment of PFOA contaminated soil', Environmental Technology & Innovation, vol. 28, pp. 102938-102938.
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Gandomi, AH & Roke, DA 2022, 'A Multiobjective Evolutionary Framework for Formulation of Nonlinear Structural Systems', IEEE Transactions on Industrial Informatics, vol. 18, no. 9, pp. 5795-5803.
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In this article, an evolutionary framework is proposed for seismic response formulation of self-centering concentrically braced frame (SC-CBF) systems. A total of 75 different SC-CBF systems were designed, and their responses were recorded under 170 earthquake records. To select the most important earthquake intensity measures, an evolutionary feature selection strategy is introduced, which tries to find the highest correlation. For the formulation of the SC-CBF response, a hybrid multiobjective genetic programming and regression analysis is implemented, considering both model accuracy and model complexity as objectives. In the hybrid approach, regression tries to connect multiple genes. Non-dominated models are presented, and the best model is selected based on the practical approach proposed here. The best model is compared with four other genetic programming models. The results show that the evolutionary procedure is highly effective for designing the SC-CBF system using a simple and accurate model for such a complex system.
Gandomi, AH, Chen, F & Abualigah, L 2022, 'Machine Learning Technologies for Big Data Analytics', Electronics, vol. 11, no. 3, pp. 421-421.
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Big data analytics is one high focus of data science and there is no doubt that big data is now quickly growing in all science and engineering fields [...]
Ganguly, D, Ngo, TV, Coleman, M, Sorrelle, N, Dominguez, A, Toombs, J, Schmidt, M, Mora, FV, Ortega, DG, Wellstein, A & Brekken, RA 2022, 'Abstract 3144: Pleiotrophin drives a pro-metastatic immune niche within breast tumor microenvironment', Cancer Research, vol. 82, no. 12_Supplement, pp. 3144-3144.
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Abstract Immune cells in the tumor microenvironment (TME) can impact cancer cell invasion, seeding and colonization of secondary metastatic sites. Our findings show that Pleiotrophin (PTN), a unique and previously under-studied heparin binding cytokine, contributes to inflammation within the TME. PTN appears to be particularly efficient in recruiting neutrophils, which have been reported to cause immune suppression. Consequently, inhibition of PTN pharmacologically or genetically results in lower breast cancer metastasis to lungs and better survival in multiple mouse models of breast cancer. Corroborating our mouse studies, we have found that higher PTN expression in breast cancer patient plasma correlates with poor prognosis. Concurrently, stage IV breast cancer patients that have lower PTN expression have a highly significant survival advantage (median survival 78.27 months) over patients expressing high levels of PTN (median survival 27.5 months). Additionally, we have also found PTN positive cancer cells are enriched in metastatic lesions suggesting that PTN-high cancer cells are more successful at colonizing and surviving at the secondary site. Uncovering the function of PTN in these unique cancer cells might have a major impact in the clinical setting. Overall, our data suggests that PTN is important in driving a pro-metastatic immune niche within the TME that promotes tumor cell escape from the primary tumor and survival of metastatic cancer cells in secondary sites. Additionally, our studies highlight that inhibition of PTN has potential to reduce metastatic burden in breast cancer and suggest that future studies testing the combination of PTN inhibition with standard chemotherapy or immune therapy are warranted. Citation Format: Debolina Ganguly, Tuong V. Ngo, Morgan Coleman, Noah Sorrelle, Adrian Dominguez, Jason Toombs, Marcel Schmidt, Fa V. Mora, David G. Ortega,...
Gao, F, Zhang, S, He, X & Sheng, D 2022, 'Experimental Study on Migration Behavior of Sandy Silt under Cyclic Load', Journal of Geotechnical and Geoenvironmental Engineering, vol. 148, no. 5.
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This paper presents experimental investigation into the effects of particle size distribution of subgrade soil on mud pumping. The results show that subgrade soils with higher fine contents do not necessarily lead to more serious mud pumping. A soil with a higher silt content tends to cause the formation of a less permeable interlayer at the bottom of the ballast, which effectively reduces the particle migration magnitude. Increasing the median particle size (d50) or reducing the coefficient of uniformity (d60/d10) of the studied sandy silt promotes the migration distance of particles. While mud pumping is essentially an internal erosion problem caused by cyclic loads, existing filter theories do not directly apply to mud pumping. The findings from this study can help selecting proper rail embankment fills to reduce mud pumping.
Gao, H, Huang, J, Tao, Y, Hussain, W & Huang, Y 2022, 'The Joint Method of Triple Attention and Novel Loss Function for Entity Relation Extraction in Small Data-Driven Computational Social Systems', IEEE Transactions on Computational Social Systems, vol. 9, no. 6, pp. 1725-1735.
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Gao, H, Luo, X, Barroso, RJD & Hussain, W 2022, 'Guest editorial: Smart communications and networking: architecture, applications, and future challenges', IET Communications, vol. 16, no. 10, pp. 1021-1024.
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Gao, H, Qin, X, Barroso, RJD, Hussain, W, Xu, Y & Yin, Y 2022, 'Collaborative Learning-Based Industrial IoT API Recommendation for Software-Defined Devices: The Implicit Knowledge Discovery Perspective', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 1, pp. 66-76.
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Gao, H, Zhang, Y & Hussain, W 2022, 'Special issue on intelligent software engineering', Expert Systems, vol. 39, no. 6.
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Gao, J, Nait Amar, M, Motahari, MR, Hasanipanah, M & Jahed Armaghani, D 2022, 'Two novel combined systems for predicting the peak shear strength using RBFNN and meta-heuristic computing paradigms', Engineering with Computers, vol. 38, no. 1, pp. 129-140.
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Gao, L, Li, S, Xu, X, Zou, C & Zhang, G 2022, 'Highly Sensitive H2 Sensors Based on Co3O4/PEI‐CNTs at Room Temperature', Journal of Nanomaterials, vol. 2022, no. 1, pp. 1-8.
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The highly dispersed Co3O4 on the surface of CNTs modified with polyethylenimine (PEI) was synthesized using the hydrothermal method. In the CNT‐Co3O4 composite materials, CNTs not only provide the substrate for the Co3O4 nanoparticles but also prevent their aggregation. Furthermore, the interaction between Co3O4 and CNTs modified with polyethylenimine (PEI) helps to improve the gas sensing performance. In particular, the CNT‐Co3O4 composite synthesized at 190°C shows the outstanding sensitive characteristics to H2 with a lower detection limit of 30 ppm at room temperature. The obtained CNT‐Co3O4 sensor displays excellent selectivity and stability to H2. The energy band model of the conductive mechanism has been built to explain the resistance change when the gas sensor is exposed to the H2. Hence, the CNT‐Co3O4 composite material presents highly promising applications in H2 gas sensing.
Gao, P, Fu, X, Liu, H & Chen, Y-J 2022, 'Free Add-Ons in Services', Service Science, vol. 14, no. 4, pp. 292-306.
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The paper examines a seller’s offering of free add-ons in services. We build a stylized model where the seller decides the level of add-on provision to enhance its core service, and consumers make discrete choices between the seller and an outside option. When the seller supplies its service through a single channel, we show that the optimal add-on provision is unimodal in the difference between the seller’s service quality and the outside option, comparable with the existing literature. When the service is supplied through multiple channels, we show that the seller may make nonidentical add-on provisions among channels. If the cost of add-on provision is low, the seller should adopt a differentiation strategy. If the cost is high, the seller should adopt a homogenization strategy. Various extensions are considered to establish the robustness of our results. Funding: P. Gao received financial support from the National Natural Science Foundation of China [Grant 72192805] and the Shenzhen Institute of Artificial Intelligence and Robotics for Society [Grant AC01202101102]. Y-J Chen received financial support fromthe Hong Kong Research Grants Council [Grant 16212821].
Gao, S, Guo, YJ, Safavi-Naeini, SA, Hong, W & Yang, X-X 2022, 'Guest Editorial Low-Cost Wide-Angle Beam-Scanning Antennas', IEEE Transactions on Antennas and Propagation, vol. 70, no. 9, pp. 7378-7383.
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Gao, S, Yu, S, Wu, L, Yao, S & Zhou, X 2022, 'Detecting adversarial examples by additional evidence from noise domain', IET Image Processing, vol. 16, no. 2, pp. 378-392.
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AbstractDeep neural networks are widely adopted powerful tools for perceptual tasks. However, recent research indicated that they are easily fooled by adversarial examples, which are produced by adding imperceptible adversarial perturbations to clean examples. Here the steganalysis rich model (SRM) is utilized to generate noise feature maps, and they are combined with RGB images to discover the difference between adversarial examples and clean examples. In particular, a two‐stream pseudo‐siamese network that fuses the subtle difference in RGB images with the noise inconsistency in noise features is proposed. The proposed method has strong detection capability and transferability, and can be combined with any model without modifying its architecture or training procedure. The extensive empirical experiments show that, compared with the state‐of‐the‐art detection methods, the proposed approach achieves excellent performance in distinguishing adversarial samples generated by popular attack methods on different real datasets. Moreover, this method has good generalization, it trained by a specific adversary can defend against other adversaries effectively.
Gao, W, Wu, J & Xu, G 2022, 'Detecting Duplicate Questions in Stack Overflow via Source Code Modeling', International Journal of Software Engineering and Knowledge Engineering, vol. 32, no. 02, pp. 227-255.
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Stack Overflow is one of the most popular Question-Answering sites for programmers. However, it faces the problem of question duplication, where newly created questions are identical to previous questions. Existing works on duplicate question detection in Stack Overflow extract a set of textual features on the question pairs and use supervised learning approaches to classify duplicate question pairs. However, they do not consider the source code information in the questions. While in some cases, the intention of a question is mainly represented by the source code. In this paper, we aim to learn the semantics of a question by combining both text features and source code features. We use word embedding and convolutional neural networks to extract textual features from questions to overcome the lexical gap issue. We use tree-based convolutional neural networks to extract structural and semantic features from source code. In addition, we perform multi-task learning by combining the duplication question detection task with a question tag prediction side task. We conduct extensive experiments on the Stack Overflow dataset and show that our approach can detect duplicate questions with higher recall and MRR compared with baseline approaches on Python and Java programming languages.
Gao, X & Zhang, Y 2022, 'What is behind the globalization of technology? Exploring the interplay of multi-level drivers of international patent extension in the solar photovoltaic industry', Renewable and Sustainable Energy Reviews, vol. 163, pp. 112510-112510.
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The increasing internationalization of economic activities highlights the necessity of expanding protections for technologies outside their home countries. Given the large scale and fast growth in international patent filings, understanding the motivations of international patenting behaviors has attracted much attention. This study extends prior literature by exploring additional important determinants of international patenting behaviors and the heterogeneity in international patenting across technology and assignee categories. This study focuses on the solar photovoltaic (PV) industry, which is a major sector in the renewable energy industry and plays a key role in achieving energy transition. As a critical application for semiconductors, PV technologies have grown into a substantial field of research and development through strong patents. In this study, we found that the quality and applicability scope of a patent, as well as the market size, manufacturing capacity, and imitation threats in a destination country, can impact international patent extensions in the solar PV industry. We also found that the strength of these motivators varies based on different types of technologies and assignees.
Gao, X, Li, H, Du, J, Zhang, T, Song, J, Bu, X, An, J & Liu, H 2022, 'Design and modeling of a single-balanced high-Tc superconducting Josephson-junction terahertz mixer', Journal of Applied Physics, vol. 131, no. 3, pp. 033902-033902.
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High-Tc superconducting (HTS) receivers are promising candidates for terahertz (THz) communication applications due to their superior sensitivity and the cooling attainability with a miniature cryogenic system. Nevertheless, currently reported HTS mixers are mostly single-junction devices, and a more complicated architecture like the balanced configuration has not been investigated. This paper presents the design of a 600-GHz single-balanced HTS mixer, where detailed electromagnetic simulations are carried out to optimize the coupling efficiency and port isolation, as well as desired amplitude and phase relationship. To predict the noise and conversion performance, we present an innovative multiport network interleaving-based modeling method that enables powerful simulation verifications for single-balanced HTS Josephson mixers. Simulation results show that the presented device exhibits very good HTS mixer performance at the 600-GHz band, which validates the effectiveness of the design and the potential of the mixer for THz communication applications.
Gao, X, Yang, F, Yan, Z, Zhao, J, Li, S, Nghiem, L, Li, G & Luo, W 2022, 'Humification and maturation of kitchen waste during indoor composting by individual households', Science of The Total Environment, vol. 814, pp. 152509-152509.
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This study evaluated the humification and maturation of kitchen waste during indoor composting by individual households. In total, 50 households were randomly selected to participate in this study using kitchen waste of their own for indoor composting using a standard 20 L sealed composter. Garden waste was also collected from their local communities and used as the bulking agent. Both effective microorganisms and lime were inoculated at 1% (wet weight) of raw composting materials to facilitate the composting initiation. Results from this study demonstrate for the first time that ordinary residents could correctly follow the instruction to operate indoor composting at household level to manage urban kitchen waste at source. Overall, 30 households provided valid and complete data to show an increase (to ~50 °C) and then decrease in temperature in response to the decline of biodegradable organic substances during indoor composting. The compost physiochemical characteristics varied significantly toward maturation with an increase in seed germination index to above 50% for most households. Furthermore, organic humification occurred continuously during indoor composting as indicated by the enhanced content of humic substances, degree of polymerization, and spectroscopic characteristics.
García-Orozco, D, Alfaro-García, VG, Merigó, JM, Espitia Moreno, IC & Gómez Monge, R 2022, 'An overview of the most influential journals in fuzzy systems research', Expert Systems with Applications, vol. 200, pp. 117090-117090.
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Garg, H, Gandomi, AH, Ali, Z & Mahmood, T 2022, 'Neutrality aggregation operators based on complex q‐rung orthopair fuzzy sets and their applications in multiattribute decision‐making problems', International Journal of Intelligent Systems, vol. 37, no. 1, pp. 1010-1051.
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Complex q-rung orthopair fuzzy sets (CQROFSs) are proposed to convey vague material in decision-making problems. The CQROFSs can enthusiastically modify the region of proof by altering the factor (Formula presented.) for real and imaginary parts based on the variation degree and, therefore, favor further uncountable options. Consequently, this set reverses over the existing theories, such as complex intuitionistic fuzzy sets (CIFSs) and complex Pythagorean fuzzy sets (CPFS). In everyday life, there are repeated situations that can occur, which involve an impartial assertiveness of the decision-makers. To determine the best decision to handle such situations, in this study, we propose modern operational laws by joining the characteristics of the truth factor sum and collaboration between the truth degrees into the analysis for CQROFSs. Based on these principles, we determined several weighted averaging neutral aggregation operators (AOs) to collect the CQROF knowledge. Subsequently, we established an original multiattribute decision-making (MADM) procedure by using the demonstrated AOs based on CQROFS. To evaluate the effectiveness, in terms of reliability and consistency, of the proposed operators, they were applied to some numerical examples. A comparative analysis of the investigated operators and other existing operators was also performed to find the dominance and validity of the introduced MADM method.
Gaur, VK, Gautam, K, Sharma, P, Gupta, P, Dwivedi, S, Srivastava, JK, Varjani, S, Ngo, HH, Kim, S-H, Chang, J-S, Bui, X-T, Taherzadeh, MJ & Parra-Saldívar, R 2022, 'Sustainable strategies for combating hydrocarbon pollution: Special emphasis on mobil oil bioremediation', Science of The Total Environment, vol. 832, pp. 155083-155083.
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The global rise in industrialization and vehicularization has led to the increasing trend in the use of different crude oil types. Among these mobil oil has major application in automobiles and different machines. The combustion of mobil oil renders a non-usable form that ultimately enters the environment thereby causing problems to environmental health. The aliphatic and aromatic hydrocarbon fraction of mobil oil has serious human and environmental health hazards. These components upon interaction with soil affect its fertility and microbial diversity. The recent advancement in the omics approach viz. metagenomics, metatranscriptomics and metaproteomics has led to increased efficiency for the use of microbial based remediation strategy. Additionally, the use of biosurfactants further aids in increasing the bioavailability and thus biodegradation of crude oil constituents. The combination of more than one approach could serve as an effective tool for efficient reduction of oil contamination from diverse ecosystems. To the best of our knowledge only a few publications on mobil oil have been published in the last decade. This systematic review could be extremely useful in designing a micro-bioremediation strategy for aquatic and terrestrial ecosystems contaminated with mobil oil or petroleum hydrocarbons that is both efficient and feasible. The state-of-art information and future research directions have been discussed to address the issue efficiently.
Gaur, VK, Sharma, P, Gupta, S, Varjani, S, Srivastava, JK, Wong, JWC & Ngo, HH 2022, 'Opportunities and challenges in omics approaches for biosurfactant production and feasibility of site remediation: Strategies and advancements', Environmental Technology & Innovation, vol. 25, pp. 102132-102132.
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Biosurfactants are molecules of 21st century. Their application(s) intercedes in daily life of living beings. Major limitation in the wide applicability of biosurfactant(s) is the economicity of production. To overcome this several strategies can be employed. This review is centered on the recent technological advancements in biosurfactant research. The advancement(s) include the use of metabolomic and sequence based omics approaches that has become a high-throughput indispensable tool for the identification of biosurfactant producers. A plethora of microorganisms synthesize biosurfactants, along with other value-added products namely ethanol, microbial lipids, and polyhydroxyalkanoates has been reported. This can significantly improve the economics of the overall process and limitations can further be dealt by employing metabolic engineering approaches. Tailoring strategy enables modification in the composition of congeners produced and improves the yield of biosurfactant. Bio-based surfactants have shown promising results against combating the pollution in terrestrial and aquatic ecosystems either by increasing their bioavailability or aqueous solubility. Owing to the ever-increasing market of biosurfactant(s), this review summarized technologically feasible advancement(s) in biosurfactant research that may enable the researchers to develop more safer and reliable technologies.
Gautam, S, Lu, Y, Taghizadeh, S, Xiao, W & Lu, DD-C 2022, 'An Enhanced Time-Delay-Based Reference Current Identification Method for Single-Phase System', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 3, no. 3, pp. 683-693.
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Gautam, S, Xiao, W, Ahmed, H & Lu, DD-C 2022, 'Enhanced Single-Phase Phase Locked Loop Based on Complex-Coefficient Filter', IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-8.
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Gautam, S, Xiao, W, Lu, DD-C, Ahmed, H & Guerrero, JM 2022, 'Development of Frequency-Fixed All-Pass Filter-Based Single-Phase Phase-Locked Loop', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 1, pp. 506-517.
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Phase-locked loops (PLL) are widely used in the synchronization of grid interfaced power converters. One solution is based on orthogonal signal generation (OSG), which requires the grid frequency information for their appropriate operation. This article developed a new solution to achieve the PLL function for single-phase grid interconnection but eradicate additional frequency feedback loops in the traditional architecture of all-pass filter PLL (APF-PLL). Four new topologies are developed along with their small-signal modeling and dynamic analysis. A thorough comparison among them on their dynamic response, steady-state accuracy, implementation, and disturbance rejection capability is carried out. Finally, the best approach of frequency-fixed (FF) APF-PLL is experimentally evaluated with frequency adaptive APF-PLL and FF PLLs belonging to time delay (TD) and second-order generalized integrator (SOGI) families.
Ge, H, Chua Kim Huat, D, Koh, CG, Dai, G & Yu, Y 2022, 'Guided wave–based rail flaw detection technologies: state-of-the-art review', Structural Health Monitoring, vol. 21, no. 3, pp. 1287-1308.
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The unavoidable increase in train speed and load, as well as the aging of railway facilities, is requiring more and more attention to rail defects detection. As a promising tool for rail, in-service high-speed inspection, guided wave–based detection technologies have been developed in succession by researches in the past two decades. However, there is a lack of a systematic review on the developments and performances of these technologies. This article reviews ultrasonic rail inspection methods comprehensively with the focus on the state-of-the-art technologies based on guided wave. Different excitation options, including train wheel, electromagnetic acoustic transducer, pulsed laser, air-coupled, and contact piezoelectric transducer, are described, respectively, along with their inspection sensitivities, regions, and potential speeds. Finally, future challenges and prospects are discussed to a certain extent to provide references for researchers in this area.
George, DJ, Sanders, YR, Bagherimehrab, M, Sanders, BC & Brennen, GK 2022, 'Entanglement in quantum field theory via wavelet representations', Physical Review D, vol. 106, no. 3.
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Quantum field theory (QFT) describes nature using continuous fields, but physical properties of QFT are usually revealed in terms of measurements of observables at a finite resolution. We describe a multiscale representation of free scalar bosonic and Ising model fermionic QFTs using wavelets. Making use of the orthogonality and self-similarity of the wavelet basis functions, we demonstrate some well-known relations such as scale-dependent subsystem entanglement entropy and renormalization of correlations in the ground state. We also find some new applications of the wavelet transform as a compressed representation of ground states of QFTs which can be used to illustrate quantum phase transitions via fidelity overlap and holographic entanglement of purification.
Ghalehno, AD, Saeedi, M, Bazaz, SR, Asadi, P, EbrahimiWarkiani, M & Yazdian-Robati, R 2022, 'Nano aptasensors for detection of streptomycin: A review', Nanomedicine Journal, vol. 9, no. 1, pp. 24-33.
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This review provides a literature update of the progress in optical and electrochemical aptasensors for the detection of streptomycin in human sera and animal-derived foods. The uncontrolled use of antibiotics and rising resistance to them, has created a global problem. Therefore, the detection and quantitation of antibiotics, i.e., streptomycin by robust, easy, and sensitive methods is in great demand. Among different strategies, new analytical methods for the efficient detection and quantitative determination of streptomycin have been developed. Aptasensors or aptamer-based biosensors have attracted more attention due to their unique recognition, simple fabrication, and significant selectivity, sensitivity, and specificity. Advantages of aptasensors will be highlighted in this review, with emphasis on methodological technique and specific properties of aptasensors developed for STR determination. In this review paper, we will focus on the recent development of aptasensors for streptomycin detection, considering the papers summarized in the data bases scopus and google scholar covering the period of time from 2013 till 2021.
Ghalehno, AD, Saeedi, M, Bazaz, SR, Asadi, P, EbrahimiWarkiani, M & Yazdian-Robati, R 2022, 'Nano aptasensors for detection of streptomycin: A review', NANOMEDICINE JOURNAL, vol. 9, no. 1, pp. 24-33.
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This review provides a literature update of the progress in optical and electrochemical aptasensors for the detection of streptomycin in human sera and animal-derived foods. The uncontrolled use of antibiotics and rising resistance to them, has created a global problem. Therefore, the detection and quantitation of antibiotics, i.e., streptomycin by robust, easy, and sensitive methods is in great demand. Among different strategies, new analytical methods for the efficient detection and quantitative determination of streptomycin have been developed. Aptasensors or aptamer-based biosensors have attracted more attention due to their unique recognition, simple fabrication, and significant selectivity, sensitivity, and specificity. Advantages of aptasensors will be highlighted in this review, with emphasis on methodological technique and specific properties of aptasensors developed for STR determination. In this review paper, we will focus on the recent development of aptasensors for streptomycin detection, considering the papers summarized in the data bases scopus and google scholar covering the period of time from 2013 till 2021.
Gharaibeh, M, Almahmoud, M, Ali, MZ, Al-Badarneh, A, El-Heis, M, Abualigah, L, Altalhi, M, Alaiad, A & Gandomi, AH 2022, 'Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches', Big Data and Cognitive Computing, vol. 6, no. 1, pp. 2-2.
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Neuroimaging refers to the techniques that provide efficient information about the neural structure of the human brain, which is utilized for diagnosis, treatment, and scientific research. The problem of classifying neuroimages is one of the most important steps that are needed by medical staff to diagnose their patients early by investigating the indicators of different neuroimaging types. Early diagnosis of Alzheimer’s disease is of great importance in preventing the deterioration of the patient’s situation. In this research, a novel approach was devised based on a digital subtracted angiogram scan that provides sufficient features of a new biomarker cerebral blood flow. The used dataset was acquired from the database of K.A.U.H hospital and contains digital subtracted angiograms of participants who were diagnosed with Alzheimer’s disease, besides samples of normal controls. Since each scan included multiple frames for the left and right ICA’s, pre-processing steps were applied to make the dataset prepared for the next stages of feature extraction and classification. The multiple frames of scans transformed from real space into DCT space and averaged to remove noises. Then, the averaged image was transformed back to the real space, and both sides filtered with Meijering and concatenated in a single image. The proposed model extracts the features using different pre-trained models: InceptionV3 and DenseNet201. Then, the PCA method was utilized to select the features with 0.99 explained variance ratio, where the combination of selected features from both pre-trained models is fed into machine learning classifiers. Overall, the obtained experimental results are at least as good as other state-of-the-art approaches in the literature and more efficient according to the recent medical standards with a 99.14% level of accuracy, considering the difference in dataset samples and the used cerebral blood flow biomarker.
Gharaibeh, M, Alzu’bi, D, Abdullah, M, Hmeidi, I, Al Nasar, MR, Abualigah, L & Gandomi, AH 2022, 'Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches', Big Data and Cognitive Computing, vol. 6, no. 1, pp. 29-29.
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Plenty of disease types exist in world communities that can be explained by humans’ lifestyles or the economic, social, genetic, and other factors of the country of residence. Recently, most research has focused on studying common diseases in the population to reduce death risks, take the best procedure for treatment, and enhance the healthcare level of the communities. Kidney Disease is one of the common diseases that have affected our societies. Sectionicularly Kidney Tumors (KT) are the 10th most prevalent tumor for men and women worldwide. Overall, the lifetime likelihood of developing a kidney tumor for males is about 1 in 466 (2.02 percent) and it is around 1 in 80 (1.03 percent) for females. Still, more research is needed on new diagnostic, early, and innovative methods regarding finding an appropriate treatment method for KT. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of machine learning can save diagnosis time, improve test accuracy, and reduce costs. Previous studies have shown that deep learning can play a role in dealing with complex tasks, diagnosis and segmentation, and classification of Kidney Tumors, one of the most malignant tumors. The goals of this review article on deep learning in radiology imaging are to summarize what has already been accomplished, determine the techniques used by the researchers in previous years in diagnosing Kidney Tumors through medical imaging, and identify some promising future avenues, whether in terms of applications or technological developments, as well as identifying common problems, describing ways to expand the data set, summarizing the knowledge and best practices, and determining remaining challenges and future directions.
Ghasemi, M, Khedri, M, Didandeh, M, Taheri, M, Ghasemy, E, Maleki, R, Shon, HK & Razmjou, A 2022, 'Removal of Pharmaceutical Pollutants from Wastewater Using 2D Covalent Organic Frameworks (COFs): An In Silico Engineering Study', Industrial & Engineering Chemistry Research, vol. 61, no. 25, pp. 8809-8820.
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Gholami, K, Azizivahed, A & Arefi, A 2022, 'Risk-oriented energy management strategy for electric vehicle fleets in hybrid AC-DC microgrids', Journal of Energy Storage, vol. 50, pp. 104258-104258.
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Gholami, K, Azizivahed, A, Li, L & Zhang, J 2022, 'Accuracy enhancement of second-order cone relaxation for AC optimal power flow via linear mapping', Electric Power Systems Research, vol. 212, pp. 108646-108646.
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Optimal power flow (OPF) has always been one of the most crucial tools for power system operations. OPF problem formulation involves non-linear alternative current (AC) power flow equations, and a wide range of challenges occur as a result. This is because the resulting non-convex optimization problems are not only complex and time-consuming, but also difficult to find a global optimum as many local optimums are present. So far, different relaxations have been provided to address these issues. One of the most effective strategies for convexifying such formulations is second-order cone programming (SOCP). Although SOCP is an efficient instrument for convexifying AC OPF equations, it is unable to reach the global optimal solution compared to other methods. The aim of this paper is therefore to provide a new method to approach the global optimum of AC OPF relaxed by SOCP. This method is obtained with the aid of a new linrear tranfsormation called semi-Lorentz transformation as it similar to the Lorentz transformation in the special relativity theory. In this method second-order cone AC OPF equations are mapped to a new model via semi-Lorentz transformation. In addition, an approximation approach is also presented to reach the best semi-Lorentz factor, the main driver in semi-Lorentz transformation, for each particular problem based on the network parameters. From the comparative analysis in case studies, the proposed OPF solution method has robust precision and higher efficiency while consuming less computing time.
Gholami, K, Islam, MR, Rahman, MM, Azizivahed, A & Fekih, A 2022, 'State-of-the-art technologies for volt-var control to support the penetration of renewable energy into the smart distribution grids', Energy Reports, vol. 8, pp. 8630-8651.
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Gil Lafuente, AM, Reverter, SB, Merigó, JM & Martínez, AT 2022, 'Preface', Lecture Notes in Networks and Systems, vol. 388 LNNS, pp. v-vi.
Giubilato, R, Le Gentil, C, Vayugundla, M, Schuster, MJ, Vidal-Calleja, T & Triebel, R 2022, 'GPGM-SLAM: A Robust SLAM System for Unstructured Planetary Environments with Gaussian Process Gradient Maps', Field Robotics, vol. 2, no. 1, pp. 1721-1753.
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Simultaneous Localization and Mapping (SLAM) techniques play a key role towards long-term autonomy of mobile robots due to the ability to correct localization errors and produce consistent maps of an environment over time. Contrarily to urban or man-made environments, where the presence of unique objects and structures offer unique cues for localization, the apperance of unstructured natural environments is often ambiguous and self-similar, hindering the performances of loop closure detection. In this paper, we present an approach to improve the robustness of place recognition in the context of a submap-based stereo SLAM based on Gaussian Process Gradient Maps (GPGMaps). GPGMaps embed a continuous representation of the gradients of the local terrain elevation by means of Gaussian Process regression and Structured Kernel Interpolation, given solely noisy elevation measurements. We leverage the imagelike structure of GPGMaps to detect loop closures using traditional visual features and Bag of Words. GPGMap matching is performed as an SE(2) alignment to establish loop closure constraints within a pose graph. We evaluate the proposed pipeline on a variety of datasets recorded on Mt. Etna, Sicily and in the Morocco desert, respectively Moon- and Mars-like environments, and we compare the localization performances with state-of-the-art approaches for visual SLAM and visual loop closure detection.
Goh, BHH, Chong, CT, Ong, HC, Milano, J, Shamsuddin, AH, Lee, XJ & Ng, J-H 2022, 'Strategies for fuel property enhancement for second-generation multi-feedstock biodiesel', Fuel, vol. 315, pp. 123178-123178.
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Fatty acids from non-edible bioresources are highly sought after as biofuel feedstock and the use of multi-stream feedstock for biodiesel production is of interest. This study explores the potential of using blended feedstock consisting of inedible jatropha oil (JO) and waste cooking oil (WO) for biodiesel production. Prior to blending, the unfavourable high acid value of jatropha oil was esterified under the most optimal conditions of 60 °C, 1% H2SO4 catalyst and alcohol to oil molar ratio of 11:1 to maximise the esterified yield (81.1 %). Based on the acid value measurement, the optimum volumetric blend of WO/EJO was determined to be 90/10 with the lowest acid value of 1.9 mg KOH g−1, which was then utilised as feedstock for base-catalysed transesterification. The KOH catalysed transesterification was optimised at 60 °C, 1 wt% KOH catalyst and alcohol to oil molar ratio of 6:1 to produce biodiesel with low acid value (0.2 mg KOH g−1), high calorific value (38.4 MJ kg−1), high oxidation stability (∼11 h) and favourable viscosity (4.7 mm2 s−1). The results show that the produced biodiesel has acceptable physicochemical properties but its properties can further be improved by blending with petroleum diesel and antioxidant. Among those produced blend derivatives, petroleum diesel and biodiesel blend (80:20) or B20 showed the best improvement with high calorific value (46.6 MJ/kg), high oxidation stability (∼37 h) and low acid value (0.3 mg KOH g−1). Based on the study, in situ feedstock blending of WO/EJO can improve the physicochemical properties of the produced biodiesel and reduce the dependency on single feedstock. Biodiesel blending with commercial diesel can enhance the biodiesel fuel properties and such derivatives can be directly applied in an existing engine.
Goh, BHH, Chong, CT, Ong, HC, Seljak, T, Katrašnik, T, Józsa, V, Ng, J-H, Tian, B, Karmarkar, S & Ashokkumar, V 2022, 'Recent advancements in catalytic conversion pathways for synthetic jet fuel produced from bioresources', Energy Conversion and Management, vol. 251, pp. 114974-114974.
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Sustainable Aviation Fuel (SAF) has become an important measure in the aviation industry's efforts to mitigate carbon emissions and reduce their overall environmental impacts. However, commercial usage is relatively stunted due to a plethora of drawbacks in the production process and economic feasibility of the fuel. In this study, the currently accepted technologies for producing synthetic jet fuels under the American Society for Testing Material (ASTM D7566) standard specification for aviation turbine fuel are reviewed. The emphasis is placed in terms of their reactions, type of catalysts used for the conversion pathways of Fisher-Tropsch (FT), Hydroprocessed Esters and Fatty Acids (HEFA) and Alcohol-to-Jet (ATJ), and the use of biomass resources as feedstock. The advancement in the production process and physicochemical properties of the uncertified biojet fuels are reviewed and discussed. Generally, Co- and Fe-based catalysts are commonly used for the FT process, while bimetallic catalysts consisting of Pt, Pd, Ni and Mo have shown excellent activities and selectivities for the HEFA process. For the ATJ process, zeolites such as HZSM-5, beta and SAPO have shown remarkable ethanol dehydration efficiency, while TiO2 and ferrierite have been studied for the combined iso-butanol dehydration and oligomerisation processes. Fundamental factors influencing the reaction efficiency including the feedstock properties, reaction conditions, catalytic reusability and catalyst supports are discussed. Finally, the key challenges and prospects for biojet fuel commercialisation are addressed.
Golbaz, D, Asadi, R, Amini, E, Mehdipour, H, Nasiri, M, Etaati, B, Naeeni, STO, Neshat, M, Mirjalili, S & Gandomi, AH 2022, 'Layout and design optimization of ocean wave energy converters: A scoping review of state-of-the-art canonical, hybrid, cooperative, and combinatorial optimization methods', Energy Reports, vol. 8, pp. 15446-15479.
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Ocean Wave energy is becoming a prominent technology, which is considered a vital renewable energy resource to achieve the Net-zero Emissions Plan by 2050. It is also projected to be commercialized widely and become a part of the industry that alters conventional energy technologies in the near future. However, wave energy technologies are not entirely yet developed and mature enough, so various criteria must be optimized to enter the energy market. In order to maximize the performance of wave energy converters (WECs) components, three challenges are mostly considered: Geometry, Power Take-off (PTO) parameters, and WECs’ layout. As each of such challenges plays a meaningful role in harnessing the maximum power output, this paper systematically reviews applied state-of-the-art optimization techniques, including standard, hybrid, cooperative, bi-level and combinatorial strategies. Due to the importance of fidelity and computational cost in numerical methods, we also discuss approaches to analyzing WECs interactions’ developments. Moreover, the benefits and drawbacks of the popular optimization methods applied to improve WEC parameters’ performance are summarized, briefly discussing their key characteristics. According to the scoping review, using a combination of bio-inspired algorithms and local search as a hybrid algorithm can outperform the other techniques in layout optimization in terms of convergence rate. A review of the geometry of WECs has emphasized the indispensability of optimizing and balancing design parameters with cost issues in multimodal and large-scale problems.
Gong, S, Ball, J & Surawski, N 2022, 'Urban land-use land-cover extraction for catchment modelling using deep learning techniques', Journal of Hydroinformatics, vol. 24, no. 2, pp. 388-405.
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AbstractThroughout the world, the likelihood of floods and managing the associated risk are a concern to many catchment managers and the population residing in those catchments. Catchment modelling is a popular approach to predicting the design flood quantiles of a catchment with complex spatial characteristics and limited monitoring data to obtain the necessary information for preparing the flood risk management plan. As an important indicator of urbanisation, land use land cover (LULC) plays a critical role in catchment parameterisation and modelling the rainfall–runoff process. Digitising LULC from remote sensing imagery of urban catchment is becoming increasingly difficult and time-consuming as the variability and diversity of land uses occur during urban development. In recent years, deep learning neural networks (DNNs) have achieved remarkable image classification and segmentation outcomes with the powerful capacity to process complex workflow and features, learn sophisticated relationships and produce superior results. This paper describes end-to-end data assimilation and processing path using U-net and DeepLabV3+, also proposes a novel approach integrated with the clustering algorithm MeanShift. These methods were developed to generate pixel-based LULC semantic segmentation from high-resolution satellite imagery of the Alexandria Canal catchment, Sydney, Australia, and assess the applicability of their outputs as inputs to different catchment modelling systems. A significant innovation is using the MeanShift clustering algorithm to reduce the spatial noise in the raw image and propagate it to the deep learning network to improve prediction. All three methods achieved excellent classification performance, where the MeanShift+U-net has the highest accuracy and consistency on the test imagery. The final suitability assessment illustrates that all three methods are more suitable for the parameterisation of semi-distr...
Gong, S, Guo, Z, Wen, S & Huang, T 2022, 'Stabilization Analysis for Linear Disturbed Event-Triggered Control System With Packet Losses', IEEE Transactions on Control of Network Systems, vol. 9, no. 3, pp. 1339-1347.
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Based on an event-triggered control strategy, the stabilization of a linear system with disturbance is the purpose of this article. In this article, we first introduce a newfangled event-triggered control with a linear diffusive term and discontinuous sign term. Then, a class of event-triggered conditions, whose threshold contains a constant term and a sampled state term, is provided for guaranteeing the stability of a disturbed linear system. The event could be directly triggered to change the control information by violating the event-triggered condition when packet losses during control information transmission are defaulting. This means that event-triggered control can markedly economize on resource waste and communication cost. In addition, the interexecution time, which is determined by the event-triggered condition, is proved to have a uniform positive lower bound to guarantee that Zeno behavior does not occur. In the presence of packet losses, we estimate the maximum allowable number of successive packet losses to maintain the desired performance of the system. Finally, the validity of the theoretical results is substantiated by an example.
Gong, S, Zou, Y, Xu, J, Hoang, DT, Lyu, B & Niyato, D 2022, 'Optimization-Driven Hierarchical Learning Framework for Wireless Powered Backscatter-Aided Relay Communications', IEEE Transactions on Wireless Communications, vol. 21, no. 2, pp. 1378-1391.
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In this paper, we employ multiple wireless-powered relays to assist information transmission from a multi-antenna access point to a single-antenna receiver. The wireless relays can operate in either the passive mode via backscatter communications or the active mode via RF communications, depending on their channel conditions and energy states. We aim to maximize the overall throughput by jointly optimizing the transmit beamforming and the relays' radio modes and operating parameters. Due to the non-convex and combinatorial problem structure, we develop a novel optimization-driven hierarchical deep deterministic policy gradient (H-DDPG) approach to adapt the beamforming and relay strategies. The optimization-driven H-DDPG algorithm firstly decomposes the binary relay mode selection into the outer-loop deep $Q$ -network (DQN) algorithm and then optimizes the continuous beamforming and relaying strategies by using the inner-loop DDPG algorithm. Secondly, to improve the learning efficiency, we integrate the model-based optimization into the inner-loop DDPG framework by providing a better-informed target estimation for DNN training. Simulation results reveal that these two special designs ensure a more stable learning performance and achieve a higher reward, up to 20%, compared to the conventional model-free DDPG approach.
Gong, Y, Li, Z, Zhang, J, Liu, W & Zheng, Y 2022, 'Online Spatio-Temporal Crowd Flow Distribution Prediction for Complex Metro System', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 2, pp. 865-880.
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Gonzalez de Vega, R, Lockwood, TE, Xu, X, Gonzalez de Vega, C, Scholz, J, Horstmann, M, Doble, PA & Clases, D 2022, 'Analysis of Ti- and Pb-based particles in the aqueous environment of Melbourne (Australia) via single particle ICP-MS', Analytical and Bioanalytical Chemistry, vol. 414, no. 18, pp. 5671-5681.
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AbstractThe analysis of natural and anthropogenic nanomaterials (NMs) in the environment is challenging and requires methods capable to identify and characterise structures on the nanoscale regarding particle number concentrations (PNCs), elemental composition, size, and mass distributions. In this study, we employed single particle inductively coupled plasma-mass spectrometry (SP ICP-MS) to investigate the occurrence of NMs in the Melbourne area (Australia) across 63 locations. Poisson statistics were used to discriminate between signals from nanoparticulate matter and ionic background. TiO2-based NMs were frequently detected and corresponding NM signals were calibated with an automated data processing platform. Additionally, a method utilising a larger mass bandpass was developed to screen for particulate high-mass elements. This procedure identified Pb-based NMs in various samples. The effects of different environmental matrices consisting of fresh, brackish, or seawater were mitigated with an aerosol dilution method reducing the introduction of salt into the plasma and avoiding signal drift. Signals from TiO2- and Pb-based NMs were counted, integrated, and subsequently calibrated to determine PNCs as well as mass and size distributions. PNCs, mean sizes, particulate masses, and ionic background levels were compared across different locations and environments. Graphical abstract
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2022, 'Compilation of parasitic immunogenic proteins from 30 years of published research using machine learning and natural language processing', Scientific Reports, vol. 12, no. 1.
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AbstractThe World Health Organisation reported in 2020 that six of the top 10 sources of death in low-income countries are parasites. Parasites are microorganisms in a relationship with a larger organism, the host. They acquire all benefits at the host’s expense. A disease develops if the parasitic infection disrupts normal functioning of the host. This disruption can range from mild to severe, including death. Humans and livestock continue to be challenged by established and emerging infectious disease threats. Vaccination is the most efficient tool for preventing current and future threats. Immunogenic proteins sourced from the disease-causing parasite are worthwhile vaccine components (subunits) due to reliable safety and manufacturing capacity. Publications with ‘subunit vaccine’ in their title have accumulated to thousands over the last three decades. However, there are possibly thousands more reporting immunogenicity results without mentioning ‘subunit’ and/or ‘vaccine’. The exact number is unclear given the non-standardised keywords in publications. The study aim is to identify parasite proteins that induce a protective response in an animal model as reported in the scientific literature within the last 30 years using machine learning and natural language processing. Source code to fulfil this aim and the vaccine candidate list obtained is made available.
Gordan, B, Raja, MA, Armaghani, DJ & Adnan, A 2022, 'Review on Dynamic Behaviour of Earth Dam and Embankment During an Earthquake', Geotechnical and Geological Engineering, vol. 40, no. 1, pp. 3-33.
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Goudarzi, A, Fahad, S, Ni, J, Ghayoor, F, Siano, P & Haes Alhelou, H 2022, 'A sequential hybridization of ETLBO and IPSO for solving reserve‐constrained combined heat, power and economic dispatch problem', IET Generation, Transmission & Distribution, vol. 16, no. 10, pp. 1930-1949.
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AbstractThe explosive demand for electricity and ecological concerns has necessitated the operation of power networks in a more cost‐effective approach. In recent years, the integration of combined heat and power units has presented a potential answer to these problems; nevertheless, a new difficult challenge has emerged: finding an optimal solution for simultaneous dispatch of power and heat. Therefore, to tackle this problem, this work presents an intelligent sequential algorithm based on a hybridization of an enthusiasm‐aided teaching and learning‐based optimization algorithm (ETLBO) with an improved version of particle swarm optimization (IPSO). The proposed method can simultaneously minimize total generating costs while considering a variety of physical and operational limitations. In addition, this research designed an adaptive violation constraint management approach combined with the formulated hybridized optimization algorithm to ensure system constraints' safe preservation during the optimization process. Finally, the performance of the proposed method is compared to the recently developed metaheuristic algorithms as well as Knitro and IPOPT (industrially used optimization packages), in which the ETLBO‐IPSO outperforms all the other methods.
Gravina da Rocha, C, Korb, S & Sacks, R 2022, 'Work structuring and product design for customized repetitive projects', Construction Management and Economics, vol. 40, no. 7-8, pp. 526-547.
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Grigorev, A, Mihaita, A-S, Lee, S & Chen, F 2022, 'Incident duration prediction using a bi-level machine learning framework with outlier removal and intra–extra joint optimisation', Transportation Research Part C: Emerging Technologies, vol. 141, pp. 103721-103721.
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Grzybowska, H, Wijayaratna, K, Shafiei, S, Amini, N & Travis Waller, S 2022, 'Ramp Metering Strategy Implementation: A Case Study Review', Journal of Transportation Engineering, Part A: Systems, vol. 148, no. 5.
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Guan, R, Zheng, H, Liu, Q, Ou, K, Li, D-S, Fan, J, Fu, Q & Sun, Y 2022, 'DIW 3D printing of hybrid magnetorheological materials for application in soft robotic grippers', Composites Science and Technology, vol. 223, pp. 109409-109409.
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A new hybrid magnetorheological material is prepared by DIW 3D printing technology, which is composed of magnetorheological fluid and magnetorheological elastomer. It does not only exhibit high magnetorheological effect of magnetorheological fluid, but also shows high mechanical stability of magnetorheological elastomer. The maxima absolute and relative magnetorheological effect of hybrid magnetorheological material are about 11.1 MPa and 7474%, which are simultaneously improved to be 2.9 times and 7.8 times comparing to single magnetorheological elastomer. Furthermore, the hybrid magnetorheological material is evaluated for application in soft robotic grippers. It shows larger clamping force (7.0 × 10−3 N) and faster response rate (ca.2.0s) comparing to other actuators. The work provides a new method to prepare hybrid magnetorheological material with high performance for various applications.
Guan, S, Lu, H, Zhu, L & Fang, G 2022, 'AFE-CNN: 3D Skeleton-based Action Recognition with Action Feature Enhancement', Neurocomputing, vol. 514, pp. 256-267.
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Guan, W, Song, X, Chang, X & Nie, L 2022, 'Preface', Synthesis Lectures on Information Concepts Retrieval and Services, pp. v-vi.
Guan, W, Wen, H, Song, X, Wang, C, Yeh, C-H, Chang, X & Nie, L 2022, 'Partially Supervised Compatibility Modeling', IEEE Transactions on Image Processing, vol. 31, pp. 4733-4745.
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Fashion Compatibility Modeling (FCM), which aims to automatically evaluate whether a given set of fashion items makes a compatible outfit, has attracted increasing research attention. Recent studies have demonstrated the benefits of conducting the item representation disentanglement towards FCM. Although these efforts have achieved prominent progress, they still perform unsatisfactorily, as they mainly investigate the visual content of fashion items, while overlooking the semantic attributes of items (e.g., color and pattern), which could largely boost the model performance and interpretability. To address this issue, we propose to comprehensively explore the visual content and attributes of fashion items towards FCM. This problem is non-trivial considering the following challenges: a) how to utilize the irregular attribute labels of items to partially supervise the attribute-level representation learning of fashion items; b) how to ensure the intact disentanglement of attribute-level representations; and c) how to effectively sew the multiple granulairites (i.e, coarse-grained item-level and fine-grained attribute-level) information to enable performance improvement and interpretability. To address these challenges, in this work, we present a partially supervised outfit compatibility modeling scheme (PS-OCM). In particular, we first devise a partially supervised attribute-level embedding learning component to disentangle the fine-grained attribute embeddings from the entire visual feature of each item. We then introduce a disentangled completeness regularizer to prevent the information loss during disentanglement. Thereafter, we design a hierarchical graph convolutional network, which seamlessly integrates the attribute- and item-level compatibility modeling, and enables the explainable compatibility reasoning. Extensive experiments on the real-world dataset demonstrate that our PS-OCM significantly outperforms the state-of-the-art baselines. We have r...
Gudigar, A, U., R, Samanth, J, Vasudeva, A, A. J., AA, Nayak, K, Tan, R-S, Ciaccio, EJ, Ooi, CP, Barua, PD, Molinari, F & Acharya, UR 2022, 'Role of Four-Chamber Heart Ultrasound Images in Automatic Assessment of Fetal Heart: A Systematic Understanding', Informatics, vol. 9, no. 2, pp. 34-34.
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The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child’s outcomes. In this regard, artificial intelligence (AI) can be used for the automatic analysis of fetal heart ultrasound images. This study reviews nondeep and deep learning approaches for assessing the fetal heart using standard four-chamber ultrasound images. The state-of-the-art techniques in the field are described and discussed. The compendium demonstrates the capability of automatic assessment of the fetal heart using AI technology. This work can serve as a resource for research in the field.
Gui, L, Xu, S, Xiao, F, Shu, F & Yu, S 2022, 'Non-Line-of-Sight Localization of Passive UHF RFID Tags in Smart Storage Systems', IEEE Transactions on Mobile Computing, vol. 21, no. 10, pp. 3731-3743.
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Gul, F, Mir, A, Mir, I, Mir, S, Islaam, TU, Abualigah, L & Forestiero, A 2022, 'A Centralized Strategy for Multi-Agent Exploration', IEEE Access, vol. 10, pp. 126871-126884.
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Gul, M, Kalam, MA, Mohd Zulkifli, NW, Hj. Hassan, M, Abbas, MM, Yousuf, S, Al-Dahiree, OS, Gaffar Abbas, MK, Ahmed, W & Imran, S 2022, 'Enhancing AW/EP tribological characteristics of biolubricant synthesized from chemically modified cotton methyl-esters by using nanoparticle as additives', Industrial Lubrication and Tribology, vol. 74, no. 4, pp. 411-420.
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PurposeThe purpose of this study is to improve the tribological characteristics of cotton-biolubricant by adding nanoparticles at extreme pressure (EP) conditions in comparison with commercial lubricant SAE-40.Design/methodology/approachThis research involved the synthesis of cotton-biolubricant by transesterification process and then the addition of nanoparticles in it to improve anti wear (AW)/EP tribological behavior. SAE-40 was studied as a reference commercial lubricant. AW/EP characteristics of all samples were estimated by the four-ball tribo-tester according to the American Society for Testing and Materials D2783 standard.FindingsThe addition of 1-Wt.% TiO2 and Al2O3 with oleic acid surfactant in cotton-biolubricant decreased wear scar diameter effectively and enhanced the lubricity, load-wear-index, weld-load and flash-temperature-parameters. This investigation revealed that cotton-biolubricant with TiO2 nano-particle additive is more effective and will help in developing new efficient biolubricant to replace petroleum-based lubricants.Research limitations/implicationsCotton biolubricant with TiO2 nano-particles appeared as an optimistic solution for the global bio-lubricant market.Originality/valueNo one has not studied the cotton biolubricant with nanoparticles for internal combustion engine applications at high temperature and EP conditions.
Gullino, ML, Albajes, R, Al-Jboory, I, Angelotti, F, Chakraborty, S, Garrett, KA, Hurley, BP, Juroszek, P, Lopian, R, Makkouk, K, Pan, X, Pugliese, M & Stephenson, T 2022, 'Climate Change and Pathways Used by Pests as Challenges to Plant Health in Agriculture and Forestry', Sustainability, vol. 14, no. 19, pp. 12421-12421.
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Climate change already challenges people’s livelihood globally and it also affects plant health. Rising temperatures facilitate the introduction and establishment of unwanted organisms, including arthropods, pathogens, and weeds (hereafter collectively called pests). For example, a single, unusually warm winter under temperate climatic conditions may be sufficient to assist the establishment of invasive plant pests, which otherwise would not be able to establish. In addition, the increased market globalization and related transport of recent years, coupled with increased temperatures, has led to favorable conditions for pest movement, invasion, and establishment worldwide. Most published studies indicate that, in general, pest risk will increase in agricultural ecosystems under climate-change scenarios, especially in today’s cooler arctic, boreal, temperate, and subtropical regions. This is also mostly true for forestry. Some pests have already expanded their host range or distribution, at least in part due to changes in climate. Examples of these pests, selected according to their relevance in different geographical areas, are summarized here. The main pathways used by them, directly and/or indirectly, are also discussed. Understanding these pathways can support decisions about mitigation and adaptation measures. The review concludes that preventive mitigation and adaptation measures, including biosecurity, are key to reducing the projected increases in pest risk in agriculture, horticulture, and forestry. Therefore, the sustainable management of pests is urgently needed. It requires holistic solutions, including effective phytosanitary regulations, globally coordinated diagnostic and surveillance systems, pest risk modeling and analysis, and preparedness for pro-active management.
Gunatilake, A, Kodagoda, S & Thiyagarajan, K 2022, 'A Novel UHF-RFID Dual Antenna Signals Combined With Gaussian Process and Particle Filter for In-Pipe Robot Localization', IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 6005-6011.
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Condition assessment of underground infrastructures such as pipe networks is crucial for aging cities around the globe. The development of robotic technologies over the years led to the application of them in the condition assessment of pipe networks. However, there is a gap for accurate localization technology due to the complexity of the environment. In this letter, we propose a novel ultra-high frequency radio frequency identification (UHF-RFID) technology dual antenna system combined with Gaussian process and Particle filter algorithms to achieve millimetre level localization accuracy. The system is capable of achieving millimetre level accuracy over 50m of length without an apparent estimation drift. The results were validated through experiments conducted using an extracted water pipe section.
Gunatilake, A, Kodagoda, S & Thiyagarajan, K 2022, 'Battery-Free UHF-RFID Sensors-Based SLAM for In-Pipe Robot Perception', IEEE Sensors Journal, vol. 22, no. 20, pp. 20019-20026.
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Guo, CA & Guo, YJ 2022, 'A General Approach for Synthesizing Multibeam Antenna Arrays Employing Generalized Joined Coupler Matrix', IEEE Transactions on Antennas and Propagation, vol. 70, no. 9, pp. 7556-7564.
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Guo, E, Li, P, Yu, S & Wang, H 2022, 'Efficient Video Privacy Protection Against Malicious Face Recognition Models', IEEE Open Journal of the Computer Society, vol. 3, pp. 271-280.
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The proliferation of powerful facial recognition systems poses a serious threat to user privacy. Attackers could train highly accurate facial recognition models using public data on social platforms. Therefore, recent works have proposed image pre-processing techniques to protect user privacy. Without affecting people's normal viewing, these techniques add special noises into images, so that it would be difficult for attackers to train models with high accuracy. However, existing protection techniques are mainly designed for image data protection, and they cannot be directly applied for video data because of high computational overhead. In this paper, we propose an efficient protection method for video privacy that exploits unique features of video protection to eliminate computation redundancy for computational acceleration. The evaluation results under various benchmarks demonstrate that our method significantly outperforms the traditional methods by reducing computation overhead by 35.5%.
Guo, H, Dai, R, Xie, M, Peng, LE, Yao, Z, Yang, Z, Nghiem, LD, Snyder, SA, Wang, Z & Tang, CY 2022, 'Tweak in Puzzle: Tailoring Membrane Chemistry and Structure toward Targeted Removal of Organic Micropollutants for Water Reuse', Environmental Science & Technology Letters, vol. 9, no. 4, pp. 247-257.
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Membrane-based water reuse through reverse osmosis (RO) and nanofiltration (NF) faces a critical challenge from organic micropollutants (OMPs). Conventional polyamide RO and NF membranes often lack adequate selectivity to achieve sufficient removal of toxic and harmful OMPs in water. Tailoring membrane chemistry and structure to allow highly selective removal of OMPs has risen as an important topic in membrane-based water reuse. However, a critical literature gap remains to be addressed: how to design membranes for more selective removal of OMPs. In this review, we critically analyzed the roles of membrane chemistry and structure on the removal of OMPs and highlighted opportunities and strategies toward more selective removal of OMPs in the context of water reuse. Specifically, we statistically analyzed rejection of OMPs by conventional polyamide membranes to illustrate their drawbacks on OMPs removal, followed by a discussion on the underlying fundamental mechanisms. Corresponding strategies to tailor membrane properties for improving membrane selectivity against OMPs, including surface modification, nanoarchitecture construction, and deployment of alternative membrane materials, were systematically assessed in terms of water permeance, OMPs rejection, and water-OMPs selectivity. In the end, we discussed the potential and challenges of various strategies for scale-up in real applications.
Guo, H, Wang, J, Li, Z, Lu, H & Zhang, L 2022, 'A non-ferrous metal price ensemble prediction system based on innovative combined kernel extreme learning machine and chaos theory', Resources Policy, vol. 79, pp. 102975-102975.
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Guo, J, Cao, L & Gong, Z 2022, 'Recurrent Coupled Topic Modeling over Sequential Documents', ACM Transactions on Knowledge Discovery from Data, vol. 16, no. 1, pp. 1-32.
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The abundant sequential documents such as online archival, social media, and news feeds are streamingly updated, where each chunk of documents is incorporated with smoothly evolving yet dependent topics. Such digital texts have attracted extensive research on dynamic topic modeling to infer hidden evolving topics and their temporal dependencies. However, most of the existing approaches focus on single-topic-thread evolution and ignore the fact that a current topic may be coupled with multiple relevant prior topics. In addition, these approaches also incur the intractable inference problem when inferring latent parameters, resulting in a high computational cost and performance degradation. In this work, we assume that a current topic evolves from all prior topics with corresponding coupling weights, forming the multi-topic-thread evolution . Our method models the dependencies between evolving topics and thoroughly encodes their complex multi-couplings across time steps. To conquer the intractable inference challenge, a new solution with a set of novel data augmentation techniques is proposed, which successfully discomposes the multi-couplings between evolving topics. A fully conjugate model is thus obtained to guarantee the effectiveness and efficiency of the inference technique. A novel Gibbs sampler with a backward–forward filter algorithm efficiently learns latent time-evolving parameters in a closed-form. In addition, the latent Indian Buffet Process compound distribution is exploited to automatically infer the overall topic number and customize the sparse topic proportions for each sequential document without bias. The proposed method is evaluated on both synthetic and real-world datasets against the competitive baselines, demonstrating its superiority over the baselines in terms of the low per-word perplexity, high coherent topics, and better document time prediction.
Guo, J, Xiao, N, Li, H, He, L, Li, Q, Wu, T, He, X, Chen, P, Chen, D, Xiang, J & Peng, X 2022, 'Transformer-Based High-Frequency Oscillation Signal Detection on Magnetoencephalography From Epileptic Patients', Frontiers in Molecular Biosciences, vol. 9, p. 822810.
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High-frequency oscillations (HFOs), observed within 80–500 Hz of magnetoencephalography (MEG) data, are putative biomarkers to localize epileptogenic zones that are critical for the success of surgical epilepsy treatment. It is crucial to accurately detect HFOs for improving the surgical outcome of patients with epilepsy. However, in clinical practices, detecting HFOs in MEG signals mainly depends on visual inspection by clinicians, which is very time-consuming, labor-intensive, subjective, and error-prone. To accurately and automatically detect HFOs, machine learning approaches have been developed and have demonstrated the promising results of automated HFO detection. More recently, the transformer-based model has attracted wide attention and achieved state-of-the-art performance on many machine learning tasks. In this paper, we are investigating the suitability of transformer-based models on the detection of HFOs. Specifically, we propose a transformer-based HFO detection framework for biomedical MEG one-dimensional signal data. For signal classification, we develop a transformer-based HFO (TransHFO) classification model. Then, we investigate the relationship between depth of deep learning models and classification performance. The experimental results show that the proposed framework outperforms the state-of-the-art HFO classifiers, increasing classification accuracy by 7%. Furthermore, we find that shallow TransHFO (<10 layers) outperforms deep TransHFO models (≥10 layers) on most data augmented factors.
Guo, K & Guo, Y 2022, 'Design and Analysis of an Outer Mover Linear-Rotary Vernier Machine', Journal of Electrical Engineering & Technology, vol. 17, no. 2, pp. 1087-1095.
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Guo, K, Guo, Y & Fang, S 2022, 'Flux Leakage Analytical Calculation in the E-Shape Stator of Linear Rotary Motor With Interlaced Permanent Magnet Poles', IEEE Transactions on Magnetics, vol. 58, no. 8, pp. 1-6.
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Guo, X, Zhang, H, Tang, W, Lu, Z, Hua, C, Siwakoti, YP, Malinowski, M & Blaabjerg, F 2022, 'Overview of Recent Advanced Topologies for Transformerless Dual-Grounded Inverters', IEEE Transactions on Power Electronics, vol. 37, no. 10, pp. 12679-12704.
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Transformerless inverters, most of which are H-bridge inverters, have been widely used and studied in grid-connected power systems in the last decades. However, the H-bridge inverter is affected by the low- and high-frequency common-mode voltage between the input and output terminals, resulting in a large common-mode leakage current. An alternative solution is to connect the ground of the input terminal to the output load or grid, that is, the dual-grounded inverter. In this case, the low- and high-frequency common-mode voltages can be mitigated or eliminated. As a matter of fact, scholars have made several research results on dual-grounded inverters. However, as of now, there is still no literature that comprehensively and systematically summarizes these research results. To fill this gap, this article classifies different types of dual-grounded inverters from the perspective of topology for the first time, and compares and summarizes their advantages and disadvantages. More than 60 works of literature have been reviewed to identify the practical implementation challenges and research opportunities in the application of dual-grounded inverters.
Guo, Y, Li, W, Dong, W, Luo, Z, Qu, F, Yang, F & Wang, K 2022, 'Self-sensing performance of cement-based sensor with carbon black and polypropylene fibre subjected to different loading conditions', Journal of Building Engineering, vol. 59, pp. 105003-105003.
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Different dosages of carbon black (CB) were used to manufacture the cost-effective and highly sensitive polypropylene (PP) fibre cement-based sensors in this paper. The distribution of conductive phases and static electrical resistivity were firstly investigated through microscopic characterization and static resistivity, respectively. Then the self-sensing performance of the CB/PP fibre cementitious composites in response to different loading conditions was comprehensively assessed by cyclic compression, notched bending, and splitting tensile conditions. The results indicate that the improvement of PP fibres on conductivity and self-sensing performance is heavily dependent on the coating efficiency of CB nanoparticles on the surfaces of PP fibres. In particular, the cement-based sensors with excellent CB coating efficiency demonstrate the most promising pre-crack flexural sensing capacity. Additionally, the strain hardening characteristics and damage sensing ability for the intrinsic cement-based sensors were explored by splitting tension together with digital image correlation tracking. Apart from a strong linear correlation between fractional change of resistivity and tensile strain during the strain hardening stage, the distinct sensing characteristics between the strain hardening stage and softening stage can give the diagnosis of damage stage (strain hardening stage or softening stage) and crack width (microcracking or macrocracking). Therefore, the intrinsic CB/PP fibre cementitious composites as robust cement-based sensors can provide a great potential to sense strain and deformation as well as detect crack and damage for concrete infrastructure subjected to various loading conditions.
Guo, Y, Li, W, Dong, W, Wang, K, He, X, Vessalas, K & Sheng, D 2022, 'Self-sensing cement-based sensors with superhydrophobic and self-cleaning capacities after silane-based surficial treatments', Case Studies in Construction Materials, vol. 17, pp. e01311-e01311.
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A novel cement-based sensors was developed with integrated self-sensing superhydrophobicity, and self-cleaning functions in this paper. The synthesis was carried out by penetrating precast graphene nanoplate/cement-based sensors with silane/isopropanol solutions. The silane-treated cement-based sensors showed satisfactory stress/strain sensing performance with an average gauge factor of 141.8, and exhibited excellent hydrophobic behaviour with the highest water contact angle of 163° on the intact surface. The contact angle decreased to 148° and 142°, for the surface with scratches and for the inner part of sensors, respectively. The reduction was due to the spalling and less amount of silane particles within the scratches and the harder entry of silane to the inner part of sensor. The self-cleaning properties of silane-treated cement-based sensor were evaluated by the visual observation of removing efficiency of hydrophilic carbon black dust and lipophilic sauces after water rinsing. It was found that the silane-treated cement-based sensor showed excellent self-cleaning performance using hydrophilic carbon dust. Despite the removing efficiency decreased for the lipophilic sauces, the silane-treated cement-based sensors maintained much less stain than that of untreated ones on the surface. The related results will promote the synthesis and practical applications of multifunctional cement-based sensors for the application of intrisic structural health monitoring.
Guo, Y, Liu, L, Ba, X, Lu, H, Lei, G, Sarker, P & Zhu, J 2022, 'Characterization of Rotational Magnetic Properties of Amorphous Metal Materials for Advanced Electrical Machine Design and Analysis', Energies, vol. 15, no. 20, pp. 7798-7798.
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Amorphous metal (AM), specifically amorphous ferromagnetic metal, is considered as a satisfactory magnetic material for exploring electromagnetic devices with high-efficiency and high-power density, such as electrical machines and transformers, benefits from its various advantages, such as reasonably low power loss and very high permeability in medium to high frequency. However, the characteristics of these materials have not been investigated comprehensively, which limits its application prospects to good-performance electrical machines that have the magnetic flux density with generally rotational and non-sinusoidal features. The appropriate characterization of AMs under different magnetizations is among the fundamentals for utilizing these materials in electrical machines. This paper aims to extensively overview AM property measurement techniques in the presence of various magnetization patterns, particularly rotational magnetizations, and AM property modeling methods for advanced electrical machine design and analysis. Possible future research tasks are also discussed for further improving AM applications.
Guo, Y, Xian, H, Shereen, T, Qiang, F, Jin, X, Daniel, M, Qiao, GG & Zhang, H 2022, 'Feasibility of corneal epithelial transplantation with polyethylene glycol hydrogel membrane as a carrier for limbal stem cell deficiency', Chinese Journal of Experimental Ophthalmology, vol. 40, no. 12, pp. 1125-1133.
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Objective To investigate whether polyethylene glycol hydrogel films (PHFs) can be used as a carrier for the expansion of corneal epithelial cells (CECs) in vitro and whether PHFs can be used in the treatment of limbal stem cell deficiency (LSCD). Methods Sebacoyl chloride, dihydroxyl PCL and glycerol ethoxylate were used to synthesize PHFs. The thickness, transmittance and mechanical tensile properties of PHFs were measured. Four clean-grade New Zealand white rabbits were selected to culture primary limbal epithelial cells. The expression of keratin marker AE1/AE3 and stem cell marker p63 in the cultured cells were observed under a fluorescence microscope. The cells were divided into negative control group cultured with common cell culture solution, positive control group cultured with cell culture solution containing 100 μmol/L H2O2, and PHFs + CECs group lined with PHFs cultured with common cell culture solution for 24 hours. The proliferation and apoptosis of cells in the three groups were observed by MTT and TUNEL staining, respectively. Fifteen clean-grade New Zealand white rabbits were divided into control group, PHFs group and PHFs+CECs group by random number table method, with 5 rabbits in each group. LSCD model was constructed in the three groups. The control group was not given any treatment after modeling. In PHFs group, empty PHFs were placed on the corneal surface of rabbits. In PHFs + CECs group, tissue-engineered grafts constructed with CECs after passage implanted on PHFs were placed on the corneal surface of rabbits. The corneal defect area of rabbits was detected and scored by fluorescein sodium staining. The histological characteristics of rabbits corneal epithelium was observed by hematoxylin-eosin staining. The use and care of animals complied with Guide for the Care and Use of Laboratory Animals by the U. S. National Research Council. The experimental protocol was approved by the Research and Clinical Trial Et...
Guo, Y, Zhang, YX & Soe, K 2022, 'Effect of sodium monofluorophosphate and phosphates on mechanical properties and water resistance of magnesium oxychloride cement', Cement and Concrete Composites, vol. 129, pp. 104472-104472.
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Guo, Z, Lian, M, Wen, S & Huang, T 2022, 'An Adaptive Multi-Agent System With Duplex Control Laws for Distributed Resource Allocation', IEEE Transactions on Network Science and Engineering, vol. 9, no. 2, pp. 389-400.
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In this paper, we present an adaptive multi-agent system with duplex control laws for non-smooth resource allocation problem, where the decisions are subjected to local constraints and network resource constraints. The multi-agent system based on the distance penalty function method is developed in three sets of coupled differential inclusions or equations, where the last set of differential equations are designated to learn an adaptive penalty vector. In the multi-agent system, proportional and integral controls can be performed from two different layers of the multiplex control network with an independent communication topology at each layer. The existence of equilibrium points and convergence of the multi-agent system are proven for achieving optimal resource allocation starting from any initial resource allocation. Finally, the simulation results of two illustrative examples are discussed to substantiate the theoretical results.
Guo, Z, Xiao, F, Sheng, B, Sun, L & Yu, S 2022, 'TWCC: A Robust Through-the-Wall Crowd Counting System Using Ambient WiFi Signals', IEEE Transactions on Vehicular Technology, vol. 71, no. 4, pp. 4198-4211.
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With the widespread of commercial communication equipment, WiFi signals are ubiquitous in human life. Therefore, utilizing WiFi signals to implement intelligent sensing applications is an inevitable trend. In WiFi sensing applications, through-the-wall crowd counting is a challenging problem. In the through-the-wall scenario, the wireless signal transmitted through the wall will carry a lot of noises and is severely attenuated. Therefore, the influence of human activities on the wireless signal is difficult to extract. To solve this problem, we propose TWCC, a through-the-wall crowd counting system using ambient WiFi signals. TWCC utilizes commercial WiFi equipments to extract the phase difference data of the channel state information (CSI) and transform it to sense the environment. First, TWCC preprocesses the data to remove uncorrelated noise, and then combines the sub-carrier correlation to achieve through-the-wall human detection. When people exist, TWCC extracts features from four domains as feature groups, namely time domain, subcarrier domain, frequency domain, and time-frequency domain. Then TWCC uses different backpropagation (BP) neural networks for the features of the four domains and combines with weighting and threshold judgment to realize the through-the-wall crowd counting detection. Extensive real-world experiments show that TWCC achieves an average recognition accuracy of about 90% and maintains strong robustness to different speeds and environments.
Gupta, BB, Chaudhary, P, Chang, X & Nedjah, N 2022, 'Smart defense against distributed Denial of service attack in IoT networks using supervised learning classifiers', Computers & Electrical Engineering, vol. 98, pp. 107726-107726.
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From smart home to industrial automation to smart power grid, IoT- based solutions penetrate into every working field. These devices expand the attack surface and turned out to be an easy target for the attacker as resource constraint nature hinders the integration of heavy security solutions. Because IoT devices are less secured and operate mostly in unattended scenario, they perfectly justify the requirements of attacker to form botnet army to trigger Denial of Service attack on massive scale. Therefore, this paper presents a Machine Learning-based attack detection approach to identify the attack traffic in Consumer IoT (CIoT). This approach operates on local IoT network-specific attributes to empower low-cost machine learning classifiers to detect attack, at the local router. The experimental outcomes unveiled that the proposed approach achieved the highest accuracy of 0.99 which confirms that it is robust and reliable in IoT networks.
Gupta, BB, Tewari, A, Cvitić, I, Peraković, D & Chang, X 2022, 'Artificial intelligence empowered emails classifier for Internet of Things based systems in industry 4.0', Wireless Networks, vol. 28, no. 1, pp. 493-503.
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In this paper, we introduce an approach to secure IoT devices from unsolicited emails by using certain AI-based features and clustering in real-time. We propose a novel approach that first filters the unwanted emails from the incoming traffic and then classifies them into spam and phishing for Internet of Things (IoTs) based systems in industry 4.0. The AI mechanism collects and analyzes emails to detect multiple features that identify patterns for classification. We divided our incoming data into batches and each batch was classified based on knowledge gained from previous batch's classification. We tested our results with a number of classifiers and results show that our approach gives highly accurate classification.
Gupta, D, Borah, P, Sharma, UM & Prasad, M 2022, 'Data-driven mechanism based on fuzzy Lagrangian twin parametric-margin support vector machine for biomedical data analysis', Neural Computing and Applications, vol. 34, no. 14, pp. 11335-11345.
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This paper proposes a fuzzy-based Lagrangian twin parametric-margin support vector machine (FLTPMSVM) to reduce the effect of the outliers presented in biomedical data. The proposed FLTPMSVM assigns the weights to each data sample on the basis of fuzzy membership values to reduce the effect of outliers. This paper also adopts the square of the 2-norm of slack variables to make the objective function more convex. The proposed FLTPMSVM solves simple linearly convergent iterative schemes instead of solving a pair of quadratic programming problems. No external toolbox is required for the proposed FLTPMSVM as compared to the other methods. To establish the applicability of the proposed FLTPMSVM in the area of biomedical data classification, numerical experiments are performed on several biomedical datasets. The proposed FLTPMSVM gives an improved generalization performance and reduced training cost as compared to support vector machine (SVM), twin support vector machine (TWSVM), fuzzy twin support vector machine (FTSVM), twin parametric-margin support vector machine (TPMSVM) and new fuzzy twin support vector machine (NFTSVM).
Gupta, S & Mahmood, AH 2022, 'A multi-method investigation into rheological properties, hydration, and early-age strength of cement composites with admixtures recovered from inorganic and bio-based waste streams', Construction and Building Materials, vol. 347, pp. 128529-128529.
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The selection of mineral admixtures to develop low-carbon cement composites must be diversified to balance the increasing demand and low supply of certain widely used admixtures. The research aims to advance scientific knowledge into the comparative influence of several admixtures recovered from different waste streams including silica fume (SF), fly ash (FA), pulverized glass powder (PGP), biochar (BC), and high-performance ash (HPA) on static yield stress, dynamic yield stress, plastic viscosity, setting, micro-structural build-up, degree of hydration and compressive strength of cementitious paste. Experimental findings show that adding 10 wt% silica fume results in a 300% increase in yield stress (under static and dynamic shear rate) and plastic viscosity than control, attributed to its fine particle size and higher gelling rate during early hydration. HPA leads to an 83% increase in static yield stress compared to control at 70 mins, but the application of a high shear rate leads to a marginal increase (18%) in yield stress than control. PGP leads to improvement in workability by reducing the dynamic and static yield stress by 50 – 55% than the control without significant change in plastic viscosity. Comparing the influence of BC, FA, and PGP, which have comparable particle size distribution in this research, the addition of biochar accelerates micro-structural build-up and final setting, evident from higher ultrasonic pulse velocity and faster hydration kinetics by 1.20 h. Cement pastes with FA, PGP and HPA demonstrate significantly lower compressive strength at 3-day and 7-day age than control but offer similar 28-day strength. The addition of SF and BC do not compromise 3-day and 7-day strength while offering 16% enhancement in 28-day strength than control attributed to a higher degree of hydration due to pozzolanic and filler effects respectively. The findings suggest that PGP, BC, and HPA could be potential alternatives to more widely used FA a...
Gupta, S, Biswas, PK, Babu, TS & Alhelou, HH 2022, 'Hunting Based Optimization Techniques Used in Controlling an Active Magnetic Bearing System', IEEE Access, vol. 10, pp. 62702-62721.
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Gupta, S, Kashani, A & Mahmood, AH 2022, 'Carbon sequestration in engineered lightweight foamed mortar – Effect on rheology, mechanical and durability properties', Construction and Building Materials, vol. 322, pp. 126383-126383.
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Haakenstad, A, Yearwood, JA, Fullman, N, Bintz, C, Bienhoff, K, Weaver, MR, Nandakumar, V, LeGrand, KE, Knight, M, Abbafati, C, Abbasi-Kangevari, M, Abdoli, A, Abeldaño Zuñiga, RA, Adedeji, IA, Adekanmbi, V, Adetokunboh, OO, Afzal, MS, Afzal, S, Agudelo-Botero, M, Ahinkorah, BO, Ahmad, S, Ahmadi, A, Ahmadi, S, Ahmed, A, Ahmed Rashid, T, Aji, B, Akande-Sholabi, W, Alam, K, Al Hamad, H, Alhassan, RK, Ali, L, Alipour, V, Aljunid, SM, Ameyaw, EK, Amin, TT, Amu, H, Amugsi, DA, Ancuceanu, R, Andrade, PP, Anjum, A, Arabloo, J, Arab-Zozani, M, Ariffin, H, Arulappan, J, Aryan, Z, Ashraf, T, Atnafu, DD, Atreya, A, Ausloos, M, Avila-Burgos, L, Ayano, G, Ayanore, MA, Azari, S, Badiye, AD, Baig, AA, Bairwa, M, Bakkannavar, SM, Baliga, S, Banik, PC, Bärnighausen, TW, Barra, F, Barrow, A, Basu, S, Bayati, M, Belete, R, Bell, AW, Bhagat, DS, Bhagavathula, AS, Bhardwaj, P, Bhardwaj, N, Bhaskar, S, Bhattacharyya, K, Bhurtyal, A, Bhutta, ZA, Bibi, S, Bijani, A, Bikbov, B, Biondi, A, Bolarinwa, OA, Bonny, A, Brenner, H, Buonsenso, D, Burkart, K, Busse, R, Butt, ZA, Butt, NS, Caetano dos Santos, FL, Cahuana-Hurtado, L, Cámera, LA, Cárdenas, R, Carneiro, VLA, Catalá-López, F, Chandan, JS, Charan, J, Chavan, PP, Chen, S, Chen, S, Choudhari, SG, Chowdhury, EK, Chowdhury, MAK, Cirillo, M, Corso, B, Dadras, O, Dahlawi, SMA, Dai, X, Dandona, L, Dandona, R, Dangel, WJ, Dávila-Cervantes, CA, Davletov, K, Deuba, K, Dhimal, M, Dhimal, ML, Djalalinia, S, Do, HP, Doshmangir, L, Duncan, BB, Effiong, A, Ehsani-Chimeh, E, Elgendy, IY, Elhadi, M, El Sayed, I, El Tantawi, M, Erku, DA, Eskandarieh, S, Fares, J, Farzadfar, F, Ferrero, S, Ferro Desideri, L, Fischer, F, Foigt, NA, Foroutan, M, Fukumoto, T, Gaal, PA, Gaihre, S, Gardner, WM, Garg, T, Getachew Obsa, A, Ghafourifard, M, Ghashghaee, A, Ghith, N, Gilani, SA, Gill, PS, Goharinezhad, S, Golechha, M, Guadamuz, JS, Guo, Y, Gupta, RD, Gupta, R, Gupta, VK, Gupta, VB, Hamiduzzaman, M, Hanif, A, Haro, JM, Hasaballah, AI, Hasan, MM, Hasan, MT, Hashi, A, Hay, SI, Hayat, K, Heidari, M, Heidari, G, Henry, NJ, Herteliu, C, Holla, R, Hossain, S, Hossain, SJ, Hossain, MBH, Hosseinzadeh, M, Hostiuc, S, Hoveidamanesh, S, Hsieh, VC-R, Hu, G, Huang, J, Huda, MM, Ifeagwu, SC, Ikuta, KS, Ilesanmi, OS, Irvani, SSN, Islam, RM, Islam, SMS, Ismail, NE, Iso, H, Isola, G, Itumalla, R, Iwagami, M, Jahani, MA, Jahanmehr, N & et al. 2022, 'Assessing performance of the Healthcare Access and Quality Index, overall and by select age groups, for 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019', The Lancet Global Health, vol. 10, no. 12, pp. e1715-e1743.
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BACKGROUND: Health-care needs change throughout the life course. It is thus crucial to assess whether health systems provide access to quality health care for all ages. Drawing from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019), we measured the Healthcare Access and Quality (HAQ) Index overall and for select age groups in 204 locations from 1990 to 2019. METHODS: We distinguished the overall HAQ Index (ages 0-74 years) from scores for select age groups: the young (ages 0-14 years), working (ages 15-64 years), and post-working (ages 65-74 years) groups. For GBD 2019, HAQ Index construction methods were updated to use the arithmetic mean of scaled mortality-to-incidence ratios (MIRs) and risk-standardised death rates (RSDRs) for 32 causes of death that should not occur in the presence of timely, quality health care. Across locations and years, MIRs and RSDRs were scaled from 0 (worst) to 100 (best) separately, putting the HAQ Index on a different relative scale for each age group. We estimated absolute convergence for each group on the basis of whether the HAQ Index grew faster in absolute terms between 1990 and 2019 in countries with lower 1990 HAQ Index scores than countries with higher 1990 HAQ Index scores and by Socio-demographic Index (SDI) quintile. SDI is a summary metric of overall development. FINDINGS: Between 1990 and 2019, the HAQ Index increased overall (by 19·6 points, 95% uncertainty interval 17·9-21·3), as well as among the young (22·5, 19·9-24·7), working (17·2, 15·2-19·1), and post-working (15·1, 13·2-17·0) age groups. Large differences in HAQ Index scores were present across SDI levels in 2019, with the overall index ranging from 30·7 (28·6-33·0) on average in low-SDI countries to 83·4 (82·4-84·3) on average in high-SDI countries. Similarly large ranges between low-SDI and high-SDI countries, respectively, were estimated in the HAQ Index for the young (40·4-89·0), working (33·8-82·8), and post-working ...
HaesAlhelou, H, Parthasarathy, H, Nagpal, N, Agarwal, V, Nagpal, H & Siano, P 2022, 'Decentralized Stochastic Disturbance Observer-Based Optimal Frequency Control Method for Interconnected Power Systems With High Renewable Shares', IEEE Transactions on Industrial Informatics, vol. 18, no. 5, pp. 3180-3192.
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Haider, JB, Haque, MI, Hoque, M, Hossen, MM, Mottakin, M, Khaleque, MA, Johir, MAH, Zhou, JL, Ahmed, MB & Zargar, M 2022, 'Efficient extraction of silica from openly burned rice husk ash as adsorbent for dye removal', Journal of Cleaner Production, vol. 380, pp. 135121-135121.
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Rice is the staple food in many countries including Bangladesh. In Bangladesh, >80% of the total irrigated area is planted with rice, which generates a huge amount of rice husk (RH) as a solid waste which requires proper management. This study, therefore, aimed to extract amorphous silica from openly burned rice husk ash (RHA) using a simple method by avoiding calcination or combustion processes. The extracted silica was then applied for the removal of environmental contaminants i.e., methylene blue dye from an aqueous solution. It was found that the yield of silica produced from sulfuric acid-pretreated RHA was 72.4%. The FTIR absorption peaks at 1057 and 783 cm−1 indicate the presence of a highly condensed silica-containing asymmetric and symmetric siloxane (Si–O–Si) network mixture. The broad maximum bond peak intensity at 2θ = 22° by x-ray diffraction analysis also indicates that the produced silica was amorphous with a mesoporous structure. The surface area of sulfuric acid treated RHA-based silica was 183 m2/g. This silica resulted in a maximum adsorption capacity of 107 mg/g of methylene blue at pH 8 with a faster equilibrium reached at 60 min. The mechanistic study indicated that both Langmuir and Freundlich adsorption isotherms were both fitted well which suggested homogeneous adsorbent surfaces involving monolayer and multilayer adsorption processes.
Hakami, M, Pradhan, S & Mastio, E 2022, '“Who you know affects what you know”: Knowledge transfer in the university–private partnership – a social capital perspective', Industry and Higher Education, vol. 36, no. 4, pp. 415-428.
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The research literature on university–private partnerships shows that these partnerships can contribute significantly to the building of a knowledge-based economy. At the heart of this contribution is the practice of knowledge transfer. Through the analytical lens of social capital theory, this paper reports on a systematic review of 23 studies, from 2000 to 2021, on partnerships between universities and private sector organisations. The findings reveal inconsistencies in knowledge transfer, especially from the perspective of the cognitive frame of this theory. Based on these findings, a more rigorous theoretical framework is proposed for the enhancement of knowledge transfer in such partnerships, as moderated by the intermediary factor, and future research directions are suggested.
Hallad, SA, Ganachari, SV, Soudagar, MEM, Banapurmath, NR, Hunashyal, AM, Fattah, IMR, Hussain, F, Mujtaba, MA, Afzal, A, Kabir, MS & Elfasakhany, A 2022, 'Investigation of flexural properties of epoxy composite by utilizing graphene nanofillers and natural hemp fibre reinforcement', Polymers and Polymer Composites, vol. 30, pp. 096739112210936-096739112210936.
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This study aims to determine the optimum reinforcement required to attain the best combination of flexural strength of modified green composites (graphene oxide + hemp fibre reinforced epoxy composites) for potential use in structural applications. An attempt was also made for the combination of graphene and hemp fibres to enhance load-bearing ability. The infusion of hemp and graphene was made by the weight of the base matrix (epoxy composite). Results showed that graphene reinforcement at 0.4 wt.% of matrix showed load-sustaining capacity of 0.76 kN or 760 MPa. In the case of hemp fibre reinforcement at 0.2 wt.% of the matrix, infusion showed enhanced load-bearing ability (0.79 kN or 790 MPa). However, the combination of graphene (0.1 wt.% graphene nanofillers) and hemp (5 wt.% hemp fibre) indicated a load-sustaining ability of 0.425 kN or 425 MPa, whereas maximum deflection was observed for specimen with hemp 7.5 % + graphene 0.2 % with 1.9 mm. Graphene addition to the modified composites in combination with natural fibres showed promising results in enhancing the mechanical properties under study. Moreover, graphene-modified composites exhibited higher thermal resistance compared to natural fibre reinforced composites. However, when nanofiller reinforcement exceeded a threshold value, the composites exhibited reduced flexural strength as a result of nanofiller agglomeration.
Hamdani, H, Sabri, FS, Harapan, H, Syukri, M, Razali, R, Kurniawan, R, Irwansyah, I, Sofyan, SE, Mahlia, TMI & Rizal, S 2022, 'HVAC Control Systems for a Negative Air Pressure Isolation Room and Its Performance', Sustainability, vol. 14, no. 18, pp. 11537-11537.
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The controlled environment room, called an isolation room, has become a must have for medical facilities, due to the spreading of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), to isolate the high risk infected patients. To avoid the transmission of the virus through airborne routes, guidelines were published by the government and the association. A medical facility must comply with this document for high-risk patient treatment. A full-scale N class isolation room was built at Syiah Kuala University to investigate the performance in terms of the controller, temperature, pressure, humidity, and energy consumption. The isolation room was equipped with a proper capacity heating, ventilating, and air conditioning (HVAC) system, which consisted of an air conditioning compressor and a negative pressure generator (NPG), and its installation was ensured to fulfil the guidelines. Since the current NPG was controlled manually, a computer-based control system was designed, implemented, and compared with the manual control. The results showed that the computer-based control outputs better stability of pressure and electric power. For that reason, a computer-based control was chosen in the real case. To investigate the performance of the isolation room, a 24 h experiment was carried out under different parameter setups. The results showed that improvement of the control strategy for temperature and humidity is still necessary. The energy consumption during the activation of the NPG for the recommended negative pressure was slightly different. An additional piece of equipment to absorb the heat from the exhaust air would be promising to improve the energy efficiency.
Hamdi, AMA, Hussain, FK & Hussain, OK 2022, 'Task offloading in vehicular fog computing: State-of-the-art and open issues', Future Generation Computer Systems, vol. 133, pp. 201-212.
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Vehicular fog computing (VFC) has been proposed as a promising solution to overcome the limitations of edge computing. In VFC, the idle resources of moving and parked vehicles can be used for compute-intensive applications of resource-limited vehicles by offloading their tasks to them. For this to succeed, selecting an appropriate target fog node needs to consider various constraints. This paper argues that the selection process should broadly follow the steps needed to form a service level agreement (SLA) to ensure that the right target fog node is selected. We identify the different requirements that need to be addressed in forming such a SLA before surveying the existing literature to determine if the existing approaches of task offloading in VFC address them or not. Based on the analysis, we conclude the paper by discussing open gaps that need to be addressed for efficient task offloading in VFC.
Hamdi, M, Wen, S & Yang, Y 2022, 'BTM: Boundary Trimming Module for Temporal Action Detection', Electronics, vol. 11, no. 21, pp. 3520-3520.
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Temporal action detection (TAD) aims to recognize actions as well as their corresponding time spans from an input video. While techniques exist that accurately recognize actions from manually trimmed videos, current TAD solutions often struggle to identify the precise temporal boundaries of each action, which are required in many real applications. This paper addresses this problem with a novel Boundary Trimming Module (BTM), a post-processing method that adjusts the temporal boundaries of the detected actions from existing TAD solutions. Specifically, BTM operates based on the classification of frames in the input video, aiming to detect the action more accurately by adjusting the surrounding frames of the start and end frames of the original detection results. Experimental results on the THUMOS14 benchmark data set demonstrate that the BTM significantly improves the performance of several existing TAD methods. Meanwhile, we establish a new state of the art for temporal action detection through the combination of BTM and the previous best TAD solution.
Hamidi, BA, Hosseini, SA & Hayati, H 2022, 'Forced torsional vibration of nanobeam via nonlocal strain gradient theory and surface energy effects under moving harmonic torque', Waves in Random and Complex Media, vol. 32, no. 1, pp. 318-333.
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Hamidi, BA, Hosseini, SA, Hayati, H & Hassannejad, R 2022, 'Forced axial vibration of micro and nanobeam under axial harmonic moving and constant distributed forces via nonlocal strain gradient theory', Mechanics Based Design of Structures and Machines, vol. 50, no. 5, pp. 1491-1505.
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Han, C, Han, R, Zhang, X, Xu, Z, Li, W, Yamauchi, Y & Huang, Z 2022, '2D boron nanosheet architectonics: opening new territories by smart functionalization', Journal of Materials Chemistry A, vol. 10, no. 6, pp. 2736-2750.
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The lack of stability hinders the applications of pristine borophene. Functionalization imparts both stability and tunable properties allowing for wide application. This review focuses on the applications of functionalized 2D boron nanosheets.
Han, C, Li, W, Wang, J & Huang, Z 2022, 'Boron leaching: Creating vacancy-rich Ni for enhanced hydrogen evolution', Nano Research, vol. 15, no. 3, pp. 1868-1873.
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Creating vacancy is often highly effective in enhancing the hydrogen evolution performance of transition metal-based catalysts. Vacancy-rich Ni nanosheets have been fabricated via topochemical formation of two-dimentional (2D) Ni2B on graphene precursor followed by boron leaching. Anchored on graphene, a few atomic layered Ni2B nanosheets are first obtained by reduction and annealing. Large number of atomic vacancies are then generated in the Ni2B layer via leaching boron atoms. When used for hydrogen evolution reaction (HER), the vacancy-rich Ni/Ni(OH)2 heterostructure nanosheets demonstrate remarkable performance with a low overpotential of 159 mV at a current density of 10 mA·cm−2 in alkaline solution, a dramatic improvement over 262 mV of its precursor. This enhancement is associated with the formation of vacancies which introduce more active sites for HER along Ni/Ni(OH)2 heterointerfaces. This work offers a facile and universal route to introduce vacancies and improve catalytic activity. [Figure not available: see fulltext.]
Han, C, Yu, X, Gao, C, Sang, N & Yang, Y 2022, 'Single image based 3D human pose estimation via uncertainty learning', Pattern Recognition, vol. 132, pp. 108934-108934.
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In monocular image scenes, 3D human pose estimation exhibits inherent ambiguity due to the loss of depth information and occlusions. Simply regressing body joints with high uncertainties will lead to model overfitting and poor generalization. In this paper, we propose an uncertainty-based framework to jointly learn 3D human poses and the uncertainty of each joint. Our proposed joint estimation framework aims to mitigate the adverse effects of training samples with high uncertainties and facilitate the training procedure. To be specific, we model each body joint as a Laplace distribution for uncertainty representation. Since visual joints often exhibit low uncertainties while occluded ones have high uncertainties, we develop an adaptive scaling factor, named the uncertainty-aware scaling factor, to ease the network optimization in accordance with the joint uncertainties. By doing so, our network is able to converge faster and significantly reduce the adverse effects caused by those ambiguous joints. Furthermore, we present an uncertainty-aware graph convolutional network by exploiting the learned joint uncertainties and the relationships among joints to refine the initial joint localization. Extensive experiments on single-person (Human3.6M) and multi-person (MuCo-3DHP & MuPoTS-3D) 3D human pose estimation datasets demonstrate the effectiveness of our method.
Han, X, Yu, X, Zhu, H, Wang, L, Yu, S, Wang, M & Zheng, M 2022, 'Elastic breakdown via multi-core high frequency discharge for lean-burn ignition', Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 236, no. 12, pp. 2661-2680.
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An advanced ignition technique is developed to achieve multi-event breakdown and multi-site ignition using a single coil for ignition quality improvements. The igniter enables a unique elastic breakdown process embracing a series of high-frequency discharge events at the spark gap. The equivalent electric circuits and current/voltage equations are identified and verified for the first time to explain the working principle that governs such an elastic breakdown process. Benchmarking tests are first performed to compare the elastic breakdown ignition with the conventional and advanced multi-electrode ignition systems. The elastic breakdown and spark events are thereafter analyzed through current and voltage measurements and high-speed imaging techniques. Finally, ignition tests in combustion chambers are performed to examine the effects on the ignition process in comparison with conventional coil-based ignition systems. The experiments show that, the elastic breakdown ignition can distribute multiple high-frequency breakdown events at all electrode pairs of a multi-electrode sparkplug while using only one ignition coil, thereby offering significant cost saving advantage and packaging practicability.
Han, Z, Huo, J, Zhang, X, Ngo, HH, Guo, W, Du, Q, Zhang, Y, Li, C & Zhang, D 2022, 'Characterization and flocculation performance of a newly green flocculant derived from natural bagasse cellulose', Chemosphere, vol. 301, pp. 134615-134615.
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A newly green natural polymer bagasse cellulose based flocculant (PBCF) was synthesized utilizing a grafting copolymerization method for effectively enhancing humic acid (HA) removal from natural water. This work aims to investigate flocculation behavior of PBCF in synthetic water containing HA, and the effects of flocculant dose and initial solution pH on flocculation performance. Results showed that PBCF functioned well at a flocculant dose of 60 mg/L and pH ranging from 6.0 to 9.0. The organic removal efficiency in synthetic water in terms of HA (UV254) and chemical oxygen demand (COD Mn) were up to 90.6% and 91.3%, respectively. Furthermore, the charge neutralization and adsorption bridging played important roles in HA removal. When applied for lake water, PBCF removed 91.6% turbidity and 50.0% dissolved organic matter, respectively. In short, PBCF demonstrates great potential in water treatment in a safe and environmentally friendly or 'green' way.
Hanaei, S, Takian, A, Majdzadeh, R, Maboloc, CR, Grossmann, I, Gomes, O, Milosevic, M, Gupta, M, Shamshirsaz, AA, Harbi, A, Burhan, AM, Uddin, LQ, Kulasinghe, A, Lam, C-M, Ramakrishna, S, Alavi, A, Nouwen, JL, Dorigo, T, Schreiber, M, Abraham, A, Shelkovaya, N, Krysztofiak, W, Ebrahimi Warkiani, M, Sellke, F, Ogino, S, Barba, FJ, Brand, S, Vasconcelos, C, Salunke, DB & Rezaei, N 2022, 'Emerging Standards and the Hybrid Model for Organizing Scientific Events During and After the COVID-19 Pandemic', Disaster Medicine and Public Health Preparedness, vol. 16, no. 3, pp. 1172-1177.
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AbstractSince the beginning of 2020, the coronavirus disease (COVID-19) pandemic has dramatically influenced almost every aspect of human life. Activities requiring human gatherings have either been postponed, canceled, or held completely virtually. To supplement lack of in-person contact, people have increasingly turned to virtual settings online, advantages of which include increased inclusivity and accessibility and a reduced carbon footprint. However, emerging online technologies cannot fully replace in-person scientific events. In-person meetings are not susceptible to poor Internet connectivity problems, and they provide novel opportunities for socialization, creating new collaborations and sharing ideas. To continue such activities, a hybrid model for scientific events could be a solution offering both in-person and virtual components. While participants can freely choose the mode of their participation, virtual meetings would most benefit those who cannot attend in-person due to the limitations. In-person portions of meetings should be organized with full consideration of prevention and safety strategies, including risk assessment and mitigation, venue and environmental sanitation, participant protection and disease prevention, and promoting the hybrid model. This new way of interaction between scholars can be considered as a part of a resilience system, which was neglected previously and should become a part of routine practice in the scientific community.
Hannan, MA, Abd Rahman, MS, Al-Shetwi, AQ, Begum, RA, Ker, PJ, Mansor, M, Mia, MS, Hossain, MJ, Dong, ZY & Mahlia, TMI 2022, 'Impact Assessment of COVID-19 Severity on Environment, Economy and Society towards Affecting Sustainable Development Goals', Sustainability, vol. 14, no. 23, pp. 15576-15576.
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The COVID-19 pandemic has affected every sector in the world, ranging from the education sector to the health sector, administration sector, economic sector and others in different ways. Multiple kinds of research have been performed by research centres, education institutions and research groups to determine the extent of how huge of a threat the COVID-19 pandemic poses to each sector. However, detailed analysis and assessment of its impact on every single target within the 17 Sustainable Development Goals (SDGs) have not been discussed so far. We report an assessment of the impact of COVID-19 effect towards achieving the United Nations SDGs. In assessing the pandemic effects, an expert elicitation model is used to show how the COVID-19 severity affects the positive and negative impact on the 169 targets of 17 SDGs under environment, society and economy groups. We found that the COVID-19 pandemic has a low positive impact in achieving only 34 (20.12%) targets across the available SDGs and a high negative impact of 54 targets (31.95%) in which the most affected group is the economy and society. The environmental group is affected less; rather it helps to achieve a few targets within this group. Our elicitation model indicates that the assessment process effectively measures the mapping of the COVID-19 pandemic impact on achieving the SDGs. This assessment identifies that the COVID-19 pandemic acts mostly as a threat in enabling the targets of the SDGs.
Hao, D, Ma, T, Jia, B, Wei, Y, Bai, X, Wei, W & Ni, B-J 2022, 'Small molecule π-conjugated electron acceptor for highly enhanced photocatalytic nitrogen reduction of BiOBr', Journal of Materials Science & Technology, vol. 109, pp. 276-281.
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Artificial ammonia synthesis using solar energy is of great significance as it can help narrow the gap to the zero-net emission target. However, the current photocatalytic activity is generally too low for mass production. Herein, we report a novel bismuth bromide oxide (BiOBr)-Tetracyanoquinodimethane (TCNQ) photocatalyst prepared via a facile self-assembly method. Due to the well-match band structure of TCNQ and BiOBr, the separation and transfer of photogenerated electron-hole pairs were significantly boosted. More importantly, the abundant delocalized π electrons of TCNQ, and the electron-withdrawing property of TNCQ made electrons efficiently accumulated on the catalysts, which can strengthen the adsorption and cleavage of nitrogen molecules. As a result, the photocatalytic activity increased significantly. The highest ammonia yield of the optimized sample reached 2.617 mg/(h gcat), which was 5.6-fold as that of pristine BiOBr and higher than the reported BiOBr-based photocatalysts. The isotope labeled 15N2 was used to confirm that the ammonia is formed form the fixation of N2. Meanwhile, the sample also had good stability. After 4-time usage, the photocatalysts still had about 81.8% as the fresh sample. The results of this work provide a new way for optimizing the electronic structure of photocatalysts to achieve highly efficient photochemical N2 reduction.
Hao, D, Wei, Y, Mao, L, Bai, X, Liu, Y, Xu, B, Wei, W & Ni, B-J 2022, 'Boosted selective catalytic nitrate reduction to ammonia on carbon/bismuth/bismuth oxide photocatalysts', Journal of Cleaner Production, vol. 331, pp. 129975-129975.
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Using solar energy to catalytically convert nitrate into ammonia is attractive for waste recycling and sustainable development. However, the rapid recombination of electron-hole pairs and the poor selectivity are obstructing photocatalytic nitrate reduction to ammonia to be mass applied currently. In this work, we reported a facile synthesis of carbon/bismuth/bismuth oxide photocatalyst via a one-pot hydrothermal reaction without using reducing reagent. Compared with α-bismuth oxide (α-Bi2O3), the photocatalytic ammonia yield of the optimum sample increased 3.65 times. In addition, the ammonia selectivity increased from 65.21% to 95.00%. The highly enhanced photocatalytic performance was attributed to the surface plasmon resonance of metallic bismuth. Meanwhile, the formation of carbon enables to boost the transfer of electrons significantly. Under light irradiation, electrons can be accumulated on metallic bismuth, effectively boosting the reduction of nitrate. The findings of this work will contribute to the recycling of nitrate for ammonia synthesis and sustainable environmental development.
Hao, J, Zhu, X, Yu, Y, Zhang, C & Li, J 2022, 'Damage localization and quantification of a truss bridge using PCA and convolutional neural network', Smart Structures and Systems, vol. 30, no. 6, pp. 673-686.
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Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of researchers and engineers. These algorithms commonly used loss functions and evaluation indices like the mean square error (MSE) which were not originally designed for SHM problems. An updated loss function which was specifically constructed for deep-learning-based structural damage detection problems has been proposed in this study. By tuning the coefficients of the loss function, the weights for damage localization and quantification can be adapted to the real situation and the deep learning network can avoid unnecessary iterations on damage localization and focus on the damage severity identification. To prove efficiency of the proposed method, structural damage detection using convolutional neural networks (CNNs) was conducted on a truss bridge model. Results showed that the validation curve with the updated loss function converged faster than the traditional MSE. Data augmentation was conducted to improve the anti-noise ability of the proposed method. For reducing the training time, the normalized modal strain energy change (NMSEC) was extracted, and the principal component analysis (PCA) was adopted for dimension reduction. The results showed that the training time was reduced by 90% and the damage identification accuracy could also have a slight increase. Furthermore, the effect of different modes and elements on the training dataset was also analyzed. The proposed method could greatly improve the performance for structural damage detection on both the training time and detection accuracy.
Hao, Y, Xiao, D, Hao, H, Li, J & Li, J 2022, 'Experimental study of reinforced concrete beams reinforced with hybrid spiral-hooked end steel fibres under static and impact loads', Advances in Structural Engineering, vol. 25, no. 15, pp. 3019-3030.
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Discrete short steel fibres were proposed to be mixed with concrete for arresting cracks and enhancing the post-cracking resistance. It has been proven in previous tests that spiral steel fibres possessed markedly higher bonding to concrete matrix, leading to significantly improved performance of steel fibre reinforced concrete (SFRC) in terms of crack controllability, impact resistance, deformability and energy absorption capability. However, at the initial stage of cracking, SFRC reinforced with spiral fibres has relatively lower resistance to crack opening as compared to that reinforced with other types of steel fibres because of spiral shape stretching. To overcome this shortcoming, in the present study, short hooked-end steel fibres that exhibit high pull-out resistance at the crack initiation stage were mixed with spiral steel fibres in the normal-strength concrete matrix. A total volume fraction of 1% of hybrid steel fibres was mixed to cast SFRC specimens. With various mix ratios between spiral and hooked-end fibres considered, five batches of SFRC specimens were tested. Uniaxial compressive tests and four-point bending tests were carried out to compare the mechanical properties of SFRC materials with hybrid fibres while three-point bending tests on SFRC structural beams under static, drop-weight impact and post-impact static loading tests were conducted to investigate the structural performances. An equal dosage of hooked-end and spiral fibres was found to outperform other blend proportions to provide synergetic reinforcement to concrete matrix in terms of post-cracking resistance, energy absorption capacity and post-impact performance.
Hao, Y, Zhang, X, Du, Q, Wang, H, Ngo, HH, Guo, W, Zhang, Y, Long, T & Qi, L 2022, 'A new integrated single-chamber air-cathode microbial fuel cell - Anaerobic membrane bioreactor system for improving methane production and membrane fouling mitigation', Journal of Membrane Science, vol. 655, pp. 120591-120591.
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Harandizadeh, H, Armaghani, DJ, Hasanipanah, M & Jahandari, S 2022, 'A novel TS Fuzzy-GMDH model optimized by PSO to determine the deformation values of rock material', Neural Computing and Applications, vol. 34, no. 18, pp. 15755-15779.
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Haribabu, K, Sivasubramanian, V, Deepanraj, B & Ong, HC 2022, 'Thematic issue: Bioenergy and biorefinery approaches for environmental sustainability', Biomass Conversion and Biorefinery, vol. 12, no. 5, pp. 1433-1433.
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Haris, A, Sepehrirahnama, S, Lee, HP & Lim, K-M 2022, 'Mitigation of vibration of ship structure via local structural modifications', Ships and Offshore Structures, vol. 17, no. 8, pp. 1684-1694.
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Hasan, ASMM, Kabir, MA, Hoq, MT, Johansson, MT & Thollander, P 2022, 'Drivers and barriers to the implementation of biogas technologies in Bangladesh', Biofuels, vol. 13, no. 5, pp. 643-655.
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In Bangladesh, despite available feedstock for producing biogas, the development of biogas production has been very slow. The objective of this research was to study the drivers for and barriers to biogas technology implementation in the country. As the research involved different types of stakeholders related to biogas production, the outcome provides clarity about the factors influencing the profusion of biogas production in Bangladesh. The outcome of the study identifies poor research and development, lack of coordination among stakeholders, an immature biogas market, lack of awareness and no feed-in tariff policy as the main barriers. In the case of drivers, the motivation of producing biogas as an efficient way of using waste, the availability of local experts, the attractiveness of a growing renewable energy market and the contribution of biogas technology in adaptation to climate change were found to be the most important factors. The study’s outcomes are found to be similar to other studies from developing countries with similar socio-economic status. In accordance with the important drivers and barriers identified in this study, recommendations for increasing the diffusion of biogas in Bangladesh are also presented at the end of the article.
Hasan, H 2022, 'NUMERICAL SIMULATION OF PERVIOUS CONCRETE PILE IN LOOSE AND SILTY SAND AFTER TREATING WITH MICROBIALLY INDUCED CALCITE PRECIPITATION', International Journal of GEOMATE, vol. 22, no. 90, pp. 32-39.
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It is essential to provide a stable foundation system for construction projects to reduce the geotechnical risk of failure due to static or dynamic loads. Pile foundations are recommended to increase bearing capacity and decrease the dynamic oscillations of soils. Recently, soil stabilization using microbially induced calcite precipitation (MICP) was widely used to increase shear strength parameters and reduce the hydraulic conductivity of sand. In this study, the technique of using MICP was reviewed based on previous studies and analyzed using Plaxis 3D to evaluate the enhancement of a single pervious concrete pile under static, free vibration and earthquake stages of loose and silty sand. In the static stage, under the applying load to reach prescribed displacement of 76 mm, the results of loose sand demonstrate that the static load capacity was increased from 470 kN of untreated loose sand to 582, 598 and 612 kN after treating by MICP along the shaft and tip of a concrete pile with 0.5,0.75 and 1 m, respectively. In the earthquake stage, the result of treated loose sand such as vertical and lateral displacement was insignificant compared with untreated loose sand. The Plaxis 3D models have clarified the benefit of using MICP with the pile foundation model.
Hasanpour, S, Forouzesh, M & Siwakoti, Y 2022, 'Full Soft-Switching Ultra-High Gain DC/DC Converter Using Three-Winding Coupled-Inductor with Modular Scalability for Renewable Energy Applications', Aut Journal of Electrical Engineering, vol. 54, no. 1, pp. 49-66.
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This paper proposes a new non-isolated single-switch DC/DC converter with an ultra-high voltage gain, low input current ripple, low voltage stress, soft-switching operation, and modular scalability for renewable sources applications. With the help of a Three-Winding Coupled-Inductor (TWCI) and Voltage Multipliers circuits, ultra-high voltage gains can be achieved without needing a large duty cycle. A regenerative clamp capacitor recycles the energy stored in the leakage inductor; thus, the maximum voltage across the single power switch is restricted. Moreover, at the turn-on instant of the power switch, a Zero Current Switching (ZCS) condition is achieved. By designing a resonant tank, the switched current value of the main switch at the turn-off instant is reduced significantly. Additionally, the leakage inductor of the TWCI helps all converter diodes to operate under the ZCS condition. Due to full soft-switching performance, the introduced topology can provide a wide output voltage range under a high conversion efficiency. The steady-state analysis and comprehensive comparisons are provided in this paper. A 160 W prototype with 24 V input and 250 V output voltage is developed to validate the theoretical analysis. Due to ZCS operation and low voltage stress (VDS ≈ 40 V), the power loss portion of the MOSFET is low. Moreover, the maximum voltage stresses of diodes are measured as 40 V, 60 V, 90 V, and 110 V, that are well below the output voltage. Furthermore, at the full-load condition, the input current ripple is about 20 % and the measured efficiency is about 96.3%.
Hasanzadeh, S, Shojaeian, H, Mohsenzadeh, MM, Heydarian-Forushani, E, Alhelou, HH & Siano, P 2022, 'Power Quality Enhancement of the Distribution Network by Multilevel STATCOM-Compensated Based on Improved One-Cycle Controller', IEEE Access, vol. 10, pp. 50578-50588.
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Hason Rudd, D, Huo, H & Xu, G 2022, 'Improved Churn Causal Analysis Through Restrained High-Dimensional Feature Space Effects in Financial Institutions', Human-Centric Intelligent Systems, vol. 2, no. 3-4, pp. 70-80.
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AbstractCustomer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Customer acquisition cost can be five to six times that of customer retention, hence investing in customers with churn risk is wise. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and identify effects and possible causes for churn. In general, this study presents a conceptual framework to discover the confounding features that correlate with independent variables and are causally related to those dependent variables that impact churn. We combine different algorithms including the SMOTE, ensemble ANN, and Bayesian networks to address churn prediction problems on a massive and high-dimensional finance data that is usually generated in financial institutions due to employing interval-based features used in Customer Relationship Management systems. The effects of the curse and blessing of dimensionality assessed by utilising the Recursive Feature Elimination method to overcome the high dimension feature space problem. Moreover, a causal discovery performed to find possible interpretation methods to describe cause probabilities that lead to customer churn. Evaluation metrics on validation data confirm the random forest and our ensemble ANN model, with %86 accuracy, outperformed other approaches. Causal analysis results confirm that some independent causal variables representing the level of super guarantee contribution, account growth, and account balance amount were identified as confounding variables that cause customer churn with a high degree of belief. This article provides a real-world customer churn analysis from current status inference to future directions in local superannuation funds.
Hassan, M, Hossain, J & Shah, R 2022, 'Threshold-free localized scheme for DC fault identification in multiterminal HVDC systems', Electric Power Systems Research, vol. 210, pp. 108081-108081.
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Hassani, S, Mousavi, M & Gandomi, AH 2022, 'A Hilbert transform sensitivity-based model-updating method for damage detection of structures with closely-spaced eigenvalues', Engineering Structures, vol. 268, pp. 114761-114761.
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In this paper, a novel method is proposed for damage detection of structures with closely-spaced eigenvalues. The proposed method uses a transformed form of the condensed frequency response function matrix each of whose columns is obtained as the sum of the unwrapped instantaneous Hilbert phase of the corresponding decomposed column of the original matrix using Empirical Mode Decomposition (EMD) algorithm. A new sensitivity-based model updating equation is then developed, which uses the constructed new matrix as input. The constructed sensitivity-based equation is solved via the least squares method through iterations to update unknown structural damage indices in a finite element model of the structure. To demonstrate the capability of the proposed method, the problem of damage detection in a composite laminate plate and a spatial truss structure, as examples of structures with closely-spaced eigenvalues, is solved. Moreover, the results obtained from the proposed method are compared against two other methods from the literature. The results show that the proposed method is far more effective at updating damage indices when incomplete highly noisy data is available.
Hassani, S, Mousavi, M & Gandomi, AH 2022, 'A mode shape sensitivity-based method for damage detection of structures with closely-spaced eigenvalues', Measurement, vol. 190, pp. 110644-110644.
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Hassani, S, Mousavi, M & Gandomi, AH 2022, 'Damage detection of composite laminate structures using VMD of FRF contaminated by high percentage of noise', Composite Structures, vol. 286, pp. 115243-115243.
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A novel highly robust-to-noise and closely-situated eigenvalues damage detection method is proposed. The proposed method employs the Variational Mode Decomposition (VMD) algorithm to construct a new set of input signals obtained from the rows of the condensed Frequency Response Function (CFRF) to be used in a sensitivity-based model updating problem. Each row of the FRF matrix is replaced by its Unwrapped Instantaneous Hilbert Phase (UIHP). However, since the signal corresponding to the rows of the CFRF might not exhibit the mono-component property, and thus the UIHP will not be well-defined, VMD is used to obtain a set of constructive mono-component modes for each row, whereby the sum of UIHPs (SUIHP) for that row is obtained. The obtained SUIHPs for all rows of the CFRF are stacked up to obtain a new matrix to be fed into the optimisation problem. The proposed method is tested on a composite laminate plate with different configurations, as an example of structures with closely-situated eigenvalues. The results of the application of highly noisy measurement data for damage detection as well as comparison with two other methods demonstrate the superiority of the proposed method in damage detection of structures with closely-situated eigenvalues using highly noisy input data.
Hastings, C 2022, 'How Do Poor Families in Australia Avoid Homelessness? An fsQCA Analysis', Housing, Theory and Society, vol. 39, no. 3, pp. 275-295.
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Hastings, C, Davenport, A & Sheppard, K 2022, 'The loneliness of a long-distance critical realist student: the story of a doctoral writing group', Journal of Critical Realism, vol. 21, no. 1, pp. 65-82.
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Hawileh, RA, Al Nuaimi, N, Nawaz, W, Abdalla, JA & Sohail, MG 2022, 'Flexural and Bond Behavior of Concrete Beams Strengthened with CFRP and Galvanized Steel Mesh Laminates', Practice Periodical on Structural Design and Construction, vol. 27, no. 1.
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Hazrat, MA, Rasul, MG, Khan, MMK, Ashwath, N, Fattah, IMR, Ong, HC & Mahlia, TMI 2022, 'Correction: Biodiesel production from transesterification of Australian Brassica napus L. oil: optimisation and reaction kinetic model development', Environment, Development and Sustainability, vol. 26, no. 1, pp. 2739-2741.
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Unfortunately, the original article contains error in Sect. 3.3. Fuel Composition. The correct data have been provided below in this correction article. 3.3. Fuel composition The fatty acid composition of the produced biodiesel through the optimisation process is shown in Table 8. From the table, it can be seen that Australian canola oil is mostly composed of methyl oleate, with 42.47 wt% included in the composition. This is followed by 27.85 wt% and 16.65 wt% methyl linoleate and methyl linoleate, respectively. A similar FAC was observed by Issariyakul and Dalai (2010) with slight difference in methyl oleate and methyl linolenate percentages. The main component of their canola oil biodiesel is methyl oleate which contains 60.92 wt% of this component. Based on the composition, canola biodiesel contains a total of 12.89 wt% saturated FAME component, 42.61 wt% monounsaturated FAME and 44.5 wt% polyunsaturated FAME. Table 9 compares the properties of produced canola biodiesel and diesel. According to the table, canola oil biodiesel has a 21.5% higher cetane number but a 6% lower LHV than diesel fuel.
Hazrat, MA, Rasul, MG, Khan, MMK, Ashwath, N, Silitonga, AS, Fattah, IMR & Mahlia, TMI 2022, 'Kinetic Modelling of Esterification and Transesterification Processes for Biodiesel Production Utilising Waste-Based Resource', Catalysts, vol. 12, no. 11, pp. 1472-1472.
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Process optimisation and reaction kinetic model development were carried out for two-stage esterification-transesterification reactions of waste cooking oil (WCO) biodiesel. This study focused on these traditional processes due to their techno-economic feasibility, which is an important factor before deciding on a type of feedstock for industrialisation. Four-factor and two-level face-centred central composite design (CCD) models were used to optimise the process. The kinetic parameters for the esterification and transesterification processes were determined by considering both pseudo-homogeneous irreversible and pseudo-homogeneous first-order irreversible processes. For the esterification process, the optimal conditions were found to be an 8.12:1 methanol to oil molar ratio, 1.9 wt.% of WCO for H2SO4, and 60 °C reaction temperature for a period of 90 min. The optimal process conditions for the transesterification process were a 6.1:1 methanol to esterified oil molar ratio, 1.2 wt.% of esterified oil of KOH, reaction temperature of 60 °C, and a reaction time of 110 min in a batch reactor system; the optimal yield was 99.77%. The overall process conversion efficiency was found to be 97.44%. Further research into reaction kinetics will aid in determining the precise reaction process kinetic analysis in future.
He, B, Armaghani, DJ & Lai, SH 2022, 'A Short Overview of Soft Computing Techniques in Tunnel Construction', The Open Construction & Building Technology Journal, vol. 16, no. 1.
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Tunnel construction is a complex technology, with a huge number of effective parameters, which cannot be accurately analyzed/designed using empirical or theoretical methods. With the rapid development of computer technologies, Soft Computing (SC) approaches have been widely used in tunnel construction. Typically, the two common tunneling methods, blasting and mechanical excavation (e.g., tunnel boring machine, shield, pipe jacking method), have been used in conjunction with some SC techniques to solve specific problems and have shown a good fit. On this basis, this paper first summarizes the current research on the application of SC techniques in the field of tunnel construction methods. For example, in the case of blasting, the application of SC techniques is focusing on the environmental problems induced by blasting, such as the prediction of peak particle velocity and over-break. As for mechanical tunnel construction, the SC techniques were used to analyze the boring characteristics of the machine, such as the estimation of penetration rate and advance rate. Additionally, an important aspect for the application of SC techniques is the identification of the influencing factors for each of the study subjects, i.e. the necessary input parameters for the SC. Finally, this paper elaborates on the working process of the supervised learning models, highlights the points that need to be taken care of in each step, and points out that the SC technique, which is synergistic with the physical process, is more useful to explain the actual phenomenon.
He, B, Armaghani, DJ & Lai, SH 2022, 'A Short Overview of Soft Computing Techniques in Tunnel Construction', The Open Construction & Building Technology Journal, vol. 16, no. 1.
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Tunnel construction is a complex technology, with a huge number of effective parameters, which cannot be accurately analyzed/designed using empirical or theoretical methods. With the rapid development of computer technologies, Soft Computing (SC) approaches have been widely used in tunnel construction. Typically, the two common tunneling methods, blasting and mechanical excavation (e.g., tunnel boring machine, shield, pipe jacking method), have been used in conjunction with some SC techniques to solve specific problems and have shown a good fit. On this basis, this paper first summarizes the current research on the application of SC techniques in the field of tunnel construction methods. For example, in the case of blasting, the application of SC techniques is focusing on the environmental problems induced by blasting, such as the prediction of peak particle velocity and over-break. As for mechanical tunnel construction, the SC techniques were used to analyze the boring characteristics of the machine, such as the estimation of penetration rate and advance rate. Additionally, an important aspect for the application of SC techniques is the identification of the influencing factors for each of the study subjects, i.e. the necessary input parameters for the SC. Finally, this paper elaborates on the working process of the supervised learning models, highlights the points that need to be taken care of in each step, and points out that the SC technique, which is synergistic with the physical process, is more useful to explain the actual phenomenon.
He, C, Cheng, R & Yazdani, D 2022, 'Adaptive Offspring Generation for Evolutionary Large-Scale Multiobjective Optimization', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 2, pp. 786-798.
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He, D, Lv, X, Xu, X, Yu, S, Li, D, Chan, S & Guizani, M 2022, 'An Effective Double-Layer Detection System Against Social Engineering Attacks', IEEE Network, vol. 36, no. 6, pp. 92-98.
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He, F, Mahmud, MAP, Kouzani, AZ, Anwar, A, Jiang, F & Ling, SH 2022, 'An Improved SLIC Algorithm for Segmentation of Microscopic Cell Images.', Biomed. Signal Process. Control., vol. 73, pp. 103464-103464.
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He, G, Xu, L, Lei, J, Xie, W, Li, Y, Fan, Y & Zhou, J 2022, 'Interlayer Restoration Deep Neural Network for Scalable High Efficiency Video Coding', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 5, pp. 3217-3234.
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He, L, Wang, X, Chen, H & Xu, G 2022, 'Online Spam Review Detection: A Survey of Literature', Human-Centric Intelligent Systems, vol. 2, no. 1-2, pp. 14-30.
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AbstractThe increasingly developed online platform generates a large amount of online reviews every moment, e.g., Yelp and Amazon. Consumers gradually develop the habit of reading previous reviews before making a decision of buying or choosing various products. Online reviews play an vital part in determining consumers’ purchase choices in e-commerce, yet many online reviews are intentionally created to confuse or mislead potential consumers. Moreover, driven by product reputations and merchants’ profits, more and more spam reviews were inserted into online platform. This kind of reviews can be positive, negative or neutral, but they had common features: misleading consumers or damaging reputations. In the past decade, many people conducted research on detecting spam reviews using statistical or deep learning method with various datasets. In view of that, this article first introduces the task of spam online reviews detection and makes a common definition of spam reviews. Then, we comprehensively conclude the existing method and available datasets. Third, we summarize the existing network-based approaches in dealing with this task and propose some direction for future research.
He, N & Ferguson, S 2022, 'Music emotion recognition based on segment-level two-stage learning', International Journal of Multimedia Information Retrieval, vol. 11, no. 3, pp. 383-394.
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AbstractIn most Music Emotion Recognition (MER) tasks, researchers tend to use supervised learning models based on music features and corresponding annotation. However, few researchers have considered applying unsupervised learning approaches to labeled data except for feature representation. In this paper, we propose a segment-based two-stage model combining unsupervised learning and supervised learning. In the first stage, we split each music excerpt into contiguous segments and then utilize an autoencoder to generate segment-level feature representation. In the second stage, we feed these time-series music segments to a bidirectional long short-term memory deep learning model to achieve the final music emotion classification. Compared with the whole music excerpts, segments as model inputs could be the proper granularity for model training and augment the scale of training samples to reduce the risk of overfitting during deep learning. Apart from that, we also apply frequency and time masking to segment-level inputs in the unsupervised learning part to enhance training performance. We evaluate our model on two datasets. The results show that our model outperforms state-of-the-art models, some of which even use multimodal architectures. And the performance comparison also evidences the effectiveness of audio segmentation and the autoencoder with masking in an unsupervised way.
He, T, Wu, M, Lu, DD-C, Song, K & Zhu, J 2022, 'Model Predictive Sliding Control for Cascaded H-Bridge Multilevel Converters With Dynamic Current Reference Tracking', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 2, pp. 1409-1421.
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He, T-X, Shannon, AG & Shiue, PJ-S 2022, 'Some identities of Gaussian binomial coefficients', Annales Mathematicae et Informaticae, vol. Accepted manuscript.
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In this paper, we present some identities of Gaussian binomial coefficients with respect to recursive sequences, Fibonomial coefficients, and complete functions by use of their relationships.
He, W, Li, X, Morsch, M, Ismail, M, Liu, Y, Rehman, FU, Zhang, D, Wang, Y, Zheng, M, Chung, R, Zou, Y & Shi, B 2022, 'Brain-Targeted Codelivery of Bcl-2/Bcl-xl and Mcl-1 Inhibitors by Biomimetic Nanoparticles for Orthotopic Glioblastoma Therapy', ACS Nano, vol. 16, no. 4, pp. 6293-6308.
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He, X, Wang, F, Li, W & Sheng, D 2022, 'Deep learning for efficient stochastic analysis with spatial variability', Acta Geotechnica, vol. 17, no. 4, pp. 1031-1051.
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Using machine-learning models as surrogate models is a popular technique to increase the computational efficiency of stochastic analysis. In this technique, a smaller number of numerical simulations are conducted for a case, and obtained results are used to train machine-learning surrogate models specific for this case. This study presents a new framework using deep learning, where models are trained with a big dataset covering any soil properties, spatial variabilities, or load conditions encountered in practice. These models are very accurate for new data without re-training. So, the small number of numerical simulations and training process are not needed anymore, which further increases efficiency. The prediction of bearing capacity of shallow strip footings is taken as an example. We start with a simple scenario, and progressively consider more complex scenarios until the full problem is considered. More than 12,000 data are used in training. It is shown that one-hidden-layer fully connected networks can give reasonable results for simple problems, but they are ineffective for complex problems, where deep neural networks show a competitive edge, and a deep-learning model achieves a very high accuracy (the root-mean-square relative error is 3.1% for unseen data). In testing examples, this model is proven very accurate if the parameters of specific cases are well in the defined limits. Otherwise, the capability of deep-learning models can be extended by simply generating more data outside the current limits and re-training the models.
He, Y, Liu, Y, Yan, M, Zhao, T, Liu, Y, Zhu, T & Ni, B-J 2022, 'Insights into N2O turnovers under polyethylene terephthalate microplastics stress in mainstream biological nitrogen removal process', Water Research, vol. 224, pp. 119037-119037.
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The ubiquitous microplastics in wastewater have raised growing concerns due to their unintended effects on microbial activities. However, whether and how microplastics affect nitrous oxide (N2O) (a potent greenhouse gas) turnovers in mainstream biological nitrogen removal (BNR) process remain unclear. This work therefore aimed to fill such knowledge gap by conducting both long-term and batch tests. After over 100 days of feeding with wastewater containing polyethylene terephthalate (PET) microplastics (0-500 μg/L), the long-term results showed that both production and reduction of N2O during denitrification were reduced, as well as the N2O production during nitrification. Accordingly, 60% reduction in N2O accumulation and 70% reduction in N2O production were observed in the denitrification and nitrification batch tests, respectively. Nevertheless, the long-term N2O emission factors under PET microplastics stress were comparable to that in the control reactor, mainly because PET microplastics led to more nitrite accumulation in anoxic period. With the aid of online N2O sensors and site-preference analysis, it was demonstrated that the heterotrophic bacteria pathway and ammonia oxidizing bacteria denitrification pathway for N2O production were negatively affected by PET microplastics, whereas a clear increase in the contribution of hydroxylamine pathway (+ 22.9%) was observed. Further investigation revealed that PET microplastics even at environmental level (i.e. 10 μg/L) significantly reshaped the BNR sludge characteristics (e.g. much larger particle size) and microbial communities (e.g. Thauera, Rhodobacte and Nitrospira) as well as the nitrogen metabolism pathways, which were chiefly responsible for the changes of N2O turnovers and N2O production pathways under the PET microplastics stress.
He, Y, Zhang, X, Xia, Z, Liu, Y, Sood, K & Yu, S 2022, 'Joint optimization of Service Chain Graph Design and Mapping in NFV-enabled networks', Computer Networks, vol. 202, pp. 108626-108626.
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Network Function Virtualization (NFV) is an emerging approach to serve diverse demands of network services by decoupling network functions and dedicated network devices. Traffic needs to traverse through a sequence of software-based Virtual Network Functions (VNFs) in a preset order, which is named as Service Function Chain (SFC). Since network operators usually deploy the same type of VNFs in different locations in NFV-enabled networks. How to steer a SFC request to an appropriate path in substrate networks to meet service demands becomes an important issue, which is typically known as SFC mapping. However, the existing research works on SFC mapping often assume that service chain graphs are given in advance. They do not consider VNF interdependency and traffic volume change, which are both theoretically challenging for NFV Management and Orchestration (MANO) framework. To this end, we study the joint optimization of Service Chain Graph Design and Mapping (SCGDM) in NFV-enabled networks. Our objective is to minimize the maximum link load factor to improve the performance of network system. We first formulate the SCGDM problem as an Integer Linear Programming (ILP) model, and prove that it is an NP-hard problem by reduction from a classical Virtual Network Embedding (VNE) problem. Further, we develop an approximation algorithm using randomized rounding method and analyze the approximation performance. Extensive simulation results show that the proposed algorithm effectively reduce the maximum link load factor.
He, Z, Luo, Q, Li, Q, Zheng, G & Sun, G 2022, 'Fatigue behavior of CFRP/Al adhesive joints — Failure mechanisms study using digital image correlation (DIC) technique', Thin-Walled Structures, vol. 174, pp. 109075-109075.
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Heggart, K & Dickson-Deane, C 2022, 'What should learning designers learn?', Journal of Computing in Higher Education, vol. 34, no. 2, pp. 281-296.
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There is widespread interest in employing designers who focus on learning, performance and education technology in many industries at a global level. In Australia, learning designers are in demand in Education, Corporate Training, Finance, Charity, Non-Government Sectors, and also in Start-Ups and Entrepreneurial arenas. This demand is despite the fact that the role of the Learning Designer is incredibly varied, contextually-based, and also unclear to many employers - and students! This suggests that there is currently an opportunity for learning designers and academics who deliver learning design content to define what it means to be a learning designer. This paper presents an Australian case study which uses design-based research methods in a pre-production mode to identify the key principles that informed the development of a course of study (what others may refer to as a program). How those principles were operationalised within the course design and more are discussed in an effort to reposition understandings of knowledge, skills and abilities for this field.
Heidari, A, Bansal, RC, Hossain, J & Zhu, J 2022, 'Strategic risk aversion of smart energy hubs in the joined energy markets applying a stochastic game approach', Journal of Cleaner Production, vol. 349, pp. 131386-131386.
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Henke, T & Deuse, J 2022, 'Application of heuristics for packing problems to optimise throughput time in fixed position assembly islands', International Journal of Productivity and Quality Management, vol. 36, no. 1, pp. 150-150.
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Henneken, J, Blamires, SJ, Goodger, JQD, Jones, TM & Elgar, MA 2022, 'Population level variation in silk chemistry but not web architecture in a widely distributed orb web spider', Biological Journal of the Linnean Society, vol. 137, no. 2, pp. 350-358.
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Abstract Spider webs are iconic examples of extended phenotypes that are remarkably plastic across different environments. Orb webs are not only effective traps for capturing prey, but can also provide information to potential mates and, in some cases, potential predators and prey through silk-based chemicals. As with regular phenotypic traits, variability in the properties of spider webs is thought to be mediated by a combination of genetic and environmental effects. Here, we examined variation in several key features of the webs of the orb-weaving spider Argiope keyserlingi across five geographically disparate populations. We documented variation in web architecture and chemical properties of webs collected directly from the field. We then probed the potential for the underlying environmental driver of local insect abundance to explain this variation, by analysing the properties of orb webs constructed by the spiders from these different populations, but under identical laboratory conditions. We found no evidence of variation across populations in the architecture of webs constructed in the laboratory, despite the large geographic distances. Nonetheless, we discovered between population variation in the composition of chemicals found on the surface of silk and in the taxonomic distribution of available prey. Furthermore, there was a positive correlation between the quantity of nitrogenous compounds in web silks and female body condition. When combined, these findings suggest that environmental mechanisms can drive variation in web traits across spider populations.
Hesam‐Shariati, N, Chang, W, Wewege, MA, McAuley, JH, Booth, A, Trost, Z, Lin, C, Newton‐John, T & Gustin, SM 2022, 'The analgesic effect of electroencephalographic neurofeedback for people with chronic pain: A systematic review and meta‐analysis', European Journal of Neurology, vol. 29, no. 3, pp. 921-936.
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AbstractBackgroundElectroencephalographic (EEG) neurofeedback has been utilized to regulate abnormal brain activity associated with chronic pain.MethodsIn this systematic review, we synthesized the evidence from randomized controlled trials (RCTs) to evaluate the effect of EEG neurofeedback on chronic pain using random effects meta‐analyses. Additionally, we performed a narrative review to explore the results of non‐randomized studies. The quality of included studies was assessed using Cochrane risk of bias tools, and the GRADE system was used to rate the certainty of evidence.ResultsTen RCTs and 13 non‐randomized studies were included. The primary meta‐analysis on nine eligible RCTs indicated that although there is low confidence, EEG neurofeedback may have a clinically meaningful effect on pain intensity in short‐term. Removing the studies with high risk of bias from the primary meta‐analysis resulted in moderate confidence that there remained a clinically meaningful effect on pain intensity. We could not draw any conclusion from the findings of non‐randomized studies, as they were mostly non‐comparative trials or explorative case series. However, the extracted data indicated that the neurofeedback protocols in both RCTs and non‐randomized studies mainly involved the conventional EEG neurofeedback approach, which targeted reinforcing either alpha or sensorimotor rhythms and suppressing theta and/or beta bands on one brain region at a time. A posthoc analysis of RCTs utilizing the conventional approach resulted in a clinically meaningful effect estimate for pain intensity.ConclusionAlthough there is promising evidence on the analgesic effect of EEG neurofeedback, further studies with larger sample si...
Heydarian-Forushani, E, Alhelou, HH & Elghali, SB 2022, 'Introduction', pp. 1-6.
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Hidayat, A, Cheema, MA, Lin, X, Zhang, W & Zhang, Y 2022, 'Continuous monitoring of moving skyline and top-k queries', The VLDB Journal, vol. 31, no. 3, pp. 459-482.
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Given a set of criteria, an object o dominates another object o′ if o is more preferable than o′ according to every criterion. A skyline query returns every object that is not dominated by any other object. A top-k query returns k most preferred objects according to a given scoring function. In this paper, we study the problem of continuously monitoring moving skyline queries and moving top-k queries where one of the criteria is the distance between the objects and the moving query. We propose safe zone-based techniques to address the challenge of efficiently updating the results as the query moves. A safe zone is the area such that the results of a query remain unchanged as long as the query lies inside this area. Hence, the results are required to be updated only when the query leaves its safe zone. We present several non-trivial optimizations and propose an efficient algorithm for safe zone construction for both the skyline queries and top-k queries. Our techniques for the moving top-k queries are generic in the sense that these are immediately applicable to any top-k query as long as its scoring function is monotonic. Furthermore, we show that the proposed techniques can also be extended to monitor various other queries for different distance metrics. Our experiments demonstrate that the cost of our techniques is reasonably close to a lower bound cost and is several orders of magnitude lower than the cost of a naïve algorithm.
Hieu, NQ, Hoang, DT, Niyato, D, Wang, P, Kim, DI & Yuen, C 2022, 'Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles With Joint Radar-Data Communications', IEEE Transactions on Communications, vol. 70, no. 8, pp. 5164-5180.
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Autonomous Vehicles (AVs) are required to operate safely and efficiently in dynamic environments. For this, the AVs equipped with Joint Radar-Communications (JRC) functions can enhance the driving safety by utilizing both radar detection and data communication functions. However, optimizing the performance of the AV system with two different functions under uncertainty and dynamic of surrounding environments is very challenging. In this work, we first propose an intelligent optimization framework based on the Markov Decision Process (MDP) to help the AV make optimal decisions in selecting JRC operation functions under the dynamic and uncertainty of the surrounding environment. We then develop an effective learning algorithm leveraging recent advances of deep reinforcement learning techniques to find the optimal policy for the AV without requiring any prior information about surrounding environment. Furthermore, to make our proposed framework more scalable, we develop a Transfer Learning (TL) mechanism that enables the AV to leverage valuable experiences for accelerating the training process when it moves to a new environment. Extensive simulations show that the proposed transferable deep reinforcement learning framework reduces the obstacle miss detection probability by the AV up to 67% compared to other conventional deep reinforcement learning approaches. With the deep reinforcement learning and transfer learning approaches, our proposed solution can find its applications in a wide range of autonomous driving scenarios from driver assistance to full automation transportation.
Hill, M & Tran, N 2022, 'miRNA:miRNA Interactions: A Novel Mode of miRNA Regulation and Its Effect On Disease', Adv Exp Med Biol, vol. 1385, pp. 241-257.
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MicroRNAs (miRNAs) are known for their role in the post-transcriptional regulation of messenger RNA (mRNA). However, recent evidence has shown that miRNAs are capable of regulating non-coding RNAs, including miRNAs, in what is known as miRNA:miRNA interactions. There are three main models for the interplay between miRNAs. These involve direct interaction between two miRNAs, either in their mature or primary form, the subsequent changes in miRNA expression due to miRNA-directed transcriptional changes, and the cell-wide impact on miRNA and mRNA levels as a result of miRNA manipulation. Networks of mRNA and miRNA regulatory connections are invaluable to the discovery of miRNA:miRNA pathways, but this cannot be applied without consideration of the specific cell type or condition.In this chapter, we discuss what is understood about miRNA:miRNA interactions, their mechanisms and consequences in disease biology, and suggest further avenues of investigation based on current gaps in the literature and in our understanding of miRNA biology. We also address the pitfalls in contemporary methods relating to the identification of miRNA:miRNA interactions. Future work in this area may ultimately change the definitional role of miRNAs, and have far-reaching impacts on therapeutic and diagnostic developments.
Ho, TK, Feng, J, Bilawal, F, Shehab, SH, Trinh, KT, Yang, Y, Rudiger, C, Walker, JP & Karmakar, NC 2022, 'Lightweight and Compact Radiometers for Soil Moisture Measurement: A review', IEEE Geoscience and Remote Sensing Magazine, vol. 10, no. 1, pp. 231-250.
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Fresh water is considered to be one of the most precious natural resources in many countries. Given that soil moisture plays a salient role in vegetation growth, the continuous and timely measurement of soil moisture content is critical to manage water shortages, especially in the agriculture sector [1], [2], which is the largest water use sector in Australia. A 10% water savings in the Australian agriculture sector would be equivalent to a reduction of more than 30% of the combined water consumption from Australian capital cities [3]. Further, soil moisture influences the process of rainfall being partitioned into runoff and infiltration, and saturated soil could turn heavy rain into floods. Therefore, soil moisture information is essential for accurate climate forecasting as well as improving flood and drought prediction [3]-[5].
Ho, W-HJ, Law, AMK, Masle-Farquhar, E, Castillo, LE, Mawson, A, O’Bryan, MK, Goodnow, CC, Gallego-Ortega, D, Oakes, SR & Ormandy, CJ 2022, 'Activation of the viral sensor oligoadenylate synthetase 2 (Oas2) prevents pregnancy-driven mammary cancer metastases', Breast Cancer Research, vol. 24, no. 1.
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AbstractBackgroundThe interferon response can influence the primary and metastatic activity of breast cancers and can interact with checkpoint immunotherapy to modulate its effects. UsingN-ethyl-N-nitrosourea mutagenesis, we found a mouse with an activating mutation in oligoadenylate synthetase 2 (Oas2), a sensor of viral double stranded RNA, that resulted in an interferon response and prevented lactation in otherwise healthy mice.MethodsTo determine if sole activation ofOas2could alter the course of mammary cancer, we combined theOas2mutation with theMMTV-PyMToncogene model of breast cancer and examined disease progression and the effects of checkpoint immunotherapy using Kaplan–Meier survival analysis with immunohistochemistry and flow cytometry.ResultsOas2mutation prevented pregnancy from increasing metastases to lung. Checkpoint immunotherapy with antibodies against programmed death-ligand 1 was more effective when theOas2mutation was present.ConclusionsThese data establish OAS2 as a therapeutic target for agents designed to reduce metastases and increase the effectiveness of checkpoint immunotherapy.
Hoang, AT, Foley, AM, Nižetić, S, Huang, Z, Ong, HC, Ölçer, AI, Pham, VV & Nguyen, XP 2022, 'Energy-related approach for reduction of CO2 emissions: A critical strategy on the port-to-ship pathway', Journal of Cleaner Production, vol. 355, pp. 131772-131772.
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The maritime sector has been searching for efficient solutions to change energy consumptions patterns of ports and ships to ensure sustainable operation and to reduce CO2 emissions to support sustainable transport in line with International Maritime Organization (IMO) policy guidelines. Therefore, pursuing smart strategies by utilizing renewable energy sources, clean fuels, smart grid, as well as measures of efficient-energy use are beneficial towards attaining the core goals of the IMO, specifically CO2 emission reduction in the future. In this review work, the main methods and criteria for monitoring CO2 emission from ports and ships are meticulously presented. Advanced renewable energy technologies connected with sources such as solar, wind, tidal, wave, and alternative fuels and their application in ports to reduce CO2 are thoroughly examined. In addition, energy-saving techniques and strategies for alternative power and fuels in ships are comprehensively evaluated. The key finding is that port-to-ship interactions such as using zero-emission energy sources or nearly zero-emission approaches could offer significant benefits for CO2 emission reduction. Finally, it is recommended that smart approaches associated with efficient and clean energy use for the port-to-ship pathways to generate net zero-CO2 emissions for the maritime shipping sector need further urgent investigation.
Hoang, AT, Huang, Z, Nižetić, S, Pandey, A, Nguyen, XP, Luque, R, Ong, HC, Said, Z, Le, TH & Pham, VV 2022, 'Characteristics of hydrogen production from steam gasification of plant-originated lignocellulosic biomass and its prospects in Vietnam', International Journal of Hydrogen Energy, vol. 47, no. 7, pp. 4394-4425.
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Hoang, D & Hoang, S 2022, 'Deep learning - cancer genetics and application of deep learning to cancer oncology', Vietnam Journal of Science and Technology, vol. 60, no. 6, pp. 885-928.
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Arguably the human body has been one of the most sophisticated systems we encounter but until now we are still far from understanding its complexity. We have been trying to replicate human intelligence by way of artificial intelligence but with limited success. We have discovered the molecular structure in terms of genetics, performed gene editing to change an organism’s DNA and much more, but their translatability into the field of oncology has remained limited. Conventional machine learning methods achieved some degree of success in solving problems that we do not have an explicit algorithm. However, they are basically shallow learning methods, not rich enough to discover and extract intricate features that represent patterns in the real environment. Deep learning has exceeded human performance in pattern recognition as well as strategic games and are powerful for dealing with many complex problems. High-throughput sequencing and microarray techniques have generated vast amounts of data and allowed the comprehensive study of gene expression in tumor cells. The application of deep learning with molecular data enables applications in oncology with information not available from clinical diagnosis. This paper provides fundamental concepts of deep learning, an essential knowledge of cancer genetics, and a review of applications of deep learning to cancer oncology. Importantly, it provides an insightful knowledge of deep learning and an extensive discussion on its challenges. The ultimate purpose is to germinate ideas and facilitate collaborations between cancer biologists and deep learning researchers to address challenging oncological problems using advanced deep learning technologies.
Hoang, LM, Andrew Zhang, J, Nguyen, DN & Thai Hoang, D 2022, 'Frequency Hopping Joint Radar-Communications With Hybrid Sub-Pulse Frequency and Duration Modulation', IEEE Wireless Communications Letters, vol. 11, no. 11, pp. 2300-2304.
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Frequency-hopping (FH) joint radar-communications (JRC) can offer excellent security for integrated sensing and communication systems. However, existing JRC schemes mainly embed information using only the sub-pulse frequencies and hence the data rate is limited. In this letter, we propose to use both sub-pulse frequencies and durations for information modulation, leading to higher communication data rates. For information demodulation, we propose a novel scheme by using the time-frequency analysis (TFA) technique and a 'you only look once' (YOLO)-based detection system. As such, our system does not require channel estimation, simplifying the transmission signal frame design. Simulation results demonstrate the effectiveness of our scheme, and show that it is robust against the Doppler shift and timing offset between the transceiver and the communication receiver.
Hoang, PM, Tuan, HD, Son, TT, Poor, HV & Hanzo, L 2022, 'Learning Unbalanced and Sparse Low-Order Tensors', IEEE Transactions on Signal Processing, vol. 70, pp. 5624-5638.
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Efficient techniques are developed for completing unbalanced and sparse low-order tensors, which cannot be effectively completed by popular matrix-rank optimization based techniques such as compressed sensing and/or the ℓq-matrix-metric. We use our previously developed 2D-index encoding technique for tensor augmentation in order to represent these incomplete low-order tensors by high-order but low-dimensional tensors with their modes building up a coarse-grained hierachy of correlations among the incomplete tensor entries. The concept of tensor-trains is then exploited for decomposing these augmented tensors into trains of balanced and sparse matrices for efficient completion. More explicitly, we develop powerful algorithms exhibiting an excellent performance vs. complexity trade-off, which are supported by numerical examples by relying on matrix data and third-order tensor data derived from color image pixels.
Hong, H, Li, X, Pan, Y & Tsang, IW 2022, 'Domain-Adversarial Network Alignment', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 7, pp. 3211-3224.
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Network alignment is a critical task to a wide variety of fields. Many existing works leverage on representation learning to accomplish this task without eliminating domain representation bias induced by domain-dependent features, which yield inferior alignment performance. This paper proposes a unified deep architecture (DANA) to obtain a domain-invariant representation for network alignment via an adversarial domain classifier. Specifically, we employ the graph convolutional networks to perform network embedding under the domain adversarial principle, given a small set of observed anchors. Then, the semi-supervised learning framework is optimized by maximizing a posterior probability distribution of observed anchors and the loss of a domain classifier simultaneously. We also develop a few variants of our model, such as, direction-aware network alignment, weight-sharing for directed networks and simplification of parameter space. Experiments on three real-world social network datasets demonstrate that our proposed approaches achieve state-of-the-art alignment results.
Hong, X, Zhou, X, Li, S, Feng, Y & Ying, M 2022, 'A Tensor Network based Decision Diagram for Representation of Quantum Circuits.', ACM Trans. Design Autom. Electr. Syst., vol. 27, pp. 60:1-60:1.
Hoque, MA, Sohel, MSI, Rahman, MM & Ahmed, N 2022, 'Site suitability analysis for the construction of water reservoirs in drought‐prone areas of Bangladesh using geospatial techniques', World Water Policy, vol. 8, no. 1, pp. 65-85.
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AbstractDrought is becoming a common phenomenon in the northwestern (NW) region of Bangladesh. To address this problem, the government has taken multiple initiatives such as the construction of deep tube wells, re‐activation of abandoned deep tube wells, re‐excavation of canals, construction of cross dams, and digging wells to hold water during monsoon. However, the construction of medium‐ to large‐sized water reservoirs is somewhat overlooked or missed, which can hold large quantities of surface water and, at the same time, can recharge groundwater. Finding suitable sites for water reservoirs is a challenge. The present study aims to fulfill these gaps. We have applied a spatial multi‐criteria technique called the analytical hierarchy process (AHP) to identify suitable sites for water reservoir construction in the drought‐prone NW region of Bangladesh. A total of 12 criteria such as settlement, land use, slope, soil, groundwater depth, road network, river network, vegetation cover, rainfall, geology, protected areas, and aquifer depth were selected. The study shows that 17 percent of the area is highly suitable for reservoir construction, followed by 24 percent moderately suitable and 25 percent marginally suitable. In contrast, approximately 30 percent of the area is unsuitable for reservoir construction. Among 16 districts of northwest region, areas of Rangpur district are mostly suitable (30 percent), followed by Gaibandha district (16 percent). Construction of water reservoir in the identified areas will lower irrigation water pumping costs and will raise groundwater levels in land adjacent to the reservoir in dry season.
Horng, S-J, Supardi, J, Zhou, W, Lin, C-T & Jiang, B 2022, 'Recognizing Very Small Face Images Using Convolution Neural Networks', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 2103-2115.
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Horng, S-J, Vu, D-T, Nguyen, T-V, Zhou, W & Lin, C-T 2022, 'Recognizing Palm Vein in Smartphones Using RGB Images', IEEE Transactions on Industrial Informatics, vol. 18, no. 9, pp. 5992-6002.
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Hossain, SM, Ibrahim, I, Choo, Y, Razmjou, A, Naidu, G, Tijing, L, Kim, J-H & Shon, HK 2022, 'Preparation of effective lithium-ion sieve from sludge-generated TiO2', Desalination, vol. 525, pp. 115491-115491.
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Hossain, SM, Tijing, L, Suzuki, N, Fujishima, A, Kim, J-H & Shon, HK 2022, 'Visible light activation of photocatalysts formed from the heterojunction of sludge-generated TiO2 and g-CN towards NO removal', Journal of Hazardous Materials, vol. 422, pp. 126919-126919.
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The feasibility of preparing TiO2/g-CN heterojunction from Ti-incorporated dried dye wastewater sludge is explored in this study. Two reaction routes of composite formation were evaluated. In the initial approach, one-step calcination of dried sludge and melamine mixture @600 °C was carried out. Detailed morphological and chemical characterizations showed that the one-step calcination route did not create TiO2/g-CN composites; instead, only N-doped anatase TiO2 composites were formed. Moreover, due to the non-uniform composition of organic content in the dried sludge, it was not easy to control the N doping level by varying melamine content (0-100%) in the precursor mix. However, successful formation of anatase TiO2 and g-CN was observed when a two-step calcination route was followed, i.e., via synthesis of anatase TiO2 from dried sludge, and later development of heterojunction by calcining (@550 °C) the TiO2 and melamine mixture. X-ray diffraction along with infrared and X-ray photoelectron spectroscopy verified the effective heterojunction. In addition, maximum atmospheric NO removal under UV and visible light were observed for the prepared composite when the melamine content in the precursor mixture was 70%. After 1 h of UV and visible light irradiation, the best TiO2/g-CN composite removed 25.71% and 13.50% of NO, respectively. Optical characterization suggested that the enhanced NO oxidation under UV/visible light was due to the bandgap narrowing and diminished photogenerated electron-hole recombination.
Hosseinzadeh, A, Zhou, JL, Altaee, A & Li, D 2022, 'Machine learning modeling and analysis of biohydrogen production from wastewater by dark fermentation process', Bioresource Technology, vol. 343, pp. 126111-126111.
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Dark fermentation process for simultaneous wastewater treatment and H2 production is gaining attention. This study aimed to use machine learning (ML) procedures to model and analyze H2 production from wastewater during dark fermentation. Different ML procedures were assessed based on the mean squared error (MSE) and determination coefficient (R2) to select the most robust models for modeling the process. The research showed that gradient boosting machine (GBM), support vector machine (SVM), random forest (RF) and AdaBoost were the most appropriate models, which were optimized by grid search and deeply analyzed by permutation variable importance (PVI) to identify the relative importance of process variables. All four models demonstrated promising performances in predicting H2 production with high R2 values (0.893, 0.885, 0.902 and 0.889) and small MSE values (0.015, 0.015, 0.016 and 0.015). Moreover, RF-PVI demonstrated that acetate, butyrate, acetate/butyrate, ethanol, Fe and Ni were of high importance in decreasing order.
Hosseinzadeh, A, Zhou, JL, Li, X, Afsari, M & Altaee, A 2022, 'Techno-economic and environmental impact assessment of hydrogen production processes using bio-waste as renewable energy resource', Renewable and Sustainable Energy Reviews, vol. 156, pp. 111991-111991.
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Hosseinzadeh, A, Zhou, JL, Zyaie, J, AlZainati, N, Ibrar, I & Altaee, A 2022, 'Machine learning-based modeling and analysis of PFOS removal from contaminated water by nanofiltration process', Separation and Purification Technology, vol. 289, pp. 120775-120775.
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Houshyar, S, Rifai, A, Zizhou, R, Dekiwadia, C, Booth, MA, John, S, Fox, K & Truong, VK 2022, 'Liquid metal polymer composite: Flexible, conductive, biocompatible, and antimicrobial scaffold', Journal of Biomedical Materials Research Part B: Applied Biomaterials, vol. 110, no. 5, pp. 1131-1139.
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AbstractGallium and its alloys, such as eutectic gallium indium alloy (EGaIn), a form of liquid metal, have recently attracted the attention of researchers due to their low toxicity and electrical and thermal conductivity for biomedical application. However, further research is required to harness EGaIn‐composites advantages and address their application as a biomedical scaffold. In this research, EGaIn‐polylactic acid/polycaprolactone composites with and without a second conductive filler, MXene, were prepared and characterized. The addition of MXene, into the EGaIn‐composite, can improve the composite's electrochemical properties by connecting the liquid metal droplets resulting in electrically conductive continuous pathways within the polymeric matrix. The results showed that the composite with 50% EGaIn and 4% MXene, displayed optimal electrochemical properties and enhanced mechanical and radiopacity properties. Furthermore, the composite showed good biocompatibility, examined through interactions with fibroblast cells, and antibacterial properties against methicillin‐resistant Staphylococcus aureus. Therefore, the liquid metal (EGaIn) polymer composite with MXene provides a first proof‐of‐concept engineering scaffold strategy with low toxicity, functional electrochemical properties, and promising antimicrobial properties.
Hsiao, L, Chen, Y-J, Xiong, H & Liu, H 2022, 'Incentives for disclosing the store brand supplier', Omega, vol. 109, pp. 102590-102590.
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Hu, B, Bao, G, Xu, X & Yang, K 2022, 'Topical hemostatic materials for coagulopathy', Journal of Materials Chemistry B, vol. 10, no. 12, pp. 1946-1959.
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We present a thorough analysis on the hemostatic mechanisms and the design principles of hemostatic materials for coagulopathy, survey their remarkable success, and briefly discuss the challenges and perspectives for their clinical translation.
Hu, JY, Zhang, SS, Chen, E & Li, WG 2022, 'A review on corrosion detection and protection of existing reinforced concrete (RC) structures', Construction and Building Materials, vol. 325, pp. 126718-126718.
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Performance deterioration of existing reinforced concrete (RC) structures due to corrosion of inside steel reinforcement has been a worldwide issue for long, in particular for RC structures in aggressive environments. Although extensive research on steel corrosion has been carried out over past decades, it is still a challenging problem in civil engineering. Starting from a brief introduction on corrosion mechanism of steel in concrete, this paper presents a comprehensive review on corrosion detection techniques and protection methods for existing RC structures where corrosion has already occurred. Direct detection methods based on electrochemical and physical principles related to the steel corrosion process, and indirect methods based on measurement of corrosion-induced damages in reinforced concrete are critically reviewed, with the basic working mechanism and state of the art of each method given. According to protecting mechanism, corrosion protection methods are categorized into “prevention solutions” and “therapy solutions”, with the former including high-performance fiber-reinforced cementitious composite (HPFRCC) overlay, anti-corrosion coating and corrosion inhibitor while the latter including cathodic protection (CP) and electrochemical chloride extraction (ECE). Among them, HPFRCC overlay is regarded as effective in corrosion prevention due to its high durability although it is mainly used for strengthening because of its excellent mechanical properties, while carbon fiber reinforced polymer (CFRP) can be acted as both strengthening material and anode in CP and ECE. The dual functions of these materials make them very promising in protecting corrosion-damaged RC structures. The paper aims to not only provide useful information to researchers working on detection and protection of steel corrosion, but also shed lights on the advanced strengthening strategies for corrosion-damaged structures.
Hu, S, Ni, W, Wang, X & Jamalipour, A 2022, 'Disguised Tailing and Video Surveillance With Solar-Powered Fixed-Wing Unmanned Aerial Vehicle', IEEE Transactions on Vehicular Technology, vol. 71, no. 5, pp. 5507-5518.
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Disguised tailing and visual monitoring of suspicious mobile targets is a promising application of security unmanned aerial vehicles (UAVs). But trajectory planning is non-trivial, especially for fixed-wing UAVs with more constrained maneuverability and dynamic models. This paper proposes a new framework to optimize collectively the propulsion power and the three-dimensional (3D) trajectory of a solar-powered, fixed-wing UAV on a disguised tailing and video surveillance mission. The multi-objective optimization strikes a balance between distance keeping, elevation variance, and power efficiency. A key aspect is that we develop a new propulsion power model of the fixed-wing UAV by analyzing the forces undergone while the UAV is ascending or descending. Another important aspect is a series of non-trivial reformulations, which convexify the multi-objective problem progressively with increasingly tightening linear approximation and solve the problem with a polynomial time-complexity. Our algorithm can control the trajectory of the UAV on-the-fly. Simulations confirm that the algorithm outperforms existing schemes in terms of visual disguise and power efficiency. The fixed-wing UAV also demonstrates its advantage of energy efficiency and sustainability to elongate the surveillance mission, over its rotary-wing counterpart.
Hu, S, Yu, S, Li, H & Piuri, V 2022, 'Guest Editorial Special Issue on Security, Privacy, and Trustworthiness in Intelligent Cyber–Physical Systems and Internet of Things', IEEE Internet of Things Journal, vol. 9, no. 22, pp. 22044-22047.
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Hu, S, Yuan, X, Ni, W & Wang, X 2022, 'Trajectory Planning of Cellular-Connected UAV for Communication-Assisted Radar Sensing', IEEE Transactions on Communications, vol. 70, no. 9, pp. 6385-6396.
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Hu, X, Jin, Z, Zhang, L, Zhou, A & Ye, D 2022, 'Privacy preservation auction in a dynamic social network', Concurrency and Computation: Practice and Experience, vol. 34, no. 16.
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SummaryThe growing popularity of users in online social network gives a big opportunity for online auction. The famous Information Diffusion Mechanism (IDM) is an excellent methods even meet the incentive compatibility and individual rationality. Although the existing auction in online social network has considered the buyers' information has not known by the seller, current mechanism still cannot preserve the information such as prices. In this paper, we propose a novel mechanism which modeled the auction process in online social network and preserved users' privacy by using differential privacy mechanism. Our mechanism can successfully process the auction and at the same time preserve clients' price information from neighbors. We achieved these by adding Laplace noise for its valuation and the number of valuation seller received in the auction process. We also formulate this mechanism on the real network to show the feasibility and effective of the proposed mechanism.
Hu, X, Li, X, Wen, S, Li, X, Zeng, T, Zhang, J, Wang, W, Bi, Y, Zhang, Q, Tian, S-H, Min, J, Wang, Y, Liu, G, Huang, H, Peng, M, Zhang, J, Wu, C, Li, Y-M, Sun, H, Ning, G & Chen, L 2022, 'Predictive Modeling the Probability of Suffering from Metabolic Syndrome Using Machine Learning: A Population-Based Study', Heliyon, vol. 8, no. 12, p. e12343.
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BACKGROUND: There is an increasing trend of Metabolic syndrome (MetS) prevalence, which has been considered as an important contributor for cardiovascular disease (CVD), cancers and diabetes. However, there is often a long asymptomatic phase of MetS, resulting in not diagnosed and intervened so timely as needed. It would be very helpful to explore tools to predict the probability of suffering from MetS in daily life or routinely clinical practice. OBJECTIVE: To develop models that predict individuals' probability of suffering from MetS timely with high efficacy in general population. METHODS: The present study enrolled 8964 individuals aged 40-75 years without severe diseases, which was a part of the REACTION study from October 2011 to February 2012. We developed three prediction models for different scenarios in hospital (Model 1, 2) or at home (Model 3) based on LightGBM (LGBM) technique and corresponding logistic regression (LR) models were also constructed for comparison. Model 1 included variables of laboratory tests, lifestyles and anthropometric measurements while model 2 was built with components of MetS excluded based on model 1, and model 3 was constructed with blood biochemical indexes removed based on model 2. Additionally, we also investigated the strength of association between the predictive factors and MetS, as well as that between the predictors and each component of MetS. RESULTS: In this study, 2714 (30.3%) participants suffer from MetS accordingly. The performances of the LGBM models in predicting the probability of suffering from MetS produced good results and were presented as follows: model 1 had an area under the curve (AUC) value of 0.993 while model 2 indicated an AUC value of 0.885. Model 3 had an AUC value of 0.859, which is close to that of model 2. The AUC values of LR model 1 and 2 for the scenario in hospital and model 3 at home were 0.938, 0.839 and 0.820 respectively, which seemed lower than that of their corresponding ...
Huang, C, Yao, L, Wang, X, Sheng, QZ, Dustdar, S, Wang, Z & Xu, X 2022, 'Intent-Aware Interactive Internet of Things for Enhanced Collaborative Ambient Intelligence', IEEE Internet Computing, vol. 26, no. 5, pp. 68-75.
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Huang, G, Zhu, Y, Wen, S, Mei, H, Liu, Y, Wang, D, Maddahfar, M, Su, QP, Lin, G, Chen, Y & Jin, D 2022, 'Single Small Extracellular Vesicle (sEV) Quantification by Upconversion Nanoparticles', Nano Letters, vol. 22, no. 9, pp. 3761-3769.
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Cancer-derived small extracellular vesicles (sEVs) are potential circulating biomarkers in liquid biopsies. However, their small sizes, low abundance, and heterogeneity in molecular makeups pose major technical challenges for detecting and characterizing them quantitatively. Here, we demonstrate a single-sEV enumeration platform using lanthanide-doped upconversion nanoparticles (UCNPs). Taking advantage of the unique optical properties of UCNPs and the background-eliminating property of total internal reflection fluorescence (TIRF) imaging technique, a single-sEV assay recorded a limit of detection 1.8 × 106 EVs/mL, which was nearly 3 orders of magnitude lower than the standard enzyme-linked immunosorbent assay (ELISA). Its specificity was validated by the difference between EpCAM-positive and EpCAM-negative sEVs. The accuracy of the UCNP-based single-sEV assay was benchmarked with immunomagnetic-beads flow cytometry, showing a high correlation (R2> 0.99). The platform is suitable for evaluating the heterogeneous antigen expression of sEV and can be easily adapted for biomarker discoveries and disease diagnosis.
Huang, H, Savkin, AV & Ni, W 2022, 'Decentralized Navigation of a UAV Team for Collaborative Covert Eavesdropping on a Group of Mobile Ground Nodes', IEEE Transactions on Automation Science and Engineering, vol. 19, no. 4, pp. 3932-3941.
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Huang, H, Savkin, AV & Ni, W 2022, 'Online UAV Trajectory Planning for Covert Video Surveillance of Mobile Targets', IEEE Transactions on Automation Science and Engineering, vol. 19, no. 2, pp. 735-746.
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This article considers the use of an unmanned aerial vehicle (UAV) for covert video surveillance of a mobile target on the ground and presents a new online UAV trajectory planning technique with a balanced consideration of the energy efficiency, covertness, and aeronautic maneuverability of the UAV. Specifically, a new metric is designed to quantify the covertness of the UAV, based on which a multiobjective UAV trajectory planning problem is formulated to maximize the disguising performance and minimize the trajectory length of the UAV. A forward dynamic programming method is put forth to solve the problem online and plan the trajectory for the foreseeable future. In addition, the kinematic model of the UAV is considered in the planning process so that it can be tracked without any later adjustment. Extensive computer simulations are conducted to demonstrate the effectiveness of the proposed technique. Note to Practitioners - The 'Follow Me' flight mode is available in many unmanned aerial vehicle (UAV) products, and this technique enables a UAV to automatically follow a target. However, this flight mode may make the UAV noticeable to the target and compromise the video surveillance missions of the UAV. Inspired by some security surveillance applications where UAV surveillance is conducted so that a target would not take actions to avoid being monitored, we propose an efficient method to construct the trajectory for the UAV. The proposed method considers the visual covertness and the battery capacity limitation of the UAV, and it can produce a trajectory online for the UAV. The proposed method and scenario can potentially extend the 'Follow Me' flight mode and generate new applications and market for UAVs.
Huang, H, Zhang, J, Yu, L, Zhang, J, Wu, Q & Xu, C 2022, 'TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization With Few Labeled Samples', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 2, pp. 853-866.
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Huang, J, Luo, K, Cao, L, Wen, Y & Zhong, S 2022, 'Learning Multiaspect Traffic Couplings by Multirelational Graph Attention Networks for Traffic Prediction', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 20681-20695.
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Temporal traffic prediction is critical for ITS yet remains challenging in handling complex spatio-temporal dynamics of traffic systems. The continuous traffic data (e.g., traffic flow, and speed) from various channels and nodes in a traffic network are coupled with each other over the time points of each channel, spatially between traffic nodes, and jointly in both spatial and temporal dimensions. Such multi-aspect traffic data couplings reflect the conditions of a real-life traffic system and evolve over traffic movement and network dynamics. The recent studies formulate traffic prediction by high-profile graph neural networks. However, they mainly focus on hidden relations captured by neural graph mechanisms while overlooking or simplifying the above multi-aspect traffic data couplings. By modeling a traffic system as a coupled traffic network, we learn the multi-aspect traffic data couplings by a Multi-relational Synchronous Graph Attention Network (MS-GAT). Specifically, MS-GAT learns three embeddings to respectively but synchronously represent the traffic data-based channel, temporal, and spatial relations between nodes by specific graph attention designs. The embeddings are further adaptively coupled according to their respective importance to prediction. Tested on five real-world datasets, MS-GAT outperforms six SOTA graph networks-based traffic predictors. MS-GAT captures not only spatial and temporal couplings but also traffic data-based channel interactions over traffic evolution.
Huang, L, Chen, X, Zhang, Y, Wang, C, Cao, X & Liu, J 2022, 'Identification of topic evolution: network analytics with piecewise linear representation and word embedding', Scientometrics, vol. 127, no. 9, pp. 5353-5383.
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Understanding the evolutionary relationships among scientific topics and learning the evolutionary process of innovations is a crucial issue for strategic decision makers in governments, firms and funding agencies when they carry out forward-looking research activities. However, traditional co-word network analysis on topic identification cannot effectively excavate semantic relationship from the context, and fixed time window method cannot scientifically reflect the evolution process of topics. This study proposes a framework of identifying topic evolutionary pathways based on network analytics: Firstly, keyword networks are constructed, in which a piecewise linear representation method is used for dividing time periods and a Word2Vec mode is used for capturing semantics from the context of titles and abstracts; Secondly, a community detection algorithm is used to identify topics in networks; Finally, evolutionary relationships between topics are represented by measuring the topic similarity between adjacent time periods, and then topic evolutionary pathways are identified and visualized. An empirical study on information science demonstrates the reliability of the methodology, with subsequent empirical validations.
Huang, L, Liu, Z, Wu, C, Liang, J & Pei, Q 2022, 'A three-dimensional indirect boundary integral equation method for the scattering of seismic waves in a poroelastic layered half-space', Engineering Analysis with Boundary Elements, vol. 135, pp. 167-181.
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Huang, Q, Li, B, Gao, M & Ying, M 2022, 'Fault Models in Superconducting quantum circuits.', CoRR, vol. abs/2212.00337.
Huang, S & Zhao, L 2022, '2021 IEEE RAS Winter School on Simultaneous Localization and Mapping in Deformable Environments [Education]', IEEE Robotics & Automation Magazine, vol. 29, no. 1, pp. 120-122.
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Simultaneous localization and mapping (SLAM) is an important research problem for robot navigation in unknown environments, particularly when GPS is not available. SLAM requires a robot to be able to build a map of the environment in real time and simultaneously estimate its own location within the map. In the past two decades, significant progress has been made in the research for SLAM in static environments. However, when an environment has deformations, such as when a surgical robot is navigating in internal body environments, SLAM needs to build a time-varying 3D map of the soft tissues and estimate the location of the robot/sensor within the map. This poses a challenging problem since the robot/sensor is moving while the environment is deforming.
Huang, S, Shi, W, Xu, Z, Tsang, IW & Lv, J 2022, 'Efficient federated multi-view learning', Pattern Recognition, vol. 131, pp. 108817-108817.
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Huang, S, Tsang, IW, Xu, Z & Lv, J 2022, 'Measuring Diversity in Graph Learning: A Unified Framework for Structured Multi-View Clustering', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 12, pp. 5869-5883.
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Graph Learning has emerged as a promising technique for multi-view clustering due to its efficiency of learning a unified graph from multiple views. Previous multi-view graph learning methods mainly try to exploit the multi-view consistency to boost learning performance. However, these methods ignore the prevalent multi-view diversity which may be induced by noise, corruptions, or even view-specific attributes. In this paper, we propose to simultaneously and explicitly leverage the multi-view consistency and the multi-view diversity in a unified framework. The consistent parts are further fused to our target graph with a clear clustering structure, on which the cluster label to each instance can be directly allocated without any postprocessing such as k-means in classical spectral clustering. In addition, our model can automatically assign suitable weight for each view based on its clustering capacity. By leveraging the subtasks of measuring the diversity of graphs, integrating the consistent parts with automatically learned weights, allocating cluster label to each instance in a joint framework, each subtask can be alternately boosted by utilizing the results of the others towards an overall optimal solution. Extensive experimental results on several benchmark multi-view datasets demonstrate the effectiveness of our model in comparison to several state-of-the-art algorithms.
Huang, S, Tsang, IW, Xu, Z & Lv, J 2022, 'Multiple partitions alignment via spectral rotation', Machine Learning, vol. 111, no. 3, pp. 1049-1072.
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Multi-view spectral clustering has drawn much attention due to the effectiveness of exploiting the similarity relationships among data points. These methods typically reveal the intrinsic structure using a predefined graph for each view. The predefined graphs are fused to a consensus one, on which the final clustering results are obtained. However, such common strategies may lead to information loss because of the inconsistency or noise among multiple views. In this paper, we propose to merge multi-view information in partition level instead of the raw feature space where the data points lie. The partition of each view is treated as a perturbation of the consensus clustering, and the multiple partitions are integrated by estimating a distinct rotation for each partition. The proposed model is formulated as a joint learning framework, i.e., with the input data matrix, our model directly outputs the final discrete clustering result. Hence it is an end-to-end single-stage learning model. An iterative updating algorithm is proposed to solve the learning problem, in which the involved variables can be optimized in a mutual reinforcement manner. Experimental results on real-world data sets illustrate the effectiveness of our model.
Huang, T, Ben, X, Gong, C, Zhang, B, Yan, R & Wu, Q 2022, 'Enhanced Spatial-Temporal Salience for Cross-View Gait Recognition', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 10, pp. 6967-6980.
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Gait recognition can be used in person identification and re-identification by itself or in conjunction with other biometrics. Although gait has both spatial and temporal attributes, and it has been observed that decoupling spatial feature and temporal feature can better exploit the gait feature on the fine-grained level. However, the spatial-temporal correlations of gait video signals are also lost in the decoupling process. Direct 3D convolution approaches can retain such correlations, but they also introduce unnecessary interferences. Instead of common 3D convolution solutions, this paper proposes an integration of decoupling process into a 3D convolution framework for cross-view gait recognition. In particular, a novel block consisting of a Parallel-insight Convolution layer integrated with a Spatial-Temporal Dual-Attention (STDA) unit is proposed as the basic block for global spatial-temporal information extraction. Under the guidance of the STDA unit, this block can well integrate spatial-temporal information extracted by two decoupled models and at the same time retain the spatial-temporal correlations. In addition, a Multi-Scale Salient Feature Extractor is proposed to further exploit the fine-grained features through context awareness extension of part-based features and adaptively aggregating the spatial features. Extensive experiments on three popular gait datasets, namely CASIA-B, OULP and OUMVLP, demonstrate that the proposed method outperforms state-of-the-art methods.
Huang, T, Chen, J, Zhang, J, Liu, Y & Liang, J 2022, 'Fast Point Cloud Sampling Network', Pattern Recognition Letters, vol. 164, pp. 216-223.
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Huang, X, Clemon, LM, Islam, MS & C. Saha, S 2022, 'Optimization of fluid characteristics in the main nozzle of an air-jet loom', Textile Research Journal, vol. 92, no. 3-4, pp. 525-538.
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As part of the propulsion system, the fluid dynamic features of the main nozzle can immediately affect the stability and efficiency of an air-jet loom. This study aims to optimize the fluid characteristics in the main nozzle of an air-jet loom. To investigate ways of weakening the effect of airflow congestion and backflow phenomenon occurring in the sudden expansion region, the computational fluid dynamics method is employed. Three-dimensional turbulence flow models for a regular main nozzle and 12 prototypes with different nozzle core tip geometry are built, simulated, and analyzed to get the optimum performance. Furthermore, a set of modified equations that consider the direction of airflow are proposed for better estimation of the friction force applied by the nozzle. The result shows that the nozzle core tip's geometry has a significant influence on the internal airflow, affecting the acceleration tube airflow velocity, turbulence intensity, and backflow strength of the sudden expansion region, and other critical fluid characteristics as well. Several proposed models have succeeded in reducing the backflow and outperforming the original design in many different aspects. Models A-60 and C-P, in particular, manage to increase the propulsion force by 37.6% and 20.2% in the acceleration tube while reducing the maximum backflow by 57.1% and 52.2%, respectively. These simulation results can provide invaluable information for the future optimization of the main nozzle.
Huang, X, Nan, Y & Guo, YJ 2022, 'Radio Frequency Camera: A Noncoherent Circular Array SAR With Uncoordinated Illuminations', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-14.
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A novel noncoherent microwave imaging principle with periodical or random radio frequency (RF) illumination is proposed in this article. Implemented with circular array synthetic aperture radar (SAR) frontend and low-complexity signal processing algorithms, the imaging device, called RF camera, achieves some desired properties similar to an optical camera, such as the capability to operate with multiple uncoordinated illuminators. Different from conventional multistatic imaging, the RF camera does not require any knowledge about an illuminator's location or signal waveform. A static illumination sensor (IS) can be used to provide a reference signal for image reconstruction. With periodical illumination, the RF camera can even operate without IS, but the imaging performance can be improved with IS. With random illumination, the IS is necessary for the RF camera operation, and the imaging distortion can be described by a point blur function. Theoretical analyses on the imaging signal-to-noise ratios are performed under different RF camera operation modes. Simulation and experimental tests are conducted using 77-GHz millimeter wave frequency to verify the noncoherent imaging principle and its performance.
Huang, X, Wang, Y, Li, S, Mei, G, Xu, Z, Wang, Y, Zhang, J & Bennamoun, M 2022, 'Robust real-world point cloud registration by inlier detection', Computer Vision and Image Understanding, vol. 224, pp. 103556-103556.
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Real-world point cloud registration is challenging because of large outliers in correspondence search. The mixture variations, such as partial overlap, noise and cross sources, are the root cause of these large outliers. Existing methods face challenges in effectively removing the large outliers. We propose a novel coarse-to-fine framework to remove the outliers by detecting the accurate inlier correspondences. Specifically, our coarse module predicts the top-K accurate correspondences. The coarse module is trained by jointly leveraging global and local structured information. Then, our refinement module checks the correspondences further using our proposed novel higher-order filter, which enables the structure conformity of correspondences to improve the quality of inlier correspondences. The final transformation matrix is calculated by using the refined inlier correspondences. Furthermore, a new cross-source point cloud dataset is proposed to further demonstrate the robustness in real-world point clouds. Experimental results demonstrate that our algorithm achieves the state-of-the-art accuracy on both indoor and outdoor, same-source and newly proposed cross-source real-world point clouds.
Huang, Y, Lee, CKC, Yam, Y-S, Mok, W-C, Zhou, JL, Zhuang, Y, Surawski, NC, Organ, B & Chan, EFC 2022, 'Rapid detection of high-emitting vehicles by on-road remote sensing technology improves urban air quality', Science Advances, vol. 8, no. 5, p. eabl7575.
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Vehicle emissions are the most important source of air pollution in the urban environment worldwide, and their detection and control are critical for protecting public health. Here, we report the use of on-road remote sensing (RS) technology for fast, accurate, and cost-effective identification of high-emitting vehicles as an enforcement program for improving urban air quality. Using large emission datasets from chassis dynamometer testing, RS, and air quality monitoring, we found that significant percentages of in-use petrol and LPG vehicles failed the emission standards, particularly the high-mileage fleets. The RS enforcement program greatly cleaned these fleets, in terms of high-emitter percentages, fleet average emissions, roadside and ambient pollutant concentrations, and emission inventory. The challenges of the current enforcement program are conservative setting of cut points, single-lane measurement sites, and lack of application experience in diesel vehicles. Developing more accurate and vertical RS systems will improve and extend their applications.
Huang, Y, Lee, CKC, Yam, Y-S, Zhou, JL, Surawski, NC, Organ, B, Lei, C & Shon, HK 2022, 'Effective emissions reduction of high-mileage fleets through a catalytic converter and oxygen sensor replacement program', Science of The Total Environment, vol. 850, pp. 158004-158004.
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High-mileage vehicles such as taxis make disproportionately large contributions to urban air pollution due to their accelerated engine deterioration rates and high operation intensities despite their small proportions of the total fleet. Controlling emissions from these high-mileage fleets is thus important for improving urban air quality. This study evaluates the effectiveness of a pilot repair program in reducing emissions from taxis in Hong Kong which account for about 2 % of the total licensed vehicles. The emission factors of a large sample of 684 in-service taxis (including 121 for an emission survey program and 563 for a pilot repair program) were measured on transient chassis dynamometers. The results showed that 63 % of the sampled taxis failed the driving cycle test before the pilot repair program. Most of failed taxis were NO related and 91 % of failed taxis exceeded the emission limits of at least two regulated pollutants simultaneously. After the pilot repair program by replacing catalytic converters and oxygen sensors, the failure rate was significantly reduced to only 7 %. In addition, the fleet average NO, HC and CO emission factors were reduced by 85 %, 82 % and 56 %, respectively. In addition, on-road remote sensing measurements confirmed the real-world emission reductions from the taxis that participated in the pilot repair program. These findings led to the implementation of a large-scale replacement program for all taxis in Hong Kong during 2013-2014, which was estimated to have reduced the total HC, CO and NO emissions by about 420, 2570 and 1000 t per year, respectively (equivalent to 5-8 % emission reductions from the whole road transport sector). Therefore, reducing emissions from the small high-mileage fleets is a highly cost-effective measure to improve urban air quality.
Huang, Y, Li, Y, Heyes, T, Jourjon, G, Cheng, A, Seneviratne, S, Thilakarathna, K, Webb, D & Xu, RYD 2022, 'Task adaptive siamese neural networks for open-set recognition of encrypted network traffic with bidirectional dropout', Pattern Recognition Letters, vol. 159, pp. 132-139.
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Existing deep learning approaches have achieved high performance in encrypted network traffic analysis tasks. However, practical requirements such as open-set recognition on dynamically changing tasks (e.g., changes in the target website list), challenge existing methods. While few-shot learning and open-set recognition methods have been proposed for domains such as computer vision, few-shot open-set recognition for encrypted network traffic remains an unexplored area. This paper proposes a task adaptive siamese neural network for open-set recognition of encrypted network traffic with bidirectional dropout data augmentation. Our contributions are three-fold: First, we introduce generated positive and negative pairs into the siamese neural network training process to shape a more precise similarity boundary through bidirectional dropout data augmentation. Second, we utilize Dirichlet Process Gaussian Mixture Model (DPGMM) distribution to fit the similarity scores of the negative pairs constructed by the support set of each query task, and create a new open-set recognition metric. Third, by leveraging the extracted features at coarse and fine granular levels, we construct a hierarchical cross entropy loss to improve the confidence of the similarity score. Extensive experiments on a network traffic dataset and the Omniglot dataset demonstrate the superiority and generalizability of our proposed approach.
Huang, Y, Ng, ECY, Surawski, NC, Zhou, JL, Wang, X, Gao, J, Lin, W & Brown, RJ 2022, 'Effect of diesel particulate filter regeneration on fuel consumption and emissions performance under real-driving conditions', Fuel, vol. 320, pp. 123937-123937.
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Diesel particulate filters (DPF) are widely adopted in diesel vehicles to meet the increasingly stringent emission regulations, which require continuous passive regenerations or/and periodic active regenerations to burn off the accumulated particulate matter (PM). In spite of many laboratory studies using DPF benches and engine/chassis dynamometers, there is currently a lack of investigation on DPF regeneration under real-world conditions. Therefore, this study was conducted to investigate the impact of active DPF regenerations on the fuel consumption and gaseous and particulate emissions performance of a diesel light goods vehicle under real-driving conditions by using the state-of-the-art portable emission measurement system. In total, 60 real-driving emission (RDE) tests (∼1200 km in total) were performed on the same route during the same periods of a day, to minimise the effect of uncontrollable real-world factors on the performance evaluation. The results showed that real-world active DPF regenerations occurred every 130 km for the studied vehicle. Although they did not occur frequently, DPF regenerations increased the trip-averaged fuel consumption rate by 13% on average. CO and THC emission factors tended to increase with DPF regenerations because the post combustion used to achieve the high exhaust temperature for regeneration of the filter occurred under oxygen-lean conditions. Total NOx emissions were not affected but NO2/NOx ratio was greatly reduced by DPF regeneration due to lower NO oxidation by the diesel oxidation catalyst and higher NO2 reduction by the DPF. Finally, DPF regenerations sharply increased PM emission factors by 27 times compared with a trip without DPF regeneration, resulting in significant exceedance of the emission limit.
Huang, Y, Wang, X, Zhang, Y, Chiavetta, D & Porter, AL 2022, '“Big data” driven tech mining and ST&I management: an introduction', Scientometrics, vol. 127, no. 9, pp. 5227-5231.
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Since the first Global Tech Mining (GTM) conference was held in Atlanta in 2011, the GTM conference has created a platform to connect tech mining researchers, exchange ideas and research progress, and promote collaborations. When it came to its 10th anniversary in 2020, COVID-19 forced the GTM conference into an online format. In tumultuous times for ST&I research activity, the GTM conference sought to focus on several issues: How to better collect and combine multiple 'large data' sources? How to analyze these data effectively? And how to utilize these results more powerfully in ST&I management? In this collection, 15 papers are selected after evaluating by the science advisory committee, the guest editor team, and our peer review experts to address the following aspects regarding 'tech mining': (1) DATA: Maximizing the potential of traditional and novel data; (2) METHODS: Advancing and integrating methods; (3) APPLICATIONS: Innovative analyses translating to usefulintelligence.
Huang, Z, Zhao, R, Leung, FHF, Banerjee, S, Lee, TT-Y, Yang, D, Lun, DP-K, Lam, K-M, Zheng, Y-P & Ling, SH 2022, 'Joint Spine Segmentation and Noise Removal From Ultrasound Volume Projection Images With Selective Feature Sharing.', IEEE Trans. Medical Imaging, vol. 41, no. 7, pp. 1610-1624.
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Volume Projection Imaging from ultrasound data is a promising technique to visualize spine features and diagnose Adolescent Idiopathic Scoliosis. In this paper, we present a novel multi-task framework to reduce the scan noise in volume projection images and to segment different spine features simultaneously, which provides an appealing alternative for intelligent scoliosis assessment in clinical applications. Our proposed framework consists of two streams: 1) A noise removal stream based on generative adversarial networks, which aims to achieve effective scan noise removal in a weakly-supervised manner, i.e., without paired noisy-clean samples for learning; 2) A spine segmentation stream, which aims to predict accurate bone masks. To establish the interaction between these two tasks, we propose a selective feature-sharing strategy to transfer only the beneficial features, while filtering out the useless or harmful information. We evaluate our proposed framework on both scan noise removal and spine segmentation tasks. The experimental results demonstrate that our proposed method achieves promising performance on both tasks, which provides an appealing approach to facilitating clinical diagnosis.
Huo, C, Wang, Y, Liu, C & Lei, G 2022, 'Study on the residual flux density measurement method for power transformer cores based on magnetising inductance', IET Electric Power Applications, vol. 16, no. 2, pp. 224-235.
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AbstractWhen a power transformer is reconnected to a power grid, if the residual flux in its iron core is large, significant inrush current may be generated and result in closing failure. Therefore, accurate residual flux measurement is necessary to avoid the harmful effects of inrush current. This work proposes a residual flux density measurement method for the power transformer core based on magnetising inductance. Firstly, when positive and negative DC voltages are applied along or opposite to the direction of the initial residual flux density, the measured positive magnetising inductance is smaller than the negative so that the direction of residual flux density can be determined by comparing their values. Secondly, the magnitude of residual flux density can calculated by analysing the empirical formula between residual flux density and positive magnetising inductance using the finite element method. Finally, this work takes the square iron core as the research object, establishes the corresponding empirical formula, and verifies its accuracy through experiments. The experimental results show that the proposed method has higher accuracy compared with the voltage integration method widely used in this field.
Huo, P, Chen, X, Yang, L, Wei, W & Ni, B-J 2022, 'Modeling of Sulfur-Driven Autotrophic Denitrification Coupled with Anammox Process', Bioresour Technol, vol. 349, pp. 126887-126887.
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While sulfur-driven autotrophic denitrification (SDAD) occurring in the anoxic reactor of the sulfate reduction, autotrophic denitrification and nitrification integrated (SANI) system has been regarded as the main nitrogen removal bioprocess, little is known about the accompanying Anammox bacteria whose presence is made possible by the co-existence of NH4+ and NO2-. Therefore, this work firstly developed an integrated SDAD-Anammox model to describe the interactions between sulfur-oxidizing bacteria and Anammox bacteria. The model was subsequently used to explore the impacts of influent conditions on the reactor performance and microbial community structure of the anoxic reactor. The results revealed that at a relatively low ratio of <1.5 mg S/mg N, Anammox bacteria could survive and even take a dominant position (up to 58.9%). Finally, a modified SANI system configuration based on the effective collaboration between SDAD and Anammox processes was proposed to improve the efficiency of the treatment of sulfate-rich saline sewage.
Huo, S, Ni, W, Song, X, Zhang, M, Wang, H & Li, K 2022, 'Insight from the synergistic effect of dopant and defect interplay in carbons for high-performance capacitive deionization', Separation and Purification Technology, vol. 281, pp. 119807-119807.
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Huo, X, Jiang, Z, Luo, Q, Li, Q & Sun, G 2022, 'Mechanical characterization and numerical modeling on the yield and fracture behaviors of polymethacrylimide (PMI) foam materials', International Journal of Mechanical Sciences, vol. 218, pp. 107033-107033.
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Polymethacrylimide (PMI) foam materials exhibit great potential in engineering applications due to their lightweight property. Nevertheless, there is lack of constitutive data regarding the PMI foam materials under complex loading conditions; therefore, this paper reports the systematic experimental characterization of the elastic, plastic and fracture properties of PMI foam materials. In this study, the quasi-biaxial stress loading conditions were generated by the Arcan fixture. A 2D digital image correlation (DIC) system was employed to identify the macroscopic deformation in the tests. Based upon the experimental data, a yield surface of the PMI foam material was calibrated and analyzed, in which both low and high densities of PMI foams were taken into account. It was found that the plastic and fracture parameters can be greatly affected by the foam density owing to different microstructure characteristics. Further, fracture behaviors of the PMI foam were experimentally investigated in the context of linear elastic fracture mechanics (LEFM). The critical energy release rates (ERRs) were extracted for the mixed mode I/II by using cracked specimens subjected to combined tension-shear loadings. A homogenized finite element model was developed for the PMI foam materials and validated with the experiment. This study is expected to gain systematic understanding of PMI foam material properties and provide an effective constitutive model for practical applications of PMI foam materials.
Huo, X, Luo, Q, Li, Q, Zheng, G & Sun, G 2022, 'On characterization of cohesive zone model (CZM) based upon digital image correlation (DIC) method', International Journal of Mechanical Sciences, vol. 215, pp. 106921-106921.
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Huq, T, Ong, HC, Chew, BT, Leong, KY & Kazi, SN 2022, 'Review on aqueous graphene nanoplatelet Nanofluids: Preparation, Stability, thermophysical Properties, and applications in heat exchangers and solar thermal collectors', Applied Thermal Engineering, vol. 210, pp. 118342-118342.
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Hurkmans, EGE, Koenderink, JB, van den Heuvel, JJMW, Versleijen-Jonkers, YMH, Hillebrandt-Roeffen, MHS, Groothuismink, JM, Vos, HI, van der Graaf, WTA, Flucke, U, Muradjan, G, Schreuder, HWB, Hagleitner, MM, Brunner, HG, Gelderblom, H, Cleton-Jansen, A-M, Guchelaar, H-J, de Bont, ESJM, Touw, DJ, Nijhoff, GJ, Kremer, LCM, Caron, H, Windsor, R, Patiño-García, A, González-Neira, A, Saletta, F, McCowage, G, Nagabushan, S, Catchpoole, D, te Loo, DMWM & Coenen, MJH 2022, 'SLC7A8 coding for LAT2 is associated with early disease progression in osteosarcoma and transports doxorubicin', Frontiers in Pharmacology, vol. 13, p. 1042989.
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Background: Despite (neo) adjuvant chemotherapy with cisplatin, doxorubicin and methotrexate, some patients with primary osteosarcoma progress during first-line systemic treatment and have a poor prognosis. In this study, we investigated whether patients with early disease progression (EDP), are characterized by a distinctive pharmacogenetic profile.Methods and Findings: Germline DNA from 287 Dutch high-grade osteosarcoma patients was genotyped using the DMET Plus array (containing 1,936 genetic markers in 231 drug metabolism and transporter genes). Associations between genetic variants and EDP were assessed using logistic regression models and associated variants (p <0.05) were validated in independent cohorts of 146 (Spain and United Kingdom) and 28 patients (Australia). In the association analyses, EDP was significantly associated with an SLC7A8 locus and was independently validated (meta-analysis validation cohorts: OR 0.19 [0.06–0.55], p = 0.002). The functional relevance of the top hits was explored by immunohistochemistry staining and an in vitro transport models. SLC7A8 encodes for the L-type amino acid transporter 2 (LAT2). Transport assays in HEK293 cells overexpressing LAT2 showed that doxorubicin, but not cisplatin and methotrexate, is a substrate for LAT2 (p < 0.0001). Finally, SLC7A8 mRNA expression analysis and LAT2 immunohistochemistry of osteosarcoma tissue showed that the lack of LAT2 expression is a prognostic factor of poor prognosis and reduced overall survival in patients without metastases (p = 0.0099 and p = 0.14, resp.).Conclusion: This study identified a novel locus ...
Hussain, W 2022, 'Approaching a Large Defect on the Lower Nasal Sidewall—A Twist on a Classic Reconstruction', Dermatologic Surgery, vol. 48, no. 2, pp. 239-241.
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Hussain, W, Gao, H, Raza, MR, Rabhi, FA & Merigó, JM 2022, 'Assessing cloud QoS predictions using OWA in neural network methods', Neural Computing and Applications, vol. 34, no. 17, pp. 14895-14912.
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AbstractQuality of Service (QoS) is the key parameter to measure the overall performance of service-oriented applications. In a myriad of web services, the QoS data has multiple highly sparse and enormous dimensions. It is a great challenge to reduce computational complexity by reducing data dimensions without losing information to predict QoS for future intervals. This paper uses an Induced Ordered Weighted Average (IOWA) layer in the prediction layer to lessen the size of a dataset and analyse the prediction accuracy of cloud QoS data. The approach enables stakeholders to manage extensive QoS data better and handle complex nonlinear predictions. The paper evaluates the cloud QoS prediction using an IOWA operator with nine neural network methods—Cascade-forward backpropagation, Elman backpropagation, Feedforward backpropagation, Generalised regression, NARX, Layer recurrent, LSTM, GRU and LSTM-GRU. The paper compares results using RMSE, MAE, and MAPE to measure prediction accuracy as a benchmark. A total of 2016 QoS data are extracted from Amazon EC2 US-West instance to predict future 96 intervals. The analysis results show that the approach significantly decreases the data size by 66%, from 2016 to 672 records with improved or equal accuracy. The case study demonstrates the approach's effectiveness while handling complexity, reducing data dimension with better prediction accuracy.
Hussain, W, Merigó, JM & Raza, MR 2022, 'Predictive intelligence using ANFIS‐induced OWAWA for complex stock market prediction', International Journal of Intelligent Systems, vol. 37, no. 8, pp. 4586-4611.
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Hussain, W, Merigó, JM, Raza, MR & Gao, H 2022, 'A new QoS prediction model using hybrid IOWA-ANFIS with fuzzy C-means, subtractive clustering and grid partitioning', Information Sciences, vol. 584, pp. 280-300.
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Quality of Service (QoS) is one of the key indicators to measure the overall performance of cloud services. The quantitative measurement of the QoS enables the service provider to manage its Service Level Agreement (SLA) in a viable way. It also supports a consumer in service selection and allows measuring the received services to comply with agreed services. There is much existing literature that tries to predict the QoS and assist stakeholders in their decision-making process. However, it is tricky to deal with multidimensional data in time series prediction methods. The computational complexity increases with an increase in data dimension, and it is a challenging task to give precise weights to each time interval. Existing prediction methods could not deal with the intricate reordering of input weights. To address this problem, we propose a novel Clustered Induced Ordered Weighted Averaging (IOWA) Adaptive Neuro-Fuzzy Inference System (ANFIS), (CI-ANFIS) model. This fuzzy time series prediction model reduces data dimension and handles the nonlinear relationship of the cloud QoS dataset. The proposed method uses an intelligent sorting mechanism that regulates uncertainty in prediction while incorporating a fuzzy neural network structure for optimal prediction results. The proposed method employs the IOWA operator to sort input arguments based on associated order-inducing variables and assign customised weights accordingly. The inputs are further classified using three fuzzy clustering methods - fuzzy c-means (FCM), subtractive clustering and grid partitioning. The inputs further pass to the ANFIS structure that takes the benefits of both the fuzzy and neural networks. The fuzzy structure in ANFIS builds understandable rules for cloud stakeholders and deals with uncertain occurrences of data. The model uses a real cloud QoS dataset extracted from the Amazon Elastic Compute Cloud (EC2) US-West instance and predict its behaviour every five minutes for th...
Hussain, W, Raza, MR, Jan, MA, Merigo, JM & Gao, H 2022, 'Cloud Risk Management With OWA-LSTM and Fuzzy Linguistic Decision Making', IEEE Transactions on Fuzzy Systems, vol. 30, no. 11, pp. 4657-4666.
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In a cloud environment, the indemnity of service level agreement (SLA) violations has an adverse effect on the service provider. It leads to the penalty fee, credit amount, license extension, and reputation decline that could significantly impact future business outcomes. Existing approaches are unable to handle complex predictions that can accommodate the temporal influence of Quality of Service (QoS) data. Moreover, no method in a cloud environment considers all possible attitudinal behavior of the service provider to mitigate the risk of an actual violation. This article proposes an SLA violation risk mitigation model that uses ordered weighted average (OWA) in long short-term memory for complex QoS prediction. The OWA operator is weighted with a minimax disparity approach to manage the risk of SLA violation. The approach intelligently predicts deviation in custom prioritized QoS parameter and recommend exigency of mitigating action by considering all possible attitudinal behavior of the service provider. This article uses linguistic variables, fuzzy and interval numbers to handle imprecise information. The analysis results demonstrate the applicability and efficiency of the proposed approach to address complex risk mitigation actions.
Hussein, F, Mughaid, A, AlZu’bi, S, El-Salhi, SM, Abuhaija, B, Abualigah, L & Gandomi, AH 2022, 'Hybrid CLAHE-CNN Deep Neural Networks for Classifying Lung Diseases from X-ray Acquisitions', Electronics, vol. 11, no. 19, pp. 3075-3075.
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Chest and lung diseases are among the most serious chronic diseases in the world, and they occur as a result of factors such as smoking, air pollution, or bacterial infection, which would expose the respiratory system and chest to serious disorders. Chest diseases lead to a natural weakness in the respiratory system, which requires the patient to take care and attention to alleviate this problem. Countries are interested in encouraging medical research and monitoring the spread of communicable diseases. Therefore, they advised researchers to perform studies to curb the diseases’ spread and urged researchers to devise methods for swiftly and readily detecting and distinguishing lung diseases. In this paper, we propose a hybrid architecture of contrast-limited adaptive histogram equalization (CLAHE) and deep convolutional network for the classification of lung diseases. We used X-ray images to create a convolutional neural network (CNN) for early identification and categorization of lung diseases. Initially, the proposed method implemented the support vector machine to classify the images with and without using CLAHE equalizer. The obtained results were compared with the CNN networks. Later, two different experiments were implemented with hybrid architecture of deep CNN networks and CLAHE as a preprocessing for image enhancement. The experimental results indicate that the suggested hybrid architecture outperforms traditional methods by roughly 20% in terms of accuracy.
Hussien, AG, Abualigah, L, Abu Zitar, R, Hashim, FA, Amin, M, Saber, A, Almotairi, KH & Gandomi, AH 2022, 'Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications', Electronics, vol. 11, no. 12, pp. 1919-1919.
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The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.
Huyen Vu, T, Dang, LC, Kang, G & Sirivivatnanon, V 2022, 'Chloride induced corrosion of steel reinforcement in alkali activated slag concretes: A critical review', Case Studies in Construction Materials, vol. 16, pp. e01112-e01112.
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Iacopi, F & Lin, C-T 2022, 'A perspective on electroencephalography sensors for brain-computer interfaces', Progress in Biomedical Engineering, vol. 4, no. 4, pp. 043002-043002.
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Abstract This Perspective offers a concise overview of the current, state-of-the-art, neural sensors for brain-machine interfaces, with particular attention towards brain-controlled robotics. We first describe current approaches, decoding models and associated choice of common paradigms, and their relation to the position and requirements of the neural sensors. While implanted intracortical sensors offer unparalleled spatial, temporal and frequency resolution, the risks related to surgery and post-surgery complications pose a significant barrier to deployment beyond severely disabled individuals. For less critical and larger scale applications, we emphasize the need to further develop dry scalp electroencephalography (EEG) sensors as non-invasive probes with high sensitivity, accuracy, comfort and robustness for prolonged and repeated use. In particular, as many of the employed paradigms require placing EEG sensors in hairy areas of the scalp, ensuring the aforementioned requirements becomes particularly challenging. Nevertheless, neural sensing technologies in this area are accelerating thanks to the advancement of miniaturised technologies and the engineering of novel biocompatible nanomaterials. The development of novel multifunctional nanomaterials is also expected to enable the integration of redundancy by probing the same type of information through different mechanisms for increased accuracy, as well as the integration of complementary and synergetic functions that could range from the monitoring of physiological states to incorporating optical imaging.
Ibrahim, I, Hossain, SM, Seo, DH, McDonagh, A, Foster, T, Shon, HK & Tijing, L 2022, 'Insight into the role of polydopamine nanostructures on nickel foam-based photothermal materials for solar water evaporation', Separation and Purification Technology, vol. 293, pp. 121054-121054.
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Ibrahim, I, Seo, DH, Park, MJ, Angeloski, A, McDonagh, A, Bendavid, A, Shon, HK & Tijing, L 2022, 'Highly stable gold nanolayer membrane for efficient solar water evaporation under a harsh environment', Chemosphere, vol. 299, pp. 134394-134394.
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Ibrahim, IA & Hossain, MJ 2022, 'A benchmark model for low voltage distribution networks with PV systems and smart inverter control techniques', Renewable and Sustainable Energy Reviews, vol. 166, pp. 112571-112571.
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Unbalanced three-phase low-voltage distribution networks (LVDNs) modeling, optimization, and control are essential for enabling high photovoltaic (PV) penetration levels. Accordingly, a new case study is developed to show the gaps and challenges at different PV penetration levels in LVDNs. In this case study, the aim is to provide a better understanding of LVDNs’ behavior in order to support the development and validation of the models and tools. Therefore, a reduction model is proposed to decrease the simulation time by lowering the number of buses in the IEEE European LV Test Feeder, with a negligible error. In addition, an OpenDSS-Julia interface is developed to demonstrate the effects of different PV penetration levels on the inverters’ behavior, active power curtailment, and voltage level in LVDNs. Results are demonstrated concerning several limitations and challenges in using existing smart inverter control techniques, in terms of the inverters’ behavior, active power curtailment, and the voltage level. These limitations and challenges include over-voltage issues using the constant power factor technique, high active power curtailment using the volt–watt technique, and high current flows in the network assets and poor power factors using the volt–var technique. In addition, state-of-the-art system models have not taken-into-account the modeling of uncertainty effects on the performance of PV modules. Similarly, such models have largely ignored the internal and standby losses in the inverter models. These neglected issues may lead to under- or over-estimation of the impacts of PV systems on LVDNs and inaccurate estimations of the network's ability to accommodate high PV penetration levels.
Ibrahim, IA, Hossain, MJ & Duck, BC 2022, 'A hybrid wind driven-based fruit fly optimization algorithm for identifying the parameters of a double-diode photovoltaic cell model considering degradation effects', Sustainable Energy Technologies and Assessments, vol. 50, pp. 101685-101685.
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The identification of unknown parameters of photovoltaic modules is the keystone to model their performance accurately. This paper introduces a novel hybrid wind driven-based fruit fly optimization algorithm to determine a double-diode photovoltaic cell model's seven unknown parameters. Due to the limitations of reaching a matured convergence of the classical wind driven optimization for complex multi-modal optimization problems, this paper presents a hybrid algorithm by integrating the wind driven optimization algorithm's exploitation and fruit fly optimization algorithm's exploration capacities. The effectiveness of the proposed model is validated using real data from three photovoltaic technologies: mono-crystalline, poly-crystalline, and thin-film. Besides, its computational efficiency and precision are compared with those of various models: deterministic- and metaheuristic-based models. The average values of the standard deviation, normalized-root-mean-square error, mean absolute percentage error, coefficient of determination, and convergence speed of the proposed model were 8.1101 × 10-9, 0.0911%, 2.5661%, 99.0115%, and 10.0112 s. for mono-crystalline PV module, 7.1129 × 10-9, 0.1029%, 2.6334%, 98.9331%, and 8.1201 s. for poly-crystalline PV module, and 6.2212 × 10-9, 0.0871%, 2.3129%, 99.1256% and 9.3211 s. for thin-film PV module. Findings indicate that the proposed model outperforms the aforementioned models in accuracy, convergence speed and feasibility. In addition, it can work blindly with any current-voltage characteristic curve on a 15-min. basis under any weather condition without the need for any initial guess or previous information about any parameter.
Ibrar, I, Yadav, S, Altaee, A, Safaei, J, Samal, AK, Subbiah, S, Millar, G, Deka, P & Zhou, J 2022, 'Sodium docusate as a cleaning agent for forward osmosis membranes fouled by landfill leachate wastewater', Chemosphere, vol. 308, no. Pt 2, pp. 136237-136237.
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Membrane cleaning is critical for economic and scientific reasons in wastewater treatment systems. Sodium docusate is a laxative agent and removes cerumen (ear wax). Docusate penetrates the hard ear wax, making it softer and easier to remove. The same concept could be applied to soften and remove fouling layers on the membrane surface. Once softened, the foulants can be easily flushed with water. This innovative approach can address the challenge of developing superior methods to mitigate membrane fouling and material degradation. In this study, we evaluated the efficiency of sodium docusate for cleaning fouled forward osmosis membranes with real landfill leachate wastewater. Experiments were conducted to examine the impact of dose rate, contact time, flow or static conditions, and process configuration (forward osmosis (FO) or pressure retarded osmosis (PRO) upon fouling created by landfill leachate dewatering. A remarkable (99%) flux recovery was achieved using docusate at a small concentration of only 0.1% for 30 min. Furthermore, docusate can also effectively restore flux with static cleaning without using pumps to circulate the cleaning solution. Furthermore, cleaning efficiency can be achieved at neutral pH compatible with most membrane materials. From an economic and energy-saving perspective, static cleaning can almost achieve the same cleaning efficiency as kinetic cleaning for fouled forward osmosis membranes without the expense of additional pumping energy compared to kinetic cleaning. Since pumping energy is a major contributor to the overall energy of the forward osmosis system, it can be minimized to a certain degree by using a static cleaning approach and can bring good energy savings when using larger membrane areas. Studies of the contact angle on the membrane surface indicated that the contact angle was decreased compared to the fouled membrane after cleaning (e.g. 70.3° to 63.2° or FO mode and static cleaning). Scanning Electron Micro...
Ibrar, I, Yadav, S, Braytee, A, Altaee, A, HosseinZadeh, A, Samal, AK, Zhou, JL, Khan, JA, Bartocci, P & Fantozzi, F 2022, 'Evaluation of machine learning algorithms to predict internal concentration polarization in forward osmosis', Journal of Membrane Science, vol. 646, pp. 120257-120257.
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Internal concentration polarization (ICP) is currently a major bottleneck in the forward osmosis process. Proper modelling of the internal concentration polarization is therefore vital for improving the process performance and efficiency. This study assessed the feasibility of several machine learning methods for internal concentration polarization prediction, including artificial neural networks, extreme gradient boosting (XGBoost), Categorical boosting (CatBoost), Random forest, and linear regression. Among the many algorithms evaluated, the CatBoost regression outperformed other methods in terms of coefficient of determination (R2) and the mean square error. The CatBoost algorithm's prediction power was then evaluated using non-training (user-provided) data and compared to solution diffusion models. The results indicated that the machine learning algorithms could predict ICP in the process with high accuracy for the provided dataset and excellent generalizability for future testing data. Furthermore, machine learning algorithms may offer insights into the input features that majorly affect ICP modelling in the forward osmosis process.
Ibrar, I, Yadav, S, Naji, O, Alanezi, AA, Ghaffour, N, Déon, S, Subbiah, S & Altaee, A 2022, 'Development in forward Osmosis-Membrane distillation hybrid system for wastewater treatment', Separation and Purification Technology, vol. 286, pp. 120498-120498.
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Ideris, F, Zamri, MFMA, Shamsuddin, AH, Nomanbhay, S, Kusumo, F, Fattah, IMR & Mahlia, TMI 2022, 'Progress on Conventional and Advanced Techniques of In Situ Transesterification of Microalgae Lipids for Biodiesel Production', Energies, vol. 15, no. 19, pp. 7190-7190.
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Global warming and the depletion of fossil fuels have spurred many efforts in the quest for finding renewable, alternative sources of fuels, such as biodiesel. Due to its auxiliary functions in areas such as carbon dioxide sequestration and wastewater treatment, the potential of microalgae as a feedstock for biodiesel production has attracted a lot of attention from researchers all over the world. Major improvements have been made from the upstream to the downstream aspects related to microalgae processing. One of the main concerns is the high cost associated with the production of biodiesel from microalgae, which includes drying of the biomass and the subsequent lipid extraction. These two processes can be circumvented by applying direct or in situ transesterification of the wet microalgae biomass, hence substantially reducing the cost. In situ transesterification is considered as a significant improvement to commercially produce biodiesel from microalgae. This review covers the methods used to extract lipids from microalgae and various in situ transesterification methods, focusing on recent developments related to the process. Nevertheless, more studies need to be conducted to further enhance the discussed in situ transesterification methods before implementing them on a commercial scale.
Ijaz, K, Tran, TTM, Kocaballi, AB, Calvo, RA, Berkovsky, S & Ahmadpour, N 2022, 'Design Considerations for Immersive Virtual Reality Applications for Older Adults: A Scoping Review', Multimodal Technologies and Interaction, vol. 6, no. 7, pp. 60-60.
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Immersive virtual reality (iVR) has gained considerable attention recently with increasing affordability and accessibility of the hardware. iVR applications for older adults present tremendous potential for diverse interventions and innovations. The iVR literature, however, provides a limited understanding of guiding design considerations and evaluations pertaining to user experience (UX). To address this gap, we present a state-of-the-art scoping review of literature on iVR applications developed for older adults over 65 years. We performed a search in ACM Digital Library, IEEE Xplore, Scopus, and PubMed (1 January 2010–15 December 2019) and found 36 out of 3874 papers met the inclusion criteria. We identified 10 distinct sets of design considerations that guided target users and physical configuration, hardware use, and software design. Most studies carried episodic UX where only 2 captured anticipated UX and 7 measured longitudinal experiences. We discuss the interplay between our findings and future directions to design effective, safe, and engaging iVR applications for older adults.
Ilahi, I, Usama, M, Qadir, J, Janjua, MU, Al-Fuqaha, A, Hoang, DT & Niyato, D 2022, 'Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning', IEEE Transactions on Artificial Intelligence, vol. 3, no. 2, pp. 90-109.
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Deep reinforcement learning (DRL) has numerous applications in the real world, thanks to its ability to achieve high performance in a range of environments with little manual oversight. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications (e.g., smart grids, traffic controls, and autonomous vehicles) unless its vulnerabilities are addressed and mitigated. To address this problem, we provide a comprehensive survey that discusses emerging attacks on DRL-based systems and the potential countermeasures to defend against these attacks. We first review the fundamental background on DRL and present emerging adversarial attacks on machine learning techniques. We then investigate the vulnerabilities that an adversary can exploit to attack DRL along with state-of-the-art countermeasures to prevent such attacks. Finally, we highlight open issues and research challenges for developing solutions to deal with attacks on DRL-based intelligent systems.
Inan, DI, Beydoun, G & Pradhan, B 2022, 'Disaster Management Knowledge Analysis Framework Validated.', Inf. Syst. Frontiers, vol. 24, no. 6, pp. 2077-2097.
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In Disaster Management (DM), reusing knowledge of best practices from past experiences is envisaged as the best approach for dealing with future disasters. But analysing and modelling processes involved in those experiences is a well-known challenge. But the efficient storage of those processes to allow reuse by others in future DM endeavours is even more challenging and less discussed. Without an efficient process in place, DM knowledge reuse becomes even more remote as the effort incurred gets construed as a hindrance to more pressing activities during the execution of disaster activities. Efficiency has to also be pursued without compromising the effectiveness of the knowledge analysis and reuse. It is important to ensure that knowledge remains meaningful and relevant after it is transformed. This paper presents and validates a DM knowledge analysis framework (DMKAF 2.0) that caters for efficient transformation of DM knowledge intended for reuse. The paper demonstrates that undertaking knowledge transformation and storage in the context of its use is crucial in DM for both, effectiveness and efficiency of the transformation process. Design Science Research methodology guides the research undertaken, by informing enhancements and how the framework is evaluated. A real case study of flood DM from the State Emergency Service of Victoria State Australia is successfully used to validate these enhancements.
Indraratna, B, Haq, S, Rujikiatkamjorn, C & Israr, J 2022, 'Microscale boundaries of internally stable and unstable soils', Acta Geotechnica, vol. 17, no. 5, pp. 2037-2046.
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This study presents a microscale approach for evaluating the internal instability of natural granular soils using the discrete element method. The coordination number and the stress reduction factor are combined to assess the internal instability of soil. Distinct boundaries are identified between various soils that are internally stable and unstable. The microscale investigations are then compared with constriction and particle size-based criteria. The findings reveal that the constriction-based criterion predicts internal instability with significantly better accuracy. The relationship between microscale parameters and the constriction-based retention ratio is also examined for practical purposes.
Indraratna, B, Medawela, SK, Athuraliya, S, Heitor, A & Baral, P 2022, 'Chemical clogging of granular media under acidic groundwater conditions', Environmental Geotechnics, vol. 9, no. 7, pp. 450-462.
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Generation of acidic groundwater attributed to pyrite oxidation in low-lying acid sulfate soil has caused substantial damage to the soil-water environment and civil infrastructure in coastal Australia. The installation of permeable reactive barriers (PRBs) is a frontier technology in the field of acid neutralisation and removal of toxic heavy metal cations – for example, soluble iron (Fe) and aluminium (Al). This study aims to assess the potential of limestone (calcite) aggregates as the PRB’s main reactive material in low-lying pyritic land. During long-term laboratory column experiments, a significant capacity of limestone for removing contaminant chemical species was observed. Nevertheless, the formation of secondary mineral precipitates upon geochemical reactivity within the granular media in the PRB caused armouring and chemical clogging, which diminished the rate of reactivity – that is, the treatment capacity of calcite aggregates – mainly at the entrance zone of the porous media. Flow properties were altered due to blockage of pores; for instance, hydraulic conductivity was reduced by 25% at the inlet zone. Non-homogeneous clogging towards the outlet was analysed, and the time-dependent effect on the longevity of a limestone column was studied and quantified.
Indraratna, B, Mehmood, F, Mishra, S, Ngo, T & Rujikiatkamjorn, C 2022, 'The role of recycled rubber inclusions on increased confinement in track substructure', Transportation Geotechnics, vol. 36, pp. 100829-100829.
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Large cyclic and impact loads exerted by heavy haul trains can cause significant deformation and degradation of ballast, leading to poor track geometry and track instability. The application of recycled rubber elements in track substructure to increase confinement of both sub-ballast and shoulder ballast is an innovative solution. In Australia, there is a lack of adequate recycling that leads to large stockpiles of waste tyres. In addition, the reusability of giant off-the-road tyres discarded from mining industry is seriously limited due to their size and weight (over 3.0 m in diameter weighing about 3 tonnes). This study presents a real-size prototype test using the Australia's first and only National Facility for Cyclic Testing of High-speed Rail to investigate the performance of a hybrid track where tyre-infilled granular waste materials were placed below the ballast layer to replace the traditional capping layer, and arc segments cut from the giant off-the-road tyres were used to confine shoulder ballast. The performance of this hybrid track is compared with an unreinforced track conducted earlier at the same loading conditions. Test results demonstrate that the use of this hybrid system with recycled rubber elements significantly decreases vertical and lateral displacements of ballast and effectively controls the distribution of vertical stress with depth, while reducing vibration and ballast breakage. The outcomes of this study provide a unique solution in a circular economy perspective to strengthen railways to cater for heavier and faster freight trains.
Indraratna, B, Qi, Y, Malisetty, RS, Navaratnarajah, SK, Mehmood, F & Tawk, M 2022, 'Recycled materials in railroad substructure: an energy perspective', Railway Engineering Science, vol. 30, no. 3, pp. 304-322.
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AbstractGiven that the current ballasted tracks in Australia may not be able to support faster and significantly heavier freight trains as planned for the future, the imminent need for innovative and sustainable ballasted tracks for transport infrastructure is crucial. Over the past two decades, a number of studies have been conducted by the researchers of Transport Research Centre (TRC) at the University of Technology Sydney (UTS) to investigate the ability of recycled rubber mats, as well as waste tyre cells and granulated rubber to improve the stability of track substructure including ballast and subballast layers. This paper reviews four applications of these novel methods, including using recycled rubber products such as CWRC mixtures (i.e., mixtures of coal wash (CW) and rubber crumbs (RC)) and SEAL mixtures (i.e., mixtures of steel furnace slag, CW and RC) to replace subballast/capping materials, tyre cells reinforcements for subballast/capping layer and under ballast mats; and investigates the energy dissipation capacity for each application based on small-scale cyclic triaxial tests and large-scale track model tests. It has been found that the inclusion of these rubber products increases the energy dissipation effect of the track, hence reducing the ballast degradation efficiently and increasing the track stability. Moreover, a rheological model is also proposed to investigate the effect of different rubber inclusions on their efficiency to reduce the transient motion of rail track under dynamic loading. The outcomes elucidated in this paper will lead to a better understanding of the performance of ballast tracks upgraded with resilient rubber products, while promoting environmentally sustainable and more affordable ballasted tracks for greater passenger comfort and increased safety.
Indraratna, B, Qi, Y, Tawk, M, Heitor, A, Rujikiatkamjorn, C & Navaratnarajah, SK 2022, 'Advances in ground improvement using waste materials for transportation infrastructure', Proceedings of the Institution of Civil Engineers - Ground Improvement, vol. 175, no. 1, pp. 3-22.
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Recycling waste materials for transport infrastructure such as coal wash (CW), steel furnace slag (SFS), fly ash (FA) and recycled tyre products is an efficient way of minimising the stockpiles of waste materials while offering significant economic and environmental benefits, as well as improving the stability and longevity of infrastructure foundations. This paper presents some of the most recent state-of-the-art studies undertaken at the University of Wollongong, Australia on the use of waste materials such as (a) CW-based granular mixtures (i.e. SFS + CW, CW + FA) for port reclamation and road base/subbase and (b) using recycled tyre products (i.e. rubber crumbs, tyre cell, under-sleeper pads and under-ballast mats) to increase track stability and reduce ballast degradation. Typical methods of applying these waste materials for different infrastructure conditions are described and the results of comprehensive laboratory and field tests are presented and discussed.
Indraratna, B, Singh, M, Nguyen, TT, Rujikiatkamjorn, C, Malisetty, RS, Arivalagan, J & Nair, L 2022, 'Internal Instability and Fluidisation of Subgrade Soil under Cyclic Loading', Indian Geotechnical Journal, vol. 52, no. 5, pp. 1226-1243.
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AbstractRapid globalisation and the rise in population have substantially increased the demand for rail infrastructure which have been critical in transporting passengers and freight across landmasses for over a century. The surge in demand often leads to the construction of railway lines along with unfavourable soil conditions which result in different forms of substructure challenges such as uneven track deformations, ballast degradation, and subgrade mud pumping. A widespread site investigation along the eastern coast of New South Wales, Australia, indicated the prevalence of mud holes or bog holes along the tracks. The field studies suggest that low-to-medium plasticity soils are highly susceptible to mud pump when subjected to heavy axle loads under impeding drainage conditions. Subsequent laboratory investigations conducted on the remoulded soil samples collected from the sites indicated the sharp rise in cyclic axial strains and excess pore pressures along with the internal redistribution of moisture content as the governing mechanism for mud pumping. Numerical simulations performed using discrete element method coupled with computational fluid dynamics show that at a high hydraulic gradient, there is a substantial loss of soil contact network which leads to the upward migration of soil particles. The role of plastic fines and the inclusion of geosynthetic layer between the ballast and subgrade are also discussed in this paper. It was observed that the addition of 10% of cohesive fines increased the resistance of subgrade soils to mud pumping. On the other hand, geosynthetic inclusions not only assist in dissipating high cyclic excess pore pressures but also inhibit the upward migration of fine particles.
Inwumoh, J, Baguley, C & Gunawardane, K 2022, 'A Dynamic Control Methodology for DC Fault Ride Through of Modular Multilevel Converter based High Voltage Direct Current Systems', Computers and Electrical Engineering, vol. 100, pp. 107940-107940.
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Inwumoh, J, Baguley, CA & Gunawardane, K 2022, 'A Fast and Accurate Fault Location Technique for High Voltage Direct Current (HVDC) Systems Une technique rapide et précise de localisation des défauts pour les systèmes de courant continu à haute tension (CCHT)', IEEE Canadian Journal of Electrical and Computer Engineering, vol. 45, no. 4, pp. 383-393.
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Irfan, S, Khan, SB, Lam, SS, Ong, HC, Aizaz Ud Din, M, Dong, F & Chen, D 2022, 'Removal of persistent acetophenone from industrial waste-water via bismuth ferrite nanostructures', Chemosphere, vol. 302, pp. 134750-134750.
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Irga, PJ, Fleck, R, Arsenteva, E & Torpy, FR 2022, 'Biosolar green roofs and ambient air pollution in city centres: Mixed results', Building and Environment, vol. 226, pp. 109712-109712.
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Irmawati, Chai, R, Basari & Gunawan, D 2022, 'Optimizing CNN Hyperparameters for Blastocyst Quality Assessment in Small Datasets', IEEE Access, vol. 10, pp. 88621-88631.
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Islam, L, Islam, MR, Akter, S, Hasan, MZ, Moni, MA & Uddin, MN 2022, 'Identifying Heterogeneity of Diabetics Mellitus Based on the Demographical and Clinical Characteristics', Human-Centric Intelligent Systems, vol. 2, no. 1-2, pp. 44-54.
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Abstract Background: Diabetes is a long-term disease, which is characterised by high blood sugar and has risen as a public health problem worldwide. It may prompt a variety of serious illnesses, including stroke, kidney failure, and heart attacks. In 2014, diabetes affected approximately 422 million people worldwide and it is expected to hit 642 million people in 2040. The aim of this study is to analyse the effect of demographical and clinical characteristics for diabetics disease in Bangladesh. Methods: This study employs the quantitative approach for data analysis. First, we analyse differences in variables between diabetic patients and controls by independent two-sample t-test for continuous variables and Pearson Chi-square test for categorical variables. Then, logistic regression (LR) identifies the risk factors for diabetes disease based on the odds ratio (OR) and the adjusted odds ratio (AOR). Results: The results of the t-test and Chi square test identify that the factors: residence, wealth index, education, working status, smoking status, arm circumference, weight and BMI group show statistically (p < 0.05) significant differences between the diabetic group and the control group. And, LR model demonstrates that 2 factors (“working status” and “smoking status”) out of 13 are the significant risk factors for diabetes disease in Bangladesh. Conclusions: We believe that our analysis can help the government to take proper preparation to tackle the potentially unprecedented situations in Bangladesh.
Islam, MR, Lu, H, Hossain, MJ & Li, L 2022, 'Coordinating Electric Vehicles and Distributed Energy Sources Constrained by User’s Travel Commitment', IEEE Transactions on Industrial Informatics, vol. 18, no. 8, pp. 5307-5317.
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Islam, MS, Rahman, MM, Arsalanloo, A, Beni, HM, Larpruenrudee, P, Bennett, NS, Collins, R, Gemci, T, Taylor, M & Gu, Y 2022, 'How SARS-CoV-2 Omicron droplets transport and deposit in realistic extrathoracic airways', Physics of Fluids, vol. 34, no. 11, pp. 113320-113320.
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The SARS-CoV-2 Omicron variant is more highly transmissible and causes a higher mortality rate compared to the other eleven variants despite the high vaccination rate. The Omicron variant also establishes a local infection at the extrathoracic airway level. For better health risk assessment of the infected patients, it is essential to understand the transport behavior and the toxicity of the Omicron variant droplet deposition in the extrathoracic airways, which is missing in the literature. Therefore, this study aims to develop a numerical model for the Omicron droplet transport to the extrathoracic airways and to analyze that transport behavior. The finite volume method and ANSYS Fluent 2020 R2 solver were used for the numerical simulation. The Lagrangian approach, the discrete phase model, and the species transport model were employed to simulate the Omicron droplet transport and deposition. Different breathing rates, the mouth and nose inhalation methods were employed to analyze the viral toxicity at the airway wall. The results from this study indicated that there was a 33% of pressure drop for a flow rate at 30 l/min, while there was only a 3.5% of pressure drop for a 7.5 l/min. The nose inhalation of SARS-CoV-2 Omicron droplets is significantly more harmful than through the mouth due to a high deposition rate at the extrathoracic airways and high toxicity in the nasal cavities. The findings of this study would potentially improve knowledge of the health risk assessment of Omicron-infected patients.
Islam, MZ, Hossain, SI, Deplazes, E & Saha, SC 2022, 'Concentration-dependent cortisone adsorption and interaction with model lung surfactant monolayer', Molecular Simulation, vol. 48, no. 18, pp. 1627-1638.
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Corticosteroids are drugs used to treat inflammatory conditions. In the case of lung diseases, corticosteroids can be administered by inhalation. The main barrier for inhaled particles is the lung surfactant monolayer (LSM) that lines the alveolar air–water interface and reduces surface tension during breathing. In this study, we use coarse-grained molecular dynamics simulations to study the concentration-dependent interaction of cortisone with an LSM composed of neutral and negatively charged phospholipids, cholesterol, and surfactant proteins. Simulations were carried out at surface tensions mimicking inhalation and exhalation conditions and different compressibilities. In-depth analysis shows that cortisone causes a concentration-dependent expansion of the monolayer that at high surface tension and high drug concentrations results in the monolayer collapsing. This instability is associated with the accumulation of drugs and surfactant proteins that prevent adsorption into the monolayer. Our findings help to improve the understanding of how corticosteroids alter lung surfactants structure and assist efforts to improve drug adsorption.
Islam, MZ, Hossain, SI, Deplazes, E & Saha, SC 2022, 'The steroid mometasone alters protein containing lung surfactant monolayers in a concentration-dependent manner', Journal of Molecular Graphics and Modelling, vol. 111, pp. 108084-108084.
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Mometasone is an investigational anti-inflammatory steroidal drug to treat inflammation via pulmonary administration. For steroid drugs to be effective they need to be adsorbed by lung surfactants, a thin monolayer at the air-water interface in alveoli that reduces surface tension. Information on the molecular-level interactions of the drug with lung surfactants is useful to understand the mechanism of adsorption. In this study, we use coarse-grained molecular dynamics simulation to understand the concentration-dependent effect of mometasone on a lung surfactant monolayer (LSM) composed of lipids and surfactant proteins, under two different breathing conditions (exhalation, at surface tension 0 mNm-1 and inhalation, surface tension 20-25 mNm-1). A series of fixed-APL and fixed-surface tension simulations were used to demonstrate that in the absence of drugs, the model LSM reproduces the surface tensions for the compressed and expanded states, as well as compressibility at different surface tensions. In-depth analysis of simulations of a LSM in the presence of five different drug concentrations shows that mometasone alters the structure and dynamics of the LSM in a concentration-dependent manner. Mometasone induces a collapse in the monolayer that is affected by the surfactant protein and surface tension. Overall, these findings suggest that the surfactant proteins, surface tension and drug concentration are all critical components affecting monolayer stability and drug adsorption. The outcomes of this study may be beneficial for a more in-depth understanding of how mometasone is adsorbed by lung surfactants.
Islam, MZ, Hossain, SI, Deplazes, E, Luo, Z & Saha, SC 2022, 'The concentration-dependent effect of hydrocortisone on the structure of model lung surfactant monolayer by using an in silico approach', RSC Advances, vol. 12, no. 51, pp. 33313-33328.
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Adsorption mechanism of corticosteroid drug hydrocortisone in the lung surfactant monolayer.
Islam, MZ, Krajewska, M, Hossain, SI, Prochaska, K, Anwar, A, Deplazes, E & Saha, SC 2022, 'Concentration-Dependent Effect of the Steroid Drug Prednisolone on a Lung Surfactant Monolayer', Langmuir, vol. 38, no. 14, pp. 4188-4199.
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The lung surfactant monolayer (LSM) is the main barrier for particles entering the lung, including steroid drugs used to treat lung diseases. The present study combines Langmuir experiments and coarse-grained (CG) molecular dynamics simulations to investigate the concentration-dependent effect of steroid drug prednisolone on the structure and morphology of a model LSM. The surface pressure-area isotherms for the Langmuir monolayers reveal a concentration-dependent decrease in area per lipid (APL). Results from simulations at a fixed surface tension, representing inhalation and exhalation conditions, suggest that at high drug concentrations, prednisolone induces a collapse of the LSM, which is likely caused by the inability of the drug to diffuse into the bilayer. Overall, the monolayer is most susceptible to drug-induced collapse at surface tensions representing exhalation conditions. The presence of cholesterol also exacerbates the instability. The findings of this investigation might be helpful for better understanding the interaction between steroid drug prednisolone and lung surfactants in relation to off-target effects.
Ismail, M, Yang, W, Li, Y, Chai, T, Zhang, D, Du, Q, Muhammad, P, Hanif, S, Zheng, M & Shi, B 2022, 'Targeted liposomes for combined delivery of artesunate and temozolomide to resistant glioblastoma', Biomaterials, vol. 287, pp. 121608-121608.
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Ismail, M, Yang, W, Li, Y, Wang, Y, He, W, Wang, J, Muhammad, P, Chaston, TB, Rehman, FU, Zheng, M, Lovejoy, DB & Shi, B 2022, 'Biomimetic Dp44mT-nanoparticles selectively induce apoptosis in Cu-loaded glioblastoma resulting in potent growth inhibition', Biomaterials, vol. 289, pp. 121760-121760.
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Ivanyos, G, Mittal, T & Qiao, Y 2022, 'Symbolic Determinant Identity Testing and Non-Commutative Ranks of Matrix Lie Algebras', Leibniz International Proceedings in Informatics Lipics, vol. 215.
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One approach to make progress on the symbolic determinant identity testing (SDIT) problem is to study the structure of singular matrix spaces. After settling the non-commutative rank problem (Garg-Gurvits-Oliveira-Wigderson, Found. Comput. Math. 2020; Ivanyos-Qiao-Subrahmanyam, Comput. Complex. 2018), a natural next step is to understand singular matrix spaces whose non-commutative rank is full. At present, examples of such matrix spaces are mostly sporadic, so it is desirable to discover them in a more systematic way. In this paper, we make a step towards this direction, by studying the family of matrix spaces that are closed under the commutator operation, that is, matrix Lie algebras. On the one hand, we demonstrate that matrix Lie algebras over the complex number field give rise to singular matrix spaces with full non-commutative ranks. On the other hand, we show that SDIT of such spaces can be decided in deterministic polynomial time. Moreover, we give a characterization for the matrix Lie algebras to yield a matrix space possessing singularity certificates as studied by Lovász (B. Braz. Math. Soc., 1989) and Raz and Wigderson (Building Bridges II, 2019).
Iyer, S, Blair, A, Dawes, L, Moses, D, White, C & Sowmya, A 2022, 'Supervised and semi-supervised 3D organ localisation in CT images combining reinforcement learning with imitation learning', Biomedical Physics & Engineering Express, vol. 8, no. 3, pp. 035026-035026.
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Abstract Computer aided diagnostics often requires analysis of a region of interest (ROI) within a radiology scan, and the ROI may be an organ or a suborgan. Although deep learning algorithms have the ability to outperform other methods, they rely on the availability of a large amount of annotated data. Motivated by the need to address this limitation, an approach to localisation and detection of multiple organs based on supervised and semi-supervised learning is presented here. It draws upon previous work by the authors on localising the thoracic and lumbar spine region in CT images. The method generates six bounding boxes of organs of interest, which are then fused to a single bounding box. The results of experiments on localisation of the Spleen, Left and Right Kidneys in CT Images using supervised and semi supervised learning (SSL) demonstrate the ability to address data limitations with a much smaller data set and fewer annotations, compared to other state-of-the-art methods. The SSL performance was evaluated using three different mixes of labelled and unlabelled data (i.e. 30:70,35:65,40:60) for each of lumbar spine, spleen left and right kidneys respectively. The results indicate that SSL provides a workable alternative especially in medical imaging where it is difficult to obtain annotated data.
Jaafari, A, Panahi, M, Mafi-Gholami, D, Rahmati, O, Shahabi, H, Shirzadi, A, Lee, S, Bui, DT & Pradhan, B 2022, 'Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides', Applied Soft Computing, vol. 116, pp. 108254-108254.
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The robustness of landslide prediction models has become a major focus of researchers worldwide. We developed two novel hybrid predictive models that combine the self-organizing, deep-learning group method of data handling (GMDH) with two swarm intelligence optimization algorithms, i.e., cuckoo search algorithm (CSA) and whale optimization algorithm (WOA) for spatially explicit prediction of landslide susceptibility. Eleven landslide-causing factors and 334 historic landslides in a 31,340 km2 landslide-prone area in Iran were used to produce geospatial training and validation datasets. The GMDH model was employed to develop a basic predictive model that was then restructured and its parameters were optimized using the CSA and WOA algorithms, yielding the novel hybrid GMDH-CSA and GMDH-WOA models. The hybrid models were validated and compared to the standalone GMDH model by calculating the area under the receiver operating characteristic (AUC) curve and root mean square error (RMSE). The results demonstrated that the hybrid models overcame the computational shortcomings of the basic GMDH model and significantly improved landslide susceptibility prediction (GMDH-CSA, AUC = 0.909 and RMSE = 0.089; GMDH-WOA, AUC = 0.902 and RMSE = 0.129; standalone GMDH, AUC = 0.791 and RMSE = 0.226). Further, the hybrid models were more robust than the standalone GMDH model, showing consistently excellent performance when the training and validation datasets were changed. Overall, the swarm intelligence-optimized models, but not the standalone model, identified the best trade-offs among objectives, accuracy, and robustness.
Jahed Armaghani, D, Harandizadeh, H & Momeni, E 2022, 'Load carrying capacity assessment of thin-walled foundations: an ANFIS–PNN model optimized by genetic algorithm', Engineering with Computers, vol. 38, no. S5, pp. 4073-4095.
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Jain, K, Pradhan, B & Mishra, V 2022, 'Preface', Springer Proceedings in Mathematics and Statistics, vol. 404, pp. v-vi.
Jamshaid, M, Masjuki, HH, Kalam, MA, Zulkifli, NWM, Arslan, A & Qureshi, AA 2022, 'Experimental investigation of performance, emissions and tribological characteristics of B20 blend from cottonseed and palm oil biodiesels', Energy, vol. 239, pp. 121894-121894.
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Japelaghi, M, Hajian, F, Gholamalifard, M, Pradhan, B, Maulud, KNA & Park, H-J 2022, 'Modelling the Impact of Land Cover Changes on Carbon Storage and Sequestration in the Central Zagros Region, Iran Using Ecosystem Services Approach', Land, vol. 11, no. 3, pp. 423-423.
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Central Zagros region in Iran is a major hotspot of carbon storage and sequestration which has experienced severe land cover change in recent decades that has led to carbon emission. In this research, using temporal Landsat images, land cover maps were produced and used in Land Change Modeler to predict land cover changes in 2020, 2030, 2040 and 2050 using Multilayer Perceptron Neural Network and Markov Chain techniques. Next, resultant maps were used as inputs to Ecosystem Services Modeler. The Intergovernmental Panel on Climate Change (IPCC) report data was used to extract carbon data. Results show that between 1989–2013 about half of forests have been destroyed. Prediction results show that by 2050 about 75% of existing forests will be lost and between 2013–2020 about 157,000 Mg carbon and by 2050 about 565,000 Mg carbon will be lost with more than US$1.9 million to 2020 and AU$3.2 million by 2050 economic compensation.
Javed, AR, Shahzad, F, Rehman, SU, Zikria, YB, Razzak, I, Jalil, Z & Xu, G 2022, 'Future smart cities: requirements, emerging technologies, applications, challenges, and future aspects', Cities, vol. 129, pp. 103794-103794.
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Future smart cities are the key to fulfilling the ever-growing demands of citizens. Information and communication advancements will empower better administration of accessible resources. The eventual fate of the world's betterment lies in its urban environment advancement. The fast influx of individuals creates possibility, yet it additionally causes challenges. Creating sustainable, reasonable space in the world's steadily extending cities is tested confronting governments worldwide. The model of the smart cities rise, where the rights and well-being of the smart city citizens are assured, the industry is in action, and the assessment of urban planning from an environmental point of view. This paper presents a survey on analyzing future technologies and requirements for future smart cities. We provide extensive research to identify and inspect the latest technology advancements, the foundation of the upcoming robust era. Such technologies include deep learning (DL), machine learning (ML), internet of things (IoT), mobile computing, big data, blockchain, sixth-generation (6G) networks, WiFi-7, industry 5.0, robotic systems, heating ventilation, and air conditioning (HVAC), digital forensic, industrial control systems, connected and automated vehicles (CAVs), electric vehicles, product recycling, flying Cars, pantry backup, calamity backup and vital integration of cybersecurity to keep the user concerns secured. We provide a detailed review of the existing future smart cities application frameworks. Furthermore, we discuss various technological challenges of future smart cities. Finally, we identify the future dimensions of smart cities to develop smart cities with the precedence of smart living.
Jawahar, M, H, S, L, JA & Gandomi, AH 2022, 'ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification', Computers in Biology and Medicine, vol. 148, pp. 105894-105894.
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Acute Lymphoblastic Leukemia (ALL) is cancer in which bone marrow overproduces undeveloped lymphocytes. Over 6500 cases of ALL are diagnosed every year in the United States in both adults and children, accounting for around 25% of pediatric cancers, and the trend continues to rise. With the advancements of AI and big data analytics, early diagnosis of ALL can be used to aid the clinical decisions of physicians and radiologists. This research proposes a deep neural network-based (ALNett) model that employs depth-wise convolution with different dilation rates to classify microscopic white blood cell images. Specifically, the cluster layers encompass convolution and max-pooling followed by a normalization process that provides enriched structural and contextual details to extract robust local and global features from the microscopic images for the accurate prediction of ALL. The performance of the model was compared with various pre-trained models, including VGG16, ResNet-50, GoogleNet, and AlexNet, based on precision, recall, accuracy, F1 score, loss accuracy, and receiver operating characteristic (ROC) curves. Experimental results showed that the proposed ALNett model yielded the highest classification accuracy of 91.13% and an F1 score of 0.96 with less computational complexity. ALNett demonstrated promising ALL categorization and outperformed the other pre-trained models.
Jayanthakumaran, M, Shukla, N & Beydoun, G 2022, 'The nexus of customer behaviour, corporate perception and banking: Australian perspective', Law and Financial Markets Review, vol. 16, no. 4, pp. 334-355.
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Jayaraman, S, Ramachandran, M, Patan, R, Daneshmand, M & Gandomi, AH 2022, 'Fuzzy Deep Neural Learning Based on Goodman and Kruskal's Gamma for Search Engine Optimization', IEEE Transactions on Big Data, vol. 8, no. 1, pp. 268-277.
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IEEE Search engine optimization (SEO) is a significant problem for enhancing a website's visibility with search engine results. SEO issues, such as Site Popularity, Content Quality, Keyword Density, and Publicity, were not considered during the search engine optimization process. Therefore, the retrieval rate of the existing techniques is inadequate. In this study, Triangular Fuzzy Deep Structured Learning-Based Predictive Page Ranking (TFDSL-PPR) Technique is proposed to solve these limitations. First, the TFDSL-PPR technique takes a number of user queries as input in the input layer, and then it employs four hidden layers in order to deeply analyze the web pages based on an input query. The first hidden layer determines the keywords from the user query. The second hidden layer measures the site popularity, content quality, keyword density and publicity of all web pages in the search engine. It then accomplishes Goodman and Kruskal's Gamma Predictive Ranking process in the third hidden layer, where it ranks the web pages by considering their similarities. The proposed TFDSL-PPR technique is applied to the ClueWeb09 Dataset with respect to a variety of user queries. The results are benchmarked by existing methods based on several metrics such as retrieval rate, time, and false-positive rate.
Jayawickrama, BA & He, Y 2022, 'Improved Layered Normalized Min-Sum Algorithm for 5G NR LDPC', IEEE Wireless Communications Letters, vol. 11, no. 9, pp. 2015-2018.
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Jazebi, M & Ahmadi, MM 2022, 'Drilled shafts in sand: failure pattern and tip resistance using numerical and analytical approaches', International Journal of Geotechnical Engineering, vol. 16, no. 8, pp. 974-990.
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Jena, KK, Bhoi, SK, Prasad, M & Puthal, D 2022, 'A fuzzy rule-based efficient hospital bed management approach for coronavirus disease-19 infected patients', Neural Computing and Applications, vol. 34, no. 14, pp. 11361-11382.
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Coronavirus disease-19 (COVID-19) is a very dangerous infectious disease for the entire world in the current scenario. Coronavirus spreads from one person to another person very rapidly. It spreads exponentially throughout the globe. Everyone should be cautious to avoid the spreading of this novel disease. In this paper, a fuzzy rule-based approach using priority-based method is proposed for the management of hospital beds for COVID-19 infected patients in the worst-case scenario where the number of hospital beds is very less as compared to the number of COVID-19 infected patients. This approach mainly attempts to minimize the number of hospital beds as well as emergency beds requirement for the treatment of COVID-19 infected patients to handle such a critical situation. In this work, higher priority has given to severe COVID-19 infected patients as compared to mild COVID-19 infected patients to handle this critical situation so that the survival probability of the COVID-19 infected patients can be increased. The proposed method is compared with first-come first-serve (FCFS)-based method to analyze the practical problems that arise during the assignment of hospital beds and emergency beds for the treatment of COVID-19 patients. The simulation of this work is carried out using MATLAB R2015b.
Jena, R, Pradhan, B, Beydoun, G, Alamri, A & Shanableh, A 2022, 'Spatial earthquake vulnerability assessment by using multi-criteria decision making and probabilistic neural network techniques in Odisha, India', Geocarto International, vol. 37, no. 25, pp. 8080-8099.
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Ji, J, Sun, X, He, W, Liu, Y, Duan, J, Liu, W, Nghiem, LD, Wang, Q & Cai, Z 2022, 'Built-in electric field enabled in carbon-doped Bi3O4Br nanocrystals for excellent photodegradation of PAHs', Separation and Purification Technology, vol. 302, pp. 122066-122066.
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A new type of solar active carbon-doped Bi3O4Br catalyst was synthesized by combining hydrothermal and post-thermal treatment. The activity of the material under sunlight and visible light was 3.3 times and 2.7 times that of Bi3O4Br, respectively. The C-doping on Bi3O4Br nanosheets increased the built-in electric field strength, thus significantly promoted the migration of charge carriers and enhanced the photocatalytic activity. In addition, replacing Br with C with a smaller atomic radius can shorten the interlayer spacing, which is beneficial to carrier separation. Experiments showed that the doping of C shortened the semiconductor band gap by 9.8% and expanded the absorption range of visible light. Among the photogenerated reactive species, h+ played a major role in the degradation of 1-methylpyrene (a typical polycyclic aromatic hydrocarbons), followed by O2∙- and •OH. Based on intermediate analysis and DFT calculation, we proposed the degradation mechanism and pathways. Quantitative structure–activity relationship (QSAR) analysis showed that some toxic intermediates were produced during the photocatalysis process, but the overall environmental risk was greatly reduced. This work provides new perspective for understanding non-metallic doping in semiconductor photocatalysts to enhance the built-in electric field, and this technology can be extended to other semiconductor materials.
Ji, Z, Natarajan, A, Vidick, T, Wright, J & Yuen, H 2022, 'Quantum Soundness of Testing Tensor Codes', Discrete Analysis, vol. 2022.
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A locally testable code is an error-correcting code that admits very efficient probabilistic tests of membership. Tensor codes provide a simple family of combinatorial constructions of locally testable codes that generalize the family of Reed-Muller codes. The natural test for tensor codes, the axis-parallel line vs. point test, plays an essential role in constructions of probabilistically checkable proofs. We analyze the axis-parallel line vs. point test as a two-prover game and show that the test is sound against quantum provers sharing entanglement. Our result implies the quantum-soundness of the low individual degree test, which is an essential component of the MIP = RE theorem. Our proof generalizes to the infinite-dimensional commuting-operator model of quantum provers.
Jia, J, Ren, P, Hu, H, Sayyadi, N, Parvin, F, Zheng, X, Shi, B, Piper, JA, Song, B, Vickery, K & Lu, Y 2022, 'Lifetime Multiplexing with Lanthanide Complexes for Luminescence In Situ Hybridisation', Analysis & Sensing, vol. 2, no. 2.
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AbstractLong‐lived luminescence probes with engineered lifetimes offer great potential for multiplexed biodetection and imaging at high sensitivity and contrast. The overwhelming majority of lifetime‐multiplexing probes are based on luminescent nanoparticles; however, these typically suffer practical limitations compared to molecular probes when applied to biological systems. By surveying commercially available ligands for lanthanide coordination, we identify three europium complexes with distinguishable lifetime from 100 μs to 1 ms. Spectroscopic analysis reveals this to be due to intrinsic radiative lifetime and non‐radiative relaxation. These europium complexes are developed as in situ hybridisation probes for luminescence lifetime imaging of bacterial biofilms. Utilising time‐resolved detection we achieve sensitive and robust detection and identification of the target bacteria species, thus demonstrating the practicality of lanthanide‐based molecular probes for lifetime multiplexing.
Jia, M, Gabrys, B & Musial, K 2022, 'Measuring Quadrangle Formation in Complex Networks', IEEE Transactions on Network Science and Engineering, vol. 9, no. 2, pp. 538-551.
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The classic clustering coefficient and the lately proposed closure coefficient quantifies the formation of triangles from two different perspectives, with the focal node at the centre or at the end in an open triad. As many networks are naturally rich in triangles, they become standard metrics to describe and analyse networks. However, their utilities could be limited in many other types of networks, where triangles are relatively few and quadrangles are overrepresented, such as the protein-protein interaction networks, the neural networks and the food webs. Here we propose two quadrangle coefficients, i.e., the i-quad coefficient and the o-quad coefficient, to quantify quadrangle formation in networks, and we further extend them to weighted networks. Through experiments on 16 networks from six different domains, we first reveal the density distribution of the two quadrangle coefficients, and then analyse their correlations with node degree. Finally, we demonstrate that at network-level, adding the average i-quad coefficient and the average o-quad coefficient leads to significant improvement in network classification, while at node-level, the i-quad and o-quad coefficients are useful features to improve link prediction.
Jia, M, Van Alboom, M, Goubert, L, Bracke, P, Gabrys, B & Musial, K 2022, 'Encoding edge type information in graphlets', PLOS ONE, vol. 17, no. 8, pp. e0273609-e0273609.
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Graph embedding approaches have been attracting increasing attention in recent years mainly due to their universal applicability. They convert network data into a vector space in which the graph structural information and properties are maximumly preserved. Most existing approaches, however, ignore the rich information about interactions between nodes, i.e., edge attribute or edge type. Moreover, the learned embeddings suffer from a lack of explainability, and cannot be used to study the effects of typed structures in edge-attributed networks. In this paper, we introduce a framework to embed edge type information in graphlets and generate a Typed-Edge Graphlets Degree Vector (TyE-GDV). Additionally, we extend two combinatorial approaches, i.e., the colored graphlets and heterogeneous graphlets approaches to edge-attributed networks. Through applying the proposed method to a case study of chronic pain patients, we find that not only the network structure of a patient could indicate his/her perceived pain grade, but also certain social ties, such as those with friends, colleagues, and healthcare professionals, are more crucial in understanding the impact of chronic pain. Further, we demonstrate that in a node classification task, the edge-type encoded graphlets approaches outperform the traditional graphlet degree vector approach by a significant margin, and that TyE-GDV could achieve a competitive performance of the combinatorial approaches while being far more efficient in space requirements.
Jia, X, Sedehi, O, Papadimitriou, C, Katafygiotis, LS & Moaveni, B 2022, 'Hierarchical Bayesian modeling framework for model updating and robust predictions in structural dynamics using modal features', Mechanical Systems and Signal Processing, vol. 170, pp. 108784-108784.
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Jia, X, Sedehi, O, Papadimitriou, C, Katafygiotis, LS & Moaveni, B 2022, 'Nonlinear model updating through a hierarchical Bayesian modeling framework', Computer Methods in Applied Mechanics and Engineering, vol. 392, pp. 114646-114646.
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Jiang, C, Ni, B-J, Zheng, X, Lu, B, Chen, Z, Gao, Y, Zhang, Y, Zhang, S & Luo, G 2022, 'The changes of microplastics’ behavior in adsorption and anaerobic digestion of waste activated sludge induced by hydrothermal pretreatment', Water Research, vol. 221, pp. 118744-118744.
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Waste activated sludge (WAS) contains high concentrations of microplastics (MPs), which could serve as vectors of various organic pollutants and heavy metals, causing synergistic transportation and pollution. The application of combined hydrothermal pretreatment (HTP) and anaerobic digestion (AD) has raised growing concerns since the low-temperature hydrothermal treatment could enhance the biogas production of WAS. However, the changes in physicochemical properties, adsorption performances, and effects on AD of MPs by HTP have not been studied. The study used three typical MPs in WAS, and it was found that the HTP (170°C & 30min) increased MPs' specific surface area and carbonyl index (CI) while decreasing the relative crystallinity. The adsorption capacity to Cd increased through the carbonylation for polyethylene microplastic (PE-MP) and polystyrene microplastic (PS-MP) while decreasing by the dechlorination for polyvinyl chloride microplastic (PVC-MP). Meanwhile, increased hydrophilicity reduced the adsorption capacities of all three typical MPs for ofloxacin. The above results indicated that the HTP could be worth blocking the adsorption of polar MPs for polar pollutants. For the pristine MPs, only PVC-MP at the highest concentration (0.5 g kg-1 VS) significantly (p < 0.05) reduced methane production by 16.2 ± 3.3% of WAS without the HTP. However, the HTP resulted in significant (p < 0.05) inhibition of methane production of WAS at high concentrations of PE-MP and PVC-MP (e.g., 0.1 and 0.5 g kg-1 VS), which was due to the acceleration of the released toxic plastic additives (dibutyl phthalate, dimethyl phthalate, and bisphenol-A). Microbial analysis showed the abundances of vital anaerobes, such as acid-producing bacteria (Acetoanerrobium and Mesotoga), proteolytic bacteria (Proteiniborus), and methanogens (Methanosaeta) clearly decreased with the PE-MP and PVC-MP after the HTP, which might result in the decreased methane production. The study provi...
Jiang, G, Wu, J, Weidhaas, J, Li, X, Chen, Y, Mueller, J, Li, J, Kumar, M, Zhou, X, Arora, S, Haramoto, E, Sherchan, S, Orive, G, Lertxundi, U, Honda, R, Kitajima, M & Jackson, G 2022, 'Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology', Water Research, vol. 218, pp. 118451-118451.
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As a cost-effective and objective population-wide surveillance tool, wastewater-based epidemiology (WBE) has been widely implemented worldwide to monitor the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentration in wastewater. However, viral concentrations or loads in wastewater often correlate poorly with clinical case numbers. To date, there is no reliable method to back-estimate the coronavirus disease 2019 (COVID-19) case numbers from SARS-CoV-2 concentrations in wastewater. This greatly limits WBE in achieving its full potential in monitoring the unfolding pandemic. The exponentially growing SARS-CoV-2 WBE dataset, on the other hand, offers an opportunity to develop data-driven models for the estimation of COVID-19 case numbers (both incidence and prevalence) and transmission dynamics (effective reproduction rate). This study developed artificial neural network (ANN) models by innovatively expanding a conventional WBE dataset to include catchment, weather, clinical testing coverage and vaccination rate. The ANN models were trained and evaluated with a comprehensive state-wide wastewater monitoring dataset from Utah, USA during May 2020 to December 2021. In diverse sewer catchments, ANN models were found to accurately estimate the COVID-19 prevalence and incidence rates, with excellent precision for prevalence rates. Also, an ANN model was developed to estimate the effective reproduction number from both wastewater data and other pertinent factors affecting viral transmission and pandemic dynamics. The established ANN model was successfully validated for its transferability to other states or countries using the WBE dataset from Wisconsin, USA.
Jiang, J, Xiao, T, Xu, J, Wen, D, Gao, L & Dou, Y 2022, 'A low-latency LSTM accelerator using balanced sparsity based on FPGA', Microprocessors and Microsystems, vol. 89, pp. 104417-104417.
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Jiang, K, Xie, W, Lei, J, Li, Z, Li, Y, Jiang, T & Du, Q 2022, 'E2E-LIADE: End-to-End Local Invariant Autoencoding Density Estimation Model for Anomaly Target Detection in Hyperspectral Image', IEEE Transactions on Cybernetics, vol. 52, no. 11, pp. 11385-11396.
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Jiang, M, Wu, T, Wang, Z, Gong, Y, Zhang, L & Liu, RP 2022, 'A Multi-Intersection Vehicular Cooperative Control Based on End-Edge-Cloud Computing', IEEE Transactions on Vehicular Technology, vol. 71, no. 3, pp. 2459-2471.
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Jiang, S, Li, K & Da Xu, RY 2022, 'Magnitude Bounded Matrix Factorisation for Recommender Systems', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 4, pp. 1856-1869.
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Jiang, T, Xie, W, Li, Y, Lei, J & Du, Q 2022, 'Weakly Supervised Discriminative Learning With Spectral Constrained Generative Adversarial Network for Hyperspectral Anomaly Detection', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 11, pp. 6504-6517.
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Jiang, Y, Fan, M, Yang, Z, Liu, X, Xu, Z, Liu, S, Feng, G, Tang, S, Li, Z, Zhang, Y, Chen, S, Yang, C, Law, W-C, Dong, B, Xu, G & Yong, K-T 2022, 'Recent advances in nanotechnology approaches for non-viral gene therapy', Biomaterials Science, vol. 10, no. 24, pp. 6862-6892.
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Gene therapy has shown great potential in treating many diseases by downregulating the expression of certain genes. Various functional non-viral vectors have been well designed to enable efficient gene therapy.
Jifroudi, HM, Mansor, SB, Pradhan, B, Halin, AA, Ahmad, N & Abdullah, AFB 2022, 'A new approach to derive buildings footprint from light detection and ranging data using rule-based learning techniques and decision tree', Measurement, vol. 192, pp. 110781-110781.
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Jin, JX, Zhou, Q, Yang, RH, Li, YJ, Li, H, Guo, YG & Zhu, JG 2022, 'A superconducting magnetic energy storage based current-type interline dynamic voltage restorer for transient power quality enhancement of composited data center and renewable energy source power system', Journal of Energy Storage, vol. 52, pp. 105003-105003.
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Jin, L, Ruckin, J, Kiss, SH, Vidal-Calleja, T & Popovic, M 2022, 'Adaptive-Resolution Field Mapping Using Gaussian Process Fusion With Integral Kernels', IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7471-7478.
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Jin, X, Kaw, HY, Liu, Y, Zhao, J, Piao, X, Jin, D, He, M, Yan, X-P, Zhou, JL & Li, D 2022, 'One-step integrated sample pretreatment technique by gas-liquid microextraction (GLME) to determine multi-class pesticide residues in plant-derived foods', Food Chemistry, vol. 367, pp. 130774-130774.
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Gas-liquid microextraction technique (GLME) has been integrated with dispersive solid phase extraction to establish a one-step sample pretreatment approach for rapid analysis of multi-class pesticides in different plant-derived foods. A 50 μL of organic solvent plus 40 mg of PSA were required throughout the 5-minute pretreatment procedure. Good trueness (recoveries of 67.2 - 105.4%) and precision (RSD ≤ 18.9%) were demonstrated by the one-step GLME method, with MLOQs ranged from 0.001 to 0.011 mg kg-1. As high as 93.6% pesticides experienced low matrix effect through this method, and the overall matrix effects (ME%) were generally better or comparable to QuEChERS. This method successfully quantified 2-phenylphenol, quintozene, bifenthrin and permethrin in the range of 0.001 - 0.008 mg kg-1 in real food samples. The multiresidue analysis feature of GLME has been validated, which displays further potential for on-site determination of organic pollutants in order to safeguard food safety and human health.
Jin, Z, Sun, X, Lei, G, Guo, Y & Zhu, J 2022, 'Sliding Mode Direct Torque Control of SPMSMs Based on a Hybrid Wolf Optimization Algorithm', IEEE Transactions on Industrial Electronics, vol. 69, no. 5, pp. 4534-4544.
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Direct torque control has been widely used to control surface-mounted permanent magnet synchronous motors (SPMSMs). To reduce the torque ripple and improve the flux tracking accuracy of SPMSM drives, sliding mode direct torque control (SMDTC) was developed. However, its optimal performance is hardly obtained by trial and error tuning of the control parameters. Hence, a hybrid wolf optimization algorithm (HWOA) is proposed to automatically adjust the controller's parameters of SMDTC for SPMSMs in this article. This algorithm combines the grey wolf optimization algorithm and coyote optimization algorithm. A conversion probability is designed to use them simultaneously. The proposed HWOA holds the advantages of the two algorithms. It converges very fast and can avoid local optimums effectively. Furthermore, a special fitness index with penalty terms is designed to enhance flux tracking accuracy and reduce the torque ripple of SPMSM drives. The superiority of the proposed control method is verified by an experiment.
Johansen, MD, Mahbub, RM, Idrees, S, Nguyen, DH, Miemczyk, S, Pathinayake, P, Nichol, K, Hansbro, NG, Gearing, LJ, Hertzog, PJ, Gallego-Ortega, D, Britton, WJ, Saunders, BM, Wark, PA, Faiz, A & Hansbro, PM 2022, 'Increased SARS-CoV-2 Infection, Protease, and Inflammatory Responses in Chronic Obstructive Pulmonary Disease Primary Bronchial Epithelial Cells Defined with Single-Cell RNA Sequencing', American Journal of Respiratory and Critical Care Medicine, vol. 206, no. 6, pp. 712-729.
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Rationale: Patients with chronic obstructive pulmonary disease (COPD) develop more severe coronavirus disease (COVID-19); however, it is unclear whether they are more susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and what mechanisms are responsible for severe disease. Objectives: To determine whether SARS-CoV-2 inoculated primary bronchial epithelial cells (pBECs) from patients with COPD support greater infection and elucidate the effects and mechanisms involved. Methods: We performed single-cell RNA sequencing analysis on differentiated pBECs from healthy subjects and patients with COPD 7 days after SARS-CoV-2 inoculation. We correlated changes with viral titers, proinflammatory responses, and IFN production. Measurements and Main Results: Single-cell RNA sequencing revealed that COPD pBECs had 24-fold greater infection than healthy cells, which was supported by plaque assays. Club/goblet and basal cells were the predominant populations infected and expressed mRNAs involved in viral replication. Proteases involved in SARS-CoV-2 entry/infection (TMPRSS2 and CTSB) were increased, and protease inhibitors (serpins) were downregulated more so in COPD. Inflammatory cytokines linked to COPD exacerbations and severe COVID-19 were increased, whereas IFN responses were blunted. Coexpression analysis revealed a prominent population of club/goblet cells with high type 1/2 IFN responses that were important drivers of immune responses to infection in both healthy and COPD pBECs. Therapeutic inhibition of proteases and inflammatory imbalances reduced viral titers and cytokine responses, particularly in COPD pBECs. Conclusions: COPD pBECs are more susceptible to SARS-CoV-2 infection because of increases in coreceptor expression and protease imbalances and have greater inflammatory responses. A prominent cluster of IFN-responsive club/goblet cells emerges during infection, which may be important drivers of immunity. Therapeutic i...
John, AR, Cao, Z, Chen, H-T, Martens, KE, Georgiades, M, Gilat, M, Nguyen, HT, Lewis, SJG & Lin, C-T 2022, 'Predicting the Onset of Freezing of Gait Using EEG Dynamics', Applied Sciences, vol. 13, no. 1, pp. 302-302.
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Freezing of gait (FOG) severely incapacitates the mobility of patients with advanced Parkinson’s disease (PD). An accurate prediction of the onset of FOG could improve the quality of life for PD patients. However, it is imperative to distinguish the possibility of the onset of FOG from that of voluntary stopping. Our previous work demonstrated the neurological differences between the transition to FOG and voluntary stopping using electroencephalogram (EEG) signals. We employed a timed up-and-go (TUG) task to elicit FOG in PD patients. Some of these TUG tasks had an additional voluntary stopping component, where participants stopped walking based on verbal instruction to “stop”. The performance of the convolutional neural network (CNN) in identifying the transition to FOG from normal walking and the transition to voluntary stopping was explored. To the best of our knowledge, this work is the first study to propose a deep learning method to distinguish the transition to FOG from the transition to voluntary stop in PD patients. The models, trained on the EEG data from 17 PD patients who manifested FOG episodes, considering a short two-second transition window for FOG occurrence or voluntary stopping, achieved close to 75% classification accuracy in distinguishing transition to FOG from the transition to voluntary stopping or normal walking. Our results represent an important step toward advanced EEG-based cueing systems for smart FOG intervention, excluding the potential confounding of voluntary stopping.
John, AR, Singh, AK, Do, T-TN, Eidels, A, Nalivaiko, E, Gavgani, AM, Brown, S, Bennett, M, Lal, S, Simpson, AM, Gustin, SM, Double, K, Walker, FR, Kleitman, S, Morley, J & Lin, C-T 2022, 'Unraveling the Physiological Correlates of Mental Workload Variations in Tracking and Collision Prediction Tasks', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, no. 99, pp. 770-781.
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Modern work environments have extensive interactions with technology and greater cognitive complexity of the tasks, which results in human operators experiencing increased mental workload. Air traffic control operators routinely work in such complex environments, and we designed tracking and collision prediction tasks to emulate their elementary tasks. The physiological response to the workload variations in these tasks was elucidated to untangle the impact of workload variations experienced by operators. Electroencephalogram (EEG), eye activity, and heart rate variability (HRV) data were recorded from 24 participants performing tracking and collision prediction tasks with three levels of difficulty. Our findings indicate that variations in task load in both these tasks are sensitively reflected in EEG, eye activity and HRV data. Multiple regression results also show that operators' performance in both tasks can be predicted using the corresponding EEG, eye activity and HRV data. The results also demonstrate that the brain dynamics during each of these tasks can be estimated from the corresponding eye activity, HRV and performance data. Furthermore, the markedly distinct neurometrics of workload variations in the tracking and collision prediction tasks indicate that neurometrics can provide insights on the type of mental workload. These findings have applicability to the design of future mental workload adaptive systems that integrate neurometrics in deciding not just 'when' but also 'what' to adapt. Our study provides compelling evidence in the viability of developing intelligent closed-loop mental workload adaptive systems that ensure efficiency and safety in complex work environments.
John, CB, Raja, SA, Deepanraj, B & Ong, HC 2022, 'Palm stearin biodiesel: preparation, characterization using spectrometric techniques and the assessment of fuel properties', Biomass Conversion and Biorefinery, vol. 12, no. 5, pp. 1679-1693.
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In the contemporary era, our planet has been experiencing an unprecedented energy shortage and degradation of the environment. The exhaustion of crude oil reserves, fluctuation in fuel prices, and the escalating environmental pollution problems are driving the researchers worldwide to search for sustainable alternative fuels. This study discusses an enhanced protocol for the production of biodiesel using crude palm stearin (CPS), the nonedible solid portion of palm oil, through alkali-catalyzed transesterification. The significant physicochemical properties of CPS and palm stearin biodiesel (PSB) were analyzed by adopting American Society for Testing and Materials (ASTM) test procedures and contrasted with the commonly used biodiesels, petro-diesel, and ASTM biodiesel standards. The kinematic viscosity, density, gross calorific value, and cetane number of PSB were noticed to be 0.566 cSt, 0.882 kg/m3, 38,676.90 kJ/kg, and 47.5, respectively. The fatty acid composition and the functional groups present in CPS and PSB were determined by gas chromatography mass spectrometry (GCMS) and Fourier transform infrared spectrometry (FTIR) techniques. GCMS spectra for PSB demonstrated a composition consisting of myristic acid, palmitoleic acid, palmitic acid, elaidic acid, oleic acid, stearic acid, linoleic acid, and eicosapentaenoic acid in varying percentages. The conversion of triglycerides in the CPS into methyl esters in PSB was confirmed by the FTIR analysis. The results of thermogravimetric analyses were also in good agreement with GCMS and FTIR. The closeness of the estimated properties of PSB with petro-diesel and the conformance with ASTM standards indicate the prospective of PSB as an alternative fuel for compressed ignition engines.
Jokar, H, Bahmani-Firouzi, B, Alhelou, HH & Siano, P 2022, 'Transmission and Distribution Substation Energy Management Considering Large-Scale Energy Storage, Demand Side Management and Security-Constrained Unit Commitment', IEEE Access, vol. 10, pp. 123723-123735.
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Joshua Tapas, M, Thomas, P, Vessalas, K & Sirivivatnanon, V 2022, 'Mechanisms of Alkali-Silica Reaction Mitigation in AMBT Conditions: Comparative Study of Traditional Supplementary Cementitious Materials', Journal of Materials in Civil Engineering, vol. 34, no. 3.
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This study investigates the mechanisms of alkali-silica reaction (ASR) mitigation by supplementary cementitious materials (SCMs) under accelerated mortar bar test (AMBT) conditions. The study compares the effect of traditional SCMs (fly ash, slag, metakaolin, and silica fume) on ASR expansion, calcium silicate hydrate (C-S-H) composition, and portlandite consumption as well as on the availability of silicon and aluminum in solution. Results show that at typical SCM replacement levels for effective ASR mitigation (15% metakaolin, 25% fly ash, and 65% slag), the Si/Ca and Al/Si ratios of C-S-H are increased to comparable values, suggesting that at these dosages the SCMs contribute almost equivalent amounts of silicon and aluminum in solution. Studies of blended cement + SCM pastes show that the order of pozzolanicity is as follows: silica fume > metakaolin > fly ash > slag, which is consistent with the order of efficacy of SCMs in mitigating ASR expansion and the measured concentrations of silicon in solution. Solubility studies of the SCMs showed formation of sodium aluminum silicate hydrate (N-A-S-H) in fly ash and metakaolin and formation of calcium aluminum silicate hydrate (C-A-S-H) in slag after 28 days of exposure to AMBT conditions. This highlights the role of alkali activation of SCMs in ASR mitigation under AMBT conditions.
Ju, M, Ding, C, Ren, W & Yang, Y 2022, 'IDBP: Image Dehazing Using Blended Priors Including Non-Local, Local, and Global Priors', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 7, pp. 4867-4871.
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In this letter, a robust and promising atmospheric scattering model (ASM)-based image dehazing technique called IDBP is developed, which overcomes the intrinsic limitation of available techniques based on single priors. It consists of two modules, i.e., an atmospheric light estimation (ALE) module and a multiple prior constraint (MPC) module. The ALE module is based on a new global brightening strategy of enhancing the brightness of image with minimum information loss. The MPC smartly blends the constrains of non-local prior, local prior, and global prior to shrink the solution space of haze removal, which avoids the limitation of using any single priors. Unlike previous works, IDBP does not require any training process, but is based on multiple priors and minimal information loss principle to impose the ASM, thereby making it easy to implement and ensuring its robustness. Numerous experiments reveal that the proposed IDBP outperforms the state-of-the-art alternates.
Juang, C-F, Chou, C-Y & Lin, C-T 2022, 'Navigation of a Fuzzy-Controlled Wheeled Robot Through the Combination of Expert Knowledge and Data-Driven Multiobjective Evolutionary Learning', IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 7388-7401.
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This article proposes a navigation scheme for a wheeled robot in unknown environments. The navigation scheme consists of obstacle boundary following (OBF), target seeking (TS), and vertex point seeking (VPS) behaviors and a behavior supervisor. The OBF behavior is achieved by a fuzzy controller (FC). This article formulates the FC design problem as a new constrained multiobjective optimization problem and finds a set of nondominated FC solutions through the combination of expert knowledge and data-driven multiobjective ant colony optimization. The TS behavior is achieved by new fuzzy proportional-integral-derivative (PID) and proportional-derivative (PD) controllers that control the orientation and speed of the robot, respectively. The VPS behavior is proposed to shorten the navigation route by controlling the robot to move toward a new subgoal determined from the vertex point of an obstacle. A new behavior supervisor that manages the switching among the OBF, TS, and VPS behaviors in unknown environments is proposed. In the navigation of a real robot, a new robot localization method through the fusion of encoders and an infrared localization sensor using a particle filter is proposed. Finally, this article presents simulations and experiments to verify the feasibility and advantages of the navigation scheme.
Jung, MC, Chai, R, Zheng, J & Nguyen, H 2022, 'Enhanced myoelectric control against arm position change with weighted recursive Gaussian process', Neural Computing and Applications, vol. 34, no. 7, pp. 5015-5028.
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Kabir, MM, Alam, F, Akter, MM, Gilroyed, BH, Didar-ul-Alam, M, Tijing, L & Shon, HK 2022, 'Highly effective water hyacinth (Eichhornia crassipes) waste-based functionalized sustainable green adsorbents for antibiotic remediation from wastewater', Chemosphere, vol. 304, pp. 135293-135293.
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Kacprzak, S & Tijing, LD 2022, 'Microplastics in indoor environment: Sources, mitigation and fate', Journal of Environmental Chemical Engineering, vol. 10, no. 2, pp. 107359-107359.
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Kajikawa, Y, Mejia, C, Wu, M & Zhang, Y 2022, 'Academic landscape of Technological Forecasting and Social Change through citation network and topic analyses', Technological Forecasting and Social Change, vol. 182, pp. 121877-121877.
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Technology and innovation management is vital emerging research fields. Technological Forecasting and Social Change (TFSC) has worked as a major forum in this field and is currently regarded as the leading journal. However, an increasing number of publications hamper a comprehensive understanding of the field and journal. In this study, we conducted a systematic review of TFSC with the support of bibliometric analysis. We used citation network analysis and topic models to extract research landscapes and trends. Our results illustrate how technology and innovation management research has developed through the interactions among theories, methods, and cases, both qualitatively and quantitatively. Based on our analysis and findings, we discuss the major branches of research, topics in the journal, and future perspectives.
Kalhori, H, Shooshtari, A, Tashakori, S & Li, B 2022, 'Mechanical behavior of a rectangular capacitive micro-plate subjected to an electrostatic load', International Journal of Dynamics and Control, vol. 10, no. 5, pp. 1337-1348.
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Kamal, MS, Dey, N, Chowdhury, L, Hasan, SI & Santosh, KC 2022, 'Explainable AI for Glaucoma Prediction Analysis to Understand Risk Factors in Treatment Planning', IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-9.
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Kamran, M, Shahani, NM & Armaghani, DJ 2022, 'Decision support system for underground coal pillar stability using unsupervised and supervised machine learning approaches', Geomechanics and Engineering, vol. 30, no. 2, pp. 107-121.
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Coal pillar assessment is of broad importance to underground engineering structure, as the pillar failure can lead to enormous disasters. Because of the highly non-linear correlation between the pillar failure and its influential attributes, conventional forecasting techniques cannot generate accurate outcomes. To approximate the complex behavior of coal pillar, this paper elucidates a new idea to forecast the underground coal pillar stability using combined unsupervised-supervised learning. In order to build a database of the study, a total of 90 patterns of pillar cases were collected from authentic engineering structures. A state-of-the art feature depletion method, t-distribution symmetric neighbor embedding (t-SNE) has been employed to reduce significance of actual data features. Consequently, an unsupervised machine learning technique K-mean clustering was followed to reassign the t-SNE dimensionality reduced data in order to compute the relative class of coal pillar cases. Following that, the reassign dataset was divided into two parts: 70 percent for training dataset and 30 percent for testing dataset, respectively. The accuracy of the predicted data was then examined using support vector classifier (SVC) model performance measures such as precision, recall, and f1-score. As a result, the proposed model can be employed for properly predicting the pillar failure class in a variety of underground rock engineering projects.
Kapeleris, J, Ebrahimi Warkiani, M, Kulasinghe, A, Vela, I, Kenny, L, Ladwa, R, O’Byrne, K & Punyadeera, C 2022, 'Clinical Applications of Circulating Tumour Cells and Circulating Tumour DNA in Non-Small Cell Lung Cancer—An Update', Frontiers in Oncology, vol. 12, p. 859152.
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Despite efforts to improve earlier diagnosis of non-small cell lung cancer (NSCLC), most patients present with advanced stage disease, which is often associated with poor survival outcomes with only 15% surviving for 5 years from their diagnosis. Tumour tissue biopsy is the current mainstream for cancer diagnosis and prognosis in many parts of the world. However, due to tumour heterogeneity and accessibility issues, liquid biopsy is emerging as a game changer for both cancer diagnosis and prognosis. Liquid biopsy is the analysis of tumour-derived biomarkers in body fluids, which has remarkable advantages over the use of traditional tumour biopsy. Circulating tumour cells (CTCs) and circulating tumour DNA (ctDNA) are two main derivatives of liquid biopsy. CTC enumeration and molecular analysis enable monitoring of cancer progression, recurrence, and treatment response earlier than traditional biopsy through a minimally invasive liquid biopsy approach. CTC-derived ex-vivo cultures are essential to understanding CTC biology and their role in metastasis, provide a means for personalized drug testing, and guide treatment selection. Just like CTCs, ctDNA provides opportunity for screening, monitoring, treatment evaluation, and disease surveillance. We present an updated review highlighting the prognostic and therapeutic significance of CTCs and ctDNA in NSCLC.
Kapeleris, J, Müller Bark, J, Ranjit, S, Irwin, D, Hartel, G, Warkiani, ME, Leo, P, O'Leary, C, Ladwa, R, O'Byrne, K, Hughes, BGM & Punyadeera, C 2022, 'Prognostic value of integrating circulating tumour cells and cell-free DNA in non-small cell lung cancer', Heliyon, vol. 8, no. 7, pp. e09971-e09971.
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BACKGROUND: Non-small cell lung cancer (NSCLC) often presents at an incurable stage, and majority of patients will be considered for palliative treatment at some point in their disease. Despite recent advances, the prognosis remains poor, with a median overall survival of 12-18 months. Liquid biopsy-based biomarkers have emerged as potential candidates for predicting prognosis and response to therapy in NSCLC patients. This pilot study evaluated whether combining circulating tumour cells and clusters (CTCs) and cell-free DNA (cfDNA) can predict progression-free survival (PFS) in NSCLC patients. METHODS: CTC and cfDNA/ctDNA from advanced stage NSCLC patients were measured at study entry (T0) and 3-months post-treatment (T1). CTCs were enriched using a spiral microfluidic chip and characterised by immunofluorescence. ctDNA was assessed using an UltraSEEK® Lung Panel. Kaplan-Meier plots were generated to investigate the contribution of the presence of CTC/CTC clusters and cfDNA for PFS. Cox proportional hazards analysis compared time to progression versus CTC/CTC cluster counts and cfDNA levels. RESULTS: Single CTCs were found in 14 out of 25 patients, while CTC clusters were found in 8 out of the 25 patients at T0. At T1, CTCs were found in 7 out of 18 patients, and CTC clusters in 1 out of the 18 patients. At T0, CTC presence and the combination of CTC cluster counts with cfDNA levels were associated with shorter PFS, p = 0.0261, p = 0.0022, respectively. CONCLUSIONS: Combining CTC cluster counts and cfDNA levels could improve PFS assessment in NSCLC patients. Our results encourage further investigation on the combined effect of CTC/cfDNA as a prognostic biomarker in a large cohort of advanced stage NSCLC patients.
Kaplan, E, Altunisik, E, Ekmekyapar Firat, Y, Datta Barua, P, Dogan, S, Baygin, M, Burak Demir, F, Tuncer, T, Palmer, E, Tan, R-S, Yu, P, Soar, J, Fujita, H & Rajendra Acharya, U 2022, 'Novel nested patch-based feature extraction model for automated Parkinson's Disease symptom classification using MRI images', Computer Methods and Programs in Biomedicine, vol. 224, pp. 107030-107030.
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Kaplan, E, Chan, WY, Dogan, S, Barua, PD, Bulut, HT, Tuncer, T, Cizik, M, Tan, R-S & Acharya, UR 2022, 'Automated BI-RADS classification of lesions using pyramid triple deep feature generator technique on breast ultrasound images', Medical Engineering & Physics, vol. 108, pp. 103895-103895.
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Ultrasound (US) is an important imaging modality used to assess breast lesions for malignant features. In the past decade, many machine learning models have been developed for automated discrimination of breast cancer versus normal on US images, but few have classified the images based on the Breast Imaging Reporting and Data System (BI-RADS) classes. This work aimed to develop a model for classifying US breast lesions using a BI-RADS classification framework with a new multi-class US image dataset. We proposed a deep model that combined a novel pyramid triple deep feature generator (PTDFG) with transfer learning based on three pre-trained networks for creating deep features. Bilinear interpolation was applied to decompose the input image into four images of successively smaller dimensions, constituting a four-level pyramid for downstream feature generation with the pre-trained networks. Neighborhood component analysis was applied to the generated features to select each network's 1,000 most informative features, which were fed to support vector machine classifier for automated classification using a ten-fold cross-validation strategy. Our proposed model was validated using a new US image dataset containing 1,038 images divided into eight BI-RADS classes and histopathological results. We defined three classification schemes: Case 1 involved the classification of all images into eight categories; Case 2, classification of breast US images into five BI-RADS classes; and Case 3, classification of BI-RADS 4 lesions into benign versus malignant classes. Our PTDFG-based transfer learning model attained accuracy rates of 79.29%, 80.42%, and 88.67% for Case 1, Case 2, and Case 3, respectively.
Kaplan, E, Ekinci, T, Kaplan, S, Barua, PD, Dogan, S, Tuncer, T, Tan, R-S, Arunkumar, N & Acharya, UR 2022, 'PFP‐LHCINCA: Pyramidal Fixed‐Size Patch‐Based Feature Extraction and Chi‐Square Iterative Neighborhood Component Analysis for Automated Fetal Sex Classification on Ultrasound Images', Contrast Media & Molecular Imaging, vol. 2022, no. 1, pp. 1-10.
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Objectives. Fetal sex determination with ultrasound (US) examination is indicated in pregnancies at risk of X‐linked genetic disorders or ambiguous genitalia. However, misdiagnoses often arise due to operator inexperience and technical difficulties while acquiring diagnostic images. We aimed to develop an efficient automated US‐based fetal sex classification model that can facilitate efficient screening and reduce misclassification.Methods. We have developed a novel feature engineering model termed PFP‐LHCINCA that employs pyramidal fixed‐size patch generation with average pooling‐based image decomposition, handcrafted feature extraction based on local phase quantization (LPQ), and histogram of oriented gradients (HOG) to extract directional and textural features and used Chi‐square iterative neighborhood component analysis feature selection (CINCA), which iteratively selects the most informative feature vector for each image that minimizes calculated feature parameter‐derived k‐nearest neighbor‐based misclassification rates. The model was trained and tested on a sizeable expert‐labeled dataset comprising 339 males’ and 332 females’ fetal US images. One transverse fetal US image per subject zoomed to the genital area and standardized to 256 × 256 size was used for analysis. Fetal sex was annotated by experts on US images and confirmed postnatally.Results. Standard model performance metrics were compared using five shallow classifiers—k‐nearest neighbor (kNN), decision tree, naïve Bayes, linear discriminant, and support vector machine (SVM)—with the hyperparameters tuned using a Bayesian optimizer. The PFP‐LHCINCA model achieved a sex classification accuracy of ≥88% with all five classifiers and the best accuracy rates (>98%) with kNN and SVM classifiers.Conclusions. US‐based fetal sex classification is feasible and accurate using the pr...
Karatopouzis, A, Voinov, AA, Kubiszewski, I, Taghikhah, F, Costanza, R & Kenny, D 2022, 'Estimating the Genuine Progress Indicator before and during the COVID pandemic in Australia', Ecological Indicators, vol. 141, pp. 109025-109025.
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Karbassiyazdi, E, Fattahi, F, Yousefi, N, Tahmassebi, A, Taromi, AA, Manzari, JZ, Gandomi, AH, Altaee, A & Razmjou, A 2022, 'XGBoost model as an efficient machine learning approach for PFAS removal: Effects of material characteristics and operation conditions', Environmental Research, vol. 215, no. Pt 1, pp. 114286-114286.
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Due to the implications of poly- and perfluoroalkyl substances (PFAS) on the environment and public health, great attention has been recently made to finding innovative materials and methods for PFAS removal. In this work, PFAS is considered universal contamination which can be found in many wastewater streams. Conventional materials and processes used to remove and degrade PFAS do not have enough competence to address the issue particularly when it comes to eliminating short-chain PFAS. This is mainly due to the large number of complex parameters that are involved in both material and process designs. Here, we took the advantage of artificial intelligence to introduce a model (XGBoost) in which material and process factors are considered simultaneously. This research applies a machine learning approach using data collected from reported articles to predict the PFAS removal factors. The XGBoost modeling provided accurate adsorption capacity, equilibrium, and removal estimates with the ability to predict the adsorption mechanisms. The performance comparison of adsorbents and the role of AI in one dominant are studied and reviewed for the first time, even though many studies have been carried out to develop PFAS removal through various adsorption methods such as ion exchange, nanofiltration, and activated carbon (AC). The model showed that pH is the most effective parameter to predict PFAS removal. The proposed model in this work can be extended for other micropollutants and can be used as a basic framework for future adsorbent design and process optimization.
Kardani, N, Bardhan, A, Roy, B, Samui, P, Nazem, M, Armaghani, DJ & Zhou, A 2022, 'A novel improved Harris Hawks optimization algorithm coupled with ELM for predicting permeability of tight carbonates', Engineering with Computers, vol. 38, no. S5, pp. 4323-4346.
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Kardani, N, Bardhan, A, Samui, P, Nazem, M, Zhou, A & Armaghani, DJ 2022, 'A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil', Engineering with Computers, vol. 38, no. 4, pp. 3321-3340.
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Karimi, M, Kinns, R & Kessissoglou, N 2022, 'Radiated Sound Power from Near-Surface Acoustic Sources', Journal of Ship Research, vol. 66, no. 02, pp. 151-158.
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Abstract This article investigates the radiated sound power from idealized propeller noise sources, characterized by elemental monopole and dipole acoustic sources near the sea surface. The free surface of the sea is modeled as a pressure-release surface. The ratio of sound power of the near surface sources to the sound power from the same sources in an unbounded fluid is presented as a function of source immersion relative to sound wavelength. We herein show that the sound power radiated by submerged monopole and horizontal dipole sources is greatly reduced by the effect of the free surface at typical blade passing frequencies. By contrast, the sound power from a submerged vertical dipole is doubled. A transition frequency for the submerged monopole and horizontal dipole is identified. Above this transition frequency, the radiated power is not significantly influenced by the sea surface. Directivity patterns for the acoustic sources are also presented. Introduction The principal sources contributing to underwater radiated noise (URN) over a wide frequency range are propellers and onboard machinery (Urick 1983; Ross 1987; Collier 1997; Carlton 2007). Propeller sources are highly complex, but simplification is possible at low frequencies where the wavelength of underwater sound is much larger than propeller dimensions. The propeller may then be regarded as a set of fluctuating forces at the propeller hub and a stationary monopole source that represents the growth and collapse of a cavitation region as each blade passes through the region of wake deficit. This type of model was used by Kinns and Bloor (2004) to examine the net fluctuating forces on a cruise ship hull due to defined propeller sources. The nature of the monopole source was considered...
Karki, D, Al-Hunaity, S, Far, H & Saleh, A 2022, 'Composite connections between CFS beams and plywood panels for flooring systems: Testing and analysis', Structures, vol. 40, pp. 771-785.
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Kashani, AR, Camp, CV, Rostamian, M, Azizi, K & Gandomi, AH 2022, 'Population-based optimization in structural engineering: a review', Artificial Intelligence Review, vol. 55, no. 1, pp. 345-452.
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Structural engineering is focused on the safe and efficient design of infrastructure. Projects can range in size and complexity, many requiring massive amounts of materials and expensive construction and operational costs. Therefore, one of the primary objectives for structural engineers is a cost-effective design. Incorporating optimality criteria into the design procedure introduces additional complexities that result in problems that are nonlinear, nonconvex, and have a discontinuous solution space. Population-based optimization algorithms (known as metaheuristics) have been found to be very efficient approaches to these problems. Many researchers have developed and applied state-of-art metaheuristics to automate and optimize the design of real-world civil engineering problems. While there is a large body of published papers in this area, there are few comprehensive reviews that list, summarize, and categorize metaheuristic optimization in structural engineering. This paper provides an extensive survey of a wide range of metaheuristic techniques to structural engineering optimization problems. Also, information is provided on available structural engineering benchmark problems, the formulation of different objective functions, and the handling of various types of constraints. The performance of different optimization techniques is compared for many benchmark problems.
Kashani, AR, Gandomi, AH, Azizi, K & Camp, CV 2022, 'Multi-objective optimization of reinforced concrete cantilever retaining wall: a comparative study', Structural and Multidisciplinary Optimization, vol. 65, no. 9.
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AbstractThis paper investigates the performance of four multi-objective optimization algorithms, namely non-dominated sorting genetic algorithm II (NSGA-II), multi-objective particle swarm optimization (MOPSO), strength Pareto evolutionary algorithm II (SPEA2), and multi-objective multi-verse optimization (MVO), in developing an optimal reinforced concrete cantilever (RCC) retaining wall. The retaining wall design was based on two major requirements: geotechnical stability and structural strength. Optimality criteria were defined as reducing the total cost, weight, CO2emission, etc. In this study, two sets of bi-objective strategies were considered: (1) minimum cost and maximum factor of safety, and (2) minimum weight and maximum factor of safety. The proposed method's efficiency was examined using two numerical retaining wall design examples, one with a base shear key and one without a base shear key. A sensitivity analysis was conducted on the variation of significant parameters, including backfill slope, the base soil’s friction angle, and surcharge load. Three well-known coverage set measures, diversity, and hypervolume were selected to compare the algorithms’ results, which were further assessed using basic statistical measures (i.e., min, max, standard deviation) and the Friedman test with a 95% level of confidence. The results demonstrated that NSGA-II has a higher Friedman rank in terms of coverage set for both cost-based and weight-based designs. SPEA2 and MOPSO outperformed both cost-based and weight-based solutions in terms of diversity in examples without and with the effects of a base shear key, respectively. However, based on the hypervolume measure, NSGA-II and MVO have a higher Friedman rank for examples without and with the effects of a base shear key, respectively, for both the cost-based and weight-based designs.
Kashyap, PK, Kumar, S, Jaiswal, A, Kaiwartya, O, Kumar, M, Dohare, U & Gandomi, AH 2022, 'DECENT: Deep Learning Enabled Green Computation for Edge Centric 6G Networks', IEEE Transactions on Network and Service Management, vol. 19, no. 3, pp. 2163-2177.
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Edge computing has received significant attention from academia and industries and has emerged as a promising solution for enhancing the information processing capability at the edge for next generation 6G networks. The technical design of 6G edge networks in terms of offloading the computationally extensive task is very critical because of the overgrowth in data volume primarily due to the explosion of smart IoT devices, and the ever-reducing size of these energy-constrained devices in IoT systems. Toward harnessing the benefits of deep recurrent neural network based on Long Short Term Memory (LSTM) in the design of next-generation edge networks, this paper presents a framework DECENT-Deep learning Enabled green Computation for Edge centric Next generation 6G networks. The data offloading problem is modeled as a Markov decision process considering joint optimization of energy consumption, computation latency, and offloading rate for network utility in 6G environment. The algorithm learns faster from previous long-term offloading experiences and solves the optimization problem with better convergence speed. Simulation results of the proposed framework DECENT shows that it maximizes the network utility by overcoming the challenges as compared to the state-of-the-art techniques.
Kathamuthu, ND, Chinnamuthu, A, Iruthayanathan, N, Ramachandran, M & Gandomi, AH 2022, 'Deep Q-Learning-Based Neural Network with Privacy Preservation Method for Secure Data Transmission in Internet of Things (IoT) Healthcare Application', Electronics, vol. 11, no. 1, pp. 157-157.
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The healthcare industry is being transformed by the Internet of Things (IoT), as it provides wide connectivity among physicians, medical devices, clinical and nursing staff, and patients to simplify the task of real-time monitoring. As the network is vast and heterogeneous, opportunities and challenges are presented in gathering and sharing information. Focusing on patient information such as health status, medical devices used by such patients must be protected to ensure safety and privacy. Healthcare information is confidentially shared among experts for analyzing healthcare and to provide treatment on time for patients. Cryptographic and biometric systems are widely used, including deep-learning (DL) techniques to authenticate and detect anomalies, andprovide security for medical systems. As sensors in the network are energy-restricted devices, security and efficiency must be balanced, which is the most important concept to be considered while deploying a security system based on deep-learning approaches. Hence, in this work, an innovative framework, the deep Q-learning-based neural network with privacy preservation method (DQ-NNPP), was designed to protect data transmission from external threats with less encryption and decryption time. This method is used to process patient data, which reduces network traffic. This process also reduces the cost and error of communication. Comparatively, the proposed model outperformed some standard approaches, such as thesecure and anonymous biometric based user authentication scheme (SAB-UAS), MSCryptoNet, and privacy-preserving disease prediction (PPDP). Specifically, the proposed method achieved accuracy of 93.74%, sensitivity of 92%, specificity of 92.1%, communication overhead of 67.08%, 58.72 ms encryption time, and 62.72 ms decryption time.
Katzmarek, DA, Pradeepkumar, A, Ziolkowski, RW & Iacopi, F 2022, 'Review of graphene for the generation, manipulation, and detection of electromagnetic fields from microwave to terahertz', 2D Materials, vol. 9, no. 2, pp. 022002-022002.
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AbstractGraphene has attracted considerable attention ever since the discovery of its unprecedented properties, including its extraordinary and tunable electronic and optical properties. In particular, applications within the microwave to terahertz frequency spectrum can benefit from graphene’s high electrical conductivity, mechanical flexibility and robustness, transparency, support of surface-plasmon-polaritons, and the possibility of dynamic tunability with direct current to light sources. This review aims to provide an in-depth analysis of current trends, challenges, and prospects within the research areas of generating, manipulating, and detecting electromagnetic fields using graphene-based devices that operate from microwave to terahertz frequencies. The properties of and models describing graphene are reviewed first, notably those of importance to electromagnetic applications. State-of-the-art graphene-based antennas, such as resonant and leaky-wave antennas, are discussed next. A critical evaluation of the performance and limitations within each particular technology is given. Graphene-based metasurfaces and devices used to manipulate electromagnetic fields, e.g. wavefront engineering, are then examined. Lastly, the state-of-the-art of detecting electromagnetic fields using graphene-based devices is discussed.
Kaushik, R, Mahela, OP, Bhatt, PK, Khan, B, Garg, AR, Alhelou, HH & Siano, P 2022, 'Recognition of Islanding and Operational Events in Power System With Renewable Energy Penetration Using a Stockwell Transform-Based Method', IEEE Systems Journal, vol. 16, no. 1, pp. 166-175.
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KC, S, Shrestha, S, Nguyen, TPL, Das Gupta, A & Mohanasundaram, S 2022, 'Groundwater governance: a review of the assessment methodologies', Environmental Reviews, vol. 30, no. 2, pp. 202-216.
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Groundwater, the world’s largest and most exploited freshwater resource is a crucial ingredient for global socio-economic development. However, the domination of human-induced drivers such as climate change, rapid demographic escalation, alteration in land use, industrialisation, and an increase in water demand has further stressed the unfrozen freshwater resources. This review provides a comprehensive literature-based analysis on different assessment methodologies for groundwater governance, and critically analysed the applicability and knowledge gaps in the assessment methodologies for evaluating groundwater governance under climatic and nonclimatic stresses. Furthermore, in the absence of a designated groundwater governance framework under stress, the study emphasized the need for developing a ready-to-use groundwater governance framework to assess the existing state of governance, tackling the prevailing knowledge gaps. A multidimensional framework consisting of key groundwater governance elements, the inclusion of the vulnerable and marginalised groups, current and future stressors, and an approach for aggregating multiple elements would overcome the limitations in previous assessment methodologies. Additionally, this framework would contribute to understanding current governance provisions and the capacity to manage those provisions, realise the strengths, gaps, and areas for improvement, and quantitatively visualise the prevailing state of groundwater governance for planning multiple strategies to possible threats and conflicts from the stresses.
Keshavarz, R & Shariati, N 2022, 'Highly Sensitive and Compact Quad-Band Ambient RF Energy Harvester', IEEE Transactions on Industrial Electronics, vol. 69, no. 4, pp. 3609-3621.
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Keshavarz, R & Shariati, N 2022, 'High-Sensitivity and Compact Time Domain Soil Moisture Sensor Using Dispersive Phase Shifter for Complex Permittivity Measurement', IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-10.
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This article presents a time domain transmissometry soil moisture sensor (TDT-SMS) using a dispersive phase shifter (DPS), consisting of an interdigital capacitor that is loaded with a stacked four-turn complementary spiral resonator (S4-CSR). Soil moisture measurement technique of the proposed sensor is based on the complex permittivity sensing property of a DPS in time domain. Soil relative permittivity which varies with its moisture content is measured by burying the DPS under a soil mass and changing its phase difference while excited with a 114-MHz sine wave (single tone). DPS output phase and magnitude are compared with the reference signal and measured with a phase/loss detector. The proposed sensor exhibits accuracy better than ±1.2% at the highest volumetric water content (VWC = 30%) for sandy-type soil. Precise design guide is developed and simulations are performed to achieve a highly sensitive sensor. The measurement results validate the accuracy of theoretical analysis and design procedure. Owning the advantages of low profile, low power consumption, and high sensitivity makes the proposed TDT-SMS a good candidate for precision farming and internet of things (IoT) systems.
Key, S, Demir, S, Gurger, M, Yilmaz, E, Barua, PD, Dogan, S, Tuncer, T, Arunkumar, N, Tan, R-S & Acharya, UR 2022, 'ViVGG19: Novel exemplar deep feature extraction-based shoulder rotator cuff tear and biceps tendinosis detection using magnetic resonance images', Medical Engineering & Physics, vol. 110, pp. 103864-103864.
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BACKGROUND AND PURPOSE: Rotator cuff tear (RCT) and biceps tendinosis (BT) are the two most common shoulder disorders worldwide. These disorders can be diagnosed using magnetic resonance imaging (MRI), but the expert interpretation is manual, time-consuming, and subjected to human errors. Therefore, a fixed-size feature extraction model was created to objectively and accurately perform automated binary classification of RCT vs. normal and BT vs. normal on MRI images. MATERIALS AND METHODS: We have developed an exemplar deep feature extraction model to diagnose RCT and BT disorders. The model was tested on a new MR image dataset comprising transverse, sagittal, and coronal MRI images of the shoulder that had been organized into three cases. BT was studied on transverse MRI images (Case 1), while RCT was studied on sagittal (Case 2) and coronal MRI images (Case 3). Our model comprised deep feature generation using a pre-trained VGG19, feature selection using iterative neighborhood component analysis (INCA), and classification using shallow standard classifiers k-nearest neighbors (KNN), support vector machine (SVM), and artificial neural network (ANN). In the feature extraction phase, two fully connected layers were used to extract deep features from the original image, and sixteen fixed-size patches obtained by the division of the original image. This model was named Vision VGG19 (ViVGG), analogous to vision transformers (ViT). The feature vector is extracted from the raw image dataset, and 16 feature vectors are extracted from each fixed-size patch. Seventeen feature vectors obtained from each image are obtained from fc6 and fc7 layers of the pre-trained VGG19, are merged to obtain final feature vector. INCA was used to choose the top features from the created features, and the chosen features were classified using shallow classifiers. RESULTS: We defined three cases to evaluate the proposed ViVGG19 to diagnose RT and BCT disorders. Our proposed ViVGG19...
Kha, J, Karimi, M, Maxit, L, Skvortsov, A & Kirby, R 2022, 'An analytical approach for modelling the vibroacoustic behaviour of a heavy fluid-loaded plate near a free surface', Journal of Sound and Vibration, vol. 538, pp. 117206-117206.
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Predictions of the vibroacoustic response of a point-force excited baffled thin rectangular plate immersed in a heavy fluid and near a free surface are presented using an analytical model. The equations of motion are solved by Fourier analysis, where the eigenfunctions of plate vibration form the basis of spatial expansion for fluid loading. Vibroacoustic indicators, including the plate velocity, acoustic pressure, and acoustic power, are predicted using the analytical approach and verification is performed by comparison with finite element simulations. The results have shown that variations in the height of the free surface can have a significant effect on these indicators. From the vibration response, added mass effect due to heavy fluid loading is altered and further investigated with the explicit evaluation of an added mass ratio for different free surface heights for the first five plate modes. For a given height of a free surface, standing waves can form between the free surface and baffled plate at specific excitation frequencies and slightly alters the acoustic pressure spectra. This condition also presents an effect on the acoustic power, where the first standing wave frequency dictates the efficient sound radiation to the far field.
Khalid, Z, Alnuwaiser, MA, Ahmad, HA, Shafqat, SS, Munawar, MA, Kamran, K, Abbas, MM, Kalam, MA & Ewida, MA 2022, 'Experimental and Computational Analysis of Newly Synthesized Benzotriazinone Sulfonamides as Alpha-Glucosidase Inhibitors', Molecules, vol. 27, no. 20, pp. 6783-6783.
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Diabetes mellitus is a chronic metabolic disorder in which the pancreas secretes insulin but the body cells do not recognize it. As a result, carbohydrate metabolism causes hyperglycemia, which may be fatal for various organs. This disease is increasing day by day and it is prevalent among people of all ages, including young adults and children. Acarbose and miglitol are famous alpha-glucosidase inhibitors but they complicate patients with the problems of flatulence, pain, bloating, diarrhea, and loss of appetite. To overcome these challenges, it is crucial to discover new anti-diabetic drugs with minimal side effects. For this purpose, benzotriazinone sulfonamides were synthesized and their structures were characterized by FT-IR, 1H-NMR and 13C-NMR spectroscopy. In vitro alpha-glucosidase inhibition studies of all synthesized hybrids were conducted using the spectrophotometric method. The synthesized compounds revealed moderate-to-good inhibition activity; in particular, nitro derivatives 12e and 12f were found to be the most effective inhibitors against this enzyme, with IC50 values of 32.37 ± 0.15 µM and 37.75 ± 0.11 µM. In silico studies, including molecular docking as well as DFT analysis, also strengthened the experimental findings. Both leading compounds 12e and 12f showed strong hydrogen bonding interactions within the enzyme cavity. DFT studies also reinforced the strong binding interactions of these derivatives with biological molecules due to their lowest chemical hardness values and lowest orbital energy gap values.
Khaliliboroujeni, S, He, X, Jia, W & Amirgholipour, S 2022, 'End-to-end metastasis detection of breast cancer from histopathology whole slide images', Computerized Medical Imaging and Graphics, vol. 102, pp. 102136-102136.
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Worldwide breast cancer is one of the most frequent and mortal diseases across women. Early, accurate metastasis cancer detection is a significant factor in raising the survival rate among patients. Diverse Computer-Aided Diagnostic (CAD) systems applying medical imaging modalities, have been designed for breast cancer detection. The impact of deep learning in improving CAD systems' performance is undeniable. Among all of the medical image modalities, histopathology (HP) images consist of richer phenotypic details and help keep track of cancer metastasis. Nonetheless, metastasis detection in whole slide images (WSIs) is still problematic because of the enormous size of these images and the massive cost of labelling them. In this paper, we develop a reliable, fast and accurate CAD system for metastasis detection in breast cancer while applying only a small amount of annotated data with lower resolution. This saves considerable time and cost. Unlike other works which apply patch classification for tumor detection, we employ the benefits of attention modules adding to regression and classification, to extract tumor parts simultaneously. Then, we use dense prediction for mask generation and identify individual metastases in WSIs. Experimental outcomes demonstrate the efficiency of our method. It provides more accurate results than other methods that apply the total dataset. The proposed method is about seven times faster than an expert pathologist, while producing even more accurate results than an expert pathologist in tumor detection.
Khan, HA, Yasir, M & Castel, A 2022, 'Performance of cementitious and alkali-activated mortars exposed to laboratory simulated microbially induced corrosion test', Cement and Concrete Composites, vol. 128, pp. 104445-104445.
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This research presents a laboratory simulated microbially induced corrosion (MIC) test method allowing to assess the performance of low calcium fly ash based geopolymer mortar (FA-GPm), alkali-activated slag-based mortar (AASm), sulphate resistant Portland cement mortar (SRPCm) and calcium aluminate cement mortar (CACm). This experiment runs for a period of six months in which sulphur oxidizing microbes (SOMs) (A. thiooxidans and T. intermedius) were grown in liquid media containing the thiosulphate ion. This test methodology aims to investigate the bacterial attachment phase (stage 2) followed by the initiation of acid attack (stage 3) of MIC. Lowering of the pH of liquid medium and the growth of microorganism was measured to evaluate the aggressiveness of this microbial environment. Visual, physical, chemical and microstructural investigations of mortars were performed over time to estimate the deteriorations. Ions leaching from the matrix and formation of sulphate (SO42−) was monitored using inductively coupled plasma mass spectroscopy (ICP-MS) and ion chromatography (IC), respectively. The results showed that the neutralization of CACm after exposure to biotic reactor was lesser compared to the other mortars, indicating its resistance towards biocorrosion. Moreover, formation of sulphuric acid (H2SO4) and the growth of SOMs showed its dependence on the type of mortar. Scanning electron microscopy (SEM) with energy-dispersive X-ray (EDX) spectroscopy was used to predict the depth of degradation and morphological variations at microstructural level. Patterns of deterioration and nucleation of minerals identified were similar to infield exposure, indicating the suitability of this testing method to simulate sewer biocorrosion.
Khan, MKI, Lee, CK & Zhang, Y 2022, 'Development of analytical model for predicting compressive behavior of engineered cementitious composites‐concrete encased steel composite columns', Structural Concrete, vol. 23, no. 4, pp. 2576-2599.
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AbstractAnalytical models which can reliably predict the load–deformation behaviors and the ultimate strength of columns are indispensable tools for the practical design. In this paper, an analytical model is developed to predict the compressive load–deformation behavior and the ultimate capacity of engineered cementitious composite confined concrete encased steel (ECC‐CES) composite columns. The proposed analytical model employs the effective material stress and strain compatibility approaches for the predictions of compressive load–deformation response of ECC‐CES columns. The material constitutive models for ECC, concrete, and steel are first established, and then used in the analytical model. The effect of ECC and steel confinement on the concrete core and the softening behavior of steel column due to compression buckling are also considered. Based on the experimental results, appropriate strength reduction factors are proposed so that the effective material stresses can be calculated. The strength predictions from the proposed analytical methods and different design codes are compared with the experimental and numerical results. It was found that the proposed analytical models using effective material stresses gave better predictions than many commonly used design codes.
Khan, MNH, Barzegarkhoo, R, Siwakoti, YP, Khan, SA, Li, L & Blaabjerg, F 2022, 'A new switched-capacitor multilevel inverter with soft start and quasi resonant charging capabilities', International Journal of Electrical Power & Energy Systems, vol. 135, pp. 107412-107412.
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Switched-capacitor multilevel inverters (SCMLIs) are gaining widespread attention in recent decades due to their simple design, voltage boosting capability, and inherent capacitor voltage balancing feature. However, the advantages offered by SCMLIs come at the cost of employing a higher number of active and passive components and capacitor voltage balancing issues with an inrush current profile. This paper introduces a novel configuration of SCMLIs with a lower number of power components with inherent voltage boost. The basic 5-level topology consists of a single capacitor, an inductor, a diode, and seven active switching elements. To improve the transient response and the inrush current profile of the converter, a soft start and quasi-resonant charging capability has been explored and implemented. Considering the inherent capacitor voltage balancing of the proposed SCMLI, a new finite control set-model predictive control (FCS-MPC) method with a single objective and a less computational burden is also developed, which contributes to injecting a fully controlled current for the grid-connected applications. The proposed topology is compared with other existing five-level inverter topologies to show its superior capabilities/advantages. And finally, the performance of the proposed topology and its associated FCS-MPC mechanism are validated by the measurement results.
Khan, MNH, Hasan, SU & Siwakoti, YP 2022, 'New PWM Strategy to Enable Dual-Mode Operation Capability in Common-Grounded Transformerless Inverters', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 6, pp. 7361-7370.
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A novel pulsewidth modulation (PWM) technique has been presented in this article, which enables the converter to operate in the conventional (boost) mode as well as in the buck mode (proposed). The proposed buck PWM method significantly reduces the current stresses of the components compared to the boost mode. Using this combined modulation technique, a dual-mode operation of such a class of inverters is demonstrated to accommodate a wide input voltage variation while maintaining the regulation of the output ac voltage. The proposed modulation technique is simple to implement and has been implemented in some of the conventional Type-I and Type-II inverters to demonstrate a very wide input voltage range (200-400 V dc), while keeping a fixed output voltage of 110 V ac rms. The theoretical analysis, key operating circuit, and waveforms are presented along with the thermal profile of various switches. Experimental results show that Type-I and Type-II inverters rated at 500 VA have around 95.5 ± 1% efficiency for a wide load range with a peak efficiency of 97.3% in buck + invert mode for Type-I and 95.5% for Type-II inverters.
Khan, R, Tao, X, Anjum, A, Malik, SR, Yu, S, Khan, A, Rehman, W & Malik, H 2022, '(τ, m)‐slicedBucket privacy model for sequential anonymization for improving privacy and utility', Transactions on Emerging Telecommunications Technologies, vol. 33, no. 6.
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AbstractIn a real‐world scenario for privacy‐preserving data publishing, the original data are anonymized and released periodically. Each release may vary in number of records due to insert, update, and delete operations. An intruder can combine, that is, correlate different releases to compromise the privacy of the individual records. Most of the literature, such as τ‐safety, τ‐safe (l, k)‐diversity, have an inconsistency in record signatures and adds counterfeit tuples with high generalization that causes privacy breach and information loss. In this paper, we propose an improved privacy model (τ, m)‐slicedBucket, having a novel idea of “Cache” table to address these limitations. We indicate that a collusion attack can be performed for breaching the privacy of τ‐safe (l, k)‐diversity privacy model, and demonstrate it through formal modeling. The objective of the proposed (τ, m)‐slicedBucket privacy model is to set a tradeoff between strong privacy and enhanced utility. Furthermore, we formally model and analyze the proposed model to show that the collusion attack is no longer applicable. Extensive experiments reveal that the proposed approach outperforms the existing models.
Khanna, P, Tanveer, M, Prasad, M & Lin, C-T 2022, 'Artificial intelligence and deep learning for biomedical applications', Multimedia Tools and Applications, vol. 81, no. 10, pp. 13137-13137.
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Khezri, M, Hu, Y, Luo, Q, Bambach, MR, Tong, L & Rasmussen, KJR 2022, 'Structural morphing induced by functionalising buckling', Thin-Walled Structures, vol. 181, pp. 110103-110103.
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The paper presents an overview of a research project at the University of Sydney aimed at developing a general framework for the analysis and design of functional components of buildings and structures, where such components achieve large shape changes (morphing) via buckling. The shape changes are optimised, e.g. to reduce energy consumption by minimising solar radiation loads or maximising natural air ventilation. The underlying driver for the project is to develop innovative building technology solutions to reduce the energy consumption of the next generation of low-, medium- and high-rise buildings. The paper first summarises recent work on plate elements supported along three edges, in which temporary intermediate restraints are used to load the plate into the post-buckling range and subsequently released to generate an abrupt shape change in response to an external signal triggered by shading or ventilation demand. This investigation is backed by an analysis of the placement of intermediate restraints to optimise the plate deflection by maximising the pre-buckling compression of the plate. Next, a study is presented on optimising the topology of plates to maximise their shading or ventilation capacities under applied compression or bending. Considering both buckling and nonlinear post-buckling, the analytical framework optimises the spatial distribution of plate thickness. Experiments on optimised plates are reported as well, in which shape memory alloy (SMA) and piezoelectric (PZT) actuators are used to induce compression and buckling. Subsequently, morphing induced by flexural–torsional buckling is investigated where simple 3-member frame geometries are devised to achieve large lateral buckling displacements and twist rotations under low-power external excitation. Lastly, an application of functionalised buckling for shading of buildings is illustrated which employs a bi-stable mechanism powered by shape memory alloy actuation.
Khodasevych, I, Rufangura, P & Iacopi, F 2022, 'Designing concentric nanoparticles for surface-enhanced light-matter interaction in the mid-infrared', Optics Express, vol. 30, no. 13, pp. 24118-24118.
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Nanosized particles with high responsivity in the infrared spectrum are of great interest for biomedical applications. We derive a closed-form expression for the polarizability of nanoparticles made of up to three concentric nanolayers consisting of a frequency dependent polar dielectric core, low permittivity dielectric spacer shell and conductive graphene outer shell, using the electrostatic Mie theory in combination with conductive layer in a dipole approximation. We use the obtained formula to investigate SiC, GaN and hBN as core materials, and graphene as conductive shell, separated by a low-permittivity dielectric spacer. Three-layer nanoparticles demonstrate up to a 12-fold increased mid-infrared (MIR) absorption as compared to their monolithic polar dielectrics, and up to 1.7 as compared to two-layer (no spacer) counterparts. They also show orders of magnitude enhancement of the nanoparticle scattering efficiency. The enhancement originates from the phonon-plasmon hybridization thanks to the graphene and polar dielectric combination, assisted by coupling via the low permittivity spacer, resulting in the splitting of the dielectric resonance into two modes. Those modes extend beyond the dielectric’s Reststrahlen band and can be tuned by tailoring the nanoparticles characteristics as they can be easily calculated through the closed-form expression. Nanoparticles with dual band resonances and enhanced absorption and scattering efficiencies in the MIR are of high technological interest for biomedical applications, such as surface -enhanced vibrational spectroscopies allowing simultaneous imaging and spectroscopy of samples, as well as assisting guided drug delivery.
Khokher, MR, Little, LR, Tuck, GN, Smith, DV, Qiao, M, Devine, C, O’Neill, H, Pogonoski, JJ, Arangio, R & Wang, D 2022, 'Early lessons in deploying cameras and artificial intelligence technology for fisheries catch monitoring: where machine learning meets commercial fishing', Canadian Journal of Fisheries and Aquatic Sciences, vol. 79, no. 2, pp. 257-266.
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Electronic monitoring (EM) is increasingly used to monitor catch and bycatch in wild capture fisheries. EM video data are still manually reviewed and adds to ongoing management costs. Computer vision, machine learning, and artificial intelligence-based systems are seen to be the next step in automating EM data workflows. Here we show some of the obstacles we have confronted and approaches taken as we develop a system to automatically identify and count target and bycatch species using cameras deployed to an industry vessel. A Convolutional Neural Network was trained to detect and classify target and bycatch species groups, and a visual tracking system was developed to produce counts. The multiclass detector achieved a mean average precision of 53.42%. Based on the detection results, the visual tracking system provided automatic fish counts for the test video data. Automatic counts were within two standard deviations of the manual counts for the target species and most times for the bycatch species. Unlike other recent attempts, weather and lighting conditions were largely controlled by mounting cameras under cover.
Khorramdel, H, Gitizadeh, M, Chung, CY, Othman, MM & Alhelou, HH 2022, 'An Adjustable Robust Economic Energy and Reserve Dispatch Problem Incorporating Large-Scale Wind Farms', IEEE Access, vol. 10, pp. 73969-73987.
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Khosravi, K, Golkarian, A, Saco, PM, Booij, MJ & Melesse, AM 2022, 'Model identification and accuracy for estimation of suspended sediment load', Geocarto International, vol. 37, no. 27, pp. 18520-18545.
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Kiani, M, Andreu-Perez, J, Hagras, H, Papageorgiou, EI, Prasad, M & Lin, C-T 2022, 'Effective Brain Connectivity for fNIRS With Fuzzy Cognitive Maps in Neuroergonomics', IEEE Transactions on Cognitive and Developmental Systems, vol. 14, no. 1, pp. 50-63.
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Kim, J, Charbel-Salloum, A, Perry, S & Palmisano, S 2022, 'Effects of display lag on vection and presence in the Oculus Rift HMD', Virtual Reality, vol. 26, no. 2, pp. 425-436.
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Kim, J, Kim, H-W, Tijing, LD, Shon, HK & Hong, S 2022, 'Elucidation of physicochemical scaling mechanisms in membrane distillation (MD): Implication to the control of inorganic fouling', Desalination, vol. 527, pp. 115573-115573.
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Kim, J, Yun, E-T, Tijing, L, Shon, HK & Hong, S 2022, 'Mitigation of fouling and wetting in membrane distillation by electrical repulsion using a multi-layered single-wall carbon nanotube/polyvinylidene fluoride membrane', Journal of Membrane Science, vol. 653, pp. 120519-120519.
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King, J-T, John, AR, Wang, Y-K, Shih, C-K, Zhang, D, Huang, K-C & Lin, C-T 2022, 'Brain Connectivity Changes During Bimanual and Rotated Motor Imagery', IEEE Journal of Translational Engineering in Health and Medicine, vol. 10, pp. 1-8.
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Motor imagery-based brain-computer interface (MI-BCI) currently represents a new trend in rehabilitation. However, individual differences in the responsive frequency bands and a poor understanding of the communication between the ipsilesional motor areas and other regions limit the use of MI-BCI therapy. Objective: Bimanual training has recently attracted attention as it achieves better outcomes as compared to repetitive one-handed training. This study compared the effects of three MI tasks with different visual feedback. Methods: Fourteen healthy subjects performed single hand motor imagery tasks while watching single static hand (traditional MI), single hand with rotation movement (rmMI), and bimanual coordination with a hand pedal exerciser (bcMI). Functional connectivity is estimated by Transfer Entropy (TE) analysis for brain information flow. Results: Brain connectivity of conducting three MI tasks showed that the bcMI demonstrated increased communications from the parietal to the bilateral prefrontal areas and increased contralateral connections between motor-related zones and spatial processing regions. Discussion/Conclusion: The results revealed bimanual coordination operation events increased spatial information and motor planning under the motor imagery task. And the proposed bimanual coordination MI-BCI (bcMI-BCI) can also achieve the effect of traditional motor imagery tasks and promotes more effective connections with different brain regions to better integrate motor-cortex functions for aiding the development of more effective MI-BCI therapy. Clinical and Translational Impact Statement The proposed bcMI-BCI provides more effective connections with different brain areas and integrates motor-cortex functions to promote motor imagery rehabilitation for patients' impairment.
Kiss, SH, Katuwandeniya, K, Alempijevic, A & Vidal-Calleja, T 2022, 'Constrained Gaussian Processes With Integrated Kernels for Long-Horizon Prediction of Dense Pedestrian Crowd Flows', IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7343-7350.
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Kiyani, A, Nasimuddin, N, Hashmi, RM, Baba, AA, Abbas, SM, Esselle, KP & Mahmoud, A 2022, 'A Single-Feed Wideband Circularly Polarized Dielectric Resonator Antenna Using Hybrid Technique With a Thin Metasurface', IEEE Access, vol. 10, pp. 90244-90253.
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A compact metasurface-based circularly polarized (CP) dielectric resonator antenna (DRA) is proposed with wideband characteristics. The antenna forms a very simple structure, composed of a rectangular DR, a single coaxial probe, and plus-shaped unit cells-based metasurface. The metasurface is realized on a grounded FR-4 substrate. Next, a rectangular DR is loaded centrally over the metasurface. The DR is fed with a perturbed probe feed at an appropriate angle of ( θ =29°), along the diagonal line. Thus, a novel hybrid technique involving the angle of feed location from the center of DR, and the N × N unit cells-based metasurface is utilized for generating a wideband CP radiation. The resonance from the rectangular DR and surface waves along the 7× 7 plus-shaped unit cells-based metasurface is exploited to achieve a wide 3-dB axial ratio (AR) and impedance matching bandwidth. The fabricated antenna prototype used for the validation of predicted results confirms the successful implementation of the proposed technique. Measured results demonstrate a wide impedance bandwidth of 32% (3.6 GHz - 7.0 GHz) and an overlapping 3-dB AR bandwidth of 20.4% (4.2 GHz - 5.2 GHz). Moreover, the antenna adopts a left-hand circular polarization (LHCP) with 6-7 dBic measured gain within the operational frequency range. Overall, the proposed antenna offers low-profile, simplicity, ease of design, and high performance.
Kobat, SG, Baygin, N, Yusufoglu, E, Baygin, M, Barua, PD, Dogan, S, Yaman, O, Celiker, U, Yildirim, H, Tan, R-S, Tuncer, T, Islam, N & Acharya, UR 2022, 'Automated Diabetic Retinopathy Detection Using Horizontal and Vertical Patch Division-Based Pre-Trained DenseNET with Digital Fundus Images', Diagnostics, vol. 12, no. 8, pp. 1975-1975.
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Diabetic retinopathy (DR) is a common complication of diabetes that can lead to progressive vision loss. Regular surveillance with fundal photography, early diagnosis, and prompt intervention are paramount to reducing the incidence of DR-induced vision loss. However, manual interpretation of fundal photographs is subject to human error. In this study, a new method based on horizontal and vertical patch division was proposed for the automated classification of DR images on fundal photographs. The novel sides of this study are given as follows. We proposed a new non-fixed-size patch division model to obtain high classification results and collected a new fundus image dataset. Moreover, two datasets are used to test the model: a newly collected three-class (normal, non-proliferative DR, and proliferative DR) dataset comprising 2355 DR images and the established open-access five-class Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 dataset comprising 3662 images. Two analysis scenarios, Case 1 and Case 2, with three (normal, non-proliferative DR, and proliferative DR) and five classes (normal, mild DR, moderate DR, severe DR, and proliferative DR), respectively, were derived from the APTOS 2019 dataset. These datasets and these cases have been used to demonstrate the general classification performance of our proposal. By applying transfer learning, the last fully connected and global average pooling layers of the DenseNet201 architecture were used to extract deep features from input DR images and each of the eight subdivided horizontal and vertical patches. The most discriminative features are then selected using neighborhood component analysis. These were fed as input to a standard shallow cubic support vector machine for classification. Our new DR dataset obtained 94.06% and 91.55% accuracy values for three-class classification with 80:20 hold-out validation and 10-fold cross-validation, respectively. As can be seen from steps of the proposed...
Kocaballi, AB, Laranjo, L, Clark, L, Kocielnik, R, Moore, RJ, Liao, QV & Bickmore, T 2022, 'Special Issue on Conversational Agents for Healthcare and Wellbeing', ACM Transactions on Interactive Intelligent Systems, vol. 12, no. 2, pp. 1-3.
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Kohn, C, Duong, HC, Hoang, NB & Nghiem, LD 2022, 'Digital Transformation of Packaged Reverse Osmosis Plants for Industrial and Sewer Mining Applications', Current Pollution Reports, vol. 8, no. 4, pp. 360-368.
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Koli, MNY, Afzal, MU & Esselle, KP 2022, 'Increasing the Gain of Beam-Tilted Circularly Polarized Radial Line Slot Array Antennas', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4392-4403.
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Kolli, MK, Opp, C, Karthe, D & Pradhan, B 2022, 'Automatic extraction of large-scale aquaculture encroachment areas using Canny Edge Otsu algorithm in Google earth engine – the case study of Kolleru Lake, South India', Geocarto International, vol. 37, no. 26, pp. 11173-11189.
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Kong, W, Li, X, Hou, L, Yuan, J, Gao, Y & Yu, S 2022, 'A Reliable and Efficient Task Offloading Strategy Based on Multifeedback Trust Mechanism for IoT Edge Computing', IEEE Internet of Things Journal, vol. 9, no. 15, pp. 13927-13941.
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Facing multidemand tasks and massive heterogeneous resources in an IoT edge computing environment, it is a challenge to obtain reliable and quick response service and allocate application tasks to resource nodes that meet task requirements and user preference. Since IoT edge computing is facing different types of severe attacks, such as message attacks, swing attacks, collusion attack, node attacks, etc., providing a reliable service environment, trust evaluation between edge nodes is necessary. Existing trust computing schemes, however, suffer from a long response period and low malicious detection rate in a dynamic environment. To alleviate these issues, we propose a reliable and efficient task offloading strategy based on the multifeedback trust mechanism (TOSMFTM). First, a reliable and efficient architecture of TOSMFTM is established, which can effectively improve the ability of trust computing and task offloading. Second, according to the broker's dynamic monitoring of data, a multifeedback trust aggregation model based on time attenuation and interaction frequency is proposed to provide a trusted running environment. Third, a trust weight k -means (TWK-means) clustering algorithm is designed based on resource attributes to enhance the reliability of service, and quickly and accurately cluster out resource nodes required by the task. Finally, we construct a task offloading model based on trust clustering to ensure user experience quality and promote system efficiency. Different from existing task processing models, which only focus on task offloading, our method also carries out resource preprocessing, trust evaluation, and resource clustering before task processing. The experiment verifies the effectiveness and feasibility of our TOSMFTM scheme.
Kong, X, Luo, J, Luo, Q, Li, Q & Sun, G 2022, 'Experimental study on interface failure behavior of 3D printed continuous fiber reinforced composites', Additive Manufacturing, vol. 59, pp. 103077-103077.
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Kon-yu, N & Booth, E 2022, 'Who is Telling ‘Australian’ Stories? The Results from the First Nations and People of Colour Writers Count', Journal of Language, Literature and Culture, vol. 69, no. 2-3, pp. 107-123.
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Koopialipoor, M, Asteris, PG, Salih Mohammed, A, Alexakis, DE, Mamou, A & Armaghani, DJ 2022, 'Introducing stacking machine learning approaches for the prediction of rock deformation', Transportation Geotechnics, vol. 34, pp. 100756-100756.
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Kotobuki, M, Zhou, C, Su, Z, Yang, L, Wang, Y, Jason, CJJ, Liu, Z & Lu, L 2022, 'Importance of substrate materials for sintering Li1.5Al0.5Ge1.5(PO4)3 solid electrolyte', Journal of Solid State Chemistry, vol. 310, pp. 123043-123043.
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Li1.5Al0.5Ge1.5(PO4)3 (LAGP) solid electrolyte with a NASICON (Na super ionic conductor) structure is a promising electrolyte for all-solid-state Li batteries. It is well-known that Al contamination occurs in garnet-type solid electrolytes when Al2O3 substrate is used for sintering. In this paper, the influence of substrate materials for sintering on properties of LAGP solid electrolyte is studied. The LAGP samples are sintered on Al2O3 or Pt substrate at 950 °C. TEM-EDS results show that oxides of Ge and Al are observed when sintering is performed on Pt substrate while phosphates of Li and Al are detected in the sample sintered on Al2O3 substrate. Also, the sample sintered on Al2O3 contains more Al due to Al contamination from the substrate. The Li ion conductivity is also affected by the substrate, and higher conductivity is observed in the sample sintered on the Al2O3 substrate. It is concluded that the Al contamination occurs in the LAGP solid electrolyte sintered on Al2O3 substrate. The selection of substrate is a key factor for development of LAGP solid electrolyte.
Koul, Y, Devda, V, Varjani, S, Guo, W, Ngo, HH, Taherzadeh, MJ, Chang, J-S, Wong, JWC, Bilal, M, Kim, S-H, Bui, X-T & Parra-Saldívar, R 2022, 'Microbial electrolysis: a promising approach for treatment and resource recovery from industrial wastewater', Bioengineered, vol. 13, no. 4, pp. 8115-8134.
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Wastewater is one of the most common by-products of almost every industrial process. Treatment of wastewater alone, before disposal, necessitates an excess of energy. Environmental concerns over the use of fossil fuels as a source of energy have prompted a surge in demand for alternative energy sources and the development of sophisticated procedures to extract energy from unconventional sources. Treatment of municipal and industrial wastewater alone accounts for about 3% of global electricity use while the amount of energy embedded in the waste is at least 2-4 times greater than the energy required to treat the same effluent. The microbial electrolysis cell (MEC) is one of the most efficient technologies for waste-to-product conversion that uses electrochemically active bacteria to convert organic matter into hydrogen or a variety of by-products without polluting the environment. This paper highlights existing obstacles and future potential in the integration of Microbial Electrolysis Cell with other processes like anaerobic digestion coupled system, anaerobic membrane bioreactor and thermoelectric micro converter.
Kridalukmana, R, Lu, H & Naderpour, M 2022, 'Self-Explaining Abilities of an Intelligent Agent for Transparency in a Collaborative Driving Context', IEEE Transactions on Human-Machine Systems, vol. 52, no. 6, pp. 1155-1165.
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A critical challenge in human-autonomy teaming is for human players to comprehend their nonhuman teammates (agents). Transparency in agents' behaviors is the key for such comprehension, which may be obtained by embedding a self-explanation ability into the agent to explain its own behaviors. Previous studies have relied on searching for the executed functions and logics to generate explanations for behaviors of goal-following logic-based agents. With the increasing number of functions and logics, current methods, such as component and process-based methods, have become impractical. This article proposes a new method exploiting the agent's artificial situation awareness states for generating explanations that involves several techniques: A Bayesian network, fuzzy theory, and Hamming distance. Our new method is evaluated in a collaborative driving context, in which a significant number of accidents recently occurred around the globe due to the lack of understanding of the autopilot agents. Using an autonomous driving simulator called Carla, two typical scenarios in collaborative driving, namely, traffic light and overtaking situations, are used. The findings show that the new method potentially reduces the search space in generating explanations and exhibits better computational performance and a lower cognitive workload. This work is important to calibrate human trust and to enhance comprehension of the agent.
Krishankumar, R, Supraja Nimmagadda, S, Mishra, AR, Pamucar, D, Ravichandran, KS & Gandomi, AH 2022, 'An integrated decision model for cloud vendor selection using probabilistic linguistic information and unknown weights', Engineering Applications of Artificial Intelligence, vol. 114, pp. 105114-105114.
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Because of the high competition among IT sectors, companies are planning to migrate to the cloud for effective growth and development. Driven by the importance of the cloud, new cloud vendors emerge each day to satisfy the demand of IT sectors. As a result, selection of an apt cloud vendor is critical. To this end, researchers have proposed different decision models, but these models do not effectively capture uncertainty during the decision-making process. To handle this issue, probabilistic linguistic information (PLI) is adopted in this paper, which associates occurrence probability to each term. Furthermore, weights of criteria are systematically determined using a deviation method, and cloud vendors are prioritized using a mathematical model under the PLI context. These methods are integrated to form the decision model, validated for its applicability using real case data from Cloud Armor. Finally, the advantages and weaknesses of the model are analyzed by using sensitivity analysis and comparison with extant models.
Kuang, B, Fu, A, Susilo, W, Yu, S & Gao, Y 2022, 'A survey of remote attestation in Internet of Things: Attacks, countermeasures, and prospects', Computers & Security, vol. 112, pp. 102498-102498.
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The explosive growth of the Internet of Things (IoT) devices is an inevitable trend, especially considering the fact that 5G technology facilitates numerous services building on IoT devices. IoT devices deliver great convenience to our daily lives; nevertheless, they are becoming attractive attacking targets. Compromised IoT devices can result in the exposure of user privacy, damage to network security, or even threats to personal safety. In a rush for convenience and marketability, the security of these devices is usually less considered during production and even ignored. Under these circumstances, Remote Attestation (RA) becomes a valuable security service. It outsources the computation and verification burden to a resource-rich party, e.g., server, to ease its on-device implementation, making it suitable for protocol extensions. In this paper, we investigate the state-of-the-art RA schemes from different perspectives, aiming to offer a comprehensive understanding of this security service. Specifically, we summarize the basis of RA. We set up an elaborate adversarial model by systematizing existing RA schemes. Then we put forward the evaluation criteria from protection capability, performance, network adaptability, and attestation quality. According to the adversarial model, we classify existing RA schemes into five categories to show the various characteristics. A comparison of representative proposals enables readers to adopt and design suitable protocols in different application scenarios. Finally, we discuss some open challenges and provision prospects for future research.
Kuluozturk, M, Kobat, MA, Barua, PD, Dogan, S, Tuncer, T, Tan, R-S, Ciaccio, EJ & Acharya, UR 2022, 'DKPNet41: Directed knight pattern network-based cough sound classification model for automatic disease diagnosis', Medical Engineering & Physics, vol. 110, pp. 103870-103870.
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Kumar Pachauri, R, Thanikanti, SB, Bai, J, Kumar Yadav, V, Aljafari, B, Ghosh, S & Haes Alhelou, H 2022, 'Ancient Chinese magic square-based PV array reconfiguration methodology to reduce power loss under partial shading conditions', Energy Conversion and Management, vol. 253, pp. 115148-115148.
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Kumar, A, Esmaili, N & Piccardi, M 2022, 'Neural Topic Model Training with the REBAR Gradient Estimator', ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 21, no. 5, pp. 1-18.
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Topic modelling is an important approach of unsupervised machine learning that allows automatically extracting the main “topics” from large collections of documents. In addition, topic modelling is able to identify the topic proportions of each individual document, which can be helpful for organizing the collections. Many topic modelling algorithms have been proposed to date, including several that leverage advanced techniques such as variational inference and deep autoencoders. However, to date topic modelling has made limited use of reinforcement learning, a framework that has obtained vast success in many other unsupervised learning tasks. For this reason, in this article we propose training a neural topic model using a reinforcement learning objective and minimizing the objective with the recently-proposed REBAR gradient estimator. Experiments performed over two probing datasets have shown that the proposed model has achieved improvements over all the compared models in terms of both model perplexity and topic coherence, and produced topics that appear qualitatively informative and consistent.
Kumar, AN, Ashok, B, Nanthagopal, K, Ong, HC, Geca, MJ, Victor, J, Vignesh, R, Jeevanantham, AK, Kannan, C & Kishore, PS 2022, 'Experimental analysis of higher alcohol–based ternary biodiesel blends in CI engine parameters through multivariate and desirability approaches', Biomass Conversion and Biorefinery, vol. 12, no. 5, pp. 1525-1540.
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The present work has enabled to aspire the enhancement of palm biodiesel viability for compression ignition (CI) engine applications using reformulation strategy by the addition of higher alcohols. In this work, 20% and 30% of 1-decanol and n-hexanol were used for ternary blend preparation along with palm biodiesel concentration as 20–30% and 50% diesel fuels for combustion, emission, and performance behaviour investigation in CI engine. Furthermore, the experimental results were also compared with 100% palm biodiesel (P100) and pure diesel (D100) and a binary blend of D50B50 fuels. The experimental study has revealed that the presence of higher alcohols in the ternary blends has improved the cylinder pressure and heat release rate whereas the same trend was not evident in binary biodiesel blend. All the ternary blends of higher alcohols-biodiesel and diesels have shown higher brake thermal efficiency and reduction in brake-specific fuel consumption. At the same time, decanol and hexanol addition in the palm biodiesel-diesel blends has favoured in all exhaust emission reductions with slight exemption in NO emission. The experimental results are optimized through multivariate and desirability analyses for identifying the effective composition of blend. Multivariate analysis has revealed that the higher alcohol proposition in the ternary blend was more influential than the type of higher alcohol. Furthermore, the desirability study has also validated the prescribed proportion with the maximum error of 6.17% for D50P22DC28 and 4.84% for D50P26HE24. Finally, the research concludes that decanol would be the preferable choice for ternary blend preparation than hexanol due to its overall better performance. x
Kumar, BP, Cherukuri, SK, Kaniganti, KR, Karuppiah, N, Muniraj, R, Babu, TS & Alhelou, HH 2022, 'Performance Enhancement of Partial Shaded Photovoltaic System With the Novel Screw Pattern Array Configuration Scheme', IEEE Access, vol. 10, pp. 1731-1744.
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Kumar, M, Jiang, G, Kumar Thakur, A, Chatterjee, S, Bhattacharya, T, Mohapatra, S, Chaminda, T, Kumar Tyagi, V, Vithanage, M, Bhattacharya, P, Nghiem, LD, Sarkar, D, Sonne, C & Mahlknecht, J 2022, 'Lead time of early warning by wastewater surveillance for COVID-19: Geographical variations and impacting factors', Chemical Engineering Journal, vol. 441, pp. 135936-135936.
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The global data on the temporal tracking of the COVID-19 through wastewater surveillance needs to be comparatively evaluated to generate a proper and precise understanding of the robustness, advantages, and sensitivity of the wastewater-based epidemiological (WBE) approach. We reviewed the current state of knowledge based on several scientific articles pertaining to temporal variations in COVID-19 cases captured via viral RNA predictions in wastewater. This paper primarily focuses on analyzing the WBE-based temporal variation reported globally to check if the reported early warning lead-time generated through environmental surveillance is pragmatic or latent. We have compiled the geographical variations reported as lead time in various WBE reports to strike a precise correlation between COVID-19 cases and genome copies detected through wastewater surveillance, with respect to the sampling dates, separately for WASH and non-WASH countries. We highlighted sampling methods, climatic and weather conditions that significantly affected the concentration of viral SARS-CoV-2 RNA detected in wastewater, and thus the lead time reported from the various climatic zones with diverse WASH situations were different. Our major findings are: i) WBE reports around the world are not comparable, especially in terms of gene copies detected, lag-time gained between monitored RNA peak and outbreak/peak of reported case, as well as per capita RNA concentrations; ii) Varying sanitation facility and climatic conditions that impact virus degradation rate are two major interfering features limiting the comparability of WBE results, and iii) WBE is better applicable to WASH countries having well-connected sewerage system.
Kumar, R, Kumar, R, Sharma, N, Khurana, N, Singh, SK, Satija, S, Mehta, M & Vyas, M 2022, 'Pharmacological evaluation of bromelain in mouse model of Alzheimer’s disease', NeuroToxicology, vol. 90, pp. 19-34.
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Kumar, S, Lyalin, A, Huang, Z & Taketsugu, T 2022, 'Catalytic Oxidative Dehydrogenation of Light Alkanes over Oxygen Functionalized Hexagonal Boron Nitride', ChemistrySelect, vol. 7, no. 1.
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AbstractThe catalytic activity of oxygen functionalized hexagonal boron nitride (h‐BN) with >B−O−O−B< and >B−O−B< active sites at the zigzag edges for oxidative dehydrogenation (ODH) of light alkanes, specifically ethane (C2H6), propane (C3H8), butane (C4H10), and isobutane (HC(CH3)3) is explored. It has been found that the reaction pathway involves two H atom transfer steps with small activation energies. We demonstrate that the synergy of two active sites, >B−O−O−B< and >B−O−B<, is crucial for the first and second H‐transfer, respectively. With the increase in molecular mass of the considered light alkanes, the ODH reaction temperature decreases. In the case of butane and isobutane, the ODH reaction occurs almost at the same temperature indicating that the reaction is independent of the shape of the isomer. The rate‐limiting nature of the first H‐transfer step is predicted. The charge redistribution during H‐transfers and localized oxygen atomic states in the conduction band are explored to suggest possible descriptors for the rational design of new catalysts. The universal action of the >B−O−O−B< and >B−O−B< active sites for ODH of the light alkanes paves the way for metal‐free BN‐based materials for future catalytic applications.
Kumari, P, Bahadur, N, Conlan, XA, Laleh, M, Kong, L, O'Dell, LA, Dumée, LF & Merenda, A 2022, 'Atomically-thin Schottky-like photo-electrocatalytic cross-flow membrane reactors for ultrafast remediation of persistent organic pollutants', Water Research, vol. 218, pp. 118519-118519.
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The remediation of persistent organic pollutants in surface and ground water represents a major environmental challenge worldwide. Conventional physico-chemical techniques do not efficiently remove such persistent organic pollutants and new remediation techniques are therefore required. Photo-electro catalytic membranes represent an emerging solution that can combine photocatalytic and electrocatalytic degradation of contaminants along with molecular sieving. Herein, macro-porous photo-electro catalytic membranes were prepared using conductive and porous stainless steel metal membranes decorated with nano coatings of semiconductor photocatalytic metal oxides (TiO2 and ZnO) via atomic layer deposition, producing highly conformal and stable coatings. The metal - semiconductor junction between the stainless steel membranes and photocatalysts provides Schottky - like characteristics to the coated membranes. The PEC membranes showed induced hydrophilicity from the nano-coatings and enhanced electro-chemical properties due to the Schottky junction. A high electron transfer rate was also induced in the coated membranes as the photocurrent efficiency increased by 4 times. The photo-electrocatalytic efficiency of the TiO2 and ZnO coated membranes were demonstrated in batch and cross flow filtration reactors for the degradation of persistent organic pollutant solution, offering increased degradation kinetic factors by 2.9 and 2.3 compared to photocatalysis and electrocatalysis, respectively. The recombination of photo-induced electron and hole pairs is mitigated during the photo-electrocatalytic process, resulting in an enhanced catalytic performance. The strategy offers outstanding perspectives to design stimuli-responsive membrane materials able to sieve and degrade simultaneously toxic contaminants towards greater process integration and self-cleaning operations.
Kumari, P, Bahadur, N, Kong, L, O’Dell, LA, Merenda, A & Dumée, LF 2022, 'Engineering Schottky-like and heterojunction materials for enhanced photocatalysis performance – a review', Materials Advances, vol. 3, no. 5, pp. 2309-2323.
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Photocatalysis with nanostructured semiconductors is emerging for environmental remediation.
Kurdkandi, NV, Marangalu, MG, Mohammadsalehian, S, Tarzamni, H, Siwakoti, YP, Islam, MR & Muttaqi, KM 2022, 'A New Six-Level Transformer-Less Grid-Connected Solar Photovoltaic Inverter With Less Leakage Current.', IEEE Access, vol. 10, pp. 63736-63753.
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This paper presents a novel structure of the transformer-less grid-connected inverters. The proposed inverter is combined with six power switches and two power diodes which can generate six voltage levels at the output. Furthermore, the proposed inverter can overcome the leakage current issue in the photovoltaic (PV) system, which is the major problem in grid-tied PV applications. Additional significant features include- reduced filter size, lower total harmonic distortion (THD) of the injected current to the grid, and voltage boosting ability. Moreover, the proposed topology provides full reactive power support to the grid. A control strategy is designed and implemented to provide a voltage boost ability without using any additional dc-dc boost converter. Finally, the performance of the proposed inverter is validated by the 770 W laboratory prototype.
Kurniawan, T, Nuryoto, N, Milenia, ND, Lestari, KD, Nandiyanto, ABD, Bilad, MR, Abdullah, H & Mahlia, TMI 2022, 'Improved Natural Mordenite as Low-Cost Catalyst for Glycerol Acetalization into Solketal – An Effective Fuel Additive', Materials Science Forum, vol. 1057, pp. 71-87.
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The increase in biodiesel production results in an excessive amount of crude glycerol by-product. Therefore, production of solketal –an effective additive of gasoline fuel-from glycerol and acetone via catalytic acetalization could improve the added value of glycerol. This study investigates enhancement of natural mordenite catalytic properties through the hydrothermal recrystallization method for glycerol acetalization. The hydrothermal temperature was varied at 150, 170 and 190 oC to form ZT 150, ZT 170 and ZT 190, respectively. The samples were characterized using the x-ray diffraction and the scanning electron microscope-Energy dispersive X-Ray. They were later used as catalysts for glycerol acetalization with acetone. The best obtained catalyst was further studied to explore the effect of acetone on glycerol ration. The glycerol conversion was deter-mined using the ASTM D7637-10 titration method. Solketal product was identified by using the Fourier transform infrared spectroscopy. The results show that the recrystallization temperature affects the intensity of the mordenite phase and quartz impurity phase in the modified zeolites. A high recrystallization temperature led to a higher phase of mordenite, peaking at 170oC, beyond which the quartz impurity phase increased. Glycerol acetalization conversions over zeolite parent, ZT 150, ZT 170 and ZT190 with acetone to glycerol ratio of 3 were 16.1%, 30.4%, 33.9% and 32.5%, respectively. When the ratio of acetone to glycerol was increased to 12, the glycerol conversion over ZT 170 catalyst reached 59%, a good starting point for further modifications. Overall finding demonstrated a straight-forward fabrication of catalyst from natural resource to enhance glycerol as the biodiesel production by-product into a higher value end-product of solketal.
Kusumo, F, Mahlia, TMI, Pradhan, S, Ong, HC, Silitonga, AS, Fattah, IMR, Nghiem, LD & Mofijur, M 2022, 'A framework to assess indicators of the circular economy in biological systems', Environmental Technology & Innovation, vol. 28, pp. 102945-102945.
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In regional and global contexts, the circular economy (CE) has gained significant traction to sustain the economy while maintaining environmental and social justice. However, the literature on CE lacks substantial information regarding the theory and methodology of putting CE into practice. The goal of this work is to create a framework for evaluating CE indicators and CE implementation in biological systems. The findings of this study suggest that CE may be more complicated than previously thought, involving a wide variety of interconnected mechanisms. The CE's guiding principles differentiate between biological and man-made (artificial) material and resource cycles. Biological cycles concern the safe and efficient movement of renewable biotic resources into and out of the biosphere. This study looks at the 13 different indicators of a circular economy, with a particular emphasis on the biological approaches that make up the biological cycle. The 13 papers were broken down as follows: four at the macro level, three at the meso level, and seven at the micro level. Furthermore, through the analysis of various literary sources, this paper proposed a framework for calculating and quantifying the CE. The framework's first steps are measurement criteria, the second are level monitoring procedures, and the third is the impact of CE. The proposed framework will aid in disseminating knowledge across regions, industries, and stakeholders, as well as accelerating CE implementation.
Kusumo, F, Mahlia, TMI, Shamsuddin, AH, Ahmad, AR, Silitonga, AS, Dharma, S, Mofijur, M, Ideris, F, Ong, HC, Sebayang, R, Milano, J, Hassan, MH & Varman, M 2022, 'Optimisation of biodiesel production from mixed Sterculia foetida and rice bran oil', International Journal of Ambient Energy, vol. 43, no. 1, pp. 4380-4390.
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The present study is to investigate the feasibility of mixed non-edible oils, Sterculia foetida (SFO), and rice bran oil (RBO) for biodiesel production. The transesterification process variables of SFO50RBO50 as the suitable blend were optimised using response surface methodology. The optimum conditions of the transesterification process are as follow; KOH catalyst concentration of 0.7% wt, the ratio of methanol to oil of 42%, the reaction time of 50.64 min, resulted in the methyl ester yield of 98.93%. The result shows that the SF50RB50 methyl ester properties satisfy the biodiesel requirements laid in ASTM D6751 and EN 14214 standards.
Kuzhiumparambil, U, Labeeuw, L, Commault, A, Vu, HP, Nguyen, LN, Ralph, PJ & Nghiem, LD 2022, 'Effects of harvesting on morphological and biochemical characteristics of microalgal biomass harvested by polyacrylamide addition, pH-induced flocculation, and centrifugation', Bioresource Technology, vol. 359, pp. 127433-127433.
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The effects of microalgae harvesting methods on microalgal biomass quality were evaluated using three species namely the freshwater green alga Chlorella vulgaris, marine red alga Porphyridium purpureum and marine diatom Phaeodactylum tricornutum. Harvesting efficiencies of polyacrylamide addition, alkaline addition, and centrifugation ranged from 85 to 95, 59-92 and 100%, respectively, across these species. Morphology of the harvested cells (i.e. compromised cell walls) was significantly impacted by alkaline pH-induced flocculation for all three species. Over 50% of C. vulgaris cells were compromised with alkaline pH compared to < 10% with polyacrylamide and centrifugation. The metabolic profiles varied depending on harvesting methods. Species-specific decrease of certain metabolites was observed. These results suggest that the method of harvest can alter the metabolic profile of the biomass amongst the three harvesting methods, polyacrylamide addition showed higher harvesting efficiency with less compromised cells and higher retention of industry important biochemicals.
La, DD, Ngo, HH, Nguyen, DD, Tran, NT, Vo, HT, Nguyen, XH, Chang, SW, Chung, WJ & Nguyen, MD-B 2022, 'Advances and prospects of porphyrin-based nanomaterials via self-assembly for photocatalytic applications in environmental treatment', Coordination Chemistry Reviews, vol. 463, pp. 214543-214543.
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A class of compounds called porphyrins are involved in the gas transport, catalysis, and light absorption processes of many animals and plants throughout the world. This natural mechanism can be obtained via supramolecular self-assembly of porphyrin derivatives. Porphyrin-based nanomaterials obtained via self-assembly can be utilized in many promising applications, such as optical energy or information storage devices, solar energy conversion, sensors, nanocatalysts, photoelectronics, and photodynamic therapy. This paper critically reviews recent advances in porphyrin nanostructures fabricated via self-assembly for visible-light photocatalytic reactions, and discusses their properties and applications, especially for environmental treatment. Firstly, it introduced porphyrin and a self-assembly method for fabricating porphyrin nanomaterials. Methods for fabricating porphyrin nanostructures via self-assembly were then presented, such as re-precipitation, coordination polymerization, and ionic self-assembly. Finally, the applications of porphyrin-based nanomaterials with a focus on photovoltaic applications were overviewed with highlights from recent works in this field.
Laccone, F, Malomo, L, Callieri, M, Alderighi, T, Muntoni, A, Ponchio, F, Pietroni, N & Cignoni, P 2022, 'Design And Construction Of a Bending-Active Plywood Structure: The Flexmaps Pavilion', Journal of the International Association for Shell and Spatial Structures, vol. 63, no. 2, pp. 98-114.
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Mesostructured patterns are a modern and efficient concept based on designing the geometry of structural material at the meso-scale to achieve desired mechanical performances. In the context of bending-active structures, such a concept can be used to control the flexibility of the panels forming a surface without changing the constituting material. These panels undergo a formation process of deformation by bending, and application of internal restraints. This paper describes a new constructional system, FlexMaps, that has initiated the adoption of bending-active mesostructures at the architectural scale. Here, these modules are in the form of four-arms spirals made of CNC-milled plywood and are designed to reach the desired target shape once assembled. All phases from the conceptual design to the fabrication are seamlessly linked within an automated workflow. To illustrate the potential of the system, the paper discusses the results of a demonstrator project entitled FlexMaps Pavilion (3.90x3.96x3.25 meters) that has been exhibited at the IASS Symposium in 2019 and more recently at the 2021 17th International Architecture Exhibition, La Biennale di Venezia. The structural response is investigated through a detailed structural analysis, and the long-term behavior is assessed through a photogrammetric survey.
Lai, Y, Paul, G, Cui, Y & Matsubara, T 2022, 'User intent estimation during robot learning using physical human robot interaction primitives', Autonomous Robots, vol. 46, no. 2, pp. 421-436.
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AbstractAs robotic systems transition from traditional setups to collaborative work spaces, the prevalence of physical Human Robot Interaction has risen in both industrial and domestic environments. A popular representation for robot behavior is movement primitives which learn, imitate, and generalize from expert demonstrations. While there are existing works in context-aware movement primitives, they are usually limited to contact-free human robot interactions. This paper presents physical Human Robot Interaction Primitives (pHRIP), which utilize only the interaction forces between the human user and robot to estimate user intent and generate the appropriate robot response during physical human robot interactions. The efficacy of pHRIP is evaluated through multiple experiments based on target-directed reaching and obstacle avoidance tasks using a real seven degree of freedom robot arm. The results are validated against Interaction Primitives which use observations of robotic trajectories, with discussions of future pHRI applications utilizing pHRIP.
Lakomy, K, Madonski, R, Dai, B, Yang, J, Kicki, P, Ansari, M & Li, S 2022, 'Active Disturbance Rejection Control Design With Suppression of Sensor Noise Effects in Application to DC–DC Buck Power Converter', IEEE Transactions on Industrial Electronics, vol. 69, no. 1, pp. 816-824.
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Lalbakhsh, A, Afzal, MU, Esselle, KP & Smith, SL 2022, 'All-Metal Wideband Frequency-Selective Surface Bandpass Filter for TE and TM Polarizations', IEEE Transactions on Antennas and Propagation, vol. 70, no. 4, pp. 2790-2800.
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A novel technique to design a low-cost frequency-selective surface (FSS) bandpass filter is presented in this article. Wideband polarization-independent FSS bandpass filters are predominantly made of multiple microwave dielectric substrates or noncommercially available composite materials with or without active components, contributing to a very high manufacturing cost. The presented FSS filter has neither microwave substrates, nor any active devices, while it has a large controllable operational frequency band, which can support all polarizations, due to its symmetrical configuration. To the best of our knowledge, such a polarization-independent wideband bandpass response has never been achieved by any low-cost fully metallic FSS filter. The proposed FSS filter is made of three thin metal sheets composed of an engineered metallic substrate (EMS) and a metallic orthogonal dipole resonator (ODR). The EMS is responsible for ensuring the mechanical integrity of the filter without imposing electromagnetic (EM) restrictions throughout the desired frequency band. The integration of EMS and ODRs realizes a fully controllable wideband bandpass verified thorough circuital and modal analyses. According to the predicted and measured results, the FSS filter has a large bandwidth of around 31%, extending from 8.76 to 11.96 GHz with sharp roll-offs for the normal incidence. Simulated and measured results show a low sensitivity of the FSS filter response to oblique angles of incidence for both TM and TE polarizations.
Lalbakhsh, A, Pitcairn, A, Mandal, K, Alibakhshikenari, M, Esselle, KP & Reisenfeld, S 2022, 'Darkening Low-Earth Orbit Satellite Constellations: A Review', IEEE Access, vol. 10, pp. 24383-24394.
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Lammers, T, Guertler, M & Skirde, H 2022, 'Can product modularization approaches help address challenges in technical project portfolio management? – Laying the foundations for a methodology transfer', International Journal of Information Systems and Project Management, vol. 10, no. 2, pp. 26-42.
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Formalized Project Portfolio Management (PPM) models struggle to provide comprehensive solutions to project selection, resource allocation and adaptability to dynamic technology project environments. In this article, we introduce a vision for a novel Modular Project Portfolio Management (MPPM) approach by drawing on well-established engineering methods for designing modular product architectures. We show how systems theory can be used to enable a transfer of methods from the area of engineering design and manufacturing to the area of PPM and how the concept of product modularity could help address challenges of existing PPM approaches. This lays the groundwork for the possible development of MPPM as a new and innovative methodology for managing complex technology and engineering project landscapes.
Lammers, T, Rashid, L, Kratzer, J & Voinov, A 2022, 'An analysis of the sustainability goals of digital technology start-ups in Berlin', Technological Forecasting and Social Change, vol. 185, pp. 122096-122096.
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Lan, Q, Zhang, Y, Lin, F, Meng, Q, Buys, N, Fan, H & Sun, J 2022, 'Sex-Specific Associations Between Serum Phosphate Concentration and Cardiometabolic Disease: A Cohort Study on the Community-Based Older Chinese Population', Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, vol. Volume 15, pp. 813-826.
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PURPOSE: This study aimed to investigate the association between sex-specific baseline serum phosphate and the incidence of new-onset cardiometabolic disease in a cohort of Shanghai-based older Chinese individuals. PATIENTS AND METHODS: A community cohort of 5000 disease-free Chinese men and women was recruited in 2013 and followed until 2017 for the development of cardiometabolic disease. Participants underwent index and follow-up health screens at the Tongji Medical School affiliated Shanghai East Hospital, including blood biochemistry analysis, anthropometric measurements, interview on health-related behaviors, and clinical evaluation. RESULTS: Higher baseline serum phosphate (>1.25 mmol/L) was significantly associated with new-onset type-2 diabetes mellitus (HR 1.730, 95% CI 1.127-2.655) and metabolic syndrome (HR 0.640, 95% CI 1.085-2.155) in women. Baseline serum phosphate was associated with age, BMI, waist circumference, SBP, total calcium, bicarbonate, and total cholesterol in women. The estimated risk of developing diabetes mellitus in women with inorganic phosphate >1.25 mmol/L was 14.54%. Inorganic phosphate accounted for 9.2% of the variance explained in a total estimated 14.52% of variance attributed to BMI, total cholesterol, total calcium, waist circumference, and inorganic phosphate. CONCLUSION: Serum phosphate concentration showed sex-specific associations with diabetes and metabolic syndrome. Higher inorganic phosphate was associated with increased risk of developing diabetes mellitus in women. These findings may be important in the assessment of individualized metabolic risk.
Lan, T, Hutvagner, G, Zhang, X, Liu, T, Wong, L & Li, J 2022, 'Density-based detection of cell transition states to construct disparate and bifurcating trajectories', Nucleic Acids Research, vol. 50, no. 21, pp. e122-e122.
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Abstract Tree- and linear-shaped cell differentiation trajectories have been widely observed in developmental biologies and can be also inferred through computational methods from single-cell RNA-sequencing datasets. However, trajectories with complicated topologies such as loops, disparate lineages and bifurcating hierarchy remain difficult to infer accurately. Here, we introduce a density-based trajectory inference method capable of constructing diverse shapes of topological patterns including the most intriguing bifurcations. The novelty of our method is a step to exploit overlapping probability distributions to identify transition states of cells for determining connectability between cell clusters, and another step to infer a stable trajectory through a base-topology guided iterative fitting. Our method precisely re-constructed various benchmark reference trajectories. As a case study to demonstrate practical usefulness, our method was tested on single-cell RNA sequencing profiles of blood cells of SARS-CoV-2-infected patients. We not only re-discovered the linear trajectory bridging the transition from IgM plasmablast cells to developing neutrophils, and also found a previously-undiscovered lineage which can be rigorously supported by differentially expressed gene analysis.
Lao, W, Zhao, Y, Tan, Y, Johnson, M, Li, Y, Xiao, L, Cheng, J, Lin, Y & Qu, X 2022, 'Regulatory Effects and Mechanism of Action of Green Tea Polyphenols on Osteogenesis and Adipogenesis in Human Adipose Tissue-Derived Stem Cells', Current Issues in Molecular Biology, vol. 44, no. 12, pp. 6046-6058.
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We previously showed that green tea polyphenols (GTPs) exert antiadipogenic effects on preadipocyte proliferation. Here, we investigated the regulatory effects of GTPs on osteogenesis and adipogenesis during early differentiation of human adipose tissue-derived stem cells (hADSC). Adipogenesis of hADSCs was determined by oil-red-O staining and triglycerides synthesis measurement. Osteoporosis of hADSC was measured using alkaline phosphatase assays and intracellular calcium levels. Immunofluorescence staining and qRT-PCR were used to detect PPARγ-CEBPA regulated adipogenic pathway regulated by PPAR-CEBPA and the osteogenic pathway mediated by RUNX2-BMP2. We found that GTPs treatment significantly decreased lipid accumulation and cellular triglyceride synthesis in mature adipocytes and attenuated pioglitazone-induced adipogenesis in a dose-dependent manner. GTPs downregulated protein and mRNA expression of Pparγ and attenuated pioglitazone-stimulated-Cebpa expression. GTPs treatment significantly enhanced hADSCs differentiation into osteoblasts compared to control and pioglitazone-treated cells. GTPs upregulated RunX2 and Bmp2 proteins and mRNA expression compared to control and significantly attenuated decreased RunX2 and Bmp2 mRNA expression by pioglitazone. In conclusion, our data demonstrates GTPs possesses great ability to facilitate osteogenesis and simultaneously inhibits hADSC differentiation into adipogenic lineage by upregulating the RUNX2-BMP2 mediated osteogenic pathway and suppressing PPARγ-induced signaling of adipogenesis. These findings highlight GTPs’ potential to combat osteoporosis associated with obesity.
Larpruenrudee, P, Surawski, NC & Islam, MS 2022, 'The Effect of Metro Construction on the Air Quality in the Railway Transport System of Sydney, Australia', Atmosphere, vol. 13, no. 5, pp. 759-759.
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Sydney Metro is the biggest project of Australia’s public transport, which was designed to provide passengers with more trains and faster services. This project was first implemented in 2017 and is planned to be completed in 2024. As presented, the project is currently in the construction stage located on the ground stations of the Sydney Trains Bankstown line (T3). Based on this stage, several construction activities will generate air pollutants, which will affect the air quality around construction areas. Moreover, it might cause health problems to people around there and also the passengers who usually take the train on the T3 line. However, there is no specific data for air quality inside the train that may be affected by the construction from each area. Therefore, the aim of this study is to investigate the air quality inside the train carriage of all related stations from the T3 line. A sampling campaign was conducted over 3 months to analyze particulate matter (PM) concentration, the main indoor pollutants including formaldehyde (HCHO) and total volatile organic compounds (TVOC). The results of the T3 line were analyzed and compared to Airport & South line (T8) that were not affected by the project’s construction. The results of this study indicate that Sydney Metro construction activities insignificantly affected the air quality inside the train. Average PM2.5 and PM10 inside the train of T3 line in the daytime were slightly higher than in the nighttime. The differences in PM2.5 and PM10 concentrations from these periods were around 6.8 μg/m3 and 12.1 μg/m3, respectively. The PM concentrations inside the train from the T3 line were slightly higher than the T8 line. However, these concentrations were still lower than those recommended by the national air quality standards. For HCHO and TVOC, the average HCHO and TVOC concentrations were less than the recommendation criteria.
Lau, CW, Qu, Z, Draper, D, Quan, R, Braytee, A, Bluff, A, Zhang, D, Johnston, A, Kennedy, PJ, Simoff, S, Nguyen, QV & Catchpoole, D 2022, 'Virtual reality for the observation of oncology models (VROOM): immersive analytics for oncology patient cohorts', Scientific Reports, vol. 12, no. 1, p. 11337.
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AbstractThe significant advancement of inexpensive and portable virtual reality (VR) and augmented reality devices has re-energised the research in the immersive analytics field. The immersive environment is different from a traditional 2D display used to analyse 3D data as it provides a unified environment that supports immersion in a 3D scene, gestural interaction, haptic feedback and spatial audio. Genomic data analysis has been used in oncology to understand better the relationship between genetic profile, cancer type, and treatment option. This paper proposes a novel immersive analytics tool for cancer patient cohorts in a virtual reality environment, virtual reality to observe oncology data models. We utilise immersive technologies to analyse the gene expression and clinical data of a cohort of cancer patients. Various machine learning algorithms and visualisation methods have also been deployed in VR to enhance the data interrogation process. This is supported with established 2D visual analytics and graphical methods in bioinformatics, such as scatter plots, descriptive statistical information, linear regression, box plot and heatmap into our visualisation. Our approach allows the clinician to interrogate the information that is familiar and meaningful to them while providing them immersive analytics capabilities to make new discoveries toward personalised medicine.
Law, AMK, Chen, J, Colino‐Sanguino, Y, Fuente, LRDL, Fang, G, Grimes, SM, Lu, H, Huang, RJ, Boyle, ST, Venhuizen, J, Castillo, L, Tavakoli, J, Skhinas, JN, Millar, EKA, Beretov, J, Rossello, FJ, Tipper, JL, Ormandy, CJ, Samuel, MS, Cox, TR, Martelotto, L, Jin, D, Valdes‐Mora, F, Ji, HP & Gallego‐Ortega, D 2022, 'ALTEN: A High‐Fidelity Primary Tissue‐Engineering Platform to Assess Cellular Responses Ex Vivo', Advanced Science, vol. 9, no. 21, pp. e2103332-2103332.
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AbstractTo fully investigate cellular responses to stimuli and perturbations within tissues, it is essential to replicate the complex molecular interactions within the local microenvironment of cellular niches. Here, the authors introduce Alginate‐based tissue engineering (ALTEN), a biomimetic tissue platform that allows ex vivo analysis of explanted tissue biopsies. This method preserves the original characteristics of the source tissue's cellular milieu, allowing multiple and diverse cell types to be maintained over an extended period of time. As a result, ALTEN enables rapid and faithful characterization of perturbations across specific cell types within a tissue. Importantly, using single‐cell genomics, this approach provides integrated cellular responses at the resolution of individual cells. ALTEN is a powerful tool for the analysis of cellular responses upon exposure to cytotoxic agents and immunomodulators. Additionally, ALTEN's scalability using automated microfluidic devices for tissue encapsulation and subsequent transport, to enable centralized high‐throughput analysis of samples gathered by large‐scale multicenter studies, is shown.
Le, A, Nimbalkar, S, Zobeiry, N & Malek, S 2022, 'An efficient multi-scale approach for viscoelastic analysis of woven composites under bending', Composite Structures, vol. 292, pp. 115698-115698.
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Le, AT, Huang, X & Guo, YJ 2022, 'A Two-Stage Analog Self-Interference Cancelation Structure for High Transmit Power In-Band Full-Duplex Radios', IEEE Wireless Communications Letters, vol. 11, no. 11, pp. 2425-2429.
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Le, AT, Huang, X, Tran, LC & Guo, YJ 2022, 'On the Impacts of I/Q Imbalance in Analog Least Mean Square Adaptive Filter for Self-Interference Cancellation in Full-Duplex Radios', IEEE Transactions on Vehicular Technology, vol. 71, no. 10, pp. 10683-10693.
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Le, L-T, Nguyen, K-QN, Nguyen, P-T, Duong, HC, Bui, X-T, Hoang, NB & Nghiem, LD 2022, 'Microfibers in laundry wastewater: Problem and solution', Science of The Total Environment, vol. 852, pp. 158412-158412.
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Data corroborated in this study highlights laundry wastewater as a primary source of microfibers (MFs) in the aquatic environment. MFs can negatively impact the aquatic ecosystem via five possible pathways, namely, acting as carriers of other contaminats, physical damage to digestive systems of aquatic organisms, blocking the digestive tract, releasing toxic chemicals, and harbouring invasive and noxious plankton and bacteria. This review shows that small devices to capture MFs during household laundry activities are simple to use and affordable at household level in developed countries. However, these low cost and small devices are unrealiable and can only achieve up to 40 % MF removal efficiency. In line filtration devices can achieve higher removal efficiency under well maintained condition but their performance is still limited compared to over 98 % MF removal by large scale centralized wastewater treatment. These results infer that effort to increase sanitation coverage to ensure adequate wastewater treatment prior to environmental discharge is likely to be more cost effective than those small devices for capturing MFs. This review also shows that natural fabrics would entail significantly less environmental consequences than synthetic materials. Contribution from the fashion industry to increase the share of natural frabics in the current textile market can also reduce the loading of plastic MFs in the environment.
Le, T-S, Nguyen, P-D, Ngo, HH, Bui, X-T, Dang, B-T, Diels, L, Bui, H-H, Nguyen, M-T & Le Quang, D-T 2022, 'Two-stage anaerobic membrane bioreactor for co-treatment of food waste and kitchen wastewater for biogas production and nutrients recovery', Chemosphere, vol. 309, no. Pt 1, pp. 136537-136537.
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Co-digestion of organic waste and wastewater is receiving increased attention as a plausible waste management approach toward energy recovery. However, traditional anaerobic processes for co-digestion are particularly susceptible to severe organic loading rates (OLRs) under long-term treatment. To enhance technological feasibility, this work presented a two-stage Anaerobic Membrane Bioreactor (2 S-AnMBR) composed of a hydrolysis reactor (HR) followed by an anaerobic membrane bioreactor (AnMBR) for long-term co-digestion of food waste and kitchen wastewater. The OLRs were expanded from 4.5, 5.6, and 6.9 kg COD m-3 d-1 to optimize biogas yield, nitrogen recovery, and membrane fouling at ambient temperatures of 25-32 °C. Results showed that specific methane production of UASB was 249 ± 7 L CH4 kg-1 CODremoved at the OLR of 6.9 kg TCOD m-3 d-1. Total Chemical Oxygen Demand (TCOD) loss by hydrolysis was 21.6% of the input TCOD load at the hydraulic retention time (HRT) of 2 days. However, low total volatile fatty acid concentrations were found in the AnMBR, indicating that a sufficiently high hydrolysis efficiency could be accomplished with a short HRT. Furthermore, using AnMBR structure consisting of an Upflow Anaerobic Sludge Blanket Reactor (UASB) followed by a side-stream ultrafiltration membrane alleviated cake membrane fouling. The wasted digestate from the AnMBR comprised 42-47% Total Kjeldahl Nitrogen (TKN) and 57-68% total phosphorous loading, making it suitable for use in soil amendments or fertilizers. Finally, the predominance of fine particles (D10 = 0.8 μm) in the ultrafiltration membrane housing (UFMH) could lead to a faster increase in trans-membrane pressure during the filtration process.
Lee, SS, Siwakoti, YP, Barzegarkhoo, R & Blaabjerg, F 2022, 'A Novel Common-Ground-Type Nine-Level Dynamic Boost Inverter', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 4, pp. 4435-4442.
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Lee, T, Min, C, Naidu, G, Huang, Y, Shon, HK & Kim, S-H 2022, 'Optimizing the performance of sweeping gas membrane distillation for treating naturally heated saline groundwater', Desalination, vol. 532, pp. 115736-115736.
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Lee, XJ, Ong, HC, Ooi, J, Yu, KL, Tham, TC, Chen, W-H & Ok, YS 2022, 'Engineered macroalgal and microalgal adsorbents: Synthesis routes and adsorptive performance on hazardous water contaminants', Journal of Hazardous Materials, vol. 423, pp. 126921-126921.
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Legg, R, Prior, J, Adams, J & McIntyre, E 2022, 'A geography of contaminated sites, mental health and wellbeing: The body, home, environment and state at Australian PFAS sites', Emotion, Space and Society, vol. 44, pp. 100910-100910.
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Lei, J, Gu, Y, Xie, W, Li, Y & Du, Q 2022, 'Boundary Extraction Constrained Siamese Network for Remote Sensing Image Change Detection', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13.
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Lei, Y, Sui, Y, Ding, S & Zhang, Q 2022, 'Taming transitive redundancy for context-free language reachability', Proceedings of the ACM on Programming Languages, vol. 6, no. OOPSLA2, pp. 1556-1582.
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Given an edge-labeled graph, context-free language reachability (CFL-reachability) computes reachable node pairs by deriving new edges and adding them to the graph. The redundancy that limits the scalability of CFL-reachability manifests as redundant derivations, i.e., identical edges can be derived multiple times due to the many paths between two reachable nodes. We observe that most redundancy arises from the derivations involving transitive relations of reachable node pairs. Unfortunately, existing techniques for reducing redundancy in transitive-closure-based problems are either ineffective or inapplicable to identifying and eliminating redundant derivations during on-the-fly CFL-reachability solving. This paper proposes a scalable yet precision-preserving approach to all-pairs CFL-reachability analysis by taming its transitive redundancy. Our key insight is that transitive relations are intrinsically ordered, and utilizing the order for edge derivation can avoid most redundancy. To address the challenges in determining the derivation order from the dynamically changed graph during CFL-reachability solving, we introduce a hybrid graph representation by combining spanning trees and adjacency lists, together with a dynamic construction algorithm. Based on this representation, we propose a fast and effective partially ordered algorithm POCR to boost the performance of CFL-reachability analysis by reducing its transitive redundancy during on-the-fly solving. Our experiments on context-sensitive value-flow analysis and field-sensitive alias analysis for C/C++ demonstrate the promising performance of POCR. On average, POCR eliminates 98.50% and 97.26% redundant derivations respectively for the value-flow and alias analysis, achieving speedups of 21.48× and 19.57× over the standard CFL-reachability algorithm. We also compare POCR with two recent open-source tools, Graspan (a CFL-reachability solver) and Soufflé (a Datalog...
Leng, D, Zhu, Z, Liu, G & Li, Y 2022, 'Neuro fuzzy logic control of magnetorheological elastomer isolation system for vibration mitigation of offshore jacket platforms', Ocean Engineering, vol. 253, pp. 111293-111293.
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Leong, KY, Hasbi, S, Ku Ahmad, KZ, Mat Jali, N, Ong, HC & Md Din, MF 2022, 'Thermal properties evaluation of paraffin wax enhanced with carbon nanotubes as latent heat thermal energy storage', Journal of Energy Storage, vol. 52, pp. 105027-105027.
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An efficient phase change material (PCM) should not only exhibit high latent heat, but also high thermal conductivity. Combination of these favourable properties leads to an efficient latent heat thermal energy storage. Paraffin wax is one of the popular options for organic PCMs. However, paraffin wax has a low thermal conductivity, hindering its heat charging and discharging process. This study explores the effect of multi-walled carbon nanotube (MWCNT) weight percentage (0–0.08 wt%) and various surfactants (gum arabic (GA), polyvinylpyrrolidone (PVP), sodium docecylbenzene sulfonate (SDBS), and sodium dodecyl sulfate (SDS)) on the thermal conductivity, melting temperature, melting latent heat and thermal stability of paraffin wax. It is found that the maximum thermal conductivity enhancement (48%) is exhibited by paraffin wax/0.06 wt% MWCNT without surfactant compared to paraffin wax. The thermal conductivity of paraffin wax/0.08 wt% MWCNT added with PVP, SDBS and SDS are higher than samples added with GA. Overall, all samples exhibit single-step decomposition characteristics, as shown in thermogravimetric analysis (TGA).
Lezana, P, Norambuena, M & Aguilera, RP 2022, 'Dual-Stage Control Strategy for a Flying Capacitor Converter Based on Model Predictive and Linear Controllers', IEEE Transactions on Industrial Informatics, vol. 18, no. 4, pp. 2203-2212.
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This article proposes a novel dual-stage control strategy for a flying capacitor converter. During transients, the proposed control scheme applies finite-control-set model predictive control to drive the system close to the desired reference, including all the known nonlinearities of the system in the converter model. When the converter state is in a neighborhood of the reference, the dual-stage controller switches to a pulsewidth modulation based linear controller with an integral action. In this case, the linear controller is reformulated in its feedback form. Thus, the internal linear controller states can be updated based on the actual input applied by the predictive controller. This allows the dual-stage controller to achieve a smooth transition when commuting between controllers and a zero-steady-state error even when load parameter errors are present. Experimental results are provided to verify the effectiveness of the proposed dual-stage controller.
Li, A, Yang, B, Hussain, FK & Huo, H 2022, 'HSR: Hyperbolic Social Recommender', Information Sciences, vol. 585, pp. 275-288.
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With the prevalence of online social media, users’ social connections have been widely studied and utilized to enhance the performance of recommender systems. In this paper, we explore the use of hyperbolic geometry for social recommendation. We present the Hyperbolic Social Recommender (HSR), a novel social recommendation framework that utilizes hyperbolic geometry to boost the performance. With the help of hyperbolic space, HSR can learn high-quality user and item representations to better model user-item interaction and user-user social relations. Through extensive experiments on four real-world datasets, we show that our proposed HSR outperforms its Euclidean counterpart and state-of-the-art social recommenders in click-through rate prediction and top-K recommendation, demonstrating the effectiveness of social recommendation in the hyperbolic space.
Li, C, Fang, J, Wu, C, Sun, G, Steven, G & Li, Q 2022, 'Phase field fracture in elasto-plastic solids: Incorporating phenomenological failure criteria for ductile materials', Computer Methods in Applied Mechanics and Engineering, vol. 391, pp. 114580-114580.
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Phase field approaches have been developed to analyze the failure behavior of ductile materials. In the previous phase field models, a constant critical energy or strain threshold is commonly introduced to the formulation of the driving force, aiming to avoid damage initiation at a low level of elastic and plastic deformations. However, it may not suffice to describe complex ductile fracture behavior of materials subject to various stress states. In this study, a new phase field approach is proposed to consider the effects of stress triaxiality and Lode angle, by incorporating phenomenological ductile fracture criteria. The proposed models are formulated using variational principles and implemented numerically in the finite element framework. Analytical homogeneous solutions for uniaxial tension, simple shear, and equibiaxial tension loads are derived to demonstrate the effectiveness of the proposed models. Three groups of numerical examples, covering a wide range of stress states, are utilized to further examine the proposed models. The results show that the models can reproduce the experimental response of the specimen in terms of force versus displacement curve, crack initiation, and crack propagation under various loading conditions. The proposed models are able to capture the stress-state dependence of fracture behavior of ductile materials.
Li, C, Yang, L, Yu, S, Qin, W & Ma, J 2022, 'SEMMI: Multi-party security decision-making scheme for linear functions in the internet of medical things', Information Sciences, vol. 612, pp. 151-167.
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In the Internet of Medical Things(IoMT), developing models using machine learning algorithms can detect and assist users effectively in identifying health issues. Due to the risk of private user information being leaked during the machine learning application process, the widespread application and development of IoMT applications are hampered. Encrypting data is a good way to protect user privacy. However, given the participants’ limited resources, processing and analyzing ciphertext data presents a significant challenge. As a result, this paper proposes a secure and efficient assisted decision-making scheme (SEMMI) that is appropriate for the IoMT applications. SEMMI performs a thorough analysis of each participant's resource constraints, divides the data circulation process into four stages, and constructs a data circulation and ciphertext calculation protocol. Data transmission security is ensured through the use of stream encryption and homomorphic encryption. Each participant sends the ciphertext to the cloud, and the cloud calculates the ciphertext data, effectively relieving each participant's computational load. The security of the final result is guaranteed by matching the result with pre-decryption. The scheme's security and efficiency are demonstrated experimentally. The results indicate that the accuracy loss of each data set under the ciphertext is no more than 3% at most and that the cloud performs most of the calculations for each participant. Finally, SEMMI is applied to the FedAvg algorithm, demonstrating the scheme's universality.
Li, C, Zhou, J, Tao, M, Du, K, Wang, S, Jahed Armaghani, D & Tonnizam Mohamad, E 2022, 'Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM', Transportation Geotechnics, vol. 36, pp. 100819-100819.
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Li, D, Liu, Z, Xiao, P, Zhou, J & Jahed Armaghani, D 2022, 'Intelligent rockburst prediction model with sample category balance using feedforward neural network and Bayesian optimization', Underground Space, vol. 7, no. 5, pp. 833-846.
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Li, D, Qing, L, Li, M, Cheng, H, Yang, G, Fu, Q & Sun, Y 2022, 'Ultra-fast self-repairing of anti-corrosive coating based on synergistic effect between cobalt octoate and linseed oil', Progress in Organic Coatings, vol. 166, pp. 106776-106776.
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A new microcapsule was prepared by a facile emulsions process for improving the self-repairing speed of anti-corrosive polymer coating. The microcapsule is based on the cobalt octoate/linseed oil and GO nanosheets, which acts as core and shell. The new microcapsule in polyurethane coating provided the ultra-fast self-repairing speed (ca.5.0 min). The result was attributed to synergistic catalytic curing reaction of linseed oil in presence of cobalt octoate and O2. Furthermore, it also improved their anti-corrosion properties due to the good barrier of the GO nanosheets. The work confirms the formation of anti-corrosive polymer coating with ultra-fast self-repairing performance for various applications.
Li, G, Zhou, H, Feng, B, Zhang, Y & Yu, S 2022, 'Efficient Provision of Service Function Chains in Overlay Networks Using Reinforcement Learning', IEEE Transactions on Cloud Computing, vol. 10, no. 1, pp. 383-395.
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IEEE Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies facilitate deploying Service Function Chains (SFCs) at clouds in efficiency and flexibility. However, it is still challenging to efficiently chain Virtualized Network Functions (VNFs) in overlay networks without knowledge of underlying network configurations. Although there are many deterministic approaches for VNF placement and chaining, they have high complexity and depend on state information of substrate networks. Fortunately, Reinforcement Learning (RL) brings opportunities to alleviate this challenge as it can learn to make suitable decisions without prior knowledge. Therefore, in this paper, we propose an RL approach for efficient SFC provision in overlay networks, where the same VNFs provided by multiple vendors are with different performance. Specifically, we first formulate the problem into an Integer Linear Programming (ILP) model for benchmarking. Then, we present the online SFC path selection into a Markov Decision Process (MDP) and propose a corresponding policy-gradient-based solution. Finally, we evaluate our proposed approach with extensive simulations with randomly generated SFC requests and a real-world video streaming dataset, and implement an emulation system for feasibility verification. Related results demonstrate that performance of our approach is close to the ILP-based method and better than deep Q-learning, random, and load-least-greedy methods.
Li, H, He, Z, Li, W, Li, JJ, Lin, J & Xing, D 2022, 'Self-assembled microtissues loaded with osteogenic MSCs for in vivo bone regeneration', Frontiers in Bioengineering and Biotechnology, vol. 10, p. 1069804.
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Bone regeneration strategies based on mesenchymal stem cell (MSC) therapy have received widespread attention. Although MSC incorporation into bone scaffolds can help with the repair process, a large number of studies demonstrate variable effects of MSCs with some noting that the inclusion of MSCs does not provide better outcomes compared to unseeded scaffolds. This may in part be related to low cell survival following implantation and/or limited ability to continue with osteogenic differentiation for pre-differentiated cells. In this study, we incorporated MSCs into gelatin microcryogels to form microtissues, and subjected these microtissues to osteogenic induction. We then mixed as-formed microtissues with those subjected to 6 days of osteogenic induction in different ratios, and investigated their ability to induce in vitro and in vivo osteogenesis during self-assembly. Using a full-thickness rat calvarial defect model, we found that undifferentiated and osteogenically induced microtissues mixed in a ratio of 2:1 produced the best outcomes of bone regeneration. This provides a new, customizable cell-based therapeutic strategy for in vivo repair of bone defects.
Li, H, Huang, X, Zhang, JA, Zhang, H & Cheng, Z 2022, 'Dual pulse shaping transmission with sinc‐function based complementary Nyquist pulses', IET Communications, vol. 16, no. 17, pp. 2091-2104.
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AbstractDue to difficulties in manufacturing, data conversion devices with extremely high sampling rate are becoming the bottleneck in realising high‐speed communication systems with a large bandwidth. Dual pulse shaping (DPS) transmission allows half‐symbol‐rate conversion devices to be used for two parallel data streams to achieve full‐rate transmission, and is proved to be an effective solution. Here, two sets of ideal sinc‐function based complementary Nyquist pulses for DPS transmission are proposed. Theoretically, it is shown that the proposed pulses satisfy the inter‐symbol and cross‐symbol interference‐free conditions, and can achieve full‐Nyquist‐rate transmission with half of the sampling rate. With reference to commercially available D/As, two sets of practical dual spectral shaping pulses are further proposed, and the close relationship between the ideal and practical pulses are disclosed. Performance analysis for linear equalisation is provided in the presence of both timing offset between dual shaping pulses and carrier‐frequency offset. Two approaches are then proposed to improve the system robustness by adjusting the clock phase of the D/As and A/Ds. Simulation results are presented to provide a comparison between the proposed DPS transmission schemes and the state of the art, in terms of the performance metrics of peak‐to‐average power ratio and bit error rate.
Li, H, Li, J & Bi, K 2022, 'A quasi-active negative stiffness damper for structural vibration control under earthquakes', Mechanical Systems and Signal Processing, vol. 173, pp. 109071-109071.
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This paper proposes a novel quasi-active negative stiffness damper (QANSD) for effective and robust seismic protection. By integrating the negative stiffness element and controllable damping element together, the proposed device enables to closely achieve active control performance with much less energy to operate compared to an active control system. Such a control system has been named as “Quasi-Active” control (QAC) in this study. To introduce the concept of QAC, this paper reiterates the fundamentals of active and semi-active vibration control systems from the perspective of control force, and numerically examines a few examples via comprehensive evaluation indices. The inherent shortfall of semi-active methods on control effectiveness is illustrated by an example of semi-active dampers. It is clearly revealed that the incapacity of semi-active control to capture the entire required active control force (RACF) is due to the fact that the amount of control force that can be generated by a semi-active control system is based on the responses of the structure, which prevents the semi-active control to achieve equivalent active control performance. To address this issue, this paper introduces the QAC concept and a specific realization, i.e. QANSD, including its principles, control strategy and realization. Furthermore, a generalized design approach and related formulae for designing the QANSD are developed with a special interest in obtaining its negative stiffness thresholds. Moreover, to demonstrate its control effectiveness and superiorities, comparative numerical studies are conducted based on a three-storey frame model. The comparisons are made among the same structure without control, with active control, semi-active control, passive control, as well as QAC. In the study, four scaled earthquakes are used as ground motion excitations and five evaluation criteria are adopted to assess the control performances. The results show that, with much less r...
Li, J, Guo, J, Zhu, X & Yu, Y 2022, 'Nonlinear characteristics of damaged bridges under moving loads using parameter optimization variational mode decomposition', Journal of Civil Structural Health Monitoring, vol. 12, no. 5, pp. 1009-1026.
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Investigation on the dynamic characteristics of bridges for structural condition assessment is challenging when nonlinear breathing cracks in the bridge and nonstationary vehicle-bridge interaction are considered. Variational mode decomposition (VMD) method has been widely used to analyze the nonlinear time-series, but its performance is highly dependent on the parameter setting, i.e., the number of modes and the penalty factor. A new method based on the parameter optimization VMD is proposed to extract the nonlinear dynamic characteristics from responses of bridge under moving vehicle loads in this paper. The Chicken Swarm Optimization algorithm is used to optimize the VMD parameters to improve the decomposition and characterization. A general breathing crack model is introduced to simulate the bridge damage. The acceleration response of bridge considering the cracks is decomposed by the proposed method. The instantaneous frequency (IF) is then obtained from the time–frequency representation of the bridge-related response component using the ridge detection. Numerical simulations are performed to investigate the effect of the crack location and extent on the IFs for structural damage detection. The effect of the vehicle–bridge interaction is also discussed. The method is further verified using the laboratory experimental results of a concrete bridge model under the vehicle load. The nonstationary and nonlinear dynamic properties of the bridge model with different damage scenarios are successfully identified. The results show that the extracted IF clearly reveals the behaviour of breathing crack that can be a potential indicator of the damage in the bridge.
Li, J, Jia, P, Fang, G, Wang, J, Qian, J, Ren, Q & Xiong, J 2022, 'Batch-producible all-silica fiber-optic Fabry–Perot pressure sensor for high-temperature applications up to 800 °C', Sensors and Actuators A: Physical, vol. 334, pp. 113363-113363.
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Li, J, Papadopoulou, AK, Gandedkar, N, Dalci, K, Darendeliler, MA & Dalci, O 2022, 'The effect of micro-osteoperforations on orthodontic space closure investigated over 12 weeks: a split-mouth, randomized controlled clinical trial', European Journal of Orthodontics, vol. 44, no. 4, pp. 427-435.
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SummaryObjectivesTo evaluate the effects of minimally invasive micro-osteoperforations (MOPs) on orthodontic tooth movement and pain.DesignProspective, split-mouth, randomized controlled trial.SettingSingle-centre, university hospital.MethodsTwenty subjects requiring maxillary first premolar extractions were included. Right and left sides of the maxilla were randomly allocated into experimental and controls. Space closure was initiated following alignment on 0.20″ stainless steel archwires, using 150 g force, applied by coil springs on power arms. Nance-TPA was used for anchorage. On the experimental side, two 5 mm deep MOPs in vertical alignment on distal aspect of the maxillary canine mid-root region were performed prior to space closure.OutcomesThe primary outcome was the amount of tooth movement during space closure, measured every 4 weeks for 12 weeks (T1, T2, and T3). Secondary outcome was the pain levels related to MOP, measured using Visual Analogue Scale (VAS) questionnaires. Significance was set at P < 0.01.RandomizationRandomization was generated using a randomization table, and allocation was concealed in sequentially numbered, opaque, sealed envelopes.BlindingBlinding was not possible during the experiment but assessor was blinded during outcome assessment.ResultsAll subjects completed the study, with tooth movement measurements available for all 20 patients for T0–T2. In three patients, space was closed on one side at T2. The average...
Li, J, Wang, W, Wu, C, Liu, Z & Wu, P 2022, 'Impact response of ultra-high performance fiber-reinforced concrete filled square double-skin steel tubular columns', Steel and Composite Structures, vol. 42, no. 3, pp. 325-351.
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This paper studies the lateral impact behavior of ultra-high performance fiber-reinforced concrete (UHPFRC) filled double-skin steel tubular (UHPFRCFDST) columns. The impact force, midspan deflection, and strain histories were recorded. Based on the test results, the influences of drop height, axial load, concrete type, and steel tube wall thickness on the impact resistance of UHPFRCFDST members were analyzed. LS-DYNA software was used to establish a finite element (FE) model of UHPFRC filled steel tubular members. The failure modes and histories of impact force and midspan deflection of specimens were obtained. The simulation results were compared to the test results, which demonstrated the accuracy of the finite element analysis (FEA) model. Finally, the effects of the steel tube thickness, impact energy, type of concrete and impact indenter shape, and void ratio on the lateral impact performances of the UHPFRCFDST columns were analyzed.
Li, J, Yu, E, Ma, J, Chang, X, Zhang, H & Sun, J 2022, 'Discrete Fusion Adversarial Hashing for cross-modal retrieval', Knowledge-Based Systems, vol. 253, pp. 109503-109503.
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Deep cross-modal hashing enables a flexible and efficient way for large-scale cross-modal retrieval. Existing cross-modal retrieval methods based on deep hashing aim to learn the unified hashing representation for different modalities with the supervision of pair-wise correlation, and then encode the out-of-samples via modality-specific hashing network. However, the semantic gap and distribution shift were not considered enough, and the hashing codes cannot be unified as expected under different modalities. At the same time, hashing is still a discrete problem that has not been solved well in the deep neural network. Therefore, we propose the Discrete Fusion Adversarial Hashing (DFAH) network for cross-modal retrieval to address these issues. In DFAH, the Modality-Specific Feature Extractor is designed to capture image and text features with pair-wise supervision. Especially, the Fusion Learner is proposed to learn the unified hash code, which enhances the correlation of heterogeneous modalities via the embedding strategy. Meanwhile, the Modality Discriminator is designed to adapt to the distribution shift cooperating with the Modality-Specific Feature Extractor in an adversarial way. In addition, we design an efficient discrete optimization strategy to avoid the relaxing quantization errors in the deep neural framework. Finally, the experiment results and analysis on several popular datasets also show that DFAH outperforms the state-of-the-art methods for cross-modal retrieval.
Li, J, Zhu, X & Guo, J 2022, 'Bridge modal identification based on successive variational mode decomposition using a moving test vehicle', Advances in Structural Engineering, vol. 25, no. 11, pp. 2284-2300.
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Bridge modal identification using an instrumented test vehicle as a moving sensor is promising but challenging. A key factor is to extract bridge dynamic components from vehicle responses measured when the bridge is operating. A new method based on an advanced adaptive signal decomposition technique, the successive variational mode decomposition (SVMD), has been developed to estimate the bridge modal parameters from the dynamic responses of a passing test vehicle. When bridge-related dynamic components are extracted from the decomposition, the natural excitation technique and/or random-decrement technique based fitting methods are used to estimate the modal frequencies and damping ratios of the bridge. Effects of measurement noise, moving speed and vehicle properties on the decomposition are investigated numerically. The superiority of SVMD in the decomposition is verified by comparing to another adaptive decomposition technique, the singular spectrum decomposition. The results of the proposed method confirm that the bridge modal frequencies can be identified from bridge related components with high accuracy, while damping ratio is more sensitive to the random operational load. Finally, the feasibility of the proposed method for bridge monitoring using a moving test vehicle is further verified by an in-situ experimental test on a cable-stayed bridge. The components related to the bridge dynamic responses are successfully extracted from vehicle responses.
Li, J, Zhu, X & Guo, J 2022, 'Enhanced drive‐by bridge modal identification via dual Kalman filter and singular spectrum analysis', Structural Control and Health Monitoring, vol. 29, no. 5.
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The drive-by bridge health monitoring is to assess the bridge condition using the acceleration responses measured on the body or axle of instrumented vehicles. The vehicle responses are greatly affected by the road surface roughness that makes the bridge dynamic information blurred. Instead of direct using vehicle responses for the bridge monitoring, the dynamic response of contact point (CP) between the vehicle and bridge is further explored to enhance the drive-by bridge modal identification. A novel three-step framework is proposed to extract the components related to the bridge vibration from vehicle responses. The first step is to identify the input forces of two successive vehicles by solving the combined state-input estimation problem using dual Kalman filter. The CP responses of two contact points are calculated using the input forces and vehicle parameters. In the second step, the subtraction technique is applied to the identified CP responses of the two instrumented vehicles and the effect of the road surface roughness can be significantly reduced. Finally, an automatic singular spectrum analysis technique (auto SSA) is incorporated to decompose the response residual. Then the mono-component modes related to the bridge response are extracted from the response residual for drive-by bridge modal identification and/or the nonstationary characteristic identification of vehicle–bridge interaction (VBI) system. Results of numerical and experimental study demonstrate that the method can significantly suppress the vehicle response component and reduce the effect of road surface roughness to enhance the bridge modal identification.
Li, JJ, Liu, H, Zhu, Y, Yan, L, Liu, R, Wang, G, Wang, B & Zhao, B 2022, 'Animal Models for Treating Spinal Cord Injury Using Biomaterials-Based Tissue Engineering Strategies', Tissue Engineering Part B: Reviews, vol. 28, no. 1, pp. 79-100.
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The aim of the study is to provide an up-to-date review of studies that used preclinical animal models for the evaluation of tissue engineering treatments for spinal cord injury (SCI), which involved the use of biomaterials with or without the addition of cells or biomolecules. Electronic search of the PubMed, Web of Science, and Embase databases was performed for relevant studies published between January 2009 and December 2019. In total, 1579 articles were retrieved, of which 58 studies were included for analysis. Among the included studies, rats were the most common species used for animal models of SCI, while complete transection was the most commonly used injury pattern. Immediate intervention after injury was conducted in the majority of studies, and 8 weeks was the most common final time point of outcome assessment. A wide range of natural and synthetic biomaterials with different morphologies were used as a part of tissue engineering treatments for SCI, including scaffolds, hydrogels, and particles. Experimental parameters in studies using SCI animal models to evaluate tissue engineering treatments should be carefully considered to match the purpose of the study. Biomaterials that have functional modifications or are applied in combination with cells and biomolecules can be effective in creating a permissive environment for SCI repair in preclinical animal models. Impact statement This review provides an up-to-date summary of the preclinical landscape where tissue engineering treatments involving biomaterials were tested in animal models of spinal cord injury (SCI). Using studies published within the last 10 years, novel perspectives were presented on the animal species used, injury pattern, timing of intervention and outcome measurement, and biomaterials selection, as well as a summary of the individual findings of each study. This review provides unique insight into biomaterials-based tissue engineering strategies that have progressed to testi...
Li, K, Duan, H, Liu, L, Qiu, R, van den Akker, B, Ni, B-J, Chen, T, Yin, H, Yuan, Z & Ye, L 2022, 'An Integrated First Principal and Deep Learning Approach for Modeling Nitrous Oxide Emissions from Wastewater Treatment Plants', Environmental Science & Technology, vol. 56, no. 4, pp. 2816-2826.
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Mathematical modeling plays a critical role toward the mitigation of nitrous oxide (N2O) emissions from wastewater treatment plants (WWTPs). In this work, we proposed a novel hybrid modeling approach by integrating the first principal model with deep learning techniques to predict N2O emissions. The hybrid model was successfully implemented and validated with the N2O emission data from a full-scale WWTP. This hybrid model is demonstrated to have higher accuracy for N2O emission modeling in the WWTP than the mechanistic model or pure deep learning model. Equally important, the hybrid model is more applicable than the pure deep learning model due to the lower requirement of data and the pure mechanistic model due to the less calibration requirement. This superior performance was due to the hybrid nature of the proposed model. It integrated the essential wastewater treatment knowledge as the first principal component and the less understood N2O production processes by the data-driven deep learning approach. The developed hybrid model was also successfully implemented under different circumstances for the prediction of N2O flux, which showed the generalizability of the model. The hybrid model also showed great potential to be applied for the N2O mitigation work. Nevertheless, the capability of the hybrid model in evaluating N2O mitigation strategies still requires validation with experiments. Going beyond N2O modeling in WWTP, the novel hybridization modeling concept can potentially be applied to other environmental systems.
Li, K, Lu, J, Zuo, H & Zhang, G 2022, 'Dynamic Classifier Alignment for Unsupervised Multi-Source Domain Adaptation', IEEE Transactions on Knowledge and Data Engineering, vol. PP, no. 99, pp. 1-1.
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Unsupervised domain adaptation leverages the previously gained knowledge from a labeled source domain to tackle the task from a different but similar unlabeled target domain. Most existing methods focus on transferring knowledge from a single source domain, but the information from a single domain may be inadequate to complete the target task. Some previous studies have turned to multi-view representations to enrich the transferable information. However, they simply concatenate multi-view features, which may result in information redundancy. In this paper, we propose a dynamic classifier alignment (DCA) method for multi-source domain adaptation, which aligns classifiers driven from multi-view features via a sample-wise automatic way. As proposed, both the importance of each view and the contribution of each source domain are investigated. To determine the important degrees of multiple views, an importance learning function is built by generating an auxiliary classifier. To learn the source combination parameters, a domain discriminator is developed to estimate the probability of a sample belonging to multiple source domains. Meanwhile, a self-training strategy is proposed to enhance the cross-domain ability of source classifiers with the assistance of pseudo target labels. Experiments on real-world visual datasets show the superiority of the proposed DCA.
Li, K, Lu, J, Zuo, H & Zhang, G 2022, 'Multi-Source Contribution Learning for Domain Adaptation', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 10, pp. 5293-5307.
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Transfer learning becomes an attractive technology to tackle a task from a target domain by leveraging previously acquired knowledge from a similar domain (source domain). Many existing transfer learning methods focus on learning one discriminator with single-source domain. Sometimes, knowledge from single-source domain might not be enough for predicting the target task. Thus, multiple source domains carrying richer transferable information are considered to complete the target task. Although there are some previous studies dealing with multi-source domain adaptation, these methods commonly combine source predictions by averaging source performances. Different source domains contain different transferable information; they may contribute differently to a target domain compared with each other. Hence, the source contribution should be taken into account when predicting a target task. In this article, we propose a novel multi-source contribution learning method for domain adaptation (MSCLDA). As proposed, the similarities and diversities of domains are learned simultaneously by extracting multi-view features. One view represents common features (similarities) among all domains. Other views represent different characteristics (diversities) in a target domain; each characteristic is expressed by features extracted in a source domain. Then multi-level distribution matching is employed to improve the transferability of latent features, aiming to reduce misclassification of boundary samples by maximizing discrepancy between different classes and minimizing discrepancy between the same classes. Concurrently, when completing a target task by combining source predictions, instead of averaging source predictions or weighting sources using normalized similarities, the original weights learned by normalizing similarities between source and target domains are adjusted using pseudo target labels to increase the disparities of weight values, which is desired to improve...
Li, K, Ni, W & Dressler, F 2022, 'Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks', IEEE Transactions on Mobile Computing, vol. 21, no. 8, pp. 2732-2744.
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Li, K, Ni, W & Dressler, F 2022, 'LSTM-Characterized Deep Reinforcement Learning for Continuous Flight Control and Resource Allocation in UAV-Assisted Sensor Network', IEEE Internet of Things Journal, vol. 9, no. 6, pp. 4179-4189.
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Unmanned aerial vehicles (UAVs) can be employed to collect sensory data in remote wireless sensor networks (WSNs). Due to UAV's maneuvering, scheduling a sensor device to transmit data can overflow data buffers of the unscheduled ground devices. Moreover, lossy airborne channels can result in packet reception errors at the scheduled sensor. This article proposes a new deep reinforcement learning-based flight resource allocation framework (DeFRA) to minimize the overall data packet loss in a continuous action space. DeFRA is based on deep deterministic policy gradient (DDPG), optimally controls instantaneous headings and speeds of the UAV, and selects the ground device for data collection. Furthermore, a state characterization layer, leveraging long short-term memory (LSTM), is developed to predict network dynamics, resulting from time-varying airborne channels and energy arrivals at the ground devices. To validate the effectiveness of DeFRA, experimental data collected from a real-world UAV testbed and energy harvesting WSN are utilized to train the actions of the UAV. Numerical results demonstrate that the proposed DeFRA achieves a fast convergence while reducing the packet loss by over 15%, as compared to the existing deep reinforcement learning solutions.
Li, K, Ni, W, Emami, Y & Dressler, F 2022, 'Data-Driven Flight Control of Internet-of-Drones for Sensor Data Aggregation Using Multi-Agent Deep Reinforcement Learning', IEEE Wireless Communications, vol. 29, no. 4, pp. 18-23.
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Li, K, Ni, W, Yuan, X, Noor, A & Jamalipour, A 2022, 'Deep-Graph-Based Reinforcement Learning for Joint Cruise Control and Task Offloading for Aerial Edge Internet of Things (EdgeIoT)', IEEE Internet of Things Journal, vol. 9, no. 21, pp. 21676-21686.
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Li, K, Zhang, Y, Zhang, X, Ni, B-J, Wei, Y, Xu, B & Hao, D 2022, 'A readily synthesized bismuth oxyiodide/attapulgite for the photodegradation of tetracycline under visible light irradiation', CrystEngComm, vol. 24, no. 16, pp. 3064-3073.
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Bismuth oxyiodide and attapulgite have proven to be potential materials for the removal of emerging contaminants in wastewater.
Li, M, Liu, Y, Chen, S-L, Hu, J & Guo, YJ 2022, 'Synthesizing Shaped-Beam Cylindrical Conformal Array Considering Mutual Coupling Using Refined Rotation/Phase Optimization', IEEE Transactions on Antennas and Propagation, vol. 70, no. 11, pp. 10543-10553.
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Li, N, Asteris, PG, Tran, TT, Pradhan, B & Nguyen, H 2022, 'Modelling the deflection of reinforced concrete beams using the improved artificial neural network by imperialist competitive optimization', Steel and Composite Structures, vol. 42, no. 6, pp. 733-745.
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This study proposed a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling the deflection of reinforced concrete beams, abbreviated as ICA-ANN model. Accordingly, the ICA was used to adjust and optimize the parameters of an ANN model (i.e., weights and biases) aiming to improve the accuracy of the ANN model in modelling the deflection reinforced concrete beams. A total of 120 experimental datasets of reinforced concrete beams were employed for this aim. Therein, applied load, tensile reinforcement strength and the reinforcement percentage were used to simulate the deflection of reinforced concrete beams. Besides, five other AI models, such as ANN, SVM (support vector machine), GLMNET (lasso and elastic-net regularized generalized linear models), CART (classification and regression tree) and KNN (k-nearest neighbours), were also used for the comprehensive assessment of the proposed model (i.e., ICA-ANN). The comparison of the derived results with the experimental findings demonstrates that among the developed models the ICA-ANN model is that can approximate the reinforced concrete beams deflection in a more reliable and robust manner.
Li, N, Nguyen, H, Rostami, J, Zhang, W, Bui, X-N & Pradhan, B 2022, 'Predicting rock displacement in underground mines using improved machine learning-based models', Measurement, vol. 188, pp. 110552-110552.
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Displacement of rock mass in tunnels and underground mines is considered one of the most hazardous phenomena that can cause the collapse of the structures. In this study, the rock properties, such as the depth of the tunnels (H), the angle of rock layers (α), anti-bending moment (Wc), the width of the tunnels (b), the tensile strength of rock layers (Rn), and monitoring distance (Lb), and observation time (t), were investigated to predict rock displacement in tunnels and underground mines. Two novel soft computing models, namely Harris Hawks optimization algorithm (HHOA)-based support vector machine (SVM) model (i.e., HHOA-SVM) and Grasshopper optimization algorithm (GOA)-based SVM model (i.e., GOA-SVM), were developed for this aim based on the field measurements. A total of 12 measurement stations and 63 observations of vertical rock mass displacement, rock properties, and observation time in some underground coal mines in the Donbas region (Ukraine) were compiled as the dataset for developing soft computing models. In addition, a constraint was also added to the proposed HHOA-SVM and GOA-SVM models to prevent the model from offering negative results in predicting rock displacement. The conventional models, such as SVM (without optimization) and artificial neural network (ANN), were also investigated to compare favorably with the two proposed HHOA-SVM and GOA-SVM models. Furthermore, linear and nonlinear equations were also established to predict rock displacement and compared to the soft computing models. The results showed that the novel HHOA-SVM and GOA-SVM models provided better performances than conventional SVM and ANN models. Besides, the sensitivity of the input variables was also analyzed to discover the certain characteristics of the rock displacement phenomenon through the properties of rock and observation time. The findings show that H, Lb, t, and α are the most influential parameters for predicting rock displacement in tunnels and undergr...
Li, Q, Wang, Z, Liu, S, Li, G & Xu, G 2022, 'Deep treatment-adaptive network for causal inference', The VLDB Journal, vol. 31, no. 5, pp. 1127-1142.
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AbstractCausal inference is capable of estimating the treatment effect (i.e., the causal effect oftreatmenton theoutcome) to benefit the decision making in various domains. One fundamental challenge in this research is that the treatment assignment bias in observational data. To increase the validity of observational studies on causal inference, representation-based methods as the state-of-the-art have demonstrated the superior performance of treatment effect estimation. Most representation-based methods assume all observed covariates are pre-treatment (i.e., not affected by the treatment) and learn a balanced representation from these observed covariates for estimating treatment effect. Unfortunately, this assumption is often too strict a requirement in practice, as some covariates are changed by doing an intervention on treatment (i.e., post-treatment). By contrast, the balanced representation learned from unchanged covariates thus biases the treatment effect estimation. In light of this, we propose a deep treatment-adaptive architecture (DTANet) that can address the post-treatment covariates and provide a unbiased treatment effect estimation. Generally speaking, the contributions of this work are threefold. First, our theoretical results guarantee DTANet can identify treatment effect from observations. Second, we introduce a novel regularization of orthogonality projection to ensure that the learned confounding representation is invariant and not being contaminated by the treatment, meanwhile mediate variable representation is informative and discriminative for predicting the outcome. Finally, we build on the optimal transport and learn a treatment-invariant representation for the unobserved confounders to alleviate the confounding bias.
Li, Q, Yuan, X, Hu, X, Meers, E, Ong, HC, Chen, W-H, Duan, P, Zhang, S, Lee, KB & Ok, YS 2022, 'Co-liquefaction of mixed biomass feedstocks for bio-oil production: A critical review', Renewable and Sustainable Energy Reviews, vol. 154, pp. 111814-111814.
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Li, R-H, Song, Q, Xiao, X, Qin, L, Wang, G, Yu, JX & Mao, R 2022, 'I/O-Efficient Algorithms for Degeneracy Computation on Massive Networks.', IEEE Trans. Knowl. Data Eng., vol. 34, no. 99, pp. 3335-3348.
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Li, S, Show, PL, Ngo, HH & Ho, S-H 2022, 'Algae-mediated antibiotic wastewater treatment: A critical review', Environmental Science and Ecotechnology, vol. 9, pp. 100145-100145.
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Li, T, Sun, X, Lei, G, Guo, Y, Yang, Z & Zhu, J 2022, 'Finite-Control-Set Model Predictive Control of Permanent Magnet Synchronous Motor Drive Systems—An Overview', IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 12, pp. 2087-2105.
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Permanent magnet synchronous motors (PMSMs) have been widely employed in the industry. Finite-control-set model predictive control (FCS-MPC), as an advanced control scheme, has been developed and applied to improve the performance and efficiency of the holistic PMSM drive systems. Based on the three elements of model predictive control, this paper provides an overview of the superiority of the FCS-MPC control scheme and its shortcomings in current applications. The problems of parameter mismatch, computational burden, and unfixed switching frequency are summarized. Moreover, other performance improvement schemes, such as the multi-vector application strategy, delay compensation scheme, and weight factor adjustment, are reviewed. Finally, future trends in this field is discussed, and several promising research topics are highlighted.
Li, W, Cao, L, Yue, P, Wen, S, Liao, J, Xia, J & Feng, X 2022, 'VAVM: A Flexible Technique for Variable-Angle Around View Monitor System Towards Articulated Engineering Vehicle', IEEE Transactions on Vehicular Technology, vol. 71, no. 4, pp. 3556-3568.
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The around view monitor system (AVM) has been widely used in passenger vehicles. However, for articulated engineering vehicles (AEV), AVM is difficult to directly apply due to the long body and the articulation angle. In this paper, a flexible technique for variable-angle around view monitor system (VAVM) towards the AEV is proposed, which includes static calibration and dynamic execution. In the static calibration stage, the camera poses in the vehicle coordinate system with the joint of AEV as the coordinate origin can be calibrated using irregularly placed multiple checkerboards without knowing their relative position. This flexible calibration approach makes the variable-angle mapping from the fisheye image to the bird's eye view image can be directly obtained by updating the camera poses on the tractor according to the articulation angle when the AEV turns. In the dynamic execution stage, an optimization algorithm based on minimizing the photometric error is designed to reduce the cumulative error of the angle sensor. Finally, all the bird's eye view images are mosaiced and fused to a variable-angle around view image (VAVI). The experimental results show that the VAVM can output globally balanced and seamless VAVIs under different angles and visual environment conditions. Furthermore, the practicability of VAVM is also demonstrated by a real AEV.
Li, W, Dong, W, Guo, Y, Wang, K & Shah, SP 2022, 'Advances in multifunctional cementitious composites with conductive carbon nanomaterials for smart infrastructure', Cement and Concrete Composites, vol. 128, pp. 104454-104454.
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Conductive carbon nanomaterials have been extensively developed for smart cementitious composites to gain multifunctionalities (e.g. self-sensing, self-healing, self-heating, and electromagnetic interference shielding). This paper critically reviewed dispersion and percolation of 0 dimension (0D), 1 dimension (1D) and 2 dimensions (2D) carbon materials used in cementitious composites and their effects on the electrical and piezoresistive performances. The different dispersion methods summarized are from mechanical dispersion, ultrasonic and high shearing, chemical modification, mineral additives, to carbon materials at multiple dimensions and hybrid dispersion methods. The electrical resistivity and piezoresistivity of cementitious composites with single carbon material or hybrid carbon materials are comprehensively analysed and compared in terms of efficiency and self-sensing mechanism. Furthermore, the existing theoretical modelling studies have been reviewed, indicating that many factors related to the electrical and piezoresistive behaviours, such as water content and nanocomposite agglomeration, have not been considered yet. Although some previous studies demonstrated the potential of applying conductive cementitious composites for self-sensing or heating pavements, further explorations still should be conducted on sustainable and economical manufacturing. Subsequently, the challenges and perspectives of the self-sensing stability, data acquisition system and sensor configuration are proposed with potential solutions for future smart infrastructure.
Li, W, Ji, J & Huang, L 2022, 'Global dynamics analysis of a water hyacinth fish ecological system under impulsive control', Journal of the Franklin Institute, vol. 359, no. 18, pp. 10628-10652.
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Control of a water hyacinth-fish ecological system is required for a healthy and sustainable environment. This paper aims to investigate the global dynamics of a water hyacinth fish ecological system under ratio-dependent state impulsive control. First, we study the positivity and boundedness of the solution of the controlled system. By studying the local stability of the equilibrium, we find that the system has two situations. One is that there are two equilibria, namely a saddle point and a boundary equilibrium. In the second case, there are four equilibria, namely, two saddle points, a boundary equilibrium, and a focus point. For the first case, when we select an appropriate ratio-dependent control threshold, the trajectory will globally converge to the boundary equilibrium. For the second case, when the control line is located below the focus point, by using Poincare mapping method, flip bifurcation theory, and vector field analysis techniques, we find that the solution of the controlled system either globally converges to the boundary equilibrium, order-1 periodic solution, or order-2 periodic solution under certain conditions. When the control line is located above the focus point, the solution of the controlled system either globally converges to the focus point, order-1 or order-2 periodic solution. Finally, we use examples to verify the correctness and validity of the theoretical results.
Li, W, Konsta-Gdoutos, M, Shi, X, Sobolev, K & Shah, SP 2022, 'Editorial: Intelligent Concrete, New Functionalities and Nanotechnology', Frontiers in Materials, vol. 9.
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Li, W, Li, S, Liu, C, Lu, L, Shi, Z & Wen, S 2022, 'Span identification and technique classification of propaganda in news articles', Complex & Intelligent Systems, vol. 8, no. 5, pp. 3603-3612.
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AbstractPropaganda is a rhetorical technique designed to serve a specific topic, which is often used purposefully in news article to achieve our intended purpose because of its specific psychological effect. Therefore, it is significant to be clear where and what propaganda techniques are used in the news for people to understand its theme efficiently during our daily lives. Recently, some relevant researches are proposed for propaganda detection but unsatisfactorily. As a result, detection of propaganda techniques in news articles is badly in need of research. In this paper, we are going to introduce our systems for detection of propaganda techniques in news articles, which is split into two tasks, Span Identification and Technique Classification. For these two tasks, we design a system based on the popular pretrained BERT model, respectively. Furthermore, we adopt the over-sampling and EDA strategies, propose a sentence-level feature concatenating method in our systems. Experiments on the dataset of about 550 news articles offered by SEMEVAL show that our systems perform state-of-the-art.
Li, W, Qiao, M, Qin, L, Zhang, Y, Chang, L & Lin, X 2022, 'Distance labeling: on parallelism, compression, and ordering.', VLDB J., vol. 31, no. 1, pp. 129-155.
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Distance labeling approaches are widely adopted to speed up the online performance of shortest-distance queries. The construction of the distance labeling, however, can be exhaustive, especially on big graphs. For a major category of large graphs, small-world networks, the state-of-the-art approach is pruned landmark labeling (PLL). PLL prunes distance labels based on a node order and directly constructs the pruned labels by performing breadth-first searches in the node order. The pruning technique, as well as the index construction, has a strong sequential nature which hinders PLL from being parallelized. It becomes an urgent issue on massive small-world networks whose index can hardly be constructed by a single thread within a reasonable time. This paper first scales distance labeling on small-world networks by proposing a parallel shortest-distance labeling (PSL) scheme. PSL insightfully converts the PLL’s node-order dependency to a shortest-distance dependence, which leads to a propagation-based parallel labeling in D rounds where D denotes the diameter of the graph. To further scale up PSL, it is critical to reduce the index size. This paper proposes effective index compression techniques based on graph properties as well as label properties; it also explores best practices in using betweenness-based node order to reduce the index size. The efficient betweenness estimation of the graph nodes proposed may be of independent interest to graph practitioners. Extensive experimental results verify our efficiency on billion-scale graphs, near-linear speedup in a multi-core environment, and up to 94 % reduction in the index size.
Li, W, Qu, F, Dong, W, Mishra, G & Shah, SP 2022, 'A comprehensive review on self-sensing graphene/cementitious composites: A pathway toward next-generation smart concrete', Construction and Building Materials, vol. 331, pp. 127284-127284.
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Two-dimension graphene-based nanomaterials (GBNs), such as multi-layers graphene (GNPs) and graphene oxide (GOs) have been extensively applied to enhance the mechanical properties, durability, and self-sensing performance of construction materials. Although there are some reviews on the mechanical properties and durability of graphene-based cementitious composites (GBCCs), very few papers have comprehensively covered the nano-, micro- and meso-scale properties, components, structures, and self-sensing properties, and the applications of the GBCCs. In this review, the characteristics of various GBNs with different dimensions were firstly illustrated and compared, and the enhancement methods for dispersion of 2D GBNs before mixed with cementitious materials were also comprehensively compared and discussed. When GBNs were mixed with cement, the nano- and micro-scale characteristics of GBCCs with respect to the hydration, phase transformations, microstructures, and pore characteristics were also systematically discussed. Macroscale performances of GBCCs, such as rheology, flowability, mechanical strength were analyzed, and the durability performances (e.g. chemical and fire attack, shrinkage and transport properties) of GBCCs were evaluated correspondingly. On the other hand, the self-sensing properties (e.g. electrical resistivity, piezoresistivity, and electromagnetic properties) of GBCCs were assessed for potential practical applications for structural health monitoring (SHM). Furthermore, some case studies and applications of GBCCs as advanced cement-based sensors for SHM were also evaluated. Finally, the application challenges and perspectives of adopting 2D GBNs for smart and sustainable concrete structures were proposed and discussed correspondingly. The conclusions of this review will promote future researchers and civil engineers in the concrete-related industry with the aim to developing sustainable and functional graphene-based concrete for the n...
Li, W, Wen, S, Shi, K, Yang, Y & Huang, T 2022, 'Neural Architecture Search With a Lightweight Transformer for Text-to-Image Synthesis', IEEE Transactions on Network Science and Engineering, vol. 9, no. 3, pp. 1567-1576.
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Despite the cross-modal text-to-imagesynthesis task has achieved great success, most of the latest works in this field are based on the network architectures proposed by predecessors, such as StackGAN, AttnGAN, etc. Since the quality for text-to-image synthesis is more and more demanding, these old and tandem architectures with simple convolution operations are no longer suitable. Therefore, a novel text-to-image synthesis network combining with the latest technologies is in urgent need of exploration. To tackle with this challenge, we creatively propose a unique architecture for text-to-image synthesis, dubbed T2IGAN, which is automatically searched by neural architecture search (NAS). In addition, considering the amazing capabilities of the popular transformer in natural language processing and computer vision, a lightweight transformer is applied in our search space to efficiently integrate the text features and image features. Ultimately, the effectiveness of our searched T2IGAN is remarkable by experimentally evaluating it on the typical text-to-image synthesis datasets. Specifically, we achieve an excellent result of IS 5.12 and FID 10.48 on CUB-200 Birds, IS 4.89 and FID 13.55 on Oxford-102 Flowers, IS 31.93 and FID 26.45 on COCO. By contrast with the state-of-the-art works, ours gets better performance on CUB-200 Birds and Oxford-102 Flowers.
Li, X, Johnson, I, Mueller, K, Wilkie, S, Hanzic, L, Bond, PL, O'Moore, L, Yuan, Z & Jiang, G 2022, 'Corrosion mitigation by nitrite spray on corroded concrete in a real sewer system', Science of The Total Environment, vol. 806, no. Pt 3, pp. 151328-151328.
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Microbially influenced concrete corrosion (MICC) in sewers is caused by the activity of sulfide-oxidizing microorganisms (SOMs) on concrete surfaces, which greatly deteriorates the integrity of sewers. Surface treatment of corroded concrete by spraying chemicals is a low-cost and non-intrusive strategy. This study systematically evaluated the spray of nitrite solution in corrosion mitigation and re-establishment in a real sewer manhole. Two types of concrete were exposed at three heights within the sewer manhole for 21 months. Nitrite spray was applied at the 6th month for half of the coupons which had developed active corrosion. The corrosion development was monitored by measuring the surface pH, corrosion product composition, sulfide uptake rate, concrete corrosion loss, and the microbial community on the corrosion layer. Free nitrous acid (FNA, i.e. HNO2), formed by spraying a nitrite solution on acidic corrosion surfaces, was shown to inhibit the activity of SOMs. The nitrite spray reduced the corrosion loss of concrete at all heights by 40-90% for six months. The sulfide uptake rate of sprayed coupons was also reduced by about 35%, leading to 1-2 units higher surface pH, comparing to the control coupons. The microbial community analysis revealed a reduced abundance of SOMs on nitrite sprayed coupons. The long-term monitoring also showed that the corrosion mitigation effect became negligible in 15 months after the spray. The results consistently demonstrated the effectiveness of nitrite spray on the MICC mitigation and identified the re-application frequencies for full scale applications.
Li, X, Kulandaivelu, J, Guo, Y, Zhang, S, Shi, J, O’Brien, J, Arora, S, Kumar, M, Sherchan, SP, Honda, R, Jackson, G, Luby, SP & Jiang, G 2022, 'SARS-CoV-2 shedding sources in wastewater and implications for wastewater-based epidemiology', Journal of Hazardous Materials, vol. 432, pp. 128667-128667.
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Wastewater-based epidemiology (WBE) approach for COVID-19 surveillance is largely based on the assumption of SARS-CoV-2 RNA shedding into sewers by infected individuals. Recent studies found that SARS-CoV-2 RNA concentration in wastewater (CRNA) could not be accounted by the fecal shedding alone. This study aimed to determine potential major shedding sources based on literature data of CRNA, along with the COVID-19 prevalence in the catchment area through a systematic literature review. Theoretical CRNA under a certain prevalence was estimated using Monte Carlo simulations, with eight scenarios accommodating feces alone, and both feces and sputum as shedding sources. With feces alone, none of the WBE data was in the confidence interval of theoretical CRNA estimated with the mean feces shedding magnitude and probability, and 63% of CRNA in WBE reports were higher than the maximum theoretical concentration. With both sputum and feces, 91% of the WBE data were below the simulated maximum CRNA in wastewater. The inclusion of sputum as a major shedding source led to more comparable theoretical CRNA to the literature WBE data. Sputum discharging behavior of patients also resulted in great fluctuations of CRNA under a certain prevalence. Thus, sputum is a potential critical shedding source for COVID-19 WBE surveillance.
Li, X, Leung, FHF, Su, SW & Ling, SH 2022, 'Sleep Apnea Detection Using Multi-Error-Reduction Classification System with Multiple Bio-Signals.', Sensors, vol. 22, no. 15, pp. 5560-5560.
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Introduction: Obstructive sleep apnea (OSA) can cause serious health problems such as hypertension or cardiovascular disease. The manual detection of apnea is a time-consuming task, and automatic diagnosis is much more desirable. The contribution of this work is to detect OSA using a multi-error-reduction (MER) classification system with multi-domain features from bio-signals. Methods: Time-domain, frequency-domain, and non-linear analysis features are extracted from oxygen saturation (SaO2), ECG, airflow, thoracic, and abdominal signals. To analyse the significance of each feature, we design a two-stage feature selection. Stage 1 is the statistical analysis stage, and Stage 2 is the final feature subset selection stage using machine learning methods. In Stage 1, two statistical analyses (the one-way analysis of variance (ANOVA) and the rank-sum test) provide a list of the significance level of each kind of feature. Then, in Stage 2, the support vector machine (SVM) algorithm is used to select a final feature subset based on the significance list. Next, an MER classification system is constructed, which applies a stacking with a structure that consists of base learners and an artificial neural network (ANN) meta-learner. Results: The Sleep Heart Health Study (SHHS) database is used to provide bio-signals. A total of 66 features are extracted. In the experiment that involves a duration parameter, 19 features are selected as the final feature subset because they provide a better and more stable performance. The SVM model shows good performance (accuracy = 81.68%, sensitivity = 97.05%, and specificity = 66.54%). It is also found that classifiers have poor performance when they predict normal events in less than 60 s. In the next experiment stage, the time-window segmentation method with a length of 60 s is used. After the above two-stage feature selection procedure, 48 features are selected as the final feature subset that give good performance (ac...
Li, X, Lu, L, Ni, W, Jamalipour, A, Zhang, D & Du, H 2022, 'Federated Multi-Agent Deep Reinforcement Learning for Resource Allocation of Vehicle-to-Vehicle Communications', IEEE Transactions on Vehicular Technology, vol. 71, no. 8, pp. 8810-8824.
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Li, X, Wang, X, Pan, X, Zhu, P, Zhang, Q, Huang, X, Deng, X, Wang, Z, Ding, Y, Liu, X & Zhou, JL 2022, 'Potential Hormetic Effects of Cimetidine on Aerobic Composting of Human Feces from Rural China', Sustainability, vol. 14, no. 21, pp. 14454-14454.
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Aerobic composting is widely used worldwide as a natural process for handling human waste. Such waste often contains pharmaceutical residues from human consumption, yet their impact on composting has not been studied. The aim of this study is to investigate the impact of the antihistamine cimetidine (10 mg/kg, 100 mg/kg) on the aerobic composting of human feces. The key results show that 10 mg/kg of cimetidine accelerates temperature increase and moisture removal of the composting substrate. The organic matter in all the groups gradually decreased, and the pH values increased first and then declined with the composting time, with no significant differences between the groups. The NH4+-N concentrations and NH3 emission reached the maximum at 1.5 days and then declined rapidly, while the NO2−-N concentrations increased and then decreased, and the NO3−-N contents tended to increase all the time during the composting. The 100 mg/kg cimetidine caused a higher maximal NH4+-N concentration of compost, and a lower maximal NH3 emission at 1.5 days, while 10 mg/kg cimetidine led to more NO2−-N and NO3−-N contents. In addition, 10 mg/kg cimetidine enhanced the aromatization and humification of dissolved organic matter and promoted the degradation of aliphatic substances. Furthermore, 100 mg/kg cimetidine generated a larger influence on the microorganisms than 10 mg/kg cimetidine, especially for the microorganisms related to nitrogen transformation. The findings imply that cimetidine has a dose-dependent impact on the decomposition of organic matter and the conversion of nitrogen in human feces during composting. It deserves further investigation of the possible hormesis effect.
Li, X, Ye, P, Li, J, Liu, Z, Cao, L & Wang, F-Y 2022, 'From Features Engineering to Scenarios Engineering for Trustworthy AI: I&I, C&C, and V&V', IEEE Intelligent Systems, vol. 37, no. 4, pp. 18-26.
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Artificial intelligence (AI)'s rapid development has produced a variety of state-of-the-art models and methods that rely on network architectures and features engineering. However, some AI approaches achieve high accurate results only at the expense of interpretability and reliability. These problems may easily lead to bad experiences, lower trust levels, and systematic or even catastrophic risks. This article introduces the theoretical framework of scenarios engineering for building trustworthy AI techniques. We propose six key dimensions, including intelligence and index, calibration and certification, and verification and validation to achieve more robust and trusting AI, and address issues for future research directions and applications along this direction.
Li, X, Zhang, JA, Wu, K, Cui, Y & Jing, X 2022, 'CSI-Ratio-Based Doppler Frequency Estimation in Integrated Sensing and Communications', IEEE Sensors Journal, vol. 22, no. 21, pp. 20886-20895.
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Estimating the Doppler frequency is an important part of sensing moving targets in integrated sensing and communications (ISAC) systems, such as human tracking and activity recognition. However, it can be highly challenging when there is clock asynchronism between the transmitter (Tx) and the receiver (Rx), in bistatic setups that are common nowadays. In this article, we propose three algorithms for Doppler frequency estimation based on the ratio of channel state information (CSI). These algorithms explore different properties of the CSI ratio, including the circle-preserving property of the Mobius transform, the periodicity of the CSI ratio, and the difference (or correlation) between segments of CSI-ratio signals. Experimental results demonstrate that the proposed algorithms can estimate Doppler frequency accurately, outperforming the commonly used approach based on cross-antenna cross correlation (CACC).
Li, XL, Tse, CK & Lu, DD-C 2022, 'Synthesis of Reconfigurable and Scalable Single-Inductor Multiport Converters With No Cross Regulation', IEEE Transactions on Power Electronics, vol. 37, no. 9, pp. 10889-10902.
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Li, Y & Whitacre, BE 2022, 'Economic Growth and Adult Obesity Rates in Rural America', REVIEW OF REGIONAL STUDIES, vol. 52, no. 3, pp. 387-410.
Li, Y, Chi, C, Li, C, Song, J, Song, Z, Wang, W & Sun, J 2022, 'Efficacy of Donated Milk in Early Nutrition of Preterm Infants: A Meta-Analysis', Nutrients, vol. 14, no. 9, pp. 1724-1724.
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Background: Preterm birth is associated with an increased risk of many complications, which is a main public health problem worldwide with social and economic consequences. Human milk from breast feeding has been proved to be the optimal nutrition strategy for preterm infants when available. However, the lack of human milk from mothers makes formula widely used in clinical practice. In recent years, donated breast milk has gained popularity as an alternative choice which can provide human milk oligosaccharides and other bioactive substances. Objective: We aimed to conduct a systematic review and meta-analysis to evaluate the nutritional effects of donated breast milk on preterm infants compared with formula. Method: In the present study, we searched Medline, Web of Science, Embase, clinicaltrials.gov, the China national knowledge infrastructure, and the Cochrane central register of controlled trials for candidate randomized controlled trials (RCTs). Results: A total of 1390 patients were enrolled in 11 RCTs and meta-analysis results showed that donated breast milk is also more advantageous in reducing the incidence of necrotizing enterocolitis (NEC, RR = 0.67, 95% CI = 0.48 to 0.93, p = 0.02), reducing the duration of parenteral nutrition (MD = −2.39, 95% CI = −3.66 to −1.13, p = 0.0002) and the time of full enteral feeding (MD = −0.33, 95% CI = −3.23 to 2.57, p = 0.0002). In comparison, formula significantly promotes the growth of premature infants, including their weight gain (MD = −3.45, 95% CI = −3.68 to −3.21, p < 0.00001), head growth (MD = −0.07, 95% CI = −0.08 to −0.06, p < 0.00001) and body length (MD = −0.13, 95% CI = −0.15 to −0.11, p < 0.00001), and reduces the time it takes for premature infants to regain birth weight (MD = 6.60, 95% CI = 6.11 to 7.08, p < 0.00001. Conclusion: Compared with formula, donated breast milk could significantly reduce the incidence of NEC, the duration of parenteral nutrition, and the time of ...
Li, Y, Huang, Y, Seneviratne, S, Thilakarathna, K, Cheng, A, Jourjon, G, Webb, D, Smith, DB & Xu, RYD 2022, 'From traffic classes to content: A hierarchical approach for encrypted traffic classification', Computer Networks, vol. 212, pp. 109017-109017.
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The vast majority of Internet traffic is now end-to-end encrypted, and while encryption provides user privacy and security, it has made network surveillance an impossible task. Various parties are using this limitation to distribute problematic content such as fake news, copy-righted material, and propaganda videos. Recent advances in machine learning techniques have shown great promise in extracting content fingerprints from encrypted traffic captured at the various points in IP core networks. Nonetheless, content fingerprinting from listening to encrypted wireless traffic remains a challenging task due to the difficulty in distinguishing re-transmissions and multiple flows on the same link. In this paper, we show the potential of fingerprinting internet traffic by passively sniffing WiFi frames in air, without connecting to the WiFi network by leveraging deep learning methods. First, we show the possibility of building a generic traffic classifier using a hierarchical approach that is able to identity most common traffic types in the Internet and reveal fine-granular details such as identifying the exact content of the traffic. Second, we demonstrate the possibility of using Multi-Layer Perceptron (MLP) and Recurrent Neural Networks (RNNs) to identify streaming traffic, such as video and music, from a closed set, by sniffing WiFi traffic that is encrypted at both Media Access Control (MAC) and Transport layers. Overall, our results demonstrate that we can achieve over 95% accuracy in identifying traffic types such as web, video streaming, and audio streaming as well as identifying the exact content consumed by the user.
Li, Y, Jiang, T, Xie, W, Lei, J & Du, Q 2022, 'Sparse Coding-Inspired GAN for Hyperspectral Anomaly Detection in Weakly Supervised Learning', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-11.
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Li, Y, Liu, Z, Yao, L, Wang, X, McAuley, J & Chang, X 2022, 'An Entropy-Guided Reinforced Partial Convolutional Network for Zero-Shot Learning', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 8, pp. 5175-5186.
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Li, Y, Storch, EA, Ferguson, S, Li, L, Buys, N & Sun, J 2022, 'The efficacy of cognitive behavioral therapy-based intervention on patients with diabetes: A meta-analysis', Diabetes Research and Clinical Practice, vol. 189, pp. 109965-109965.
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Li, Y, Tan, VYF & Tomamichel, M 2022, 'Optimal Adaptive Strategies for Sequential Quantum Hypothesis Testing', Communications in Mathematical Physics, vol. 392, no. 3, pp. 993-1027.
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Li, Y, Wang, Q & Li, S 2022, 'On quotients of formal power series', Information and Computation, vol. 285, pp. 104874-104874.
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Li, Y, Zhang, X, Ngo, HH, Guo, W, Zhang, D, Wang, H & Long, T 2022, 'Magnetic spent coffee biochar (Fe-BC) activated peroxymonosulfate system for humic acid removal from water and membrane fouling mitigation', Journal of Water Process Engineering, vol. 49, pp. 103185-103185.
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Li, Z, Hu, Y, Wu, J & Lu, J 2022, 'P4Resilience: Scalable Resilience for Multi-failure Recovery in SDN with Programmable Data Plane', Computer Networks, vol. 208, pp. 108896-108896.
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Li, Z, Tang, Y, Huang, T & Wen, S 2022, 'Formation Control of Multiagent Networks: Cooperative and Antagonistic Interactions', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 5, pp. 2809-2818.
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This article studies the formation control problem of second-order multiagent networks, in which cooperative and antagonistic interactions of the agents spontaneously coexist in the communication process. Based on the convex analysis theory, several convex polytopes that do not require some kinds of system constraints are constructed in the presence of these interactions. Then, the matrix perturbation theory and some mathematical techniques are utilized to analyze these convex polytopes. The obtained results show that the agents with cooperative interactions monotonously converge to their own specified formation shape while maintaining the desired relative position of the other agents with antagonistic interactions. Subsequently, two numerical examples are presented to illustrate the obtained results.
Liang, M, Huang, S, Pan, S, Gong, M & Liu, W 2022, 'Learning multi-level weight-centric features for few-shot learning', Pattern Recognition, vol. 128, pp. 108662-108662.
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Few-shot learning is currently enjoying a considerable resurgence of interest, aided by the recent advance of deep learning. Contemporary approaches based on weight-generation scheme delivers a straightforward and flexible solution to the problem. However, they did not fully consider both the representation power for unseen categories and weight generation capacity in feature learning, making it a significant performance bottleneck. This paper proposes a multi-level weight-centric feature learning to give full play to feature extractor's dual roles in few-shot learning. Our proposed method consists of two essential techniques: a weight-centric training strategy to improve the features’ prototype-ability and a multi-level feature incorporating a mid- and relation-level information. The former increases the feasibility of constructing a discriminative decision boundary based on a few samples. Simultaneously, the latter helps improve the transferability for characterizing novel classes and preserve classification capability for base classes. We extensively evaluate our approach to low-shot classification benchmarks. Experiments demonstrate our proposed method significantly outperforms its counterparts in both standard and generalized settings and using different network backbones.
Liang, Y, Liu, Y, Zhou, Y, Shi, Q, Zhang, M, Li, Y, Wen, W, Feng, L & Wu, J 2022, 'Efficient and stable electrorheological fluids based on chestnut-like cobalt hydroxide coupled with surface-functionalized carbon dots', Soft Matter, vol. 18, no. 20, pp. 3845-3855.
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The synergistic effect of the lipophilic groups on the surface of CDs and the biomimetic chestnut-like structure give Co(OH)2@CDs good wettability with silicone oil, great electrorheological efficiency and dynamic shear stress stability.
Liao, Q, Wang, D & Xu, M 2022, 'Category attention transfer for efficient fine-grained visual categorization', Pattern Recognition Letters, vol. 153, pp. 10-15.
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Fine-Grained Visual Categorization (FGVC) aims at distinguishing subordinate-level categories with subtle interclass differences. Although previous research shows the impressive effectiveness of the recurrent multi-attention models and the second-order feature encoding, they often require an enormous amount of both computation and memory space, making them inadequate for mobile applications. This paper proposed a Category Attention Transfer CNN (CAT-CNN) to address the efficiency issue in solving FGVC problems. We transfer part attention knowledge from a very large-scale FGVC network to a small but efficient network to significantly improve its presentation ability. Using the proposed CAT-CNN, the accuracy of the efficient networks, such as ShuffleNet, MobilieNet, and EfficientNet, can be improved by up to 5.7% on the CUB-2011-200 dataset without increasing computation complexity or memory cost. Our experiments show that the proposed CAT-CNN can be applied to multiple structures to enhance their performance. With a single efficient network structure and single inference, the proposed CAT-MobileNet-large-1.0 and the CAT-EfficientNet-b0 can achieve accuracies of 86.5% and 86.7%, respectively, on the CUB-2011-200 dataset, which is close to or better than the results from state-of-the-art methods using large scale networks and multiple inferences, and make FGVC feasible on mobile devices.
Lidfors Lindqvist, A, Zhou, S & Walker, PD 2022, 'Direct yaw moment control of an ultra-lightweight solar-electric passenger vehicle with variation in loading conditions', Vehicle System Dynamics, vol. 60, no. 4, pp. 1393-1415.
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Large variations load-to-curb weight ratios are linked to significant changes in parameters critical to control design for vehicle stability control system. Unique and highly customised vehicles, such as the lightweight solar car in this paper, are more susceptible to the impact of such variations when developing control methods. The purpose of this study is to study the influence of variation in loading conditions, the effect of ignoring changes in inertial parameters, and develop and compare a number of alternative vehicle stability control methods that can be applied to rear-wheel driven vehicles via in-wheel motors. In this paper a Sliding Mode Control (SMC) both nominal and when including uncertainty, Dynamic Curvature Control (DCC) and a Proportional–Integral Control (PI) strategies are compared to the baseline open-loop control case. Each controller is implemented through co-simulation via MATLAB® Simulink® and Siemens Amesim™ using a 15-DOF non-linear vehicle model. The results show that SMC achieves the best performance, whilst DCC tends to overshoot target conditions prior to settling, indicating that SMC is the preferred control strategy. It is also demonstrated that by ignoring the change in the inertial parameters in simulation environments can produce an incorrect translation of the control performance.
Lim, AC, Tang, SGH, Zin, NM, Maisarah, AM, Ariffin, IA, Ker, PJ & Mahlia, TMI 2022, 'Chemical Composition, Antioxidant, Antibacterial, and Antibiofilm Activities of Backhousia citriodora Essential Oil', Molecules, vol. 27, no. 15, pp. 4895-4895.
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The essential oil of Backhousia citriodora, commonly known as lemon myrtle oil, possesses various beneficial properties due to its richness in bioactive compounds. This study aimed to characterize the chemical profile of the essential oil isolated from leaves of Backhousia citriodora (BCEO) and its biological properties, including antioxidant, antibacterial, and antibiofilm activities. Using gas chromatography–mass spectrometry, 21 compounds were identified in BCEO, representing 98.50% of the total oil content. The isomers of citral, geranial (52.13%), and neral (37.65%) were detected as the main constituents. The evaluation of DPPH radical scavenging activity and ferric reducing antioxidant power showed that BCEO exhibited strong antioxidant activity at IC50 of 42.57 μg/mL and EC50 of 20.03 μg/mL, respectively. The antibacterial activity results showed that BCEO exhibited stronger antibacterial activity against Gram-positive bacteria (Staphylococcus aureus and Staphylococcus epidermidis) than against Gram-negative bacteria (Escherichia coli and Klebsiella pneumoniae). For the agar disk diffusion method, S. epidermidis was the most sensitive to BCEO with an inhibition zone diameter of 50.17 mm, followed by S. aureus (31.13 mm), E. coli (20.33 mm), and K. pneumoniae (12.67 mm). The results from the microdilution method showed that BCEO exhibited the highest activity against S. epidermidis and S. aureus, with the minimal inhibitory concentration (MIC) value of 6.25 μL/mL. BCEO acts as a potent antibiofilm agent with dual actions, inhibiting (85.10% to 96.44%) and eradicating (70.92% to 90.73%) of the biofilms formed by the four tested bacteria strains, compared with streptomycin (biofilm inhibition, 67.65% to 94.29% and biofilm eradication, 49.97% to 89.73%). This study highlights that BCEO can potentially be a natural antioxidant agent, antibacterial agent, and antibiofilm agent that could be applied in the pharmaceutical and food industries. To ...
Lim, S-M, Indraratna, B, Heitor, A, Yao, K, Jin, D, Albadri, WM & Liu, X 2022, 'Influence of matric suction on resilient modulus and CBR of compacted Ballina clay', Construction and Building Materials, vol. 359, pp. 129482-129482.
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Lin, C, Cheruiyot, NK, Bui, X-T & Ngo, HH 2022, 'Composting and its application in bioremediation of organic contaminants', Bioengineered, vol. 13, no. 1, pp. 1073-1089.
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This review investigates the findings of the most up-to-date literature on bioremediation via composting technology. Studies on bioremediation via composting began during the 1990s and have exponentially increased over the years. A total of 655 articles have been published since then, with 40% published in the last six years. The robustness, low cost, and easy operation of composting technology make it an attractive bioremediation strategy for organic contaminants prevalent in soils and sediment. Successful pilot-and large-scale bioremediation of organic contaminants, e.g., total petroleum hydrocarbons, plasticizers, and persistent organic pollutants (POPs) by composting, has been documented in the literature. For example, composting could remediate >90% diesel with concentrations as high as 26,315 mg kg-a of initial composting material after 24 days. Composting has unique advantages over traditional single- and multi-strain bioaugmentation approaches, including a diverse microbial community, ease of operation, and the ability to handle higher concentrations. Bioremediation via composting depends on the diverse microbial community; thus, key parameters, including nutrients (C/N ratio = 25-30), moisture (55-65%), and oxygen content (O2 > 10%) should be optimized for successful bioremediation. This review will provide bioremediation and composting researchers with the most recent finding in the field and stimulate new research ideas.
Lin, C-T, Fan, H-Y, Chang, Y-C, Ou, L, Liu, J, Wang, Y-K & Jung, T-P 2022, 'Modelling the Trust Value for Human Agents Based on Real-Time Human States in Human-Autonomous Teaming Systems', Technologies, vol. 10, no. 6, pp. 115-115.
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The modelling of trust values on agents is broadly considered fundamental for decision-making in human-autonomous teaming (HAT) systems. Compared to the evaluation of trust values for robotic agents, estimating human trust is more challenging due to trust miscalibration issues, including undertrust and overtrust problems. From a subjective perception, human trust could be altered along with dynamic human cognitive states, which makes trust values hard to calibrate properly. Thus, in an attempt to capture the dynamics of human trust, the present study evaluated the dynamic nature of trust for human agents through real-time multievidence measures, including human states of attention, stress and perception abilities. The proposed multievidence human trust model applied an adaptive fusion method based on fuzzy reinforcement learning to fuse multievidence from eye trackers, heart rate monitors and human awareness. In addition, fuzzy reinforcement learning was applied to generate rewards via a fuzzy logic inference process that has tolerance for uncertainty in human physiological signals. The results of robot simulation suggest that the proposed trust model can generate reliable human trust values based on real-time cognitive states in the process of ongoing tasks. Moreover, the human-autonomous team with the proposed trust model improved the system efficiency by over 50% compared to the team with only autonomous agents. These results may demonstrate that the proposed model could provide insight into the real-time adaptation of HAT systems based on human states and, thus, might help develop new ways to enhance future HAT systems better.
Lin, C-T, Tian, Y, Wang, Y-K, Do, T-TN, Chang, Y-L, King, J-T, Huang, K-C & Liao, L-D 2022, 'Effects of Multisensory Distractor Interference on Attentional Driving', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 10395-10403.
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Distracted driving refers to multisensory integration and attention shifts between attentional driving and different interferences from different modalities, including visual and auditory stimuli. Here, we compared the behavioral performance with interacting multisensory distractors during attentional driving. Then, the independent component analysis (ICA) and event-related spectral perturbation (ERSP) were applied to investigate the neural oscillation changes. The behavioral results showed that the response times (RTs) increased when distractors appeared in response to attentional driving. Moreover, the RTs were longer when the distractor interference was presented in the auditory modality compared with the visual modality. Eye movement intervals showed shorter tracking saccades under distractor interference. These results may indicate that attentional driving performance was impaired under the exposure to multisensory distractor interference. The ERSPs under visual and auditory distraction exposure showed decreased beta power in the frontal area, increased theta and delta power in the central area, and decreased alpha power in the parietal area. During this process, distracted driving under cross-modal sensory interference required more neural oscillation involvement. Moreover, the visual modality showed increased gamma power in the frontal, central, parietal and occipital areas, while the auditory modality showed decreased gamma power in the frontal area, indicating that auditory interference could intervene in top-down attentional processing.
Lin, C-T, Wang, Y-K, Huang, P-L, Shi, Y & Chang, Y-C 2022, 'Spatial-temporal attention-based convolutional network with text and numerical information for stock price prediction', Neural Computing and Applications, vol. 34, no. 17, pp. 14387-14395.
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AbstractIn the financial market, the stock price prediction is a challenging task which is influenced by many factors. These factors include economic change, politics and global events that are usually recorded in text format, such as the daily news. Therefore, we assume that real-world text information can be used to forecast stock market activity. However, only a few works considered both text and numerical information to predict or analyse stock trends. These works used preprocessed text features as the model inputs; therefore, latent information in text may be lost because the relationships between the text and stock price are not considered. In this paper, we propose a fusion network, i.e. a spatial-temporal attention-based convolutional network (STACN) that can leverage the advantages of an attention mechanism, a convolutional neural network and long short-term memory to extract text and numerical information for stock price prediction. Benefiting from the utilisation of an attention mechanism, reliable text features that are highly relevant to stock value can be extracted, which improves the overall model performance. The experimental results on real-world stock data demonstrate that our STACN model and training scheme can handle both text and numerical data and achieve high accuracy on stock regression tasks. The STACN is compared with CNNs and LSTMs with different settings, e.g. a CNN with only stock data, a CNN with only news titles and LSTMs with only stock data. CNNs considering only stock data and news titles have mean squared errors of 28.3935 and 0.1814, respectively. The accuracy of LSTMs is 0.0763. The STACN can achieve an accuracy of 0.0304, outperforming CNNs and LSTMs in stock regression tasks.
Lin, J, Sun, G, Beydoun, G & Li, L 2022, 'Applying Machine Translation and Language Modelling Strategies for the Recommendation Task of Micro Learning Service', Educational Technology and Society, vol. 25, no. 1, pp. 205-212.
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A newly emerged micro learning service offers a flexible formal, informal, or non-formal online learning opportunity to worldwide users with different backgrounds in real-time. With the assist of big data technology and cloud computing service, online learners can access tremendous fine-grained learning resources through micro learning service. However, big data also causes serious information overload during online learning activities. Hence, an intelligent recommender system is required to filter out not-suitable learning resources and pick the one that matches the learner’s learning requirement and academic background. From the perspective of natural language processing (NLP), this study proposed a novel recommender system that utilises machine translation and language modelling. The proposed model aims to overcome the defects of conventional recommender systems and further enhance distinguish ability of the recommender system for different learning resources.
Lin, J, Sun, G, Shen, J, Pritchard, DE, Yu, P, Cui, T, Xu, D, Li, L & Beydoun, G 2022, 'From computer vision to short text understanding: Applying similar approaches into different disciplines', Intelligent and Converged Networks, vol. 3, no. 2, pp. 161-172.
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Lin, W, Chen, R, Liu, X, Hao Ngo, H, Nan, J, Li, G, Ma, J, He, X & Ding, A 2022, 'Deep mechanism of enhanced dewaterability of residual sludge by Na+: Comprehensive analyses of intermolecular forces, hydrophilicity and water-holding capacity of EPS', Chemical Engineering Journal, vol. 450, pp. 138505-138505.
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Extracellular polymeric substance (EPS) is generally considered as the limiting factor affecting sludge dewatering due to its complex components and water-holding capacity. Conventional flocculation conditioning could improve the dewaterability by generating a certain number of channels for water discharge. However, the hydrophilicity and water-holding capacity of EPS still cannot change, resulting in the inability to further consolidate sludge dewaterability. To overcome this challenge, the study explores the application ability of sodium chloride via Na+ conditioning for sludge dewatering and compared with calcium chloride (CaCl2) and ferric chloride (FeCl3) conditioning effects. Results confirmed that the specific resistance to filtration (SRF) and water content (WC) fell dramatically from 14.3 × 1012 m/kg to 8.1 × 1012 m/kg and 80.8 % to 75.4 %, respectively, at the Na+ concentration of 80 mmol/L. The mechanism investigations indicated that addition of Na+ clearly destroyed the structure of EPS and promoted the declines in hydrophilicity and water-holding capability of EPS, resulting in much less bound water, changes in secondary structure and functional groups (e.g. N[sbnd]H, and C[dbnd]O) of EPS proteins. Furthermore, analyses of surface thermodynamic illustrated that the aggregation ability of sludge enhanced after the conditioning of Na+ combined with re-flocculation. Additionally, compared with Ca2+ and Fe3+, applying the combined conditioning method led to stronger hydrophobicity of EPS through the analysis of two-dimension correlation spectroscopy (2D-COS). This work can drive innovation in applying salty water containing sodium for effectively sludge dewatering.
Lin, W, Ding, A, Ngo, HH, Ren, Z, Nan, J, Li, G & Ma, J 2022, 'Effects of the metabolic uncoupler TCS on residual sludge treatment: Analyses of the microbial community and sludge dewaterability potential', Chemosphere, vol. 288, pp. 132473-132473.
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Lin, W, Liu, X, Ding, A, Ngo, HH, Zhang, R, Nan, J, Ma, J & Li, G 2022, 'Advanced oxidation processes (AOPs)-based sludge conditioning for enhanced sludge dewatering and micropollutants removal: A critical review', Journal of Water Process Engineering, vol. 45, pp. 102468-102468.
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Applying advanced oxidation processes (AOPs) in sludge dewatering to improve sludge treatment, disposal and environmental protection has attracted much interest due to the difficulties arising in extracellular polymeric substances (EPS) degradation during the sludge dewatering process. Oxidants can produce different types of free radicals and exert specific oxidation effects through different action mechanisms on water saturated sludge. This plays an important role in sludge dewatering, sludge minimization and removal of different types of micropollutants and/or their transformation. The current review critically evaluates the role of AOP in improving the efficiency of sludge dewatering. Characteristics of advanced oxidation methods applied to sludge dewatering are systematically illustrated through different mechanisms using free radical reactions and various sludge dewatering conditions. Factors which impact on influencing the minimization of sludge and removals of typical micropollutants during the sludge conditioning process are also analyzed. Finally, applications of advanced oxidation methods in the future are proposed based on a technoeconomic analyses of dewatering efficiency and operation cost. This review provides theoretical support regarding the application of advanced oxidation processes in sludge dewatering and avenues for practical engineering. In the current review, it is determined that the efficiency of AOP for the improvement of sludge dewatering, micropollutants removal and sludge minimization in the treatment and disposal of sludge have been fully investigated. Unfortunately, there is still lack of comparing the ability of different free radicals on published review.
Lin, W, Zeng, J, Zhang, R, He, X, Nan, J, Li, G, Ma, J, Ngo, HH & Ding, A 2022, 'Selection of metal ions in different valence states on sludge conditioning: Analysis of hydrophobicity and evaluation of resource recovery capacity', Journal of Water Process Engineering, vol. 50, pp. 103297-103297.
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Lin, W-T, Chen, G & Huang, Y 2022, 'Incentive edge-based federated learning for false data injection attack detection on power grid state estimation: A novel mechanism design approach', Applied Energy, vol. 314, pp. 118828-118828.
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With the growing concern in security and privacy of smart grid, false data injection attack detection on power grid state estimation now faces new challenges including unknown system parameters and small decentralized data sets with strategic data owners. To deal with these technical bottlenecks, this paper proposes a novel edge-based federated learning framework for false data injection attack detection on power grid state estimation, which has great potential in real-world applications with unknown system parameters. Furthermore, to seek a high detection accuracy with small decentralized data set and strategic data owners, an incentive mechanism is designed to encourage the desired data owners contributing to false data injection attack detection. To explore the impact of the incentive mechanism on the detection accuracy, a bi-level model depicting the data owners’ participation in false data injection attack detection is formulated, based on which the impact is quantified. Moreover, a novel preference criterion is proposed for optimal mechanism design. It can achieve the optimal detection accuracy under a certain incentive budget. The incentive mechanism is designed and tested for 100 Monte Carlo scenarios. Simulations of false data injection attack detection on power grid state estimation show that the proposed framework outperforms the existing works without mechanism design.
Lin, Y, Huo, P, Li, F, Chen, X, Yang, L, Jiang, Y, Zhang, Y, Ni, B-J & Zhou, M 2022, 'A critical review on cathode modification methods for efficient Electro-Fenton degradation of persistent organic pollutants', Chemical Engineering Journal, vol. 450, pp. 137948-137948.
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The Electro-Fenton (EF) technology has received significant research attention because of its efficacy in the degradation of persistent organic pollutants (POPs), which mainly relies on the in-situ generation of H2O2 via the 2-electron oxygen reduction reaction and the subsequent formation of •OH. However, the practical application of the EF technology still needs to deal with shortcomings such as the limited performance of the traditional heterogeneous catalyst and the restricted generation of •OH that could be overcome by performing modification on the cathode. This work reviewed the reported cathode modification methods including thermal and (electro)chemical treatment and modification based on materials such as metals, graphene, carbon nanotubes, and polymers. Furthermore, the documented performances of the EF systems with differently modified cathodes in degrading specific POPs were presented. Finally, the advantages and limitations of these cathode modification methods were discussed, and some research perspectives were proposed to improve the practicability and feasibility of the EF technology.
Lin, Y, Zhang, J & Li, L 2022, 'A model predictive control approach to a water pumping system in an Australian cotton farm microgrid', Cleaner Energy Systems, vol. 3, pp. 100026-100026.
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Ling, L, Yelland, N, Hatzigianni, M & Dickson-Deane, C 2022, 'The use of Internet of Things devices in early childhood education: A systematic review', Education and Information Technologies, vol. 27, no. 5, pp. 6333-6352.
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Linghu, Q, Zhang, F, Lin, X, Zhang, W & Zhang, Y 2022, 'Anchored coreness: efficient reinforcement of social networks', The VLDB Journal, vol. 31, no. 2, pp. 227-252.
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The stability of a social network has been widely studied as an important indicator for both the network holders and the participants. Existing works on reinforcing networks focus on a local view, e.g., the anchored k-core problem aims to enlarge the size of the k-core with a fixed input k. Nevertheless, it is more promising to reinforce a social network in a global manner: considering the engagement of every user (vertex) in the network. Since the coreness of a user has been validated as the “best practice” for capturing user engagement, we propose and study the anchored coreness problem in this paper: anchoring a small number of vertices to maximize the coreness gain (the total increment of coreness) of all the vertices in the network. We prove the problem is NP-hard and show it is more challenging than the existing local-view problems. An efficient greedy algorithm is proposed with novel techniques on pruning search space and reusing the intermediate results. The algorithm is also extended to distributed environment with a novel graph partition strategy to ensure the computing independency of each machine. Extensive experiments on real-life data demonstrate that our model is effective for reinforcing social networks and our algorithms are efficient.
Lionnie, R, Apriono, C, Chai, R & Gunawan, D 2022, 'Curvature Best Basis: A Novel Criterion to Dynamically Select a Single Best Basis as the Extracted Feature for Periocular Recognition', IEEE Access, vol. 10, pp. 113523-113542.
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Litov, N, Falkner, B, Zhou, H, Mehta, A, Gondwe, W, Thalakotuna, DN, Mirshekar-Syahkal, D, Esselle, K & Nakano, H 2022, 'Radar Cross Section Analysis of Two Wind Turbines via a Novel Millimeter-Wave Technique and Scale Model Measurements', IEEE Access, vol. 10, pp. 17897-17907.
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Liu, A, Lu, J, Song, Y, Xuan, J & Zhang, G 2022, 'Concept Drift Detection Delay Index', IEEE Transactions on Knowledge and Data Engineering, vol. PP, no. 99, pp. 1-1.
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Data streams may encounter data distribution changes, which can significantly impair the accuracy of models. Concept drift detection tracks data distribution changes and signals when to update models. Many drift detection methods apply thresholds to distinguish between drift or non-drift streams and to claim their method outperforms others with non-aligned drift thresholds. We consider that selecting a proper drift threshold could be more important than developing a new drift detection algorithm, and different drift detection algorithms may end up with very similar performance with aligned drift thresholds. To better understand this process, we propose a novel threshold selection algorithm to align the drift thresholds of a set of algorithms so that they are all at the same sensitivity level. Based on comprehensive experiment evaluations, we observed that several state-of-the-art drift detection algorithms could achieve similar results by aligning their thresholds, providing a novel insight to explain how drift detection algorithms contribute to data stream learning. We noticed that a higher detection sensitivity improves accuracy for data streams with frequent distribution change. The evaluation results are showing that drift thresholds should not be fixed during stream learning. Rather, they should adjust dynamically based on the prevailing conditions of the data stream.
Liu, B, Ding, M, Shaham, S, Rahayu, W, Farokhi, F & Lin, Z 2022, 'When Machine Learning Meets Privacy', ACM Computing Surveys, vol. 54, no. 2, pp. 1-36.
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The newly emerged machine learning (e.g., deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy and machine learning are still in an infancy stage, as most existing solutions only focus on privacy problems during the machine learning process. Therefore, a comprehensive study on the privacy preservation problems and machine learning is required. This article surveys the state of the art in privacy issues and solutions for machine learning. The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning-aided privacy protection, and (iii) machine learning-based privacy attack and corresponding protection schemes. The current research progress in each category is reviewed and the key challenges are identified. Finally, based on our in-depth analysis of the area of privacy and machine learning, we point out future research directions in this field.
Liu, B, Li, L, Xiao, Q, Ni, W & Yang, Z 2022, 'Remote Sensing Fine-Grained Ship Data Augmentation Pipeline With Local-Aware Progressive Image-to-Image Translation', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16.
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Liu, B, Ni, W, Liu, RP, Zhu, Q, Guo, YJ & Zhu, H 2022, 'Novel Integrated Framework of Unmanned Aerial Vehicle and Road Traffic for Energy-Efficient Delay-Sensitive Delivery', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 10692-10707.
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Unmanned aerial vehicle (UAV) has demonstrated its usefulness in goods delivery. However, the delivery distances are often restrained by the battery capacity of UAVs. This paper integrates UAVs into intelligent transportation systems for energy-efficient, delay-sensitive goods delivery. Dynamic programming (DP) is first applied to minimize the energy consumption of a UAV and ensure its timely arrival at its destination, by optimizing the control policy of the UAV. The control policy involves decisions including flight speed, hitchhiking (on collaborative ground vehicles), or recharging at roadside charging stations. Another key aspect is that we reveal the conditions of the remaining flight distance or the elapsed time, only under which the optimal action of the UAV changes. Accordingly, thresholds are derived, and the optimal control policy can be instantly made by comparing the remaining flight distance and the elapsed time with the thresholds. Simulations show that the proposed algorithms can improve the flight distance by 48%, as compared with existing alternatives. The proposed threshold-based technique can achieve the same performance as the DP-based solution, while significantly reducing the computational complexity.
Liu, C, Lu, J, Fu, W & Zhou, Z 2022, 'Second-hand housing batch evaluation model of zhengzhou city based on big data and MGWR model', Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4221-4240.
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How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03–0.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model.
Liu, C, Wang, X, Wang, S, Wang, Y, Lei, G & Zhu, J 2022, 'Magnetothermal Coupling Analysis of Permanent Magnet Claw Pole Machine Using Combined 3D Magnetic and Thermal Network Method', IEEE Transactions on Applied Superconductivity, vol. 32, no. 6, pp. 1-5.
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The permanent magnet claw pole machine (PMCPM) is a special kind of transverse flux machine, different from conventional electrical machines the main magnetic flux path of PMCPM is 3D. Therefore, for accurate performance analysis,the 3D finite element method (FEM) is required to calculate both the electromagnetic characteristics and thermal distribution. However, it is time consuming especially when the coupling effect needs to be considered. In this paper, a combined 3D magnetic and thermal network method is proposed to obtain the performance of PMCPM. As the developed thermal network shares the same structure as the magnetic network, the calculated core loss can be regarded as the heat source in the thermal analysis easily, in which 3D rotational core loss is calculated as the 3D network is adopted. The proposed method holds the advantages of close magnetothermal coupling and fast calculating speed.For the calculation results verification, 3D FEM is used.
Liu, C, Wang, X, Wang, Y, Lei, G, Guo, Y & Zhu, J 2022, 'Comparative study of rotor PM transverse flux machine and stator PM transverse flux machine with SMC cores', Electrical Engineering, vol. 104, no. 3, pp. 1153-1161.
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Liu, F, Han, R, Zhang, G, Zhang, M, Chen, J & Sun, X 2022, 'Molten Salt Assisted Synthesis of Boron Carbon Nitride Nanosheets for Enhanced Photocatalytic Activity of Silver Phosphate', Journal of Nanoelectronics and Optoelectronics, vol. 17, no. 3, pp. 560-568.
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Thin 2D boron carbon nitride nanosheets (BCNNS) possess high thermal and chemical stability as well as tunable electronic properties, but the lack of effective synthesis methods hinders their practical application. Herein, a facile and efficient approach for the synthesis of large-area boron carbon nitride nanosheets in molten KCl–NaCl salt media has been proposed. A Single precursor compound, ethylenediamine bisborane, was first heated to 1000 °C in KCl–NaCl salt melts and then held for only two minutes to produce BCNNS. Benefiting from the effective solvation of precursors and reduced surface energy in liquid salt melt, the lateral size of resultant BCNNS can reach up to 12 microns. The as-prepared products are subsequently used as a co-catalyst with silver phosphate (Ag3PO4) for degradation methyl orange under simulated sunlight. Due to the improved electronic property and interfacial effect of BCNNS, the photocatalytic performance of Ag3PO4 was significantly improved. The photodegradation rate increased from 0.369 min−1 of Ag3PO4 to 1.006 min−1 of BCN/Ag3PO4 composite with only 0.6 wt% BCNNS loading, a 2.73-fold higher value than that of pure Ag3PO4.
Liu, G, Zhang, W, Li, X, Fan, K & Yu, S 2022, 'VulnerGAN: a backdoor attack through vulnerability amplification against machine learning-based network intrusion detection systems', Science China Information Sciences, vol. 65, no. 7.
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Machine learning-based network intrusion detection systems (ML-NIDS) are extensively used for network security against unknown attacks. Existing intrusion detection systems can effectively defend traditional network attacks, however, they face AI based threats. The current known AI attacks cannot balance the escape rate and attack effectiveness. In addition, the time cost of existing AI attacks is very high. In this paper, we propose a backdoor attack called VulnerGAN, which features high concealment, high aggressiveness, and high timeliness. The backdoor can make the specific attack traffic bypass the detection of ML-NIDS without affecting the performance of ML-NIDS in identifying other attack traffic. VulnerGAN uses generative adversarial networks (GAN) to calculate poisoning and adversarial samples based on machine learning model vulnerabilities. It can make traditional network attack traffic escape black-box online ML-NIDS. At the same time, model extraction and fuzzing test are used to enhance the convergence of VulnerGAN. Compared with the state-of-the-art algorithms, the VulnerGAN backdoor attack increases 33.28% in concealment, 18.48% in aggressiveness, and 46.32% in timeliness.
Liu, H, Jiang, L, Cao, B, Du, H, Lu, H, Ma, Y, Wang, H, Guo, H, Huang, Q, Xu, B & Guo, S 2022, 'Van der Waals Interaction-Driven Self-Assembly of V2O5Nanoplates and MXene for High-Performing Zinc-Ion Batteries by Suppressing Vanadium Dissolution', ACS Nano, vol. 16, no. 9, pp. 14539-14548.
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Liu, H, Li, X, Zhang, Z, Nghiem, LD & Wang, Q 2022, 'Urine pretreatment significantly promotes methane production in anaerobic waste activated sludge digestion', Science of The Total Environment, vol. 853, pp. 158684-158684.
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Methane production of waste activated sludge (WAS) in anaerobic digestion is hindered due to the rate-limited hydrolysis process and the low methane potential of WAS. Pretreatment of WAS is a common and appealing strategy to improve methane production in anaerobic digestion. In this study, we proposed to use urine, an easily obtained human waste with high ammonium concentration and pH, as a novel pretreatment strategy for anaerobic WAS digestion. Urine pretreatment at levels of 5-30 % (Vurine/Vurine+WAS) could substantially enhance methane production by 5-35 % in biochemical methane potential (BMP) tests, with the highest methane production of 179.6 ± 3.3 mL/g volatile solids (VS) achieved under the highest level of urine (i.e. 30 % urine addition). Based on the model analysis, the biochemical methane potential (B0) and hydrolysis rate of WAS (k) rose from 131.9 mL/g VS and 0.19 d-1 in the control without pretreatment to 136.3-178.2 mL/g VS and 0.22-0.30 d-1, respectively, after the urine pretreatment (5-30 % addition). Urine pretreatment with 5-30 % addition also improved the degradation extent (Y) of WAS by 3-35 %. The promising results indicate that urine pretreatment in anaerobic digestion is a promising technology to improve the efficiency of anaerobic digestion with environmental and economic benefits.
Liu, H, Zhang, C, Yao, Y, Wei, X-S, Shen, F, Tang, Z & Zhang, J 2022, 'Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Open-Set Noise and Utilizing Hard Examples', IEEE Transactions on Multimedia, vol. 24, no. 99, pp. 546-557.
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Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators. As such, learning directly from web images for fine-grained recognition has attracted broad attention. However, the presence of label noise and hard examples in web images are two obstacles for training robust fine-grained recognition models. To this end, in this paper, we propose a novel approach to remove irrelevant samples from real-world web images during training, while employing useful hard examples to update the network. Thus, our approach can alleviate the harmful effects of irrelevant noisy web images and hard examples to achieve better performance. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is far superior to current state-of-the-art web-supervised methods.
Liu, J, Bo, F, Chang, L, Dong, C-H, Ou, X, Regan, B, Shen, X, Song, Q, Yao, B, Zhang, W, Zou, C-L & Xiao, Y-F 2022, 'Emerging material platforms for integrated microcavity photonics', Science China Physics, Mechanics & Astronomy, vol. 65, no. 10.
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Liu, J, Gao, A, Liu, Y, Sun, Y, Zhang, D, Lin, X, Hu, C, Zhu, Y, Du, Y, Han, H, Li, Y, Xu, S, Liu, T, Zhang, C, Zhu, J, Dong, R, Zhou, Y & Zhao, Y 2022, 'MicroRNA Expression Profiles of Epicardial Adipose Tissue-Derived Exosomes in Patients with Coronary Atherosclerosis', Reviews in Cardiovascular Medicine, vol. 23, no. 6, pp. 206-206.
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Background and Aims: Epicardial adipose tissue, exosomes, and miRNAs have important activities in atherosclerosis. The purpose of this study was to establish miRNA expression profiles of epicardial adipose tissue-derived exosomes in patients with coronary atherosclerosis. Methods: Biopsies of epicardial adipose tissue were obtained from patients with and without coronary artery disease (CAD, n = 12 and NCAD, n = 12) during elective open-heart surgeries. Tissue was incubated with DMEM-F12 for 24 hours. Exosomes were isolated, then nanoparticle tracking analysis, transmission electron microscopy, and immunoblotting were performed to confirm the existence of exosomes. Total RNA in exosomes was subjected to high-throughput sequencing to identify differentially expressed miRNAs. MicroRNA target gene prediction was performed, and target genes were analyzed by Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and mirPath to identify function. Reverse transcription quantitative PCR was performed to confirm the differentially expressed miRNAs. Results: Fifty-three unique miRNAs were identified (adjusted p < 0.05, fold of change >2), among which 32 miRNAs were upregulated and 21 miRNAs were downregulated in coronary artery disease patients. Reverse transcription quantitative PCR validated the results for seven miRNAs including miR-141-3p, miR-183-5p, miR-200a-5p, miR-205-5p, miR-429, miR-382-5p and miR-485-3p, with the last two downregulated. GO and KEGG analysis by mirPath indicated that these differentially expressed miRNAs were enriched in cell survival, apoptosis, proliferation, and differentiation. Conclusions: Coronary artery disease patients showed differential epicardial adipose tissue exosomal miRNA expression compared with patients without coronary artery disease. The results provide clues for further studies of mechanisms of atherosclerosis.
Liu, J, Li, J, Fang, J, Liu, K, Su, Y & Wu, C 2022, 'Investigation of ultra-high performance concrete slabs under contact explosions with a calibrated K&C model', Engineering Structures, vol. 255, pp. 113958-113958.
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Karagozian and Case (K&C) concrete model is extensively adopted in the numerical simulations of ultra-high performance concrete (UHPC) structural members subjected to impulsive loads such as impact and blast. In this study, a calibration of the K&C concrete model was conducted for UHPC in terms of three strength surfaces, equation of state, shear dilatancy, damage evolution and strain rate effect to offer simple and general guidelines on the determination of key model parameters for this new class of concrete. With the calibrated concrete model, a single element method was adopted to verify its accuracy through a comparison to the results from the static tests of the uniaxial compression, direct tension and triaxial compression. Furthermore, the numerical simulations of contact explosion tests on the UHPC slabs with the incorporation of the strain rate effect were performed and the numerical results exhibited good predictions regarding the failure mode, crater and scabbing damage as compared to the test results. More importantly, this proposed numerical model and simulation methodology are reasonable to be generally used for structural members constructed of UHPC materials under contact explosions when lacking sufficient static and dynamic test data. Using the calibrated and validated K&C concrete model, parametric studies were conducted to derive a new empirical equation for predicting the local damage mode of UHPC slabs under contact explosions.
Liu, J, Li, J, Fang, J, Su, Y & Wu, C 2022, 'Ultra-high performance concrete targets against high velocity projectile impact – a-state-of-the-art review', International Journal of Impact Engineering, vol. 160, pp. 104080-104080.
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Known for its high mechanical strength and ductility, ultra-high performance concrete (UHPC) emerges as a promising material in civil and military constructions to resist hazardous loads such as high velocity projectile impact (HVPI). Due to its unique material properties, structures built with UHPC perform differently to its counterparts made of conventional concrete under HVPI, and thus the empirical and semi-empirical resistant functions for conventional concrete against HVPI require careful evaluation before application to UHPC structures. This study presents a comprehensive review of the research advances in thick UHPC targets to resist HVPI for projectiles at normal incidence. First, the static and dynamic material properties of UHPC are briefly introduced in comparison to conventional concrete. Second, based on physical tests, key aspects in UHPC design to resist HVPI are reviewed, which include fibre reinforcement, high strength coarse aggregate, alternative binder system as well as structural reinforcement and designs. Third, in a view of the development in material constitutive models under complex dynamic loads and computational techniques, numerical simulations of UHPC under HVPI are reviewed and discussed. Further, empirical and semi-empirical formulae to predict the depth of penetration (DOP) on conventional concrete are collected and evaluated on their suitability for UHPC.
Liu, J, Liu, C, Qu, K, Li, J & Wu, C 2022, 'Calibration of Holmquist Johnson Cook (HJC) model for projectile penetration of geopolymer-based ultra-high performance concrete (G-UHPC)', Structures, vol. 43, pp. 149-163.
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Holmquist Johnson Cook (HJC) model has been extensively adopted to simulate the projectile penetration of concrete targets. In this study, based on the available experimental data of uniaxial compression, triaxial compression, split Hopkinson pressure bar (SHPB) and Hugoniot tests, HJC model parameters in terms of the strength surface, strain rate effect, damage evolution and equation of state (EOS) were systematically calibrated for a newly fabricated ultra-high performance concrete termed as geopolymer-based ultra-high performance concrete (G-UHPC). Using the HJC model with calibrated model parameters, numerical simulations of projectile penetration into plain and fibre reinforced G-UHPC targets were performed in a commercial finite element program LS-DYNA. The numerical results for the depth of penetration (DOP) exhibited fair agreement with the test data. The numerical projectile velocity and displacement evolutions were also validated through comparing to the semi-analytical model. These observations demonstrated the applicability and validity of the calibrated HJC model to estimate DOP of G-UHPC targets subjected to projectile impact. With the calibrated and validated HJC model, parametric studies were further conducted to explore the effect of uniaxial compressive strength of G-UHPC, projectile impact velocity, mass, diameter and nose shape on the final DOP values. Based on the numerical results from the parametric studies, an empirical equation concerning the aforementioned variables was proposed, which could help design G-UHPC protective barriers against projectile penetration.
Liu, J, Liu, C, Xu, S, Li, J, Fang, J, Su, Y & Wu, C 2022, 'G-UHPC slabs strengthened with high toughness and lightweight energy absorption materials under contact explosions', Journal of Building Engineering, vol. 50, pp. 104138-104138.
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This study investigates the dynamic characteristics of geopolymer-based ultra-high performance concrete (G-UHPC) slabs strengthened with high toughness and lightweight energy absorption materials under the 1 kg TNT contact explosions. A total of four slabs were tested, including plain G-UHPC slab (G-UHPC-P), 20-layer basalt textile reinforced G-UHPC slab (G-UHPC-BFM), 20-layer steel wire mesh reinforced G-UHPC slab (G-UHPC-SWM) and 1.5 vol-% steel fibre reinforced G-UHPC slab with polyurethane coating (G-UHPC–SF–PU). The test results revealed that the steel wire mesh reinforcement was more effective in resisting contact explosions than the basalt textile reinforcement for G-UHPC. The polyurethane coating on the rear face of the slab exhibited its high tensile strength and deformability to absorb the blast-induced energy so as to enhance the anti-explosion performance of the slab, and additionally prevented the splash of slab fragments upon contact explosions to minimise secondary hazards. Based on the multi-material arbitrary Lagrangian-Eulerian (ALE) algorithm, local damage of G-UHPC-SWM and G-UHPC–SF–PU induced by contact explosions was reproduced using the explicit finite element software LS-DYNA. Fair agreement between the numerical and test results demonstrated that the numerical model could simulate the response of G-UHPC-SWM and G-UHPC–SF–PU with reasonable accuracy. Extensive numerical studies by varying polyurethane strain rate, coating thickness and the bonding between the polyurethane coating and the slab were further performed to analyse their effect on the maximum bulge depth of the polyurethane coating subjected to contact explosions.
Liu, J, Singh, AK & Lin, C-T 2022, 'Corrections to “Predicting the Quality of Spatial Learning via Virtual Global Landmarks”', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 2971-2971.
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IN THE above article [1], we detected an error in reporting the 10-fold cross-validation result. The correct 10-fold cross-validation result in Table IV is uploaded in this letter. The corrected result of the cross-validation is consistent with our findings [1] that the EEG data associated with virtual global landmark (VGL) [2], [3] stimuli from the VGL group had an overall improvement in Acc and F1 scores compared to local landmarks from the non-VGL group, where Acc and F1 scores improved averagely 14.89% and 21.77%, respectively. (Table Presented).
Liu, J, Singh, AK & Lin, C-T 2022, 'Predicting the Quality of Spatial Learning via Virtual Global Landmarks', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 2418-2425.
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Analyzing the effects landmarks have on spatial learning is an active area of research in the study of human navigation processes and one that is key to understanding the links between human brain dynamics, landmark encoding, and spatial learning outcomes. This article presents a study on whether electroencephalography (EEG) signals related to virtual global landmarks combined with deep learning can be used to predict the accuracy and efficacy of spatial learning. Virtual global landmarks are silhouettes of actual landmarks projected into the navigator's vision via a heads-up display. They serve as a notable frame of reference in addition to the local landmarks we all typically use for route navigation. From a mobile virtual reality scenario involving 55 participants, the results of the study suggest that the EEG data associated with those who were exposed to global landmarks shows a visibly better capacity for predicting the quality of spatial learning levels than those who were not. As such, the EEG features associated with processing VGLs have a greater functional relation to the quality of spatial learning. This finding opens up a future direction of enquiry into landmark encoding and navigational ability. It may also provide a potential avenue for the early diagnosis of Alzheimer's disease.
Liu, J, Singh, AK & Lin, C-T 2022, 'Using virtual global landmark to improve incidental spatial learning', Scientific Reports, vol. 12, no. 1, p. 6744.
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AbstractTo reduce the decline of spatial cognitive skills caused by the increasing use of automated GPS navigation, the virtual global landmark (VGL) system is proposed to help people naturally improve their sense of direction. Designed to accompany a heads-up navigation system, VGL system constantly displays silhouette of global landmarks in the navigator’s vision as a notable frame of reference. This study exams how VGL system impacts incidental spatial learning, i.e., subconscious spatial knowledge acquisition. We asked 55 participants to explore a virtual environment and then draw a map of what they had explored while capturing electroencephalogram (EEG) signals and eye activity. The results suggest that, with the VGL system, participants paid more attention during exploration and performed significantly better at the map drawing task—a result that indicates substantially improved incidental spatial learning. This finding might kickstart a redesigning navigation aids, to teach users to learn a route rather than simply showing them the way.
Liu, J, Singh, AK, Wunderlich, A, Gramann, K & Lin, C-T 2022, 'Redesigning navigational aids using virtual global landmarks to improve spatial knowledge retrieval', npj Science of Learning, vol. 7, no. 1, p. 17.
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AbstractAlthough beacon- and map-based spatial strategies are the default strategies for navigation activities, today’s navigational aids mostly follow a beacon-based design where one is provided with turn-by-turn instructions. Recent research, however, shows that our reliance on these navigational aids is causing a decline in our spatial skills. We are processing less of our surrounding environment and relying too heavily on the instructions given. To reverse this decline, we need to engage more in map-based learning, which encourages the user to process and integrate spatial knowledge into a cognitive map built to benefit flexible and independent spatial navigation behaviour. In an attempt to curb our loss of skills, we proposed a navigation assistant to support map-based learning during active navigation. Called the virtual global landmark (VGL) system, this augmented reality (AR) system is based on the kinds of techniques used in traditional orienteering. Specifically, a notable landmark is always present in the user’s sight, allowing the user to continuously compute where they are in relation to that specific location. The efficacy of the unit as a navigational aid was tested in an experiment with 27 students from the University of Technology Sydney via a comparison of brain dynamics and behaviour. From an analysis of behaviour and event-related spectral perturbation, we found that participants were encouraged to process more spatial information with a map-based strategy where a silhouette of the compass-like landmark was perpetually in view. As a result of this technique, they consistently navigated with greater efficiency and better accuracy.
Liu, J, Wang, X, Shen, S, Fang, Z, Yu, S, Yue, G & Li, M 2022, 'Intelligent Jamming Defense Using DNN Stackelberg Game in Sensor Edge Cloud', IEEE Internet of Things Journal, vol. 9, no. 6, pp. 4356-4370.
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To ensure an accurate power allocation against increasing intelligent jamming attacks on the offloading link of computation tasks, we investigate interactions between a cluster head node and an intelligent jammer using a Stackelberg game framework, under the constraint of the total power to use and the limited knowledge of its own channel gain for each player. In this game, the intelligent jammer gathers channel gain information and processes it using a deep neural network (DNN) to infer the accurate jamming power as an attack strategy. The cluster head node also exploits DNN to infer an accurate transmission power as a defense strategy according to the varying channel gain. We model the optimization of the attack and defense strategies using single channel jamming DNN (SJnet), multiple channel jamming DNN (MJnet), single channel sensor DNN (SSnet), and multiple channel sensor DNN (MSnet) for the single (multiple) channel jamming attacks. In addition, we extend the design to the scenario where the intelligent jammer can launch a hybrid mode jamming attack, and propose a DNN Stackelberg game-based defense scheme. Numerical simulation results demonstrate that our proposed mechanism is superior to other power allocation mechanisms under different scenarios in the sensor edge cloud.
Liu, K, Song, R, Li, J, Guo, T, Li, X, Yang, J & Yan, Z 2022, 'Effect of steel fiber type and content on the dynamic tensile properties of ultra-high performance cementitious composites (UHPCC)', Construction and Building Materials, vol. 342, pp. 127908-127908.
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As one of the most promising construction and building materials in the past 30 years, ultra-high performance concrete (UHPC) has attracted a great deal of attention and the studies on its improvement and dynamic tensile behaviors become more important for its wider use in the related fields. In this contribution, a novel ultra-high performance cementitious composites (UHPCC) was developed with lower cement content, 20% of cement is replaced with fly ash and blast furnace slag, to reduce the raw material cost and carbon emission in production. Uniaxial compression, quasi-static and dynamic splitting tests were carried out to investigate the influences of steel fiber type (straight fiber and waved fiber) and content (volume fraction (0%, 1%, 2%)) on the uniaxial compressive strength, static tensile strength and dynamic tensile behaviors for current UHPCC material. The dynamic splitting tests were conducted on 100 disc specimens (75 mm in diameter and 37.5 mm in thickness) under five different impact pressure at a strain-rate range of 20–110 s−1 to study the strain rate effect by using a Split Hopkinson pressure bar (SHPB) system and the samples’ failure processes were captured by a high-speed camera. Based on the test results, the variations of energy absorption with the fiber type and content under static and dynamic tensile conditions are also analyzed, and combined with the experimental data from previous studies, an improved empirical tensile strength rate sensitivity (DIFft) model is proposed, which agrees well with the experiment results and can be used in the numerical simulation. At last, the failure process and pattern of different types of UHPCCs in the dynamic splitting test are also studied.
Liu, K, Wu, C, Li, X, Tao, M, Li, J, Liu, J & Xu, S 2022, 'Fire damaged ultra-high performance concrete (UHPC) under coupled axial static and impact loading', Cement and Concrete Composites, vol. 126, pp. 104340-104340.
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The load bearing structural components such as columns could experience axial static loads during the service life. High temperature induced by fire would have a significant detrimental impact on the mechanical properties of concrete materials. The structure could be severely damaged as the column was simultaneously loaded by other impact loads. In this study, the behavior of fire damaged ultra-high performance concrete (UHPC) with a compressive strength of 128.3 MPa under coupled axial static and impact loading was studied. UHPC specimens were heated to the target temperatures (250, 500 and 750 °C) in an electric furnace and then naturally cooled down to room temperature. The results demonstrated that the P-wave velocity and compressive strength of the heated-cooled treatment UHPC degraded significantly as the target temperature exceeded 250 °C. The impact tests were then conducted on the heated-cooled treatment UHPC specimens with axial static compression. The experimental results indicated that the axial static compression could enhance the dynamic mechanical properties such as compressive strength and elastic modulus in the elastic phase and weaken the dynamic mechanical properties in the plastic phase. In addition, the dynamic increase factor (DIF) of UHPC exhibited an increase with the temperature. The UHPC specimen could withstand a temperature of 250 °C, but lost most of its strength at temperatures of 500 and 750 °C. Thus, the axially loaded static force accelerated the failure of the specimen after being heated to above 250 °C.
Liu, L, Guo, Y, Yin, W, Lei, G & Zhu, J 2022, 'Design and Optimization Technologies of Permanent Magnet Machines and Drive Systems Based on Digital Twin Model', Energies, vol. 15, no. 17, pp. 6186-6186.
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One of the keys to the success of the fourth industrial revolution (Industry 4.0) is to empower machinery with cyber–physical systems connectivity. The digital twin (DT) offers a promising solution to tackle the challenges for realizing digital and smart manufacturing which has been successfully projected in many scenes. Electrical machines and drive systems, as the core power providers in many appliances and industrial equipment, are supposed to be reinforced on the verge of Industry 4.0 in the fields of design optimization, fault prognostic and coordinated control. Therefore, this paper aims to investigate the DT modelling method and the applications in electrical drive systems. Firstly, taking the high-speed permanent-magnet machine drive system as an example, multi-disciplinary design fundamentals and technologies, aiming at building initial mechanism and simulation models, are reviewed. The state-of-the-art of DT technologies is figured out to serve for high-precision and multi-scale dynamic modelling, by which a framework for DT models of electrical drive systems is presented. More importantly, fault diagnosis and optimization strategies of electrical drive systems in the decision and application layer are also discussed for the DT models, followed by the conclusions presenting open questions and possible directions.
Liu, L, Ji, J, Li, B, Miao, Z & Zhou, J 2022, 'Distributed Stochastic Consensus of Networked Nonholonomic Mobile Robots and Its Formation Application', Journal of Dynamic Systems, Measurement, and Control, vol. 144, no. 11.
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Abstract This paper proposes a novel distributed stochastic consensus seeking algorithm for networked nonholonomic wheeled mobile robots (NNWMRs) and its application to consensus-based formation. Time-varying delays and noisy measurement are incorporated into the dynamic model to represent two key issues inherently appearing in the communication and information exchange process among robots. Based on backstepping technique and sliding mode approach, the proposed consensus algorithm integrates kinematic controller and dynamic torque controller into the control protocol. A key feature of the proposed consensus algorithm is the introduction of the consensus gains, which characterizes the effects of time delays and noisy measurement. A unified methodology is provided for the convergence analysis of the networked system by using the generalized stochastic delayed Halanay inequality. It is shown that time delays and noisy measurement can play crucial roles in distributed consensus seeking in collaborative multirobot systems. Illustrative examples and simulations are provided to demonstrate and validate the theoretical results.
Liu, L, Yang, R, Cui, J, Chen, P, Ri, HC, Sun, H, Piao, X, Li, M, Pu, Q, Quinto, M, Zhou, JL, Shang, H-B & Li, D 2022, 'Circular Nonuniform Electric Field Gel Electrophoresis for the Separation and Concentration of Nanoparticles', Analytical Chemistry, vol. 94, no. 23, pp. 8474-8482.
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A circular nonuniform electric field strategy coupled with gel electrophoresis was proposed to control the precise separation and efficient concentration of nano- and microparticles. The circular nonuniform electric field has the feature of exponential increase in the electric field intensity along the radius, working with three functional zones of migration, acceleration, and concentration. The distribution form of electric field lines is regulated in functional zones to control the migration behaviors of particles for separation and concentration by altering the relative position of the ring electrode (outside) and rodlike electrode (inner). The circular nonuniform electric field promotes the target-type and high-precision separation of nanoparticles based on the difference in charge-to-size ratio. The concentration multiple of nanoparticles is also controlled randomly with the alternation of radius, taking advantage of vertical extrusion and concentric converging of the migration path. This work provides a brand new insight into the simultaneous separation and concentration of particles and is promising for developing a versatile tool for the separation and preparation of various samples instead of conventional methods.
Liu, L-Y, Xie, G-J, Ding, J, Liu, B-F, Xing, D-F, Ren, N-Q & Wang, Q 2022, 'Microbial methane emissions from the non-methanogenesis processes: A critical review', Science of The Total Environment, vol. 806, no. Pt 4, pp. 151362-151362.
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Methane, a potent greenhouse gas of global importance, has traditionally been considered as an end product of microbial methanogenesis of organic matter. Paradoxically, growing evidence has shown that some microbes, such as cyanobacteria, algae, fungi, purple non-sulfur bacteria, and cryptogamic covers, produce methane in oxygen-saturated aquatic and terrestrial ecosystems. The non-methanogenesis process could be an important potential contributor to methane emissions. This systematic review summarizes the knowledge of microorganisms involved in the non-methanogenesis process and the possible mechanisms of methane formation. Cyanobacteria-derived methane production may be attributed to either demethylation of methyl phosphonates or linked to light-driven primary productivity, while algae produce methane by utilizing methylated sulfur compounds as possible carbon precursors. In addition, fungi produce methane by utilizing methionine as a possible carbon precursor, and purple non-sulfur bacteria reduce carbon dioxide to methane by nitrogenase. The microbial methane distribution from the non-methanogenesis processes in aquatic and terrestrial environments and its environmental significance to global methane emissions, possible mechanisms of methane production in each open water, water-to-air methane fluxes, and the impact of climate change on microorganisms are also discussed. Finally, future perspectives are highlighted, such as establishing more in-situ experiments, quantifying methane flux through optimizing empirical models, distinguishing individual methane sources, and investigating nitrogenase-like enzyme systems to improve our understanding of microbial methane emission from the non-methanogenesis process.
Liu, M, Blankenship, JR, Levi, AE, Fu, Q, Hudson, ZM & Bates, CM 2022, 'Miktoarm Star Polymers: Synthesis and Applications', Chemistry of Materials, vol. 34, no. 14, pp. 6188-6209.
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Polymers with precisely controlled structure and function are in high demand across a diverse array of applications spanning the life sciences and nanotechnology. One prototypical example is a class of branched block copolymers known as miktoarm stars (μ-stars), which contain two or more arm compositions connected at a common junction. Miktoarm stars have attracted considerable attention since their physical properties can be different from conventional linear block copolymers. This perspective highlights the latest developments and historical context in the field of miktoarm star polymers, including design strategies, synthetic techniques, and advanced characterization tools used to avoid common preparation pitfalls and tailor properties for emerging applications. Our contemporary perspective on μ-star polymers is a resource for inspiring future research into this exciting class of materials at the intersection of chemistry, physics, and advanced technology.
Liu, M, Nothling, MD, Zhang, S, Fu, Q & Qiao, GG 2022, 'Thin film composite membranes for postcombustion carbon capture: Polymers and beyond', Progress in Polymer Science, vol. 126, pp. 101504-101504.
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Liu, Q & Cao, L 2022, 'Modeling time evolving COVID-19 uncertainties with density dependent asymptomatic infections and social reinforcement', Scientific Reports, vol. 12, no. 1, p. 5891.
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AbstractThe COVID-19 pandemic has posed significant challenges in modeling its complex epidemic transmissions, infection and contagion, which are very different from known epidemics. The challenges in quantifying COVID-19 complexities include effectively modeling its process and data uncertainties. The uncertainties are embedded in implicit and high-proportional undocumented infections, asymptomatic contagion, social reinforcement of infections, and various quality issues in the reported data. These uncertainties become even more apparent in the first 2 months of the COVID-19 pandemic, when the relevant knowledge, case reporting and testing were all limited. Here we introduce a novel hybrid approach SUDR by expanding the foundational compartmental epidemic Susceptible-Infected-Recovered (SIR) model with two compartments to a Susceptible-Undocumented infected-Documented infected-Recovered (SUDR) model. First, SUDR (1) characterizes and distinguishes Undocumented (U) and Documented (D) infections commonly seen during COVID-19 incubation periods and asymptomatic infections. Second, SUDR characterizes the probabilistic density of infections by capturing exogenous processes like clustering contagion interactions, superspreading, and social reinforcement. Lastly, SUDR approximates the density likelihood of COVID-19 prevalence over time by incorporating Bayesian inference into SUDR. Different from existing COVID-19 models, SUDR characterizes the undocumented infections during unknown transmission processes. To capture the uncertainties of temporal transmission and social reinforcement during COVID-19 contagion, the transmission rate is modeled by a time-varying density function of undocumented infectious cases. By sampling from the mean-field posterior distribution with reasonable priors, SUDR handles the randomness, noise and sparsity of COVID-19 observations widely seen in the public COVID-19 case data. The results demonstrat...
Liu, Q, Zhang, Q, Jiang, S, Du, Z, Zhang, X, Chen, H, Cao, W, Nghiem, LD & Ngo, HH 2022, 'Enhancement of lead removal from soil by in-situ release of dissolved organic matters from biochar in electrokinetic remediation', Journal of Cleaner Production, vol. 361, pp. 132294-132294.
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Liu, T, Hu, X, Xu, H, Shu, T & Nguyen, DN 2022, 'High-accuracy low-cost privacy-preserving federated learning in IoT systems via adaptive perturbation', Journal of Information Security and Applications, vol. 70, pp. 103309-103309.
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Liu, T, Zhang, W, Li, J, Ueland, M, Forbes, SL, Zheng, WX & Su, SW 2022, 'A Multiscale Wavelet Kernel Regularization-Based Feature Extraction Method for Electronic Nose', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 11, pp. 7078-7089.
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Liu, W, Wang, H, Shen, X & Tsang, IW 2022, 'The Emerging Trends of Multi-Label Learning', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 11, pp. 7955-7974.
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Exabytes of data are generated daily by humans, leading to the growing needs for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is an active and rapidly growing research area that deals with classification tasks with extremely large number of classes or labels; utilizing massive data with limited supervision to build a multi-label classification model becomes valuable for practical applications, etc. Besides these, there are tremendous efforts on how to harvest the strong learning capability of deep learning to better capture the label dependencies in multi-label learning, which is the key for deep learning to address real-world classification tasks. However, it is noted that there have been a lack of systemic studies that focus explicitly on analyzing the emerging trends and new challenges of multi-label learning in the era of big data. It is imperative to call for a comprehensive survey to fulfil this mission and delineate future research directions and new applications.
Liu, X, Deng, Q, Zheng, Y, Wang, D & Ni, B-J 2022, 'Microplastics aging in wastewater treatment plants: Focusing on physicochemical characteristics changes and corresponding environmental risks', Water Research, vol. 221, pp. 118780-118780.
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Microplastics (MPs) have been frequently detected in effluent wastewater and sludge in wastewater treatment plants (WWTPs), the discharge and agricultural application of which represent a primary source of environmental MPs contamination. As important as quantitative removal is, changes of physicochemical characteristics of MPs (e.g., shapes, sizes, density, crystallinity) in WWTPs are crucial to their environmental behaviors and risks and have not been put enough attention yet. This review is therefore to provide a current overview on the changes of physicochemical characteristics of MPs in WWTPs and their corresponding environmental risks. The changes of physicochemical characteristics as well as the underlying mechanisms of MPs in different successional wastewater and sludge treatment stages that mainly driven by mechanical (e.g., mixing, pumping, filtering), chemical (e.g., flocculation, advanced oxidation, ultraviolet radiation, thermal hydrolysis, incineration and lime stabilization), biological (e.g., activated sludge process, anaerobic digestion, composition) and their combination effects were first recapitulated. Then, the inevitable correlations between physicochemical characteristics of MPs and their environmental behaviors (e.g., migration, adsorption) and risks (e.g., animals, plants, microbes), are comprehensively discussed with particular emphasis on the leaching of additives and physicochemical characteristics that affect the co-exist pollutants behavior of MPs in WWTPs on environmental risks. Finally, knowing the summarized above, some relating unanswered questions and concerns that need to be unveiled in the future are prospected. The physicochemical properties of MPs change after passing through WWTP, leading to subsequent changes in co-contaminant adsorption, migration, and toxicity. This could threaten our ecosystems and human health and must be worth investigating.
Liu, X, Duan, X, Bao, T, Hao, D, Chen, Z, Wei, W, Wang, D, Wang, S & Ni, B-J 2022, 'High-performance photocatalytic decomposition of PFOA by BiOX/TiO2 heterojunctions: Self-induced inner electric fields and band alignment', Journal of Hazardous Materials, vol. 430, pp. 128195-128195.
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BiOX (X = Cl, Br and I) and BiOX/TiO2 photocatalysts were prepared by a facile hydrothermal approach. The BiOX/TiO2 heterojunctions demonstrated significantly enhanced efficiency for photocatalytic decomposition of perfluorooctanoic acid (PFOA) compared with sole BiOX or TiO2. PFOA (10 mg L1) was completely degraded by BiOCl(Br)/TiO2 in 8 h. Moreover, BiOCl/TiO2 attained deep decomposition of PFOA with a high defluorination ratio of 82%. The p-n heterojunctions between BiOX and TiO2 were confirmed by a series of characterizations. The photo-induced holes would migrate from the valance band (VB) of TiO2 to BiOX, driven by the built-in electric field (BIEF) near the interfaces of p-n heterojunctions, the inner electric fields (IEF) in BiOX and the higher VB position of BiOX. The X-ray diffraction (XRD) and TEM characterizations indicated that TiO2 combined with BiOX along the [110] facet, which facilitated photo-induced electron transfer in the [001] direction, thus benefiting PFOA decomposition.
Liu, X, Wang, D, Chen, Z, Wei, W, Mannina, G & Ni, B-J 2022, 'Advances in pretreatment strategies to enhance the biodegradability of waste activated sludge for the conversion of refractory substances', Bioresource Technology, vol. 362, pp. 127804-127804.
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Anaerobic digestion (AD) is a low-cost technology widely used to divert waste activated sludge (WAS) to renewable energy production, but is generally restricted by its poor biodegradability which mainly caused by the endogenous and exogenous refractory substances present in WAS. Several conventional methods such as thermal-, chemical-, and mechanical-based pretreatment have been demonstrated to be effective on organics release, but their functions on refractory substances conversion are overlooked. This paper firstly reviewed the presence and role of endogenous and exogenous refractory substances in anaerobic biodegradability of WAS, especially on their inhibition mechanisms. Then, the pretreatment strategies developed for enhancing WAS biodegradability by facilitating refractory substances conversion were comprehensively reviewed, with the conversion pathways and underlying mechanisms being emphasized. Finally, the future research needs were directed, which are supposed to improve the circular bioeconomy of WAS management from the point of removing the hindering barrier of refractory substances on WAS biodegradability.
Liu, X, Wang, L, Luo, Q, Bai, Z, Li, Q & Hu, J 2022, 'A New Stress-Based Formulation for Modeling Notched Fiber-Reinforced Laminates', Polymers, vol. 14, no. 24, pp. 5552-5552.
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Laminated plates are often modeled with infinite dimensions in terms of the so-called Whitney–Nuismer (WN) stress criteria, which form a theoretical basis for predicting the residual properties of open-hole structures. Based upon the WN stress criteria, this study derived a new formulation involving finite width; the effects of notch shape and size on the applicability of new formulae and the tensile properties of carbon-fiber-reinforced plastic (CFRP) laminates were investigated via experimental and theoretical analyses. The specimens were prepared by using laminates reinforced by plain woven carbon fiber fabrics and machined with or without an open circular hole or a straight notch. Standard tensile tests were performed and measured using the digital image correlation (DIC) technique, aiming to characterize the full-field surface strain. Continuum damage mechanics (CDMs)-based finite element models were developed to predict the stress concentration factors and failure processes of notched specimens. The characteristic distances in the stress criterion models were calibrated using the experimental results of un-notched and notched specimens, such that the failure of carbon fiber laminates with or without straight notches could be analytically predicted. The experimental results demonstrated well the effectiveness of the present formulations. The new formula provides an effective approach to implementing a finite-width stress criterion for evaluating the tensile properties of notched fiber-reinforced laminates. In addition, the notch size has a great effect on strength prediction while the fiber direction has a great influence on the fracture mode.
Liu, X, Zhu, T, Jiang, C, Ye, D & Zhao, F 2022, 'Prioritized Experience Replay based on Multi-armed Bandit', Expert Systems with Applications, vol. 189, pp. 116023-116023.
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Experience replay has been widely used in deep reinforcement learning. The learning algorithm allows online reinforcement learning agents to remember and reuse experiences from the past. In order to further improve the sampling efficiency for experience replay, the most useful experiences are expected to be sampled with higher frequency. Existing methods usually designed their sampling strategy according to a few criteria, but they tended to combine different criteria in a linear or fixed manner, where the strategy were static and independent of the agent learner. This ignores the dynamic attribute of the environment and thus can only lead to a suboptimal performance. In this work, we propose a dynamic experience replay strategy according to the interaction between the agent and environment, which is called Prioritized Experience Replay based on Multi-armed Bandit (PERMAB). PERMAB can adaptively combine multiple priority criteria to measure the importance of the experience. In particular, the weight of each assessing criterion can be adaptively adjusted from episode to episode according to their respective contribution to the agent performance, which guarantees useful criterion to be weighted more in its current state. The proposed replay strategy is able to take both sample informativeness and diversity into consideration, which could significantly boosts learning ability and speed of the game agent. Experimental results show that PERMAB accelerates the network learning and achieves a better performance compared to baseline algorithms on seven benchmark environments with various difficulties.
Liu, Y, Dong, D, Petersen, IR & Yonezawa, H 2022, 'Fault-tolerant H∞ control for optical parametric oscillators with pumping fluctuations', Automatica, vol. 140, pp. 110236-110236.
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Liu, Y, Dong, D, Petersen, IR, Gao, Q, Ding, SX, Yokoyama, S & Yonezawa, H 2022, 'Fault-Tolerant Coherent $H^\infty$ Control for Linear Quantum Systems', IEEE Transactions on Automatic Control, vol. 67, no. 10, pp. 5087-5101.
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Liu, Y, Liu, Z, Gao, K, Huang, Y & Zhu, C 2022, 'Efficient Graphical Algorithm of Sensor Distribution and Air Volume Reconstruction for a Smart Mine Ventilation Network', Sensors, vol. 22, no. 6, pp. 2096-2096.
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The accurate and reliable monitoring of ventilation parameters is key to intelligent ventilation systems. In order to realize the visualization of airflow, it is essential to solve the airflow reconstruction problem using few sensors. In this study, a new concept called independent cut set that depends on the structure of the underlying graph is presented to determine the minimum number and location of sensors. We evaluated its effectiveness in a coal mine owned by Jinmei Corporation Limited (Jinmei Co., Ltd., Shanghai, China). Our results indicated that fewer than 30% of tunnels needed to have wind speed sensors set up to reconstruct the well-posed airflow of all the tunnels (>200 in some mines). The results showed that the algorithm was feasible. The reconstructed air volume of the ventilation network using this algorithm was the same as the actual air volume. The algorithm provides theoretical support for flow reconstruction.
Liu, Y, Luo, G, Ngo, HH & Zhang, S 2022, 'New approach of bioprocessing towards lignin biodegradation', Bioresource Technology, vol. 361, pp. 127730-127730.
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Liu, Y, Wang, L, Shi, T & Li, J 2022, 'Detection of spam reviews through a hierarchical attention architecture with N-gram CNN and Bi-LSTM', Information Systems, vol. 103, pp. 101865-101865.
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Liu, Y, Wang, W, Zhang, D, Sun, Y, Li, F, Zheng, M, Lovejoy, DB, Zou, Y & Shi, B 2022, 'Brain co‐delivery of first‐line chemotherapy drug and epigenetic bromodomain inhibitor for multidimensional enhanced synergistic glioblastoma therapy', Exploration, vol. 2, no. 4.
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AbstractGlioblastoma (GBM) is a central nervous system tumor with poor prognosis due to the rapid development of resistance to mono chemotherapy and poor brain targeted delivery. Chemoimmunotherapy (CIT) combines chemotherapy drugs with activators of innate immunity that hold great promise for GBM synergistic therapy. Herein, we chose temozolomide, TMZ, and the epigenetic bromodomain inhibitor, OTX015, and further co‐encapsulated them within our well‐established erythrocyte membrane camouflaged nanoparticle to yield ApoE peptide decorated biomimetic nanomedicine (ABNM@TMZ/OTX). Our nanoplatform successfully addressed the limitations in brain‐targeted drug co‐delivery, and simultaneously achieved multidimensional enhanced GBM synergistic CIT. In mice bearing orthotopic GL261 GBM, treatment with ABNM@TMZ/OTX resulted in marked tumor inhibition and greatly extended survival time with little side effects. The pronounced GBM treatment efficacy can be ascribed to three key factors: (i) improved nanoparticle‐mediated GBM targeting delivery of therapeutic agents by greatly enhanced blood circulation time and blood–brain barrier penetration; (ii) inhibited cellular DNA repair and enhanced TMZ sensitivity to tumor cells; (iii) enhanced anti‐tumor immune responses by inducing immunogenic cell death and inhibiting PD‐1/PD‐L1 conjugation leading to enhanced expression of CD4+ and CD8+ T cells. The study validated a biomimetic nanomedicine to yield a potential new treatment for GBM.
Liu, Y, Yao, L, Li, B, Sammut, C & Chang, X 2022, 'Interpolation graph convolutional network for 3D point cloud analysis', International Journal of Intelligent Systems, vol. 37, no. 12, pp. 12283-12304.
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The feature analysis of point clouds, a popular representation of three-dimensional (3D) objects, is rising as a hot research topic nowadays. Point cloud data bear a sparse and unordered nature, making many commonly used feature extraction methods, for example, Convolutional Neural Networks (CNNs) inapplicable, while previous models suitable for the task are usually complex. We aim to reduce model complexity by reducing the number of parameters while achieving better (or at least comparable) performance. We propose an Interpolation Graph Convolutional Network (IGCN) for extracting features of point clouds. IGCN uses the point cloud graph structure and a specially designed Interpolation Convolution Kernel to mimic the operations of CNN for feature extraction. On the basis of weight postfusion and multilevel-resolution aggregation, IGCN not only reduces the cost of calculating the interpolation operation but also improves the model's performance. We validate the performance of IGCN on both point cloud classification and segmentation tasks and explore the contribution of each module of our model through ablation experiments. Furthermore, we embed the IGCN point cloud feature extraction module as a plug-and-play module into other frameworks and perform point cloud registration experiments.
Liu, Y, Zhang, S, Fang, H, Wang, Q, Jiang, S, Zhang, C & Qiu, P 2022, 'Inactivation of antibiotic resistant bacterium Escherichia coli by electrochemical disinfection on molybdenum carbide electrode', Chemosphere, vol. 287, no. Pt 4, pp. 132398-132398.
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Antibiotic-resistant bacteria (ARB) pose a substantial threat to public health worldwide. Electrochemistry, as a low energy consumption and environmentally friendly technique, is ideal for inactivating ARB. This study explored the utility of electrochemical disinfection (ED) for inactivating ARB (Escherichia coli K-12 LE392 resistant to kanamycin, tetracycline, and ampicillin) and the regrowth potential of the treated ARB. The results revealed that 5.12-log ARB removal was achieved within 30 min of applying molybdenum carbide as the anode and cathode material under a voltage of 2.0 V. No ARB regrowth was observed in the cathode chamber after 60 min of incubation in unselective broth, demonstrating that the process in the cathode chamber was more effective for permanent inactivation of ARB. The mechanisms underlying the ARB inactivation were verified based on intercellular reactive oxygen species (ROS) measurement, membrane integrity detection, and genetic damage assessment. Higher ROS production and membrane permeability were observed in the cathode and anode groups (p < 0.001) compared to the control group (0 V). In addition, the DNA was more likely to be damaged during the ED process. Collectively, our results demonstrate that ED is a promising technology for disinfecting water to prevent the spread of ARB.
Liu, Z, Gao, Y, Yang, J, Xu, X, Fang, J & Xie, F 2022, 'Multi-objective optimization framework of a vehicle door design in the slamming event for optimal dynamic performances', Applied Acoustics, vol. 187, pp. 108526-108526.
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Liu, Z, Jahed Armaghani, D, Fakharian, P, Li, D, Vladimirovich Ulrikh, D, Nikolaevna Orekhova, N & Mohamed Khedher, K 2022, 'Rock Strength Estimation Using Several Tree-Based ML Techniques', Computer Modeling in Engineering & Sciences, vol. 133, no. 3, pp. 799-824.
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Liu, Z, Li, Y, Yao, L, Wang, X & Nie, F 2022, 'Agglomerative Neural Networks for Multiview Clustering', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 7, pp. 2842-2852.
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Conventional multiview clustering methods seek a view consensus through minimizing the pairwise discrepancy between the consensus and subviews. However, pairwise comparison cannot portray the interview relationship precisely if some of the subviews can be further agglomerated. To address the above challenge, we propose the agglomerative analysis to approximate the optimal consensus view, thereby describing the subview relationship within a view structure. We present an agglomerative neural network (ANN) based on constrained Laplacian rank to cluster multiview data directly without a dedicated postprocessing step (e.g., using K-means). We further extend ANN with a learnable data space to handle data of complex scenarios. Our evaluations against several state-of-the-art multiview clustering approaches on four popular data sets show the promising view-consensus analysis ability of ANN. We further demonstrate ANN's capability in analyzing complex view structures, extensibility through our case study and robustness and effectiveness of data-driven modifications.
Liu, Z, Wang, X, Li, Y, Yao, L, An, J, Bai, L & Lim, E-P 2022, 'Face to purchase: Predicting consumer choices with structured facial and behavioral traits embedding', Knowledge-Based Systems, vol. 235, pp. 107665-107665.
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Lloret-Cabot, M & Sheng, D 2022, 'Assessing the accuracy and efficiency of different order implicit and explicit integration schemes', Computers and Geotechnics, vol. 141, pp. 104531-104531.
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A first order accurate fully implicit integration scheme and four different order explicit substepping integration schemes with automatic error control are used in this paper to integrate the constitutive relations of a critical state model for saturated soils. Their respective computational performance in terms of accuracy and efficiency is assessed in order to provide practical guidance for deciding which of the five is most suitable for solving numerical problems in geotechnical engineering involving critical state models. Even though existing literature of integration schemes applied to geotechnical problems has traditionally been focussed on the first order accurate implicit backward Euler and on the second order accurate explicit modified Euler with substepping almost exclusively, the findings of this paper suggest that the little extra work required in the implementation of an explicit third order Runge-Kutta substepping scheme is worth the effort, especially in terms of computational cost.
Loban, R 2022, '“I never asked for it, but I got it and now I feel that my knowledge about history is even greater!”: Play, Encounter and Research in Europa Universalis IV', Journal of Games Criticism, vol. 5, no. 1.
Loban, R 2022, 'Torres Strait Virtual Reality: A Reflection on the Intersection between Culture, Game Design and Research', Games and Culture, vol. 17, no. 3, pp. 311-327.
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This article is a reflection on the development of Torres Strait Virtual Reality (TSVR), a virtual reality game created to raise awareness of one of Australia’s First Nations people, Torres Strait Islanders. Through the development of TSVR, the author discovered that the processes of cultural protocols, game design and research, intersected and enriched each other to produce a culturally sound and culturally centred game. This article explores project examples of these intersections, such as the converging practices of community engagement and playtesting, and the role of Indigenous cultural influences in game design choices. The cultural focus of TSVR is best represented through the Torres Strait Cultural Tree as a conceptual framework. The Torres Strait Cultural Tree exemplifies how cultural traditions and knowledge can be used to anchor cultural reproductions in new mediums and offers an Indigenous cultural framework for developing cultural-centred games.
Loengbudnark, W, Khalilpour, K, Bharathy, G, Taghikhah, F & Voinov, A 2022, 'Battery and hydrogen-based electric vehicle adoption: A survey of Australian consumers perspective', Case Studies on Transport Policy, vol. 10, no. 4, pp. 2451-2463.
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Loganathan, P, Kandasamy, J, Jamil, S, Ratnaweera, H & Vigneswaran, S 2022, 'Ozonation/adsorption hybrid treatment system for improved removal of natural organic matter and organic micropollutants from water – A mini review and future perspectives', Chemosphere, vol. 296, pp. 133961-133961.
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Loh, HW, Ooi, CP, Barua, PD, Palmer, EE, Molinari, F & Acharya, UR 2022, 'Automated detection of ADHD: Current trends and future perspective', Computers in Biology and Medicine, vol. 146, pp. 105525-105525.
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Attention deficit hyperactivity disorder (ADHD) is a heterogenous disorder that has a detrimental impact on the neurodevelopment of the brain. ADHD patients exhibit combinations of inattention, impulsiveness, and hyperactivity. With early treatment and diagnosis, there is potential to modify neuronal connections and improve symptoms. However, the heterogeneous nature of ADHD, combined with its comorbidities and a global shortage of diagnostic clinicians, means diagnosis of ADHD is often delayed. Hence, it is important to consider other pathways to improve the efficiency of early diagnosis, including the role of artificial intelligence. In this study, we reviewed the current literature on machine learning and deep learning studies on ADHD diagnosis and identified the various diagnostic tools used. Subsequently, we categorized these studies according to their diagnostic tool as brain magnetic resonance imaging (MRI), physiological signals, questionnaires, game simulator and performance test, and motion data. We identified research gaps include the paucity of publicly available database for all modalities in ADHD assessment other than MRI, as well as a lack of focus on using data from wearable devices for ADHD diagnosis, such as ECG, PPG, and motion data. We hope that this review will inspire future work to create more publicly available datasets and conduct research for other modes of ADHD diagnosis and monitoring. Ultimately, we hope that artificial intelligence can be extended to multiple ADHD diagnostic tools, allowing for the development of a powerful clinical decision support pathway that can be used both in and out of the hospital.
Loh, HW, Ooi, CP, Seoni, S, Barua, PD, Molinari, F & Acharya, UR 2022, 'Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022)', Computer Methods and Programs in Biomedicine, vol. 226, pp. 107161-107161.
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BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) has branched out to various applications in healthcare, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box. This lack of trust remains the main reason for their low use in practice, especially in healthcare. Hence, explainable artificial intelligence (XAI) has been introduced as a technique that can provide confidence in the model's prediction by explaining how the prediction is derived, thereby encouraging the use of AI systems in healthcare. The primary goal of this review is to provide areas of healthcare that require more attention from the XAI research community. METHODS: Multiple journal databases were thoroughly searched using PRISMA guidelines 2020. Studies that do not appear in Q1 journals, which are highly credible, were excluded. RESULTS: In this review, we surveyed 99 Q1 articles covering the following XAI techniques: SHAP, LIME, GradCAM, LRP, Fuzzy classifier, EBM, CBR, rule-based systems, and others. CONCLUSION: We discovered that detecting abnormalities in 1D biosignals and identifying key text in clinical notes are areas that require more attention from the XAI research community. We hope this is review will encourage the development of a holistic cloud system for a smart city.
Loh, HW, Xu, S, Faust, O, Ooi, CP, Barua, PD, Chakraborty, S, Tan, R-S, Molinari, F & Acharya, UR 2022, 'Application of photoplethysmography signals for healthcare systems: An in-depth review', Computer Methods and Programs in Biomedicine, vol. 216, pp. 106677-106677.
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Lou, B, Barbieri, DM, Passavanti, M, Hui, C, Gupta, A, Hoff, I, Lessa, DA, Sikka, G, Chang, K, Fang, K, Lam, L, Maharaj, B, Ghasemi, N, Qiao, Y, Adomako, S, Foroutan Mirhosseini, A, Naik, B, Banerjee, A, Wang, F, Tucker, A, Liu, Z, Wijayaratna, K, Naseri, S, Yu, L, Chen, H, Shu, B, Goswami, S, Peprah, P, Hessami, A, Abbas, M & Agarwal, N 2022, 'Air pollution perception in ten countries during the COVID-19 pandemic', Ambio, vol. 51, no. 3, pp. 531-545.
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AbstractAs largely documented in the literature, the stark restrictions enforced worldwide in 2020 to curb the COVID-19 pandemic also curtailed the production of air pollutants to some extent. This study investigates the perception of the air pollution as assessed by individuals located in ten countries: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the USA. The perceptions towards air quality were evaluated by employing an online survey administered in May 2020. Participants (N = 9394) in the ten countries expressed their opinions according to a Likert-scale response. A reduction in pollutant concentration was clearly perceived, albeit to a different extent, by all populations. The survey participants located in India and Italy perceived the largest drop in the air pollution concentration; conversely, the smallest variation was perceived among Chinese and Norwegian respondents. Among all the demographic indicators considered, only gender proved to be statistically significant.
Lowe, D, Goldfinch, T, Kadi, A, Willey, K & Wilkinson, T 2022, 'Engineering graduates professional formation: the connection between activity types and professional competencies', European Journal of Engineering Education, vol. 47, no. 1, pp. 8-29.
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Lu, L, Xiao, Y, Chang, X, Wang, X, Ren, P & Ren, Z 2022, 'Deformable attention-oriented feature pyramid network for semantic segmentation', Knowledge-Based Systems, vol. 254, pp. 109623-109623.
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In the field of computer vision, the use of pyramid features can significantly improve network performance. However, the misalignment of semantic information and the scale limitation of small-scale features lead to an imbalance of feature contributions, which severely limits the performance of the feature pyramid network. In order to solve the problem of model efficiency decline caused by feature contribution imbalance, in this paper, we propose a deformable attention-oriented feature pyramid network (DAFPN). Unlike previous models, which focus solely on the semantic information between features, DAFPN uses the deformable attention mechanism to model the relationship between multiple features and then merges them in the pyramid feature fusion process. Based on DAFPN, we further propose a fully transformer-based semantic segmentation head, which achieves high performance and good scalability. Comparisons on multiple backbones reveal that our proposed model outperforms the baseline model. Under the same conditions, our method can improve the mIoU by 1∼4%, which is higher than the baseline semantic segmentation model.
Lu, W, Wang, R, Wang, S, Peng, X, Wu, H & Zhang, Q 2022, 'Aspect-Driven User Preference and News Representation Learning for News Recommendation', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 25297-25307.
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Intelligent human-device interfaces play key roles in fully automated vehicles (FAVs), ensuring smooth interactions and improving the driving experience. Listening to news is a popular method of relaxing during a journey; as a result, travelers require automatic recommendations of preferred news programs. Most existing news recommender systems usually learn topic-level representations of users and news for recommendations while neglecting to learn more informative aspect-level features, resulting in limited recommendation performance. To bridge this significant gap, we propose a novel Aspect-driven News Recommender System (ANRS) built on aspect-level user preferences and news representation learning. In ANRS, a news aspect-level encoder and a user aspect-level encoder are devised to learn the fine-grained aspect-level representations of users’ preferences and news characteristics respectively. These representations are subsequently fed into a click predictor to predict the probability of a given user clicking on the candidate news item. Extensive experiments demonstrate the superiority of our method over state-of-the-art baseline methods.
Lu, W, Zhao, P, Li, Y, Wang, S, Huang, H, Shi, S & Wu, H 2022, 'Chinese sentence semantic matching based on multi-level relevance extraction and aggregation for intelligent human–robot interaction', Applied Soft Computing, vol. 131, pp. 109795-109795.
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With the development of Internet of Things and cloud computing, intelligent question-answering (QA) has brought great convenience to human's daily activities. As one of the core technologies, sentence semantic matching (SSM) plays a critical role in a variety of intelligent QA systems. However, existing SSM methods usually first encode sentences on either character or word level, and then model semantic interactions on sentence level. Consequently, they fail to capture the rich interactions on multi-levels (i.e., character, word and sentence levels). In this paper, we propose Chinese sentence semantic matching based on Multi-level Relevance Extraction and Aggregation (MREA) for intelligent QA. MREA can comprehensively capture and aggregate various semantic relevance on character, word and sentence levels respectively based on multiple attention mechanisms. Extensive experiments on two real-world datasets demonstrate that MREA outperforms the best-performing baselines by 0.5% and 0.89% w.r.t. ACC. and F1 respectively, and achieves comparable performance with BERT-based methods.
Lu, X, Cong Luong, N, Hoang, DT, Niyato, D, Xiao, Y & Wang, P 2022, 'Secure Wirelessly Powered Networks at the Physical Layer: Challenges, Countermeasures, and Road Ahead', Proceedings of the IEEE, vol. 110, no. 1, pp. 193-209.
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Harvesting wireless power to energize miniature devices has been envisioned as a promising solution to sustain future-generation energy-sensitive networks, e.g., Internet-of-Things systems. However, due to the limited computing and communication capabilities, wirelessly powered networks (WPNs) may be incapable of employing complex security practices, e.g., encryption, which may incur considerable computation and communication overheads. This challenge makes securing energy harvesting communications an arduous task and, thus, limits the use of WPNs in many high-security applications. In this context, security at the physical layer (PHY) that exploits the intrinsic properties of the wireless medium to achieve secure communication has emerged as an alternative paradigm. This article first introduces the fundamental principles of primary PHY attacks, covering jamming, eavesdropping, and detection of covert, and then presents an overview of the prevalent countermeasures to secure both active and passive communications in WPNs. Furthermore, a number of open research issues are identified to inspire possible future research.
Lu, X, Qiu, J, Lei, G & Zhu, J 2022, 'Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia', Applied Energy, vol. 308, pp. 118296-118296.
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LÜ, Y, GAO, Q, LÜ, J, PAN, Y & DONG, D 2022, 'Recent advances of quantum neural networks on the near term quantum processor', SCIENTIA SINICA Technologica, vol. 52, no. 4, pp. 547-564.
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Lu, Y, Xiao, W & Lu, DD-C 2022, 'Optimal Dynamic and Steady-State Performance of PV-Interfaced Converters Using Adaptive Observers', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 12, pp. 4909-4913.
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Lu, Z, Xu, Y, Peng, L, Liang, C, Liu, Y & Ni, B-J 2022, 'A two-stage degradation coupling photocatalysis to microalgae enhances the mineralization of enrofloxacin', Chemosphere, vol. 293, pp. 133523-133523.
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The coupling of photocatalytic and algal processes has been used for the removal of widespread antibiotics. The removal capacities of the individual and the combined system against enrofloxacin were tested and compared in this work. Due to the low tolerance of the algae to enrofloxacin, the target compound was barely degraded during the individual algal treatment. In the individual photocatalytic process, the mineralization efficiency (defined as the ratio between the produced carbon dioxide and the initial) reached ∼57% with the remaining formed as transformation products. In contrast, a two-stage treatment incorporating photocatalytic and algal processes removed enrofloxacin completely and increased the mineralization efficiency to ∼64% or more. The addition of the citric acid as external co-substrate further elevated the mineralization efficiency with a factor of 1.25 compared to that of the individual photocatalysis. Different degradation products in both individual and integrated processes were identified and compared. The degradation pathways were found to involve the attack of the piperazine moiety and quinolone core. The results indicated the potential application of the combined photocatalytic-algal treatment in removal of veterinary antibiotics and improved our understanding of the underlying mechanisms and pathways.
Lu, Z-H, Wang, J, Tang, Z, Zhao, Y-G & Li, W 2022, 'A novel cohesive zone model for predicting the interface bonding behaviours of the ballastless track of high-speed railway', Structures, vol. 41, pp. 1-14.
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The interface damage is one of the main concerns for high-speed railway ballastless track structures during its whole service life, as it would affect the reliability of track structures or even cause the high-speed train accident. In this paper, various existing cohesive zone models (CZMs) were identified and compared regarding the performance in modeling the interface behaviors of ballastless track structures. The results show that the existing CZMs cannot accurately represent the nonlinear relationship of the ascending and descending sections in the interface traction-separation curve concurrently. Therefore, a new CZM was proposed in this study, and the feasibility of the proposed model was examined by the experimental results. It indicates that the proposed model can reasonably describe the nonlinear behaviors of the ascending and descending sections in the traction-separation curve by introducing exponential coefficients. The proposed CZM also exhibits higher accuracy in predicting the normal and tangential cracking behaviors of the interlayer interface of the ballastless track structures in comparison with the existing models, with the coefficients of determination (R2) in the fitting results all above 0.85. As a result, this model can be used for the analysis of the interface behaviours of ballastless track structures.
Lukomskyj, AO, Rao, N, Yan, L, Pye, JS, Li, H, Wang, B & Li, JJ 2022, 'Stem Cell-Based Tissue Engineering for the Treatment of Burn Wounds: A Systematic Review of Preclinical Studies', Stem Cell Reviews and Reports, vol. 18, no. 6, pp. 1926-1955.
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AbstractBurn wounds are a devastating type of skin injury leading to severe impacts on both patients and the healthcare system. Current treatment methods are far from ideal, driving the need for tissue engineered solutions. Among various approaches, stem cell-based strategies are promising candidates for improving the treatment of burn wounds. A thorough search of the Embase, Medline, Scopus, and Web of Science databases was conducted to retrieve original research studies on stem cell-based tissue engineering treatments tested in preclinical models of burn wounds, published between January 2009 and June 2021. Of the 347 articles retrieved from the initial database search, 33 were eligible for inclusion in this review. The majority of studies used murine models with a xenogeneic graft, while a few used the porcine model. Thermal burn was the most commonly induced injury type, followed by surgical wound, and less commonly radiation burn. Most studies applied stem cell treatment immediately post-burn, with final endpoints ranging from 7 to 90 days. Mesenchymal stromal cells (MSCs) were the most common stem cell type used in the included studies. Stem cells from a variety of sources were used, most commonly from adipose tissue, bone marrow or umbilical cord, in conjunction with an extensive range of biomaterial scaffolds to treat the skin wounds. Overall, the studies showed favourable results of skin wound repair in animal models when stem cell-based tissue engineering treatments were applied, suggesting that such strategies hold promise as an improved therapy for burn wounds.Graphical abstract
Luo, J, Luo, Q, Zhang, G, Li, Q & Sun, G 2022, 'On strain rate and temperature dependent mechanical properties and constitutive models for additively manufactured polylactic acid (PLA) materials', Thin-Walled Structures, vol. 179, pp. 109624-109624.
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Luo, Q, Tong, L, Bambach, M, Rasmussen, KJR & Khezri, M 2022, 'Active nonlinear buckling control of optimally designed laminated plates using SMA and PZT actuators', Thin-Walled Structures, vol. 181, pp. 110134-110134.
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Luo, Q, Tong, L, Khezri, M, Rasmussen, KJR & Bambach, MR 2022, 'Optimal design of thin laminate plates for efficient airflow in ventilation via buckling', Thin-Walled Structures, vol. 170, pp. 108582-108582.
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Luo, T, Xu, Q, Wei, W, Sun, J, Dai, X & Ni, B-J 2022, 'Performance and Mechanism of Fe3O4 Improving Biotransformation of Waste Activated Sludge into Liquid High-Value Products', Environmental Science & Technology, vol. 56, no. 6, pp. 3658-3668.
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This study demonstrated that Fe3O4 simultaneously improves the total production and formation rate of medium-chain fatty acids (MCFAs) and long-chain alcohols (LCAs) from waste activated sludge (WAS) in anaerobic fermentation. Results revealed that when Fe3O4 increased from 0 to 5 g/L, the maximal MCFA and LCA production increased significantly, and the optimal fermentation time was also remarkably shortened from 24 to 9 days. Moreover, Fe3O4 also enhanced WAS degradation, and the corresponding degradation rate in the fermentation system increased from 43.86 to 72.38% with an increase in Fe3O4 from 0 to 5 g/L. Further analysis showed that Fe3O4 promoted the microbe activities of all the bioprocesses (including hydrolysis, acidogenesis, and chain elongation processes) involved in the MCFA and LCA production from WAS. Microbial community analysis indicated that Fe3O4 increased the abundances of key microbes involved in abovementioned bioprocesses correspondingly. Mechanistic investigations showed that Fe3O4 increased the conductivity of the fermented sludge, providing a better conductive environment for the anaerobic microbes. The redox cycle of Fe(II) and Fe(III) existed in the fermentation system with Fe3O4, which was likely to act as electron shuttles to conduct electron transfer (ET) from the electron donor to the acceptor, thus increasing ET efficiency. This study provides an effective method for enhancing the biotransformation of WAS into high-value products, potentially bringing economic benefits to WAS treatment.
Luo, Z, Li, W, Wang, K, Shah, SP & Sheng, D 2022, 'Nano/micromechanical characterisation and image analysis on the properties and heterogeneity of ITZs in geopolymer concrete', Cement and Concrete Research, vol. 152, pp. 106677-106677.
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Heterogeneity of interfacial transition zones (ITZs) is a key factor for the properties and failure mechanism of geopolymer concrete. The nano/microscale properties and heterogeneity of the ITZs (the top, bottom and lateral interfaces) prepared by encompassing polished aggregates in the modelled fly ash-based geopolymer concrete were statistically investigated in this study. The nanoindentation and nanoscratch results show that the nano/micromechanical properties of the gel-related phases of ITZs at the top and bottom boundaries are higher than the corresponding ones at the lateral boundaries and bulk paste. The mechanism of the better properties of ITZs at the top and bottom boundaries is unveiled based on quantitative image analysis of the amount, diameter and proportion distribution of fly ash particles. A strategy of controlling heterogeneity of ITZs and using polished aggregates, rapid scratch and statistical analysis is proposed to investigate more complicated ITZs within acceptable testing duration.
Luu, HM, Walsum, TV, Mai, HS, Franklin, DR, Nguyen, TTT, Le, TM, Moelker, A, Le, VK, Vu, DL, Le, NH, Long, TQ, Duc, TC & Trung, NL 2022, 'Automatic scan range for dose-reduced multiphase CT imaging of the liver utilizing CNNs and Gaussian models.', Medical Image Anal., vol. 78, pp. 102422-102422.
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Multiphase CT scanning of the liver is performed for several clinical applications; however, radiation exposure from CT scanning poses a nontrivial cancer risk to the patients. The radiation dose may be reduced by determining the scan range of the subsequent scans by the location of the target of interest in the first scan phase. The purpose of this study is to present and assess an automatic method for determining the scan range for multiphase CT scans. Our strategy is to first apply a CNN-based method for detecting the liver in 2D slices, and to use a liver range search algorithm for detecting the liver range in the scout volume. The target liver scan range for subsequent scans can be obtained by adding safety margins achieved from Gaussian liver motion models to the scan range determined from the scout. Experiments were performed on 657 multiphase CT volumes obtained from multiple hospitals. The experiment shows that the proposed liver detection method can detect the liver in 223 out of a total of 224 3D volumes on average within one second, with mean intersection of union, wall distance and centroid distance of 85.5%, 5.7 mm and 9.7 mm, respectively. In addition, the performance of the proposed liver detection method is comparable to the best of the state-of-the-art 3D liver detectors in the liver detection accuracy while it requires less processing time. Furthermore, we apply the liver scan range generation method on the liver CT images acquired from radiofrequency ablation and Y-90 transarterial radioembolization (selective internal radiation therapy) interventions of 46 patients from two hospitals. The result shows that the automatic scan range generation can significantly reduce the effective radiation dose by an average of 14.5% (2.56 mSv) compared to manual performance by the radiographer from Y-90 transarterial radioembolization, while no statistically significant difference in performance was found with the CT images from intra RFA intervent...
Luu, HM, Walsum, TV, Mai, HS, Franklin, DR, Nguyen, TTT, Le, TM, Moelker, A, Le, VK, Vu, DL, Le, NH, Long, TQ, Duc, TC & Trung, NL 2022, 'Automatic scan range for dose-reduced multiphase CT imaging of the liver utilizing CNNs and Gaussian models.', Medical Image Anal., vol. 78, pp. 102424-102424.
Lv, X, Ako, RT, Bhaskaran, M, Sriram, S, Fumeaux, C & Withayachumnankul, W 2022, 'Frequency-Selective-Surface-Based Mechanically Reconfigurable Terahertz Bandpass Filter', IEEE Transactions on Terahertz Science and Technology, vol. 12, no. 3, pp. 257-266.
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Ly, QV, Truong, VH, Ji, B, Nguyen, XC, Cho, KH, Ngo, HH & Zhang, Z 2022, 'Exploring potential machine learning application based on big data for prediction of wastewater quality from different full-scale wastewater treatment plants', Science of The Total Environment, vol. 832, pp. 154930-154930.
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Lyu, B, Qin, L, Lin, X, Zhang, Y, Qian, Z & Zhou, J 2022, 'Maximum and top-k diversified biclique search at scale.', VLDB J., vol. 31, no. 6, pp. 1365-1389.
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Maximum biclique search, which finds the biclique with the maximum number of edges in a bipartite graph, is a fundamental problem with a wide spectrum of applications in different domains, such as E-Commerce, social analysis, web services, and bioinformatics. Unfortunately, due to the difficulty of the problem in graph theory, no practical solution has been proposed to solve the issue in large-scale real-world datasets. Existing techniques for maximum clique search on a general graph cannot be applied because the search objective of maximum biclique search is two-dimensional, i.e., we have to consider the size of both parts of the biclique simultaneously. In this paper, we divide the problem into several subproblems each of which is specified using two parameters. These subproblems are derived in a progressive manner, and in each subproblem, we can restrict the search in a very small part of the original bipartite graph. We prove that a logarithmic number of subproblems is enough to guarantee the algorithm correctness. To minimize the computational cost, we show how to reduce significantly the bipartite graph size for each subproblem while preserving the maximum biclique satisfying certain constraints by exploring the properties of one-hop and two-hop neighbors for each vertex. Furthermore, we study the diversified top-k biclique search problem which aims to find k maximal bicliques that cover the most edges in total. The basic idea is to repeatedly find the maximum biclique in the bipartite graph and remove it from the bipartite graph k times. We design an efficient algorithm that considers to share the computation cost among the k results, based on the idea of deriving the same subproblems of different results. We further propose two optimizations to accelerate the computation by pruning the search space with size constraint and refining the candidates in a lazy manner. We use several real datasets from various application domains, one of which contai...
Lyu, B, Ramezani, P, Hoang, DT & Jamalipour, A 2022, 'IRS-Assisted Downlink and Uplink NOMA in Wireless Powered Communication Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 1, pp. 1083-1088.
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This paper studies the integration of the newly-emerged intelligent reflecting surface (IRS) technology into non-orthogonal multiple access (NOMA)-based wireless powered communication networks (WPCNs). We consider two WPCNs which communicate with a common hybrid access point (HAP), where there exists two types of devices in each WPCN, namely information receiving device (IRD) and harvest-then-transmit device (HTTD). Downlink communication from the HAP to IRDs, downlink energy transfer (ET) from the HAP to HTTDs, and uplink information transmission (IT) from the HTTDs to the HAP are assisted by two IRSs, one in each WPCN. Under this setup, we propose efficient algorithms to optimize reflection coefficients, beamforming vectors, and resource allocation for the sake of uplink sum-rate maximization, taking into account the minimum rate requirement at the IRDs. Numerical results show the considerable performance gain of the proposed NOMA-based scheme as compared to the conventional orthogonal multiple access (OMA)-based counterpart.
M. B., B, B., RP, Tripathi, A, Yadav, S, John, NS, Thapa, R, Altaee, A, Saxena, M & Samal, AK 2022, 'A Unique Bridging Facet Assembly of Gold Nanorods for the Detection of Thiram through Surface-Enhanced Raman Scattering', ACS Sustainable Chemistry & Engineering, vol. 10, no. 22, pp. 7330-7340.
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Concerns have grown in recent years about the widespread use of the pesticide thiram (TRM), which has been linked to negative effects on local ecosystems. This highlights the critical need for quick and accurate point-of-need pesticide analysis tools for real-time applications. The detection of TRM using gold nanorods (Au NRs) with a limit of detection of 10-11M (10 pM) and an enhancement factor of 2.8 × 106along with 6.2% of signal homogeneity (with respect to the peak at 1378 cm-1) is achieved through surface-enhanced Raman scattering (SERS). The formation of an Au-S bond emphasizes the adsorption of TRM on Au NRs. The addition of Au NRs to TRM of higher and lower concentrations yields a side-by-side assembly (SSA) and a bridging facet assembly (BFA), respectively, and exhibited excellent hotspots for the ultralow detection of TRM. Bridging facets of Au NRs, such as (5 12 0) and (5 0 12) planes, are mainly responsible for the BFA. This kind of interaction is observed for the first time and not reported elsewhere. The detailed facets of Au NRs, namely, side facets, bridging facets, and pyramid facets were demonstrated with the 3D model of Au NRs. The computational studies confirming the SSA and BFA for Au NRs with varying concentrations of TRM are in well agreement with the experimental results. The interaction of Au NRs with TRM is highly sensitive, and the ultralow detection of hazardous TRM through SERS is an ideal technique for environmental protection, real-time applications, and analysis of one-of-a-kind materials.
Ma, B, Teng, J, Li, H, Zhang, S, Cai, G & Sheng, D 2022, 'A New Strength Criterion for Frozen Soil Considering Pore Ice Content', International Journal of Geomechanics, vol. 22, no. 7, p. 04022107.
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Pore ice content is crucial in evaluating the mechanical properties of frozen soils. Existing strength criterion models are usually empirical and ignore the influence of pore ice content. By assuming that the critical state shear stress ratio of soil is a function of the stress level, a critical state line of frozen soil is proposed to consider pore ice content. By combining the Mohr-Coulomb (M-C) and Drucker-Prager strength criteria to describe the failure shape characteristics on the deviatoric plane, a new strength criterion is established for complex stress conditions. The proposed model is validated against existing models and measured data in the literature. In addition, the proposed model can uniformly describe the CSL of different types of geotechnical materials and has a clear physical meaning, which may provide a theoretical basis for constitutive models.
Ma, B, Wang, X, Ni, W & Liu, RP 2022, 'Personalized Location Privacy With Road Network-Indistinguishability', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 20860-20872.
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The proliferation of location-based services (LBS) leads to increasing concern about location privacy. Location obfuscation is a promising privacy-preserving technique but yet to be adequately tailored for vehicles in road networks. Existing obfuscation schemes are based primarily on the Euclidean distances and can lead to infeasible results, e.g., off-road locations. In this paper, we define Road Network-Indistinguishability (RN-I) to evaluate obfuscation-based location privacy-preserving schemes in road networks. To protect drivers' location privacy in road networks, we propose a Personalized Location Privacy-Preserving (PLPP) scheme and prove it achieves RN-I. The PLPP scheme employs a dual-obfuscation algorithm, consisting of a connection perturbation and an interval perturbation, to obfuscate on-road locations. An efficient personalization algorithm is designed for the PLPP scheme to fine-tune location privacy budgets for capturing drivers' sensitive locations and privacy requirements. Experiments upon two real-world datasets confirm the location privacy-preserving capability, data utility, and efficiency of the proposed PLPP scheme.
Ma, F, Wu, Y, Yu, X & Yang, Y 2022, 'Learning With Noisy Labels via Self-Reweighting From Class Centroids', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 11, pp. 6275-6285.
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Although deep neural networks have been proved effective in many applications, they are data hungry, and training deep models often requires laboriously labeled data. However, when labeled data contain erroneous labels, they often lead to model performance degradation. A common solution is to assign each sample with a dynamic weight during optimization, and the weight is adjusted in accordance with the loss. However, those weights are usually unreliable since they are measured by the losses of corrupted labels. Thus, this scheme might impede the discriminative ability of neural networks trained on noisy data. To address this issue, we propose a novel reweighting method, dubbed self-reweighting from class centroids (SRCC), by assigning sample weights based on the similarities between the samples and our online learned class centroids. Since we exploit statistical class centers in the image feature space to reweight data samples in learning, our method is robust to noise caused by corrupted labels. In addition, even after reweighting the noisy data, the decision boundaries might still suffer distortions. Thus, we leverage mixed inputs that are generated by linearly interpolating two random images and their labels to further regularize the boundaries. We employ the learned class centroids to evaluate the confidence of our generated mixed data via measuring feature similarities. During the network optimization, the class centroids are updated as more discriminative feature representations of original images are learned. In doing so, SRCC will generate more robust weighting coefficients for noisy and mixed data and facilitates our feature representation learning in return. Extensive experiments on both the synthetic and real image recognition tasks demonstrate that our method SRCC outperforms the state of the art on learning with noisy data.
Ma, F, Zhu, L & Yang, Y 2022, 'Weakly Supervised Moment Localization with Decoupled Consistent Concept Prediction', International Journal of Computer Vision, vol. 130, no. 5, pp. 1244-1258.
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Localizing moments in a video via natural language queries is a challenging task where models are trained to identify the start and the end timestamps of the moment in a video. However, it is labor intensive to obtain the temporal endpoint annotations. In this paper, we focus on a weakly supervised setting, where the temporal endpoints of moments are not available during training. We develop a decoupled consistent concept prediction (DCCP) framework to learn the relations between videos and query texts. Specifically, the atomic objects and actions are decoupled from the query text to facilitate the recognition of these concepts in videos. We introduce a concept pairing module to temporally localize the objects and actions in the video. The classification loss and the concept consistency loss are proposed to leverage the mutual benefits of object and action cues for building relations between languages and videos. Extensive experiments on DiDeMo, Charades-STA, and ActivityNet Captions demonstrate the effectiveness of our model.
Ma, H, Li, L, Fan, Y, Guo, Y, Jin, Z & Luo, J 2022, 'A Discrete Current Controller for High Power-Density Synchronous Machines', Energies, vol. 15, no. 17, pp. 6396-6396.
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This paper proposes a complex vector discrete current controller based on the flux-linkage data to solve the current loop oscillation problem of high power-density synchronous machines. An offline flux-linkage table measurement method considering cross saturation is introduced, and the data are used to deduce the symmetrical complex vector model. The influence of latch and delay of inverters on the line voltage of machines at high speed is analyzed and compensated during the controller design process. The proposed controller, which only needs to tune one parameter, can deal with the inductance mismatch issues caused by iron core saturation. The controller can be adopted in the current loop of saturated salient or nonsalient synchronous machines. Simulations and experiments have verified the effectiveness of the proposed method.
Ma, T, Mafi, R, Cami, B, Javankhoshdel, S & Gandomi, AH 2022, 'NURBS Surface-Altering Optimization for Identifying Critical Slip Surfaces in 3D Slopes', International Journal of Geomechanics, vol. 22, no. 9.
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The evaluation of slope stability of a three-dimensional slope requires identifying the critical slip surface with the minimum factor of safety, which is a complex optimization problem. Failure to identify the critical slip surface can lead to unconservative conclusions about the stability of a slope. This paper proposes a novel 3D surface-altering optimization method, which iteratively alters the geometry of a 3D slip surface to find the critical slip surface representing the minimum factor of safety in a slope. The geometry of the slip surface is defined via nonuniform rational basis spline (NURBS) curves formed over a plan grid of control points. The proposed method includes a series of five subroutines that apply various forms of transformations to the control points. These subroutines include minimization problems, which determine the optimal transformation parameters for minimizing the obtained factor of safety of the resulting slip surfaces. Given that any geometrically defined slip surface can be approximated using an equivalent series of NURBS control points, the proposed method can be used in efforts to further reduce the global factor of safety first obtained via conventional search methods, such as those involving spherical or ellipsoidal slip surfaces. To demonstrate its effectiveness, the proposed method was applied to further optimize the critical ellipsoidal slip surfaces reported in some numerical examples. Comparing the results with those limited to ellipsoidal slip surfaces, the proposed method was consistently able to identify slip surfaces with significantly lower factors of safety. The postaltered slip surfaces also matched closely with finite element shear strength reduction results. As such, the proposed method is effective in searching for critical slip surfaces and can be used as a final step in the critical surface searching routine.
Ma, X-L, Shang, F, Ni, W, Zhu, J, Luo, B & Zhang, Y-Q 2022, 'Correction to: MicroRNA-338-5p plays a tumor suppressor role in glioma through inhibition of the MAPK-signaling pathway by binding to FOXD1', Journal of Cancer Research and Clinical Oncology, vol. 148, no. 3, pp. 749-750.
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Ma, Y, Huang, Y, Wu, J, E, J, Zhang, B, Han, D & Ong, HC 2022, 'A review of atmospheric fine particulate matters: chemical composition, source identification and their variations in Beijing', Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 44, no. 2, pp. 4783-4807.
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Fine particulate matter (PM2.5) is a major air pollutant worldwide. Characterizing its chemical compositions and source contributions is a critical prerequisite for effective control of PM2.5 pollution. This paper systematically reviews the sampling methods, chemical compositions, and source apportionments of PM2.5. Sampling methods have significant influences on the identification of chemical compositions and source contributions, with Quartz and Teflon filters being the most widely used. Receptor models are commonly adopted for identifying the sources of PM2.5, such as positive matrix factorization, chemical mass balance, principal component analysis, and UNMIX models, which have their respective advantages and limitations that determine their applications. The variations of PM2.5 compositions and sources in the past two decades in Beijing are also reviewed, which is the political, economic, and cultural center of China and is experiencing severe haze pollution events frequently. It was found that organic matters were the largest component (28.2%) in PM2.5, followed by sulfate (15.1%) during 2004–2013, which was overtaken by nitrate (14.9%) after 2013. Each PM2.5 source demonstrated significant seasonal and annual variations due to changes in climatic conditions and anthropogenic activities. Future research on the impacts of these external factors is urgently needed. This review is expected to provide valuable advice and evidence for those fast-growing megacities like Beijing to identify and control their PM2.5-related air pollution problems.
Mahdavi, M, Alhelou, HH & Hesamzadeh, MR 2022, 'An Efficient Stochastic Reconfiguration Model for Distribution Systems With Uncertain Loads', IEEE Access, vol. 10, pp. 10640-10652.
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Mahdavi, M, Alhelou, HH, Siano, P & Loia, V 2022, 'Robust Mixed-Integer Programing Model for Reconfiguration of Distribution Feeders Under Uncertain and Variable Loads Considering Capacitor Banks, Voltage Regulators, and Protective Relays', IEEE Transactions on Industrial Informatics, vol. 18, no. 11, pp. 7790-7803.
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Mahdavi, M, Schmitt, K, Ramos, RAV & Alhelou, HH 2022, 'Role of hydrocarbons and renewable energies in Iran's energy matrix focusing on bioenergy: review', IET Renewable Power Generation, vol. 16, no. 15, pp. 3384-3405.
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AbstractIran is one of the most potent energy exporters and fastest‐growing energy consumers in the world. Its large amount of energy exported can directly impact the economy of importer countries. Iran's energy matrix mostly consists of hydrocarbons, while the remaining portion is compounded by a blend of biofuels, hydropower, wind, solar, and other renewable sources. Even though the country has an excellent potential to reach a large amount of renewable power, it is most likely that Iran will deal with a shortage in its electricity segment during the upcoming years. Besides wind and solar energy, bioenergy appears to be a good alternative for enhancing the country's energy matrix and transit Iran's energy consumption pattern from a high‐level usage of hydrocarbons to a more renewable scenario. Therefore, in the present paper, the role of Iran in global energy production is discussed, and its potential for renewable energy generation is described. The review presents a vast set of data related to environmental, infrastructural, economic, and social aspects to clarify the potential and benefits of Iran's transition to a more renewable energy matrix. The investigations revealed the country's extremely favourable conditions to use bioenergy resources as a key solution to avoiding future energy crises, besides improving quality of life. The context can help researchers, power suppliers, and energy consumers know how renewable energies, especially bioenergy, can modify the energy matrix of a country rich in hydrocarbon resources.
Mahmood, A, Siddiqui, SA, Sheng, QZ, Zhang, WE, Suzuki, H & Ni, W 2022, 'Trust on wheels: Towards secure and resource efficient IoV networks', Computing, vol. 104, no. 6, pp. 1337-1358.
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Mahmood, AH, Afroz, S, Kashani, A, Kim, T & Foster, SJ 2022, 'The efficiency of recycled glass powder in mitigating the alkali-silica reaction induced by recycled glass aggregate in cementitious mortars', Materials and Structures, vol. 55, no. 6.
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AbstractWith the potential for a decline in fly ash (FA) production over time, due to the phasing down of coal fired power plants, alternative supplementary cementitious materials need to be identified. The efficiency of pulverised glass powder (PGP) was studied for its reactivity and its capacity for inhibiting alkali-silica reaction (ASR) that results from utilisation of recycled glass as a fine aggregate (sand) replacement. Characterisations of pastes containing PGP reveal that PGP may possess latent hydraulic properties, resulting in a more than 75% strength activity index, together with better strength gain than FA-blended pastes. PGP also offered increased heat of hydration compared to FA, from a combination of the dilution effect, filler effect and early-age reactions of PGP. A comparable efficiency of PGP and FA in ASR expansion mitigation was confirmed with mortar bar expansions of less than 0.10% at cement replacement levels of at least 10%. Both PGP and FA provided alkali dilution and reduced the mass transport in hydrated cement paste from the refinement of larger pores to below 60 nm. The FA mix consumed calcium hydroxide and, thus, performed marginally better than the PGP mix in mitigating ASR. This pozzolanic reactivity is not evident for PGP, whereas in the literature glass powders are often regarded as pozzolanic. Microscopic images confirm that PGP and FA significantly limit the occurrence of ASR gels without altering its composition. It was concluded that PGP is a comparable ASR inhibitor to FA, despite the underlying differences in their mechanisms. The result of this research support the utilisation of recycled glass both as an aggregate, and as an ASR-inhibiting SCM in cementitious systems.
Mahmood, AH, Babaee, M, Foster, SJ & Castel, A 2022, 'Capturing the early-age physicochemical transformations of alkali-activated fly ash and slag using ultrasonic pulse velocity technique', Cement and Concrete Composites, vol. 130, pp. 104529-104529.
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Mahmud, MA, Zheng, J, Tang, S, Wang, G, Bing, J, Bui, AD, Qu, J, Yang, L, Liao, C, Chen, H, Bremner, SP, Nguyen, HT, Cairney, J & Ho‐Baillie, AWY 2022, 'Cation‐Diffusion‐Based Simultaneous Bulk and Surface Passivations for High Bandgap Inverted Perovskite Solar Cell Producing Record Fill Factor and Efficiency', Advanced Energy Materials, vol. 12, no. 36, pp. 2201672-2201672.
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AbstractHigh bandgap perovskite solar cells are integral to perovskite‐based multi‐junction tandem solar cells with efficiency potentials over 40%. However, at present, high bandgap perovskite devices underperform compared to their mid bandgap counterparts in terms of voltage outputs and fill factors resulting in lower than ideal efficiencies. Here, the low fill factor aspect of high bandgap perovskite is addressed by developing a cation‐diffusion‐based double‐sided interface passivation scheme that simultaneously provides bulk passivation for a 1.75 eV perovskite cell that is also compatible with a p‐i‐n cell architecture. The champion cell achieves a record fill factor of 86.5% and a power conversion efficiency of 20.2%. Results of ionic distribution profiling, Fourier transform infrared spectroscopy, and X‐ray diffraction crystallography reveal evidence of cation diffusion from the surface perovskite passivation layer into bulk. The diffused cations reduce Shockley–Read–Hall recombination in the perovskite bulk and at the surfaces with the latter being more dominant as confirmed by light‐intensity dependent and temperature‐dependent open‐circuit voltage measurements as well as thermal admittance spectroscopy. This concurrent bulk and surface passivation scheme renders record fill factor and efficiency in the double‐side passivated cells. This provides new insights for future passivation strategies based on ionic diffusion of functionalized materials.
Mahmud, MAP, Bazaz, SR, Dabiri, S, Mehrizi, AA, Asadnia, M, Warkiani, ME & Wang, ZL 2022, 'Advances in MEMS and Microfluidics‐Based Energy Harvesting Technologies', Advanced Materials Technologies, vol. 7, no. 7, pp. 2101347-2101347.
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AbstractEnergy harvesting from mechanical vibrations, thermal gradients, electromagnetic radiations, and solar radiations has experienced rapid progress in recent times not only to develop an alternative power source that can replace conventional batteries to energize portable and personal electronics smartly but also to achieve sustainable self‐sufficient micro/nanosystems. Utilizing micro‐electromechanical system (MEMS) and microfluidics technologies through selective designs and fabrications effectively, those energy harvesters can be considerably downsized while ensuring a stable, portable, and consistent power supply. Although ambient energy sources such as solar radiation are harvested for decades, recent developments have enabled ambient vibrations, electromagnetic radiation, and heat to be harvested wirelessly, independently, and sustainably. Developments in the field of microfluidics have also led to the design and fabrication of novel energy harvesting devices. This paper reviews the recent advancements in energy harvesting technologies such as piezoelectric, electromagnetic, electrostatic, thermoelectric, radio frequency, and solar to drive self‐powered portable electronics. Moreover, the potential application of MEMS and microfluidics as well as MEMS‐based structures and fabrication techniques for energy harvesting are summarized and presented. Finally, a few crucial challenges affecting the performance of energy harvesters are addressed.
Mahmud, S, Haider, ASMR, Shahriar, ST, Salehin, S, Hasan, ASMM & Johansson, MT 2022, 'Bioethanol and biodiesel blended fuels — Feasibility analysis of biofuel feedstocks in Bangladesh', Energy Reports, vol. 8, pp. 1741-1756.
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In 2019–2020, Bangladesh imported 5.2 million metric tonnes of petroleum products, worth 2.5 billion USD, and 50% of the imports were consumed by the transportation sector. Having limited natural oil reserves and being heavily dependent on oil imports, the country is vulnerable to shocks in the international oil market, which can jeopardize its consistent economic growth. The Government announced a 5% blending of bioethanol with gasoline in 2017, with broken rice, maize, and molasses as the feedstocks, but sourcing biofuel from food crops can hamper the country's food security. This study explores second and third generation feedstocks e.g., organic plants, seeds, agricultural residues, and waste animal fat or skin that can be collected and processed for the extraction of biofuels. Technical potential of biofuel from the feedstocks is analysed which shows that Bangladesh has a potential to extract 44.4 million metric tonnes of bioethanol in a year from agricultural residues with rice residue having the highest potential (71%). Ground nut and rubber seeds can be major feedstocks for biodiesel production having a potential of 61,000 and 42,000 metric tonnes per year, respectively. Waste chicken skin can be another promising feedstock for the extraction of biodiesel. Biofuels extracted from these non-edible feedstocks and blended with existing transport fuels can lessen Bangladesh's import bills through a sustainable, environmentally friendly manner.
Maidi, AM, Kalam, MA & Begum, F 2022, 'Photonic Crystal Fiber Sensor for Detecting Sulfuric Acid in Different Concentrations', Photonics, vol. 9, no. 12, pp. 958-958.
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A high-performance photonic crystal fiber sensor for sulfuric acid detection is designed and investigated, undertaken through a full vector Finite Element Method on COMSOL Multiphysics software to establish the optical properties of effective refractive index, power fraction, relative sensitivity, confinement loss, chromatic dispersion, and propagation constant. Different aqueous sulfuric acid concentrations of 0%, 10%, 20%, 30%, and 40% were selected as the test analytes. The dimensions of two cladding rings of the hexagon- and circular-shaped air holes and a circular core hole denoted outstanding outcomes of relative sensitivity and confinement loss. At 1.1 µm optimum wavelength, 0%, 10%, 20%, 30%, and 40% sulfuric acid concentrations depict relative sensitivities of 97.08%, 97.67%, 98.06%, 98.39%, and 98.67%, respectively, and confinement losses of 1.32 × 10−12 dB/m, 4.11 × 10−12 dB/m, 1.46 × 10−12 dB/m, 6.34 × 10−12 dB/m, and 2.12 × 10−12 dB/m, respectively.
Maithri, M, Raghavendra, U, Gudigar, A, Samanth, J, Prabal Datta Barua, Murugappan, M, Chakole, Y & Acharya, UR 2022, 'Automated emotion recognition: Current trends and future perspectives', Computer Methods and Programs in Biomedicine, vol. 215, pp. 106646-106646.
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Majidi Nezhad, M, Heydari, A, Neshat, M, Keynia, F, Piras, G & Garcia, DA 2022, 'A Mediterranean Sea Offshore Wind classification using MERRA-2 and machine learning models', Renewable Energy, vol. 190, pp. 156-166.
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Majidi Nezhad, M, Neshat, M, Piras, G & Astiaso Garcia, D 2022, 'Sites exploring prioritisation of offshore wind energy potential and mapping for wind farms installation: Iranian islands case studies', Renewable and Sustainable Energy Reviews, vol. 168, pp. 112791-112791.
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Makhanbet, M, Lv, T, Ni, W & Orynbet, M 2022, 'Energy-Delay-Aware Power Control for Reliable Transmission of Dynamic Cell-Free Massive MIMO', IEEE Transactions on Communications, vol. 70, no. 1, pp. 276-290.
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Makhdoom, I, Abolhasan, M & Lipman, J 2022, 'A comprehensive survey of covert communication techniques, limitations and future challenges', Computers & Security, vol. 120, pp. 102784-102784.
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Data encryption aims to protect the confidentiality of data at storage, during transmission, or while in processing. However, it is not always the optimum choice as attackers know the existence of the ciphertext. Hence, they can exploit various weaknesses in the implementation of encryption algorithms and can thus decrypt or guess the related cryptographic primitives. Moreover, in the case of proprietary applications such as online social networks, users are at the mercy of the vendor's security measures. Therefore, users are vulnerable to various security and privacy threats. Contrary to this, covert communication techniques hide the existence of communication and thus achieve security through obscurity and hidden communication channels. Over the period, there has been a significant advancement in this field. However, existing literature fails to encompass all the aspects of covert communications in a single document. This survey thus endeavors to highlight the latest trends in covert communication techniques, related challenges, and future directions.
Makhdoom, I, Lipman, J, Abolhasan, M & Challen, D 2022, 'Science and Technology Parks: A Futuristic Approach', IEEE Access, vol. 10, pp. 31981-32021.
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Most of the existing science and technology parks resort to various conventional ways to attract different stakeholders to the park. Some of these traditional measures include business support, workspaces, laboratories, networking events, accommodation, and essential commodities. Besides, with rampantly changing multidisciplinary technologies and increased data-oriented business models, the classic science and technology park value-creation strategies may not be instrumental in the near future. Hence, we foresee that future science and a technology parks should be fully integrated, sustainable, and innovative living science cities. Where park tenants can actively interact and contribute to emerging technologies. Therefore, this paper carries out an in-depth study of world s best practices in smart cities and science and technology parks, their characteristics, and value-added contributions that excite the prospective tenants. Developing on the detailed survey, we propose a unique feature of Autonomous Systems as a Service to bestow a futuristic look to the science and technology parks. It is envisaged that autonomous systems will not only provide value-added services to the park tenants but will also provide an infrastructure for testing new technologies within park premises. Furthermore, this study evaluates security and privacy challenges associated with autonomous systems and data-oriented services and recommends appropriate security measures. The role of universities in the success of a science and technology park is also delineated. Finally, the components deemed essential for the attainment of science and technology parks objectives are highlighted.
Malik, K, Kumar, D, Perissin, D & Pradhan, B 2022, 'Estimation of ground subsidence of New Delhi, India using PS-InSAR technique and Multi-sensor Radar data', Advances in Space Research, vol. 69, no. 4, pp. 1863-1882.
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Globally the land subsidence is a significant problem of the rapidly growing urban area. The factor responsible for the land subsidence caused by over-exploitation of the underground fluid such as water, petroleum, and gas respectively. In present study we present the result of detail investigation of active ground subsidance in New Delhi, National Capital Region (NCR). This area indicates a high rate of urban growth during the past decades. To analyze the land subsidence, we used multiple SAR sensor data and exploited the PS-InSAR technique. The data used for this study are Cosmo-skymed acquired between 08/06/2011 to 15/11/2017, Sentinel-1A-B (18-12-2014 to 27-11-2018), and ALOS PALSAR acquired between 19/01/2007 and 20/01/2011. These radar sensors operate in X, C, and L-band, which covers over ten years, from 2007 to 2018. The PSI results of Cosmo-skymed reveals that the Delhi NCR region has undergone an average deformation ± 15 mm/y, a maximum surface deformation observed from ALOS-PALSAR is 10 to 18 mm/y and the ground displacement observed from the SENTINAL-1A data is −2 to 16 mm/y. Groundwater level data also collected for the same period and a ground water level depletion compared with the subsidence. Monitoring land subsidence with ground-based conventional technology is time-consuming and can be carried out in a limited area due to the financial implication. PS-InSAR is an established method to detect the surface movement using the SAR sensor's time-series data. The result shows that a twenty centimeter of land subsidence is visible in some areas, validated with the collected ground evidence. The affected area is also showing resemblance to the groundwater depleting condition in those areas. This study also establish that multiple sensor data can be used to monitor the long term land subsidence.
Manjunath, Suresh, HNR, Rajanna, S, Thanikanti, SB & Alhelou, HH 2022, 'Hybrid interconnection schemes for output power enhancement of solar photovoltaic array under partial shading conditions', Iet Renewable Power Generation, vol. 16, no. 13, pp. 2859-2880.
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Partial shading conditions (PSC) across the solar photovoltaic (PV) array reduce the overall power yield of the PV plant. Therefore, it is essential to decrease mismatch power loss (MPL) incurred under PSC. For this purpose, total-cross-tied (TCT)-based ‘hybrid interconnection’ schemes of solar PV array are investigated in the present research work. Proposed novel hybrid bridge-link (BL)-TCT and honey-comb (HC)-TCT interconnection schemes are found to be effective alternatives to achieve increased solar PV array output power. The performance of the proposed ‘hybrid interconnection’ schemes is studied by subjecting the 9 × 9 sized solar PV array under four different categories of PSC. The efficacy of the proposed ‘hybrid interconnection’ schemes is justified to be superior by comparing the simulation results obtained from the proposed interconnection schemes with the earlier reported conventional ‘S-P, B-L, H-C, and traditional TCT’ interconnection schemes. Further, it is observed that output power is additionally enhanced using a bypass diode connected across individual modules of the solar PV array. The simulation results indicate that the proposed ‘hybrid interconnection’ schemes with values of 17.5%, 21.28%, 54.33%, and 50.4% under four categories of typical PSC have the lowest MPL relatively compared to that of literature-reported conventional interconnection schemes. In addition, the proposed ‘hybrid interconnection’ schemes with the performance enhancement (PE) values of 82.5%, 78.7%, 45.66%, and 49.6% under four categories of typical PSC have the highest PE relatively compared to literature-reported conventional interconnection schemes. Based on the comparative analysis of results obtained upon four typical PSC, it is concluded that the proposed ‘hybrid interconnection’ schemes provide a relatively superior output performance compared to that of the conventional ‘S-P, B-L, H-C, and traditional TCT’ interconnection schemes.
Mannina, G, Gulhan, H & Ni, B-J 2022, 'Water reuse from wastewater treatment: The transition towards circular economy in the water sector', Bioresource Technology, vol. 363, pp. 127951-127951.
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Water is crucial for economic development since it interacts with the agricultural, production, and energy sectors. However, the increasing demand and climate change put pressure on water sources. This paper argued the necessity of using reclaimed water for irrigation within the scope of a circular economy. The barriers (i.e., technological and economic, institutional/regulatory, and social) to water reuse practices were revealed. Lessons on how to overcome the barriers were learned from good practices. The roadmaps adopted in the European Union for the transition towards the circular economy were reviewed. It has been observed that these roadmaps are generally on the circularity of solid wastes. However, water is too important for the economy to be ignored in the transition towards circular economy. Research needs and perspective for a comprehensive roadmap to widen water-smart solutions such as water reuse were drawn.
Manon, SM, Phuong, JM, Moles, RJ, Kelly, A, Center, JR, Luckie, K, White, C & Carter, SR 2022, 'The role of community pharmacists in delivering interventions for osteoporosis: A systematic review', Journal of the American Pharmacists Association, vol. 62, no. 6, pp. 1741-1749.e10.
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Mansouri, M, Eskandari, M, Asadi, Y, Siano, P & Alhelou, HH 2022, 'Pre-Perturbation Operational Strategy Scheduling in Microgrids by Two-Stage Adjustable Robust Optimization', IEEE Access, vol. 10, pp. 74655-74670.
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Mao, S, Onggowarsito, C, Feng, A, Zhang, S, Fu, Q & Nghiem, LD 2022, 'A cryogel solar vapor generator with rapid water replenishment and high intermediate water content for seawater desalination', Journal of Materials Chemistry A, vol. 11, no. 2, pp. 858-867.
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By selecting appropriate polymer precursors, we developed a novel cryogel solar vapor generator for seawater desalination with high intermediate water content for lower evaporation enthalpy and interconnected macropores for rapid water replenishment.
Mao, T, Mihaita, A-S, Chen, F & Vu, HL 2022, 'Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 7112-7141.
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Traffic control optimization is a challenging task for various traffic centers around the world and the majority of existing approaches focus only on developing adaptive methods for normal (recurrent) traffic conditions. Optimizing the control plans when severe incidents occur still remains an open problem, especially when a high number of lanes or entire intersections are affected. This paper presents a novel methodology for optimizing the traffic signal timings in signalized urban intersections, under non-recurrent traffic incidents. With the purpose of producing fast and reliable decisions, we combine the fast running Machine Learning (ML) algorithms and the reliable Genetic Algorithms (GA) into a single optimization framework. Firstly, we deploy a typical GA algorithm by considering the phase duration as the decision variable and the objective function as the total travel time in the network. We fine tune the GA for crossover, mutation, fitness calculation and obtain the optimal parameters. Secondly, we train several regression models to predict the total travel time in the studied traffic network, and select the best performing model which we further hyper-tune. Lastly, we propose a new algorithm BGA-ML combining the GA algorithm and the extreme-gradient decision-tree (XGBT), which is the best performing regression model, together in a single optimization framework. Comparison and results are generated by two experiments (one synthetic and one from real urban traffic network) and show that the new BGA-ML is much faster than the original GA algorithm and can reduce the total travel time by almost half when used under incident conditions.
Marimuthu, P, Rajesh, T, Rajeswaran, N & Alhelou, HH 2022, 'Comparative Performance Analysis of Deregulated Hydrothermal System With Dual Mode Controller and Diverse Source of Generation Employing Imperialistic Competition Algorithm', IEEE Access, vol. 10, pp. 51008-51020.
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Marjanovic, O 2022, 'A novel mechanism for business analytics value creation: improvement of knowledge-intensive business processes', Journal of Knowledge Management, vol. 26, no. 1, pp. 17-44.
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PurposeThis paper focuses on the “how” of business analytics (BA) value creation, which remains an open research problem and a practical challenge. The main purpose of this paper is to propose a novel BA value creation mechanism that is BA-enabled improvement of Knowledge-intensive Business Processes (KIBPs), with experiential knowledge of decision makers as the key to a more sustainable BA-enabled competitive differentiation.Design/methodology/approachThis research uses a qualitative research case study, conducted in a large retail distribution company. The research insights were observed through a combined lens of work systems theory and the knowledge-based view (KBV) of the firm, using an interpretive approach.FindingsThe proposed theoretical model identifies three stages of KIBP improvement through BA and explains how they lead to a sustainable BA-enabled competitive differentiation. Stage 1 focusses on BA support for individual knowledge-intensive tasks, Stage 2 focusses on individual decision makers and their ability to gain KIBP-related analytical insights and turn them into action; and Stage 3 on sharing of the acquired experiential knowledge amongst decision makers using BA.Originality/valueIn addition to proposing a novel mechanism for BA value creation, this research demonstrates the importance of leveraging experiential knowledge of decision makers as a pathway to a more sustainable competitive differentiation through BA. This, in turn, creates new opportunities for knowledge management researchers to engage in BA-related research. It also opens a new approach fo...
Marjanovic, O & Murthy, V 2022, 'The Emerging Liquid IT Workforce: Theorizing Their Personal Competitive Advantage', Information Systems Frontiers, vol. 24, no. 6, pp. 1775-1793.
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In this paper we aim to contribute to a better understanding of an emerging phenomenon of ‘liquid workforce’, which according to industry press, is rapidly growing. Our specific focus is on the broad research questions: How do liquid IT workers remain competitive? What are suitable strategies for their management and engagement? Using the research insights from the interviews with independent liquid IT professionals working on the same mission-critical compliance program in a large financial institution, we propose a new conceptual model of their ‘personal competitive advantage’ (PCA). Drawing from the theories of human capital and social capital, we theorize PCA as a complex, mutually enhancing (a triple-helix-like) interplay of three highly-intertwined components of ‘Doing’, ‘Relating’ and ‘Becoming’. Based on the proposed model, we then articulate an initial set of strategies for management and engagement of the liquid workforce. In doing so, we expand and challenge the current IS research on IT workforce that remains focused on its retention and prevention of turnover. Instead, we propose to focus on specific management strategies for building and maintaining social capital within and beyond organizational boundaries.
Marjanovic, O, Cecez-Kecmanovic, D & Vidgen, R 2022, 'Theorising Algorithmic Justice', European Journal of Information Systems, vol. 31, no. 3, pp. 269-287.
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The mounting evidence of unintended harmful social consequences of automated algorithmic decision-making (AADM), powered by AI and big data, in transformative services (e.g., welfare services), is startling. The algorithmic harm experienced by individuals, communities and society-at-large involves new injustice claims and disputes that go beyond issues of social justice. Drawing from the theory of “abnormal justice” in this paper we articulate a new theory of algorithmic justice that addresses the questions: WHAT is the matter of algorithmic justice? WHO counts as a subject of algorithmic justice? HOW are algorithmic justices performed? and How to address and resolve disputes about the WHAT, WHO and HOW of algorithmic justice? We illustrate the theory of algorithmic justice by drawing from a case of AADM in social welfare services, widely adopted by governments around the world. Our research points to datafication, technological inscribing and the systemic nature of injustices as important IS-specific aspects of algorithmic justice. Our main practical contribution comes from the articulation of algorithmic justice as a framework that (1) makes visible the injustices related to the “what”, “who”, and “how” of AADM in transformative services, and (2) provides further insights into how we might address and resolve these algorithmic injustices.
Martin, K, Arbour, S, McGregor, C & Rice, M 2022, 'Silver linings: Observed reductions in aggression and use of restraints and seclusion in psychiatric inpatient care during COVID‐19', Journal of Psychiatric and Mental Health Nursing, vol. 29, no. 2, pp. 381-385.
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Accessible SummaryWhat is known about the subject?In a survey conducted by the World Health Organization (WHO) in the summer of 2020, 93% of countries worldwide acknowledged negative impacts on their mental health services.Previous research during the H1N1 pandemic in 2009 established an increase of patient aggression in psychiatric facilities.What the paper adds to existing knowledge?Despite expected worsening of mental health, our hospital observed reductions in aggressive behaviour among inpatients and subsequent use of coercive interventions by staff in the months following Covid‐19 pandemic restrictions being implemented.The downward trend in incidents observed during the pandemic has suggested that aggression in mental health hospitals may be more situation‐specific and less so a factor of mental illness.What are the implications for practice?We believe that the reduction in aggressive behaviour observed during the pandemic is related to changes in our organization that occurred in response to concerns about patient well‐being; our co‐design approach shifted trust, choice and power. Therefore, practices that support these constructs are needed to maintain the outcomes we experienced.Rather than return to normal in the wake of the pandemic, we are strongly encouraged to sustain the changes we made and continue to find better ways to support and work with the ...
Martin-Jimenez, D, Ruppert, MG, Ihle, A, Ahles, S, Wegner, HA, Schirmeisen, A & Ebeling, D 2022, 'Chemical bond imaging using torsional and flexural higher eigenmodes of qPlus sensors', Nanoscale, vol. 14, no. 14, pp. 5329-5339.
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Non-contact atomic force microscopy (AFM) with CO-functionalized tips allows visualizing the chemical structure of individual adsorbed molecules. Particularly high image contrast is observed by exciting a torsional eigenmode of the AFM sensor.
Masangkay, J, Munasinghe, N, Watterson, P & Paul, G 2022, 'Simulation and experimental characterisation of a 3D-printed electromagnetic vibration sensor', Sensors and Actuators A: Physical, vol. 338, pp. 113470-113470.
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Additive manufacturing, also known as 3D printing has already transformed from a rapid prototyping tool to a final end-product manufacturing technique. 3D printing can be used to develop various types of sensors. This paper investigates the ability to use the electromagnetic induction properties of 3D printed carbon-based filament for developing sensors. The paper presents a novel prototype vibration sensor which is 3D-printable, except for an included NdFeB magnet. Motion is detected from the voltage induced by the relative motion of the magnet. The devised vibration sensor is simulated using ANSYS, and a novel prototype is 3D-printed for physical testing to characterise and understand its electromagnetic properties. Simulation helped establish constraints for the design. Two types of experimental setups were physically tested, one setup with a magnet freely sliding inside a cylindrical cavity within an oscillating coil, and the other setup with a stationary coil and oscillating magnet. At a frequency of 10 Hz and a motion travel of about 12 mm, the induced voltage for the moving coil case varied from 5.4 mV RMS for pure sliding motion of the internal magnet to 22.1 mV RMS. The findings of this paper suggest that future sensors can be developed using the electromagnetic induction properties of the carbon-based filament.
Masrur, H, Shafie-Khah, M, Hossain, MJ & Senjyu, T 2022, 'Multi-Energy Microgrids Incorporating EV Integration: Optimal Design and Resilient Operation', IEEE Transactions on Smart Grid, vol. 13, no. 5, pp. 3508-3518.
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Mateos, MK, Marshall, GM, Barbaro, PM, Quinn, MCJ, George, C, Mayoh, C, Sutton, R, Revesz, T, Giles, JE, Barbaric, D, Alvaro, F, Mechinaud, F, Catchpoole, D, Lawson, JA, Chenevix-Trench, G, MacGregor, S, Kotecha, RS, Dalla-Pozza, L & Trahair, TN 2022, 'Methotrexate-related central neurotoxicity: clinical characteristics, risk factors and genome-wide association study in children treated for acute lymphoblastic leukemia', Haematologica, vol. 107, no. 3, pp. 635-643.
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Symptomatic methotrexate-related central neurotoxicity (MTX neurotoxicity) is a severe toxicity experienced during acute lymphoblastic leukemia (ALL) therapy with potential long-term neurologic complications. Risk factors and long-term outcomes require further study. We conducted a systematic, retrospective review of 1,251 consecutive Australian children enrolled on Berlin-Frankfurt-Münster or Children's Oncology Group-based protocols between 1998-2013. Clinical risk predictors for MTX neurotoxicity were analyzed using regression. A genome-wide association study (GWAS) was performed on 48 cases and 537 controls. The incidence of MTX neurotoxicity was 7.6% (n=95 of 1,251), at a median of 4 months from ALL diagnosis and 8 days after intravenous or intrathecal MTX. Grade 3 elevation of serum aspartate aminotransferase (P=0.005, odds ratio 2.31 [range, 1.28–4.16]) in induction/consolidation was associated with MTX neurotoxicity, after accounting for the only established risk factor, age ≥10 years. Cumulative incidence of CNS relapse was increased in children where intrathecal MTX was omitted following symptomatic MTX neurotoxicity (n=48) compared to where intrathecal MTX was continued throughout therapy (n=1,174) (P=0.047). Five-year central nervous system relapse-free survival was 89.2 4.6% when intrathecal MTX was ceased compared to 95.4 0.6% when intrathecal MTX was continued. Recurrence of MTX neurotoxicity was low (12.9%) for patients whose intrathecal MTX was continued after their first episode. The GWAS identified single-nucletide polymorphism associated with MTX neurotoxicity near genes regulating neuronal growth, neuronal differentiation and cytoskeletal organization (P<1x10-6). In conclusion, increased serum aspartate aminotransferase and age ≥10 years at diagnosis were independent risk factors for MTX neurotoxicity. Our data do not support cessation of intrathecal MTX after a first MTX neurotoxicity event.
Mathew, M, Rad, MA, Mata, JP, Mahmodi, H, Kabakova, IV, Raston, CL, Tang, Y, Tipper, JL & Tavakoli, J 2022, 'Hyperbranched polymers tune the physicochemical, mechanical, and biomedical properties of alginate hydrogels', Materials Today Chemistry, vol. 23, pp. 100656-100656.
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The current research aimed to fabricate an alginate-hyperbranched polymer (HBP) complex, using a vortex fluidic device (VFD), to control the physicochemical, structural, and mechanical properties of alginate hydrogel; thus, providing a dominant biomaterial system for different biomedical applications. Samples were prepared by mixing alginate (6%w/w) with HBP (0.85 μM) before cross-linking with Ca2+ (100 mM). Magnet stirrer (600 rpm) and VFD (6000 rpm) were used to prepare experimental samples, and alginate was used as control. Comprehensive evaluations of bulk and surface morphology, microstructural analysis, swelling kinetics, mechanical characteristics, cytotoxicity, and formation of hydrogen bonds were conducted. The findings from this study revealed that the addition of HBP to alginate structure led to a higher swelling capability (86%), increased diffusion coefficient (66-fold), and enhanced failure mechanical properties (160% and 20% increases for failure stress and elongation at break, respectively) than control. Traditional mixing affected the surface morphology, while the bulk structure remained unchanged. Moreover, the rate of degradation was not significantly different between alginate and alginate-HBP samples. When VFD was incorporated, a higher swelling ratio (30%) was observed than the control sample and the coefficient of diffusion increased (34-fold). The associated degradation rate increased 30-fold, and the failure stress and elongation at break were increased 310% and 83%, respectively, compared to the control sample. The micromixing of alginate with HBP under high shear stress using a VFD created a micro-hybrid composite formed by alginate microparticles embedded in an alginate sheet.
Matin, SS & Pradhan, B 2022, 'Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review', Geocarto International, vol. 37, no. 21, pp. 6186-6212.
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Matta, SM, Selam, MA, Manzoor, H, Adham, S, Shon, HK, Castier, M & Abdel-Wahab, A 2022, 'Predicting the performance of spiral-wound membranes in pressure-retarded osmosis processes', Renewable Energy, vol. 189, pp. 66-77.
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A process simulator has been developed to model and predict the performance of spiral-wound membrane modules in pressure retarded osmosis processes. This has involved automation of generalized protocols for the numerical integration of the solvent and solute flux equations (in conjunction with a suitable electrolyte equation of state) along the surface area of a spiral-wound membrane leaf. Performance equations are solved for discrete area elements and the spiral-wound character of the module as a whole is realized through the programmed sequence in which discrete elements are evaluated. This arrangement allows for mirroring the parabolic flow pattern of the feed stream in the spiral-wound membrane leaf. The total permeation (and, by extension, power density) is thus calculated in a manner that accounts for the driving force profile consistent with flow patterns specific to spiral-wound membranes. This effective treatment of each discrete element as a flat-sheet membrane enables the transferability of membrane parameters characterized in standard, coupon-scale experiments to the simulation of spiral-wound modules. This transferability is illustrated through comparisons of model predictions with published pilot-scale PRO data.
Maxit, L, Karimi, M, Guasch, O & Michel, F 2022, 'Numerical analysis of vibroacoustic beamforming gains for acoustic source detection inside a pipe conveying turbulent flow', Mechanical Systems and Signal Processing, vol. 171, pp. 108888-108888.
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McCourt, LR, Routley, BS, Ruppert, MG, Keast, VJ, Sathish, CI, Borah, R, Goreham, RV & Fleming, AJ 2022, 'Single-Walled Carbon Nanotubes as One-Dimensional Scattering Surfaces for Measuring Point Spread Functions and Performance of Tip-Enhanced Raman Spectroscopy Probes', ACS Applied Nano Materials, vol. 5, no. 7, pp. 9024-9033.
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This Article describes a method for the characterization of the imaging performance of tip-enhanced Raman spectroscopy probes. The proposed method identifies single-walled carbon nanotubes that are suitable as one-dimensional Raman scattering objects by using atomic force microscope maps and exciting the radial breathing mode using 785 nm illumination. High-resolution cross sections of the nanotubes are collected, and the point spread functions are calculated along with the optical contrast and spot diameter. The method is used to characterize several probes, which results in a set of imaging recommendations and a summary of limitations for each probe. Elemental analysis and boundary element simulations are used to explain the formation of multiple peaks in the point spread functions as a consequence of random grain formation on the probe surface.
McGowan, B, Grace, H, Beste, D, Frey, S, Bridges, J, Sun, J & Nair, RG 2022, 'Factors influencing oral cancer screening preferences in patients attending Tertiary Care University Oral Health Clinic', Australian Dental Journal, vol. 67, no. 1, pp. 55-68.
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AbstractBackgroundUnderstanding factors that influence patients' preferences towards oral cancer (OC) screening is imperative to provide high‐quality evidence‐based OC screening interventions that can be targeted for population‐level uptake. This study determined adult patients' knowledge and awareness of OC, and how health behaviours influenced their preferences towards OC screening.MethodsThis cross‐sectional study used a 42‐point questionnaire, between February and May 2020 using a combination of in‐person and telephone interviews. Chi‐square test and multiple logistic regression analysis were applied to confounding factors that returned statistical significance against OC knowledge and awareness. Significance of P < 0.05 was accepted.ResultsSixty‐eight (38.6%) participants out of a total 176 had good knowledge of OC and 89 (50.6%) had good awareness. A total of 31.8% reported preference for OC screening by a general dental practitioner (GDP) over a general medical practitioner (GMP). Majority (72.7%) reported acceptance of OC screening at their next GDP visit. Ages 56–70 (OR = 0.357, 95% CI) and previous smokers (OR = 0.336, 95% CI) significantly influenced screening preferences. Knowledge of risk factors did not significantly influence OC screening preferences (χ2 = 3.178, P = 0.075).ConclusionsSignificant gaps in OC knowledge, screening and role of GDPs exist with smoking history and age influencing OC screening preferences.
Mckie, I, Narayan, B & Kocaballi, B 2022, 'Conversational Voice Assistants and a Case Study of Long-Term Users: A Human Information Behaviours Perspective', Journal of the Australian Library and Information Association, vol. 71, no. 3, pp. 233-255.
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Medawela, S, Indraratna, B, Athuraliya, S, Lugg, G & Nghiem, LD 2022, 'Monitoring the performance of permeable reactive barriers constructed in acid sulfate soils', Engineering Geology, vol. 296, pp. 106465-106465.
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Two pilot-scale permeable reactive barriers (PRBs) were installed in an acidic terrain to treat contaminated groundwater with low pH and high concentrations of Al and Fe. The first pilot-scale barrier (PRB-1) was installed in 2006 using recycled concrete aggregates (RCA) as the reactive material, and the second barrier (PRB-2) was installed in late 2019 using limestone aggregates (LA) as the reactive material. Although the initial material cost of the recycled concrete aggregates is low, laboratory trials conducted before the field applications deduced that limestone is capable of more reliable and efficient pH neutralisation in the long term, reducing frequent maintenance or material replacement in the PRB. The performance of PRB-1 has been monitored continuously over the past 14 years. In particular, both internal (within PRB) and external (upgradient and downgradient) variations in acidity (pH), ion concentrations, and the flow conditions, including the piezometric heads, have been analysed. These decade long field observations have resulted in a comprehensive understanding of the temporal variations of treatment by RCA along the groundwater flow path through the alkaline granular mass and its biogeochemical clogging. For instance, acid neutralisation at the entrance of PRB-1 decreased by 31% over 14 years, whereas the corresponding reduction at the outlet is only 6%. The non-homogeneous biogeochemical clogging in different PRB zones was evident by a 48% reduction in hydraulic conductivity at the inlet and a 34% reduction at the outlet.
Meena, MS, Pare, S, Singh, P, Rana, A & Prasad, M 2022, 'A Robust Illumination and Intensity invariant Face Recognition System', International Journal of Circuits, Systems and Signal Processing, vol. 16, pp. 974-984.
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Face recognition has achieved more attention in computer vision with the focus on modelling the expression variations of human. However, in computer vision system, face recognition is a challenging task, due to variation in expressions, poses, and lighting conditions. This paper proposes a facial recognition technique based on 2D Hybrid Markov Model (2D HMM), Cat Swam Optimization (CSO), Local Directional Pattern (LDP), and Tetrolet Transform. Skin segmentation method is used for pre-processing followed by filtering to extract the region of interest. Resultant image is fed to proposed feature extraction method comprising of Tetrolet Transform and LDP. Extracted features are classified using proposed classifier “CSO trained 2D-HMM classification method”. To prove the superiority of method, four face datasets are used, and comparative results are presented. Quantitively results are measured by False Acceptance Rate (FAR), False Rejection Rate (FRR) and Accuracy and the values are 0.0025, 0.0035 and 99.65% respectively
Mehami, J, Falque, R, Vidal-Calleja, T & Alempijevic, A 2022, 'Multi-Modal Non-Isotropic Light Source Modelling for Reflectance Estimation in Hyperspectral Imaging', IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10336-10343.
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Estimating reflectance is key when working with hyperspectral cameras. The modelling of light sources can aid reflectance estimation, however, it is commonly overlooked. The key contribution of this letter is a physics-based, data-driven model formed by a Gaussian Process (GP) with a unique mean function capable of modelling a light source with an asymmetric radiant intensity distribution (RID) and a configurable attenuation function. This is referred to as the light-source-mean model. Moreover, we argue that by utilising multi-modal sensing information, we can achieve improved reflectance estimation using the proposed light source model with shape information obtained by depth cameras. An existing reflectance estimation method, that solves the dichromatic reflectance model (DRM) via quadratic programming optimisation, is augmented with terms that allow input of shape information. Experiments in simulation show that the light-source-mean GP model had less error when compared to a parametric model. The improved reflectance estimation outperforms existing methods in simulation by reducing the error by 96.8% on average when compared to existing works. We further validate the improved reflectance estimation method through a multi-modal classification application.
Mehrabi, N & Khabbaz, H 2022, 'A trustful transition zone for high-speed rail using stone columns', Australian Journal of Civil Engineering, vol. 20, no. 1, pp. 56-66.
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The high-speed railway projects have encountered several geotechnical challenges. One of the most important challenges is the differential settlement control in transition zones. Cement-treated soil is a common method to prevent the differential settlement at transition zones. An alternative method uses stone columns for controlling the differential settlement in approaching embankment of bridges. In this study, numerical modelling using PLAXIS 2D is selected for the assessment of stone columns in the reduction of total and differential settlements. One of the overpass bridges of the track constructed for the Tehran–Isfahan railway, the first high-speed railway in the country, is chosen as the case study. Three models are created based on the properties of the selected case study. The first one is a typical approaching embankment. The second one is the bridge abutment section, and the last one is a typical reinforced approaching embankment with stone columns.
Mehraj, S, Mushtaq, S, Parah, SA, Giri, KJ, Sheikh, JA, Gandomi, AH, Hijji, M & Muhammad, K 2022, 'Spatial Domain-Based Robust Watermarking Framework for Cultural Images', IEEE Access, vol. 10, pp. 117248-117260.
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Mei, F, Li, J, Zhang, L, Gao, J, Li, H, Zhou, D, Xing, D & Lin, J 2022, 'Posterior-Stabilized Versus Cruciate-Retaining Prostheses for Total Knee Arthroplasty: An Overview of Systematic Reviews and Risk of Bias Considerations', Indian Journal of Orthopaedics, vol. 56, no. 11, pp. 1858-1870.
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BACKGROUND: Numerous systematic reviews have been published comparing the outcomes of patients undergoing posterior stabilized (PS) versus cruciate-retaining (CR) procedures in total knee arthroplasty (TKA), but with some overlaps and contradictions. The objectives of this study were (1) to perform an overview of current systematic reviews comparing PS versus CR in TKA, by evaluating their methodological quality and risk of bias, and (2) to provide recommendations through the best evidence. METHODS: A systematic search of systematic reviews comparing PS and CR in TKA, published until June 2021 was conducted using the MEDLINE, EMBASE, and Cochrane Library databases. Included systematic reviews were assessed for methodological quality and risk of bias by the AMSTAR2 instrument and ROBIS tool, respectively. The choice of best evidence was conducted according to the Jadad decision algorithm. RESULTS: A total of eight systematic reviews were eligible for inclusion in this study. The Jadad decision algorithm suggested that reviews with the highest AMSTAR2 scores should be selected. According to the ROBIS tool, there were three reviews with a low risk of bias and five with a high risk of bias. Consequently, one systematic review conducted by Verra et al. with the highest AMSTAR2 score and low risk of bias was selected as the best evidence. CONCLUSIONS: Although current systematic reviews demonstrated some statistical differences in clinical presentation and functional outcomes between PS and CR, the current outcome indicators cannot be taken to provide recommendations for undergoing PS or CR. The decision for prosthesis selection could be made mostly based on the surgeon's preference, indications and other indicators.
Mei, F, Li, J, Zhang, L, Gao, J, Wang, B, Zhou, Q, Xu, Y, Zhou, C, Zhao, J, Li, P, Zhao, Y, Yuan, T, Fu, W, Li, C, Jin, Y, Yang, P, Xing, D & Lin, J 2022, 'Preference of Orthopedic Practitioners Toward the Use of Topical Medicine for Musculoskeletal Pain Management in China: A National Survey', Orthopaedic Surgery, vol. 14, no. 10, pp. 2470-2479.
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ObjectiveMusculoskeletal pain is having growing impacts worldwide with clinical challenge in pain management. The purpose of the present study is to investigate the preferences of orthopedic surgeons of China for using medicine in musculoskeletal pain.MethodsA questionnaire was developed, including the following domains, personal information, medication preference for pain treatment, and perceptions of topical medicine. Ten participants were selected to confirm the consistency of questionnaire. A cross‐sectional survey was conducted in orthopedic physicians with different specialties in different regions of China via the online survey platform. The participants' survey results were analyzed one‐way and multi‐way using chi‐square test and logistic regression.ResultsThe pre‐survey analysis results of 10 randomly selected investigators were a mean weighted kappa coefficient of 0.76 (range 0.61–0.89), which indicated the substantial consistency of the present questionnaire. A total of 1099 orthopedic surgeons (mean age, 41.67 ± 8.31 years) responded to our survey, most of whom were male (90.72%), and most of whom worked in level III hospitals (63.24%) and trained in modern medicine (71.43%). Most surgeons who participated in the survey had used topical analgesics in their clinical work (95.81%), and most preferred to use topical analgesics (39.50%) or a combination of oral analgesics (28.87%). Primary reasons for preferring topical analgesics were as follows: less adverse reactions (68.01%); ease of use (60.90%); and not interfering with other oral medications (49.60%). The preference for prescribing topical analgesics increased with the education level of the respondent, where statistically significant differences were seen (P < 0.05)...
Melhem, MM, Caprani, CC & Stewart, MG 2022, 'Reliability updating of partial factors for empirical codes: Application to Super-T PSC girders designs at the ultimate limit state in bending', Structures, vol. 35, pp. 233-242.
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Reliability design code calibrations typically involve the comparison of calculated levels of safety (β) of designs to a range of prospective partial safety factors with the minimum acceptable level of safety (βT). When updating the calibration and the original βT is unknown or undocumented, design-specific probability models and the code-implied level of safety are necessary. This study presents a methodology for updating capacity reduction factors ϕ for a suite of PSC bridge girder section designs for ultimate strength in bending for a design code for which βT is unknown. In the methodology, the code-implied safety as inferred from the notional traffic design load, and the designed girder safety under actual traffic loading are computed. The method is applied to the suite of prestressed concrete Super-T girders designed to the Australian bridge standards AS 5100, in which the implicit βT is not known. The results find both code-implied safety and designed girder safety far surpasses the usual recommendations for βT for all designs and regardless of ϕ. As such, only through the relative comparison of code-implied safety and designed girder safety can recommendations be made on increasing ϕ in AS 5100 for Super-T girder ultimate strength in bending. Moreover, the comparison with code-implied safety is taken to indicate the desired degree of reserve capacity available for future traffic growth. The results inform on possible improvements for the next version of AS 5100. More significantly, the work illustrates a way to reliability-update partial factors of design codes when βT is not known, and future-proofing structures is seen as necessary.
Meng, X, Li, X, Nghiem, LD, Ruiz, E, Johir, MA, Gao, L & Wang, Q 2022, 'Improved stormwater management through the combination of the conventional water sensitive urban design and stormwater pipeline network', Process Safety and Environmental Protection, vol. 159, pp. 1164-1173.
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With rapid urbanization, flooding events become more frequently in daily life, causing enormous economic damage and loss of life. Water Sensitive Urban Design (WSUD) is a common approach for mitigating stormwater runoff. However, it showed limited performance in big catchment areas (>1000 ha). This study proposed an innovative approach by combining conventional WSUD projects with the stormwater pipeline network through linear connections for better stormwater runoff management for a big catchment. The performance of combined WSUD projects and conventional WSUD was evaluated using the urban water system of a catchment (over 1200 ha) in Sydney, Australia, through the water mass balance modelling approach using annual rainfall data of 70 years (from 1950 to 2020). Combined WSUD reduced the stormwater runoff by over 124 ML/yr compared to that of the conventional WSUD model in accommodating future development. Combined WSUD restored the evapotranspiration and infiltration under high, average and low annual rainfall scenarios with an increasing 20–30% increase of evapotranspiration and infiltration in combined WSUD than the conventional WSUD. The results obtained from the study demonstrated that combining WSUDs with the stormwater pipeline network through linear connections is a promising approach in stormwater management and restoring the natural hydrological cycle.
Merino-Arteaga, I, Alfaro-García, VG & Merigó, JM 2022, 'Fuzzy systems research in the United States of America and Canada: A bibliometric overview', Information Sciences, vol. 617, pp. 277-292.
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Mi, J, Ni, W, Huang, P, Hong, J, Jia, R, Deng, S, Yu, X, Wei, H & Yang, W 2022, 'Effect of acetylated distarch adipate on the physicochemical characteristics and structure of shrimp (Penaeus vannamei) myofibrillar protein', Food Chemistry, vol. 373, pp. 131530-131530.
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Milano, J, Shamsuddin, AH, Silitonga, AS, Sebayang, AH, Siregar, MA, Masjuki, HH, Pulungan, MA, Chia, SR & Zamri, MFMA 2022, 'Tribological study on the biodiesel produced from waste cooking oil, waste cooking oil blend with Calophyllum inophyllum and its diesel blends on lubricant oil', Energy Reports, vol. 8, pp. 1578-1590.
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Biodiesel or biodiesel–diesel fuel is the current fuel used to power transportation engines. Contamination on lubricating oil is a common issue due to leakage or extensive use of engines. This study explores the lubricant oil blend's friction and wear with the biodiesel derived from waste cooking oil, waste cooking oil blend withCalophyllum inophyllum oil, and biodiesel–diesel blend. The blending of biodiesels and biodiesel–diesel blend with lubricant oil varies from 5% to 25% of biodiesels and biodiesel–diesel with 95% to 75% of lubricating oil based on volume ratio. The test was conducted using a four-ball tribotester according to the ASTM D 4172. The result showed that blending of BWCIL75 with biodiesel–diesel has the lowest friction coefficient (0.072) among tested oil. The wear scar on the ball bearing lubricated with the blending mixture showed an acceptable diameter value. The wear morphology has shown that a worn surface with black spots provides more protection to the tested ball. The result found that fatty acid contained in the biodiesel and the low viscosity of biodiesel significantly reduced the frictional coefficient of the lubricating oil and worked as wear prevention. Mechanical efficiency of machinery component favour low coefficient of friction. This study indicated that biodiesel produced from waste cooking oil blended with Calophyllum inophyllum oil shows better lubricity and can be used as an additive to petroleum-based lubricant for better automotive engine performance.
Miller, HD, Akbarnezhad, A, Mesgari, S & Foster, SJ 2022, 'Effects of silane treatment on the bond between steel fibres and mortar', Magazine of Concrete Research, vol. 74, no. 10, pp. 528-540.
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The ability of fibres to resist crack growth in fibre-reinforced concrete can be significantly influenced by the fibre–matrix bond. This investigation reveals surface treatment of fibres as a viable technique for developing a uniform bond along the fibre–cement interface to resist growth of microcracks and thereby complement the physical restraint against pull-out provided by fibres’ shape and friction. Previous reports have shown effective chemical treatment of glass, carbon and polypropylene fibres. However, research into chemical surface treatment processes for steel fibres, the most common in concrete, is scarce and focused on corrosion and dispersion, rather than the fibre–matrix bond. Here, a silane treatment technique is proposed to strengthen the steel fibre–cementitious matrix bond. Surface energy measurements and X-ray photoelectron spectroscopy demonstrate the effectiveness of this treatment. Fibre pull-out tests conducted on silane-treated fibres show an apparent increase in pull-out energy, accompanied by a delay in reaching the peak load, compared with untreated fibres, suggesting increased resistance to crack initiation and growth. Furthermore, the results indicate improved flexural strength and direct tensile strength of mortar reinforced with silane-treated fibres compared with untreated fibres. The improvements are further corroborated by results from restrained drying shrinkage and volume of permeable voids.
Min, C, Kim, JE, Shon, HK & Kim, S-H 2022, 'Low energy resonance vibration submerged membrane system for microalgae harvesting: Performance and feasibility', Desalination, vol. 539, pp. 115895-115895.
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This study investigated an energy-efficient harvesting method to collect microalgae of Chlorella Vulgaris (C. vulgaris). The method proposed in the current study was a combination of a resonance vibration submerged membrane (RVSM) system and centrifugation. The result showed that the RVSM system was able to concentrate the C. vulgaris solution by 17 times (0.61 g·L−1 to 10.4 g·L−1) without chemical cleaning during filtration with intermittent relaxation (i.e., filtration for 9 min and relaxation for 1 min) at a flux of 40 LMH (L·m−2·h−1) until the transmembrane pressure (TMP) reached 70 kPa. In addition, extracellular polymeric substances such as polysaccharides and protein were found mainly responsible for membrane fouling during the operation of concentrating C. vulgaris solution. Integrating the RVSM system with the centrifugation process required the total specific energy consumption of 0.56 kWh·m−3 (0.09 kWh·m−3 for the RVSM and 0.47 kWh·m−3 for the centrifugation). This study demonstrated the combination of the RVSM system and centrifugation to be a feasible C. vulgaris harvesting method by showing lower energy consumption than other conventional processes.
Mir, I, Gul, F, Mir, S, Khan, MA, Saeed, N, Abualigah, L, Abuhaija, B & Gandomi, AH 2022, 'A Survey of Trajectory Planning Techniques for Autonomous Systems', Electronics, vol. 11, no. 18, pp. 2801-2801.
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This work offers an overview of the effective communication techniques for space exploration of ground, aerial, and underwater vehicles. We not only comprehensively summarize the trajectory planning, space exploration, optimization, and other challenges encountered but also present the possible directions for future work. Because a detailed study like this is uncommon in the literature, an attempt has been made to fill the gap for readers interested in path planning. This paper also includes optimization strategies that can be used to implement terrestrial, underwater, and airborne applications. This study addresses numerical, bio-inspired, and hybrid methodologies for each dimension described. Throughout this study, we endeavored to establish a centralized platform in which a wealth of research on autonomous vehicles (on the land and their trajectory optimizations), airborne vehicles, and underwater vehicles, is published.
Mir, T, Waqas, M, Tu, S, Fang, C, Ni, W, MacKenzie, R, Xue, X & Han, Z 2022, 'Relay Hybrid Precoding in UAV-Assisted Wideband Millimeter-Wave Massive MIMO System', IEEE Transactions on Wireless Communications, vol. 21, no. 9, pp. 7040-7054.
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Miri-Lavasani, Z, Torabi, S, Solhi, R, Shokouhian, B, Afsharian, P, Heydari, Z, Piryaei, A, Farzaneh, Z, Hossein-khannazer, N, Es, HA, Zahmatkesh, E, Nussler, A, Hassan, M, Najimi, M & Vosough, M 2022, 'Conjugated Linoleic Acid Treatment Attenuates Cancerous features in Hepatocellular Carcinoma Cells', Stem Cells International, vol. 2022, pp. 1-14.
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Background. A growing number of hepatocellular carcinoma (HCC), and recurrence frequency recently have drawn researchers’ attention to alternative approaches. The concept of differentiation therapies (DT) relies on inducing differentiation in HCC cells in order to inhibit recurrence and metastasis. Hepatocyte nuclear factor 4 alpha (HNF4α) is the key hepatogenesis transcription factor and its upregulation may decrease the invasiveness of cancerous cells by suppressing epithelial-mesenchymal transition (EMT). This study aimed to evaluate the effect of conjugated linoleic acid (CLA) treatment, natural ligand of HNF4α, on the proliferation, migration, and invasion capacities of HCC cells in vitro. Materials and Method. Sk-Hep-1 and Hep-3B cells were treated with different doses of CLA or BIM5078 [1-(2 ′ -chloro-5 ′ -nitrobenzenesulfonyl)−2-methylbenzimidazole], an HNF4α antagonist. The expression levels of HNF4a and EMT related genes were evaluated and associated to hepatocytic functionalities, migration, and colony formation capacities, as well as to viability and proliferation rate of HCC cells. Results. In both HCC lines, CLA treatment induced HNF4α expression in parallel to significantly decreased EMT marker levels, migration, colony formation capacity, and proliferation rate, whereas BIM5078 treatment resulted in the opposite effects. Moreover, CLA supplementation also upregulated ALB, ZO1, and HNF4α proteins as well as glycogen storage capacity in the treated HCC cells. Conclusion. CLA treatment can induce a remarkable hepatoc...
Mishra, A, Alzoubi, YI, Anwar, MJ & Gill, AQ 2022, 'Attributes impacting cybersecurity policy development: An evidence from seven nations', Computers & Security, vol. 120, pp. 102820-102820.
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Cyber threats have risen as a result of the growing usage of the Internet. Organizations must have effective cybersecurity policies in place to respond to escalating cyber threats. Individual users and corporations are not the only ones who are affected by cyber-attacks; national security is also a serious concern. Different nations' cybersecurity rules make it simpler for cybercriminals to carry out damaging actions while making it tougher for governments to track them down. Hence, a comprehensive cybersecurity policy is needed to enable governments to take a proactive approach to all types of cyber threats. This study investigates cybersecurity regulations and attributes used in seven nations in an attempt to fill this research gap. This paper identified fourteen common cybersecurity attributes such as telecommunication, network, Cloud computing, online banking, E-commerce, identity theft, privacy, and smart grid. Some nations seemed to focus, based on the study of key available policies, on certain cybersecurity attributes more than others. For example, the USA has scored the highest in terms of online banking policy, but Canada has scored the highest in terms of E-commerce and spam policies. Identifying the common policies across several nations may assist academics and policymakers in developing cybersecurity policies. A survey of other nations' cybersecurity policies might be included in the future research.
Mishra, A, Alzoubi, YI, Gill, AQ & Anwar, MJ 2022, 'Cybersecurity Enterprises Policies: A Comparative Study', Sensors, vol. 22, no. 2, pp. 538-538.
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Cybersecurity is a critical issue that must be prioritized not just by enterprises of all kinds, but also by national security. To safeguard an organization’s cyberenvironments, information, and communication technologies, many enterprises are investing substantially in cybersecurity these days. One part of the cyberdefense mechanism is building an enterprises’ security policies library, for consistent implementation of security controls. Significant and common cybersecurity policies of various enterprises are compared and explored in this study to provide robust and comprehensive cybersecurity knowledge that can be used in various enterprises. Several significant common security policies were identified and discussed in this comprehensive study. This study identified 10 common cybersecurity policy aspects in five enterprises: healthcare, finance, education, aviation, and e-commerce. We aimed to build a strong infrastructure in each business, and investigate the security laws and policies that apply to all businesses in each sector. Furthermore, the findings of this study reveal that the importance of cybersecurity requirements differ across multiple organizations. The choice and applicability of cybersecurity policies are determined by the type of information under control and the security requirements of organizations in relation to these policies.
Mishra, DK, Ghadi, MJ, Li, L, Zhang, J & Hossain, MJ 2022, 'Active distribution system resilience quantification and enhancement through multi-microgrid and mobile energy storage', Applied Energy, vol. 311, pp. 118665-118665.
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The functional capability of the active distribution network is continually challenged by extreme weather and unforeseen events. A complete resilience quantification framework is required to assess the resilience of a distribution system. With this objective, a framework for demonstrating resilience enhancement through the utilization of multi-microgrids (MMGs) and mobile energy storage in extreme operating conditions is developed in this paper. In the proposed framework, four resilience indices, that is, withstand, recovery, adapt, and prevent (WRAP), are introduced. Withstand index signifies the coping capability after the event, where the MG plays a vital role. The recovery index measures the restoration after the event ends through the system reconfiguration using MGs, tie-lines, and mobile energy storage. The adapt index shows the stability of the system before and during the events. Finally, the prevent index suggests how different resources are important and responsible for fast recovery and minimizing consequences. WRAP, as a resilience quantification framework, is formulated in this study, and indices are quantified and enhanced through the MMG and mobile energy storages. The IEEE 33-bus system is considered for this study, and simulation is performed with different scenarios and measured resilience indices. It is found that appropriate reconfiguration through the use of MMG, tie-lines, and mobile storages can remarkably enhance the resilience of a distribution system.
Mishra, DK, Ray, PK, Li, L, Zhang, J, Hossain, MJ & Mohanty, A 2022, 'Resilient control based frequency regulation scheme of isolated microgrids considering cyber attack and parameter uncertainties', Applied Energy, vol. 306, pp. 118054-118054.
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Cyber-physical attacks and parameter uncertainties are becoming a compelling issue on load frequency control, directly affecting the resilience (i.e., reliability plus security) of the microgrid and multi-microgrid systems enabled by internet of things and the fifth generation communication system. A resilient system aims to endure and quickly restore a system's transients during extreme events. Therefore, it is critically important to have a resilient system to evade the total system failure or blackout in order to make them attack-resilient. With this objective, this paper presents a resilience-based frequency regulation scheme in a microgrid under different operating conditions, such as, step and random change in load and different wind speed patterns. Furthermore, a cyber-attack model is considered in the problem formulation to make the system robust against external attacks. To protect against the cyber-attack and parameter uncertainties in the system, different control schemes are employed, and their robustness characteristics are compared through various performance indices. Besides, the proposed control schemes are validated through a real-time software synchronisation environment, i.e., OPAL-RT. As noted, the proposed type-2 fuzzy proportional-integral-derivative based controller provides the most significant improvement in the dynamic performance for frequency regulation compared to that of the others under the cyber-attack and uncertainties.
Mishra, DK, Złotecka, D & Li, L 2022, 'Significance of SMES Devices for Power System Frequency Regulation Scheme considering Distributed Energy Resources in a Deregulated Environment', Energies, vol. 15, no. 5, pp. 1766-1766.
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Nowadays, the restructuring of power systems is extremely urgent due to the depletion of fossil fuels on the one hand and the environmental impact on the other. In the restructured environment, the incorporation of renewable energy sources and storage devices is key as they have helped achieve a milestone in the form of microgrid technology. As the restructuring of the power system increases, there are several types of generation sources, and distribution companies express their interest in trading in a deregulated environment to operate economically. When considering the power system deregulation, the contract value deviates in some situations, resulting in an imbalance between the generation and the energy consumption, which can bring the system into a power outage condition. In particular, load frequency control has been a great challenge over the past few decades to ensure the stable operation of power systems. This study considers two generation sources: mini-hydro in GENCO-1 and 3 and microgrid (combination of wind, fuel cell, battery storage, and diesel engine) in GENCO-2 and 4. It is two equal-area networks; in area-1, GENCO-1 and 2, and in area-2, GENCO-3 and 4 are considered, respectively. In addition, a FOPID controller and two ancillary devices, such as a unified power flow controller and a superconducting magnetic energy storage system, have been incorporated. Three different test networks have been formed according to the contract value, such as unilateral, bilateral, and agreement violations. The simulation results show that ancillary devices and controller participation significantly enhance the system response by reducing the frequency and tie-line power fluctuation. To validate the efficacy of the proposed method, respective performance indices and percentages of improvement have been obtained. Finally, this study demonstrated the effectiveness of the proposed restructured power system in a deregulated environment.
Mishra, M, Chaudhuri, S, Kshetrimayum, RS, Alphones, A & Esselle, KP 2022, 'Space Efficient Meta-Grid Lines for Mutual Coupling Reduction in Two-Port Planar Monopole and DRA Array', IEEE Access, vol. 10, pp. 49829-49838.
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Mittal, A, Shivakumara, P, Pal, U, Lu, T & Blumenstein, M 2022, 'A new method for detection and prediction of occluded text in natural scene images', Signal Processing: Image Communication, vol. 100, pp. 116512-116512.
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Text detection from natural scene images is an active research area for computer vision, signal, and image processing because of several real-time applications such as driving vehicles automatically and tracing person behaviors during sports or marathon events. In these situations, there is a high probability of missing text information due to the occlusion of different objects/persons while capturing images. Unlike most of the existing methods, which focus only on text detection by ignoring the effect of missing texts, this work detects and predicts missing texts so that the performance of the OCR improves. The proposed method exploits the property of DCT for finding significant information in images by selecting multiple channels. For chosen DCT channels, the proposed method studies texture distribution based on statistical measurement to extract features. We propose to adopt Bayesian classifier for categorizing text pixels using extracted features. Then a deep learning model is proposed for eliminating false positives to improve text detection performance. Further, the proposed method employs a Natural Language Processing (NLP) model for predicting missing text information by using detected and recognition texts. Experimental results on our dataset, which contains texts occluded by objects, show that the proposed method is effective in predicting missing text information. To demonstrate the effectiveness and objectiveness of the proposed method, we also tested it on the standard datasets of natural scene images, namely, ICDAR 2017-MLT, Total-Text, and CTW1500.
Mofijur, M, Ashrafur Rahman, SM, Nguyen, LN, Mahlia, TMI & Nghiem, LD 2022, 'Selection of microalgae strains for sustainable production of aviation biofuel', Bioresource Technology, vol. 345, pp. 126408-126408.
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This study develops and applies the PROMETHEE-GAIA method as a new tool to select microalgae strains for aviation fuel production. Assessment involves 19 criteria with equal weighting in three aspects, namely biomass production, lipid quality, and fatty acid methylester properties. Here, the method is demonstrated for evaluating 17 candidate microalgae strains. Chlorella sp. NT8a is assessed as the most suitable strain for aviation fuel production. The results also show that unmodified biofuel from the most suitable strain could not meet all jet fuel standards. In particular, microalgae-based fuel could not satisfy the required density, heating value and freezing points of the international jet fuel standards. These results highlight the need for a broad action plan including improvement in the processing or modification of biofuel produced from microalgae and revision of the current jet fuel standards to facilitate the introduction of microalgae-based biofuel for the aviation industry.
Moghaddam, MP, Zamani, R, Alhelou, HH & Siano, P 2022, 'Preface', pp. xv-xv.
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Mohamed, BA, Bilal, M, Salama, E-S, Periyasamy, S, Fattah, IMR, Ruan, R, Awasthi, MK & Leng, L 2022, 'Phenolic-rich bio-oil production by microwave catalytic pyrolysis of switchgrass: Experimental study, life cycle assessment, and economic analysis', Journal of Cleaner Production, vol. 366, pp. 132668-132668.
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This study aims to determine the environmental impacts and feasibility of optimizing the production of phenolic-rich bio-oil, via switchgrass microwave catalytic pyrolysis. K3PO4 (Tripotassium phosphate) was used as the catalyst, at different temperatures, throughout this life cycle assessment (LCA) study. Results were compared with non-catalytic microwave pyrolysis (SiC-400) and conventional pyrolysis. K3PO4 (KP) was used as the microwave absorber and catalyst to enhance the low microwave absorption of switchgrass during microwave pyrolysis, and to improve the bio-oil quality and selectivity for phenolics production. Pyrolysis temperatures made a considerable difference to the LCA. There was an 86% reduction in the pyrolysis time when heating the sample to 300 °C (KP-300), as compared to 400 °C (KP-400), resulting in a significant reduction of the amount of energy required, and GHG's emitted. The total global warming potential (GWP) for microwave catalytic pyrolysis is observed within 159–223 kg CO2-eq/1000 kg of dried switchgrass (SG), with the baseline case (SiC-400) being the highest, and KP-300 being the lowest. Using the produced biochar, which is rich in nutrients for soil application, brings the net GWP to negative values through carbon sequestration. KP-300 also showed the highest selectivity for phenol and alkylphenols production, which increased by 252% and 420% respectively, compared to the baseline. The results clearly indicate that introducing K3PO4 showed great potential for accelerating microwave heating, and improving bio-oil selectivity towards alkylphenols, which can be used to replace petroleum-based phenol. This in turn can reduce GHG emissions, due to higher conversion efficiencies and lower energy consumption compared with non-catalytic microwave pyrolysis and conventional pyrolysis.
Mohamed, BA, Fattah, IMR, Yousaf, B & Periyasamy, S 2022, 'Effects of the COVID-19 pandemic on the environment, waste management, and energy sectors: a deeper look into the long-term impacts', Environmental Science and Pollution Research, vol. 29, no. 31, pp. 46438-46457.
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The COVID-19 pandemic not only has caused a global health crisis but also has significant environmental consequences. Although many studies are confirming the short-term improvements in air quality in several countries across the world, the long-term negative consequences outweigh all the claimed positive impacts. As a result, this review highlights the positive and the long-term negative environmental effects of the COVID-19 pandemic by evaluating the scientific literature. Remarkable reduction in the levels of CO (3 - 65%), NO2 (17 - 83%), NOx (24 - 47%), PM2.5 (22 - 78%), PM10 (23 - 80%), and VOCs (25 - 57%) was observed during the lockdown across the world. However, according to this review, the pandemic put enormous strain on the present waste collection and treatment system, resulting in ineffective waste management practices, damaging the environment. The extensive usage of face masks increased the release of microplastics/nanoplastics (183 to 1247 particles piece-1) and organic pollutants in land and water bodies. Furthermore, the significant usages of anti-bacterial hand sanitizers, disinfectants, and pharmaceuticals have increased the accumulation of various toxic emerging contaminants (e.g., triclocarban, triclosan, bisphenol-A, hydroxychloroquine) in the treated sludge/biosolids and discharged wastewater effluent, posing great threats to the ecosystems. This review also suggests strategies to create long-term environmental advantages. Thermochemical conversions of solid wastes including medical wastes and for treated wastewater sludge/biosolids offer several advantages through recovering the resources and energy and stabilizing/destructing the toxins/contaminants and microplastics in the precursors.
Mohammadi, E, Jahanandish, M, Ghahramani, A, Nikoo, MR, Javankhoshdel, S & Gandomi, AH 2022, 'Stochastic optimization model for determining support system parameters of a subway station', Expert Systems with Applications, vol. 203, pp. 117509-117509.
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Mohammadi, M, Rashidi, M, Mousavi, V, Yu, Y & Samali, B 2022, 'Application of TLS Method in Digitization of Bridge Infrastructures: A Path to BrIM Development', Remote Sensing, vol. 14, no. 5, pp. 1148-1148.
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Over the past years, bridge inspection practices and condition assessments were predicated upon long-established manual and paper-based data collection methods which were generally unsafe, time-consuming, imprecise, and labor-intensive, influenced by the experience of the trained inspectors involved. In recent years, the ability to turn an actual civil infrastructure asset into a detailed and precise digital model using state-of-the-art emerging technologies such as laser scanners has become in demand among structural engineers and managers, especially bridge asset managers. Although advanced remote technologies such as Terrestrial Laser Scanning (TLS) are recently established to overcome these challenges, the research on this subject is still lacking a comprehensive methodology for a reliable TLS-based bridge inspection and a well-detailed Bridge Information Model (BrIM) development. In this regard, the application of BrIM as a shared platform including a geometrical 3D CAD model connected to non-geometrical data can benefit asset managers, and significantly improve bridge management systems. Therefore, this research aims not only to provide a practical methodology for TLS-derived BrIM but also to serve a novel sliced-based approach for bridge geometric Computer-Aided Design (CAD) model extraction. This methodology was further verified and demonstrated via a case study on a cable-stayed bridge called Werrington Bridge, located in New South Wales (NSW), Australia. In this case, the process of extracting a precise 3D CAD model from TLS data using the sliced-based method and a workflow to connect non-geometrical information and develop a BrIM are elaborated. The findings of this research confirm the reliability of using TLS and the sliced-based method, as approaches with millimeter-level geometric accuracy, for bridge inspection subjected to precise 3D model extraction, as well as bridge asset management and BrIM development.
Mojiri, A, Ozaki, N, Kazeroon, RA, Rezania, S, Baharlooeian, M, Vakili, M, Farraji, H, Ohashi, A, Kindaichi, T & Zhou, JL 2022, 'Contaminant Removal from Wastewater by Microalgal Photobioreactors and Modeling by Artificial Neural Network', Water, vol. 14, no. 24, pp. 4046-4046.
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The potential of microalgal photobioreactors in removing total ammonia nitrogen (TAN), chemical oxygen demand (COD), caffeine (CAF), and N,N-diethyl-m-toluamide (DEET) from synthetic wastewater was studied. Chlorella vulgaris achieved maximum removal of 62.2% TAN, 52.8% COD, 62.7% CAF, and 51.8% DEET. By mixing C. vulgaris with activated sludge, the photobioreactor showed better performance, removing 82.3% TAN, 67.7% COD, 85.7% CAF, and 73.3% DEET. Proteobacteria, Bacteroidetes, and Chloroflexi were identified as the dominant phyla in the activated sludge. The processes were then optimized by the artificial neural network (ANN). High R2 values (>0.99) and low mean squared errors demonstrated that ANN could optimize the reactors’ performance. The toxicity testing showed that high concentrations of contaminants (>10 mg/L) and long contact time (>48 h) reduced the chlorophyll and protein contents in microalgae. Overall, a green technology for wastewater treatment using microalgae and bacteria consortium has demonstrated its high potentials in sustainable management of water resources.
Mojiri, A, Ozaki, N, Zhou, JL, Kazeroon, RA, Zahed, MA, Rezania, S, Vakili, M, Gavanji, S & Farraji, H 2022, 'Integrated Electro-Ozonation and Fixed-Bed Column for the Simultaneous Removal of Emerging Contaminants and Heavy Metals from Aqueous Solutions', Separations, vol. 9, no. 10, pp. 276-276.
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In the current study, an integrated physiochemical method was utilized to remove tonalide (TND) and dimethyl phthalate (DMP) (as emerging contaminants, ECs), and nickel (Ni) and lead (Pb) (as heavy metals), from synthetic wastewater. In the first step of the study, pH, current (mA/cm2), and voltage (V) were set to 7.0, 30, and 9, respectively; then the removal of TND, DMP, Ni, and Pb with an electro-ozonation reactor was optimized using response surface methodology (RSM). At the optimum reaction time (58.1 min), ozone dosage (9.4 mg L−1), initial concentration of ECs (0.98 mg L−1), and initial concentration of heavy metals (28.9 mg L−1), the percentages of TND, DMP, Ni, and Pb removal were 77.0%, 84.5%, 59.2%, and 58.2%, respectively. For the electro-ozonation reactor, the ozone consumption (OC) ranged from 1.1 kg to 3.9 kg (kg O3/kg Ecs), and the specific energy consumption (SEC) was 6.95 (kWh kg−1). After treatment with the optimum electro-ozonation parameters, the synthetic wastewater was transferred to a fixed-bed column, which was filled with a new composite adsorbent (named BBCEC), as the second step of the study. BBCEC improved the efficacy of the removal of TND, DMP, Ni, and Pb to more than 92%.
Mojiri, A, Zhou, JL, Nazari V, M, Rezania, S, Farraji, H & Vakili, M 2022, 'Biochar enhanced the performance of microalgae/bacteria consortium for insecticides removal from synthetic wastewater', Process Safety and Environmental Protection, vol. 157, pp. 284-296.
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The presence of pesticides in aquatic environments has threatened marine food resources, aquaculture, fisheries and human health; therefore, two most used insecticides were removed during this study. Two photobioreactors, including biochar and Chlorella vulgaris/activated sludge (reactor 1), and Chlorella vulgaris/activated sludge (reactor 2) were run to remove chlorpyrifos (CPF) and cypermethrin (CYP). Proteobacteria, Bacteroidetes and Chloroflexi were the dominant phyla of activated sludge. The optimization performance of both reactors was conducted by response surface methods. The performance of first photobioreactor was better than that in the second reactor, achieving abatement of 88.80% CPF and 93.12% CYP, at 69.7 h contact time and 0.32 mg/L initial concentration. The toxicity of CPF and CYP to Chlorella vulgaris was monitored under 0–4 mg/L of insecticide concentrations and 0–72 h contact time. The minimum chlorophyll content (2 mg/L) and protein (16.7%), and maximum growth inhibition (89.7%) were recorded at 4 mg/L insecticides concentration and 72 h contact time. Moreover, molecular docking simulation for catalytic enzyme degradation of Proteobacteria, Bacteroidetes and microalgae was carried out using individual hydrolase enzymes: carboxypeptidase in microalgae, isochorismatase hydrolase in Proteobacteria and alpha-L-arabinofuranosidase in Bacteroidetes. Ligand-binding energy, affinity and dimensions of ligands-binding sites in the enzyme cavity were calculated in each case. Hydrolase is an enzyme group that offers a promising practical application for the degradation of CYP and CPF due to its cavity features. This analysis demonstrated the mode of interaction of ligands with hydrolase enzymes in different species.
Mojiri, A, Zhou, JL, Ratnaweera, H, Rezania, S & Nazari V, M 2022, 'Pharmaceuticals and personal care products in aquatic environments and their removal by algae-based systems', Chemosphere, vol. 288, no. Pt 2, pp. 132580-132580.
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The consumption of pharmaceuticals and personal care products (PPCPs) has been widely increasing, yet up to 90-95% of PPCPs consumed by human are excreted unmetabolized. Moreover, the most of PPCPs cannot be fully removed by wastewater treatment plants (WWTPs), which release PPCPs to natural water bodies, affecting aquatic ecosystems and potentially humans. This study sought to review the occurrence of PPCPs in natural water bodies globally, and assess the effects of important factors on the fluxes of pollutants into receiving waterways. The highest ibuprofen concentration (3738 ng/L) in tap water was reported in Nigeria, and the highest naproxen concentration (37,700 ng/L) was reported in groundwater wells in Penn State, USA. Moreover, the PPCPs have affected aquatic organisms such as fish. For instance, up to 24.4 × 103 ng/g of atenolol was detected in P. lineatus. Amongst different technologies to eliminate PPCPs, algae-based systems are environmentally friendly and effective because of the photosynthetic ability of algae to absorb CO2 and their flexibility to grow in different wastewater. Up to 99% of triclosan and less than 10% of trimethoprim were removed by Nannochloris sp., green algae. Moreover, variable concentrations of PPCPs might adversely affect the growth and production of algae. The exposure of algae to high concentrations of PPCPs can reduce the content of chlorophyll and protein due to producing reactive oxygen species (ROS), and affecting expression of some genes in chlorophyll (rbcL, psbA, psaB and psbc).
Mokaramian, E, Shayeghi, H, Sedaghati, F, Safari, A & Alhelou, HH 2022, 'An Optimal Energy Hub Management Integrated EVs and RES Based on Three-Stage Model Considering Various Uncertainties', IEEE Access, vol. 10, pp. 17349-17365.
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Mong, GR, Chong, CT, Chong, WWF, Ng, J-H, Ong, HC, Ashokkumar, V, Tran, M-V, Karmakar, S, Goh, BHH & Mohd Yasin, MF 2022, 'Progress and challenges in sustainable pyrolysis technology: Reactors, feedstocks and products', Fuel, vol. 324, pp. 124777-124777.
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Pyrolysis is a thermo-chemical decomposition process that converts organic or inorganic materials into solid, liquid and gaseous products. The pyrolysis process involves multiple complex chemical reactions, and the derived products are highly dependent on the pyrolysis operating parameters and type of feedstock. In the present review, progress on the state-of-the-art pyrolysis technology, feedstock and properties of the end products are thoroughly reviewed. The potential application of the pyrolysis products in the industries is discussed: solid leftover can be upgraded and used as a bio-adsorbant, soil amendment, fertilizer or solid fuel; pyrolysis liquid can be used as a bio-chemical source or upgraded into liquid fuel; gaseous products can be used as recirculating gas for the pyrolysis environment or burnt as fuel for heat and power generation. Despite the potential of pyrolysis in processing agricultural or industrial wastes, studies regarding the economic feasibility and environment sustainability of scaled-up pyrolysis plant are scarce. A comprehensive overview on the type of pyrolysis reactor technology, potential feedstock and the properties of the derived products is presented. Further, the sustainability of the technology is assessed from the aspects of energy balance, environment and economics. In spite of the potential benefits to the environment and recovery of valuable products, there are several challenges that need to be addressed to ensure the sustainability and commercialibility of the pyrolysis technologies.
Monjurul Hasan, ASM, Trianni, A, Shukla, N & Katic, M 2022, 'A novel characterization based framework to incorporate industrial energy management services', Applied Energy, vol. 313, pp. 118891-118891.
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Energy management has been widely considered as an effective means for achieving energy efficiency and sustainable competitiveness in industrial organizations. However, several barriers prevent its diffused implementation. It is thus crucial to assess and evaluate energy management and the corresponding services in the industrial context in order to further promote them. Extent literature has neither defined industrial energy management services adequately, nor developed any models that consider the characterization of energy management services to support industrial decision making. In light of this, our study aims to provide a comprehensive framework to help key industrial decision-makers and policymakers in making better informed decisions regarding the adoption of energy management activities. We accomplish this by explicitly taking into consideration the characteristics of energy management services based on 25 attributes belonging to four categories i.e., implementation, impacted area, impact on production resources and productivity. In addition, we shed further light on the practical implementation of energy management activities by also placing focus on the link between the implications of their adoption on production resources and the subsequent impact on industrial operations. The framework is validated by a sample of selected energy management experts within Australian organizations, followed by an application in an industrial context. The study concludes with suggestions for industrial decision makers and an outlook on further research avenues.
Moradi, S, Kamal, A, Aboulkheyr Es, H, Farhadi, F, Ebrahimi, M, Chitsaz, H, Sharifi-Zarchi, A & Baharvand, H 2022, 'Pan-cancer analysis of microRNA expression profiles highlights microRNAs enriched in normal body cells as effective suppressors of multiple tumor types: A study based on TCGA database', PLOS ONE, vol. 17, no. 4, pp. e0267291-e0267291.
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BackgroundMicroRNAs (miRNAs) are frequently deregulated in various types of cancer. While antisense oligonucleotides are used to block oncomiRs, delivery of tumour-suppressive miRNAs holds great potential as a potent anti-cancer strategy. Here, we aim to determine, and functionally analyse, miRNAs that are lowly expressed in various types of tumour but abundantly expressed in multiple normal tissues.MethodsThe miRNA sequencing data of 14 cancer types were downloaded from the TCGA dataset. Significant differences in miRNA expression between tumor and normal samples were calculated using limma package (R programming). An adjusted p value < 0.05 was used to compare normal versus tumor miRNA expression profiles. The predicted gene targets were obtained using TargetScan, miRanda, and miRDB and then subjected to gene ontology analysis using Enrichr. Only GO terms with an adjusted p < 0.05 were considered statistically significant. All data from wet-lab experiments (cell viability assays and flow cytometry) were expressed as means ± SEM, and their differences were analyzed using GraphPad Prism software (Student’s t test, p < 0.05).ResultsBy compiling all publicly available miRNA profiling data from The Cancer Genome Atlas (TCGA) Pan-Cancer Project, we reveal a small set of tumour-suppressing miRNAs (which we designate as ’normomiRs’) that are highly expressed in 14 types of normal tissues but poorly expressed in corresponding tumour tissues. Interestingly, muscle-enriched miRNAs (e.g. miR-133a/b and miR-206) and miRNAs from DLK1-DIO3 locus (e...
Morgan, AL, Torpy, FR, Irga, PJ, Fleck, R, Gill, RL & Pettit, T 2022, 'The botanical biofiltration of volatile organic compounds and particulate matter derived from cigarette smoke', Chemosphere, vol. 295, pp. 133942-133942.
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Moridian, P, Ghassemi, N, Jafari, M, Salloum-Asfar, S, Sadeghi, D, Khodatars, M, Shoeibi, A, Khosravi, A, Ling, SH, Subasi, A, Abdulla, SA, Alizadehsani, R, Górriz, JM & Acharya, UR 2022, 'Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review.', CoRR, vol. abs/2206.11233, pp. 1-32.
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Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.
Moridian, P, Shoeibi, A, Khodatars, M, Jafari, M, Pachori, RB, Khadem, A, Alizadehsani, R & Ling, SH 2022, 'Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works.', WIREs Data Mining Knowl. Discov., vol. 12, no. 6.
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Apnea is a sleep disorder that stops or reduces airflow for a short time during sleep. Sleep apnea may last for a few seconds and happen for many while sleeping. This reduction in breathing is associated with loud snoring, which may awaken the person with a feeling of suffocation. So far, a variety of methods have been introduced by researchers to diagnose sleep apnea, among which the polysomnography (PSG) method is known to be the best. Analysis of PSG signals is very complicated. Many studies have been conducted on the automatic diagnosis of sleep apnea from biological signals using artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods. This research reviews and investigates the studies on the diagnosis of sleep apnea using AI methods. First, computer aided diagnosis system (CADS) for sleep apnea using ML and DL techniques along with its parts including dataset, preprocessing, and ML and DL methods are introduced. This research also summarizes the important specifications of the studies on the diagnosis of sleep apnea using ML and DL methods in a table. In the following, a comprehensive discussion is made on the studies carried out in this field. The challenges in the diagnosis of sleep apnea using AI methods are of paramount importance for researchers. Accordingly, these obstacles are elaborately addressed. In another section, the most important future works for studies on sleep apnea detection from PSG signals and AI techniques are presented. Ultimately, the essential findings of this study are provided in the conclusion section. This article is categorized under: Technologies > Artificial Intelligence Application Areas > Data Mining Software Tools Algorithmic Development > Biological Data Mining.
Morris, A, Mitchell, E, Wilson, S, Ramia, G & Hastings, C 2022, 'Loneliness within the Home among International Students in the Private Rental Sector in Sydney and Melbourne', Urban Policy and Research, vol. 40, no. 1, pp. 67-81.
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Morshedi Rad, D, Rezaei, M, Radfar, P & Ebrahimi Warkiani, M 2022, 'Microengineered filters for efficient delivery of nanomaterials into mammalian cells', Scientific Reports, vol. 12, no. 1, p. 4383.
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AbstractIntracellular delivery of nanomaterials into the cells of interest has enabled cell manipulation for numerous applications ranging from cell-based therapies to biomedical research. To date, different carriers or membrane poration-based techniques have been developed to load nanomaterials to the cell interior. These biotools have shown promise to surpass the membrane barrier and provide access to the intracellular space followed by passive diffusion of exogenous cargoes. However, most of them suffer from inconsistent delivery, cytotoxicity, and expensive protocols, somewhat limiting their utility in a variety of delivery applications. Here, by leveraging the benefits of microengineered porous membranes with a suitable porosity, we demonstrated an efficient intracellular loading of diverse nanomaterials to different cell types based on inducing mechanical disruption to the cell membrane. In this work, for the first time, we used ultra-thin silicon nitride (SiN) filter membranes with uniform micropores smaller than the cell diameter to load impermeable nanomaterials into adherent and non-adherent cell types. The delivery performance using SiN microsieves has been validated through the loading of functional nanomaterials from a few nanometers to hundreds of nanometers into mammalian cells with minimal undesired impacts. Besides the high delivery efficiency and improved cell viability, this simple and low-cost approach offers less clogging and higher throughput (107 cell min−1). Therefore, it yields to the efficient introduction of exogenous nanomaterials into the large population of cells, illustrating the potential of these microengineered filters to be widely used in the microfiltroporation (MFP) setup.
Mortazavi, H, Mortazavy Beni, H & Islam, MS 2022, 'Thermal/fluid characteristics of the inline stacked plain‐weave screen as solar‐powered Stirling engine heat regenerators', IET Renewable Power Generation, vol. 16, no. 5, pp. 956-965.
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AbstractThe present work demonstrates the results of a 3D numerical analysis of heat transfer and fluid flow through a fabricated aluminium wire filament, bonded mesh deployed as a regenerator surface in the Stirling engine. Here, two inline regenerator models with the same mass but different geometry were simulated by computational fluid dynamics (CFD) and finite element method (FEM). The results show that the temperature efficiency of both inline with non‐uniform and uniform wire diameters is almost equal. However, the non‐uniform diameter model is shorter and reaches the maximum thermal efficiency faster in reduced length . Also, it shows approximately 5% a greater mean thermal efficiency. In general, from this simulation, it can be seen that changes in the geometry of the heat regenerator can have a direct effect on thermal efficiency and pressure drop in the same mass. Therefore, using the inline stacked plain‐weave screen with a non‐uniform wire diameter regenerator in the powered Stirling engine is recommended due to space constraints. Small changes in the regenerator geometry lead to immense leaps in increasing a solar power plant's overall efficiency.
Morvan, A, Andersen, TI, Mi, X, Neill, C, Petukhov, A, Kechedzhi, K, Abanin, DA, Michailidis, A, Acharya, R, Arute, F, Arya, K, Asfaw, A, Atalaya, J, Bardin, JC, Basso, J, Bengtsson, A, Bortoli, G, Bourassa, A, Bovaird, J, Brill, L, Broughton, M, Buckley, BB, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Chen, Z, Chiaro, B, Collins, R, Conner, P, Courtney, W, Crook, AL, Curtin, B, Debroy, DM, Del Toro Barba, A, Demura, S, Dunsworth, A, Eppens, D, Erickson, C, Faoro, L, Farhi, E, Fatemi, R, Flores Burgos, L, Forati, E, Fowler, AG, Foxen, B, Giang, W, Gidney, C, Gilboa, D, Giustina, M, Grajales Dau, A, Gross, JA, Habegger, S, Hamilton, MC, Harrigan, MP, Harrington, SD, Hoffmann, M, Hong, S, Huang, T, Huff, A, Huggins, WJ, Isakov, SV, Iveland, J, Jeffrey, E, Jiang, Z, Jones, C, Juhas, P, Kafri, D, Khattar, T, Khezri, M, Kieferová, M, Kim, S, Kitaev, AY, Klimov, PV, Klots, AR, Korotkov, AN, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Lau, K-M, Laws, L, Lee, J, Lee, KW, Lester, BJ, Lill, AT, Liu, W, Locharla, A, Malone, F, Martin, O, McClean, JR, McEwen, M, Meurer Costa, B, Miao, KC, Mohseni, M, Montazeri, S, Mount, E, Mruczkiewicz, W, Naaman, O, Neeley, M, Nersisyan, A, Newman, M, Nguyen, A, Nguyen, M, Niu, MY, O’Brien, TE, Olenewa, R, Opremcak, A, Potter, R, Quintana, C, Rubin, NC, Saei, N, Sank, D, Sankaragomathi, K, Satzinger, KJ, Schurkus, HF, Schuster, C, Shearn, MJ, Shorter, A, Shvarts, V, Skruzny, J, Smith, WC, Strain, D, Sterling, G, Su, Y, Szalay, M, Torres, A, Vidal, G, Villalonga, B, Vollgraff-Heidweiller, C, White, T, Xing, C, Yao, Z, Yeh, P, Yoo, J, Zalcman, A, Zhang, Y, Zhu, N, Neven, H, Bacon, D, Hilton, J, Lucero, E, Babbush, R, Boixo, S, Megrant, A, Kelly, J, Chen, Y, Smelyanskiy, V, Aleiner, I, Ioffe, LB & Roushan, P 2022, 'Formation of robust bound states of interacting microwave photons', Nature, vol. 612, no. 7939, pp. 240-245.
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AbstractSystems of correlated particles appear in many fields of modern science and represent some of the most intractable computational problems in nature. The computational challenge in these systems arises when interactions become comparable to other energy scales, which makes the state of each particle depend on all other particles1. The lack of general solutions for the three-body problem and acceptable theory for strongly correlated electrons shows that our understanding of correlated systems fades when the particle number or the interaction strength increases. One of the hallmarks of interacting systems is the formation of multiparticle bound states2–9. Here we develop a high-fidelity parameterizable fSim gate and implement the periodic quantum circuit of the spin-½ XXZ model in a ring of 24 superconducting qubits. We study the propagation of these excitations and observe their bound nature for up to five photons. We devise a phase-sensitive method for constructing the few-body spectrum of the bound states and extract their pseudo-charge by introducing a synthetic flux. By introducing interactions between the ring and additional qubits, we observe an unexpected resilience of the bound states to integrability breaking. This finding goes against the idea that bound states in non-integrable systems are unstable when their energies overlap with the continuum spectrum. Our work provides experimental evidence for bound states of interacting photons and discovers their stability beyond the integrability limit.
Mossa, MA, Echeikh, H, El Ouanjli, N & Alhelou, HH 2022, 'Enhanced Second-Order Sliding Mode Control Technique for a Five-Phase Induction Motor', International Transactions on Electrical Energy Systems, vol. 2022, pp. 1-19.
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Recently, several research papers have addressed multiphase induction motor (IM) drives, owing to their several benefits compared to the three-phase motors, including increasing the torque pulsations frequency and reducing the rotor harmonic current losses. Thus, designing a robust controller to ensure the proper operation of such motors became a challenge. The present study reports the design of an effective second-order sliding mode control (SO-SMC) approach for a five-phase IM drive. The proposed control approach finds its strongest justification for the problem of using a law of nonlinear control robust to the system uncertainties of the model without affecting the system’s simplicity. The formulation of the proposed SO-SMC approach is a prescribed process to ensure the stability and proper dynamics of the five-phase IM. A detailed stability analysis is also presented for this purpose. To validate the effectiveness of the proposed controller, the five-phase IM drive is tested under different dynamic situations, including load changes and system uncertainties. The presented numerical results prove the ability of the designed SO-SMC to handle high system nonlinearities and maintain high robustness against uncertainties.
Mousavi, M, Gandomi, AH, Abdel Wahab, M & Glisic, B 2022, 'Monitoring onsite‐temperature prediction error for condition monitoring of civil infrastructures', Structural Control and Health Monitoring, vol. 29, no. 12.
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Mousavi, M, Gandomi, AH, Holloway, D, Berry, A & Chen, F 2022, 'Machine learning analysis of features extracted from time–frequency domain of ultrasonic testing results for wood material assessment', Construction and Building Materials, vol. 342, pp. 127761-127761.
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Mousavi, M, Holloway, D, Olivier, JC & Gandomi, AH 2022, 'Quaternion analysis of beam multi‐type vibration data for damage detection', Structural Control and Health Monitoring, vol. 29, no. 2.
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In this paper two damage sensitive features (DSF), one baseline dependent (DSF1) and the other one baseline free (DSF2), are proposed based on the Quaternion analysis of the phase trajectory of a beam vibration due to a moving mass. To that end, new Quaternions are constructed with the displacement, velocity and acceleration of the beam mid-span as the elements of the Quaternion vectors. The new DSFs are obtained based on the concept of the angular velocity of a curve in 3D space characterised by a Quaternion. For the baseline dependent case, the proposed DSF1 needs to be calculated for both intact and damaged beam subjected to the same experiment, whereas in the baseline free case DSF2 data from only the damaged beam are sufficient for damage detection. The performance of the proposed DSFs is compared against another technique introduced in the literature through several examples and Monte Carlo simulations. Both road roughness and 10% measurement noise effects are taken into account in performance evaluation of the proposed DSFs. The results show that the proposed DSFs are more successful in locating damage on a simply supported beam.
Mughal, F, Raffe, W, Stubbs, P, Kneebone, I & Garcia, J 2022, 'Fitbits for Monitoring Depressive Symptoms in Older Aged Persons: Qualitative Feasibility Study', JMIR Formative Research, vol. 6, no. 11, pp. e33952-e33952.
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Background In 2022, an estimated 1.105 billion people used smart wearables and 31 million used Fitbit devices worldwide. Although there is growing evidence for the use of smart wearables to benefit physical health, more research is required on the feasibility of using these devices for mental health and well-being. In studies focusing on emotion recognition, emotions are often inferred and dependent on external cues, which may not be representative of true emotional states. Objective The aim of this study was to evaluate the feasibility and acceptability of using consumer-grade activity trackers for apps in the remote mental health monitoring of older aged people. Methods Older adults were recruited using criterion sampling. Participants were provided an activity tracker (Fitbit Alta HR) and completed weekly online questionnaires, including the Geriatric Depression Scale, for 4 weeks. Before and after the study period, semistructured qualitative interviews were conducted to provide insight into the acceptance and feasibility of performing the protocol over a 4-week period. Interview transcripts were analyzed using a hybrid inductive-deductive thematic analysis. Results In total, 12 participants enrolled in the study, and 9 returned for interviews after the study period. Participants had positive attitudes toward being remotely monitored, with 78% (7/9) of participants experiencing no inconvenience throughout the study period. Moreover, 67% (6/9) were interested in trialing our prototype when it is implemented. Participants stated they would feel more comfortable if ...
Muhit, IB, Masia, MJ & Stewart, MG 2022, 'Monte-Carlo laboratory testing of unreinforced masonry veneer wall system under out-of-plane loading', Construction and Building Materials, vol. 321, pp. 126334-126334.
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This paper presents the results of a probabilistic experimental study into the behaviour of full-scale unreinforced masonry (URM) veneer walls with flexible backup subjected to out-of-plane loading. The actual safety and reliability of the contemporary Australian URM structures are unknown due to the absence of information regarding the probabilistic behaviour of the whole veneer wall system and material characterisation of the wall constituent materials. The study focused on masonry typologies representative of modern URM buildings in the Australian context. In this study, 18 full-scale URM veneer wall systems with theoretically identical geometries and properties were tested under inward and outward out-of-plane loading. For each loading type, one specimen was tested under semi-cyclic loading to check whether the monotonic loading can capture the overall behaviour of the cyclic response. For each mortar batch mixed, bond wrench testing was conducted at the same age as the test for the associated wall constructed using that mix. Batch to batch variabilities were statistically analysed, and probability distributions for flexural tensile strength were established. Lognormal distributions with aggregated means of 0.40 MPa and 0.42 MPa for inward and outward loading, respectively, were estimated for flexural tensile strengths. After the wall tests, all timber studs used to build the veneer walls were tested to evaluate the probabilistic characterisation of timber stiffness. This probabilistic information is essential for a stochastic finite element analysis (FEA) to conduct the reliability analysis. From the wall tests, veneer wall system behaviour was observed and measured until the collapse or 20% post-peak drop of the peak load. Outward loaded specimens exhibited higher variabilities for masonry cracking and system peak load compared to inward loading due to variabilities from materials, testing arrangements and failure mechanism. The true coefficient o...
Muhit, IB, Masia, MJ, Stewart, MG & Isfeld, AC 2022, 'Spatial variability and stochastic finite element model of unreinforced masonry veneer wall system under Out-of-plane loading', Engineering Structures, vol. 267, pp. 114674-114674.
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Muhit, IB, Stewart, MG & Masia, MJ 2022, 'Probabilistic constitutive law for masonry veneer wall ties', Australian Journal of Structural Engineering, vol. 23, no. 2, pp. 97-118.
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In a masonry veneer wall system, tie strengths and stiffnesses vary randomly and so are not consistent for all ties throughout the wall. To ensure an economical and safe design, this paper uses tie calibration experimental approach in accordance with the standard AS2699.1 to investigate the tie failure load under compression and tension loading. Probabilistic wall tie characterisations are accomplished by estimating the mean, coefficient of variation and characteristic axial compressive and tensile strength from 50 specimens. The displacement across the cavity is recorded, which resulted the complete load versus displacement response. Using the maximum likelihood method, a range of probability distributions are fitted to tie strengths at different displacement histogram data sets, and a best-fitted probability distribution is selected for each case. The inverse cumulative distribution function plots are also used along with the Anderson-Darling test to infer a goodness-of-fit for the probabilistic models. An extensive statistical correlation analysis is also conducted to check the correlation between different tie strengths and associated displacement for both compression and tension loading. Based on the findings, a wall tie constitutive law is proposed to define probabilistic tie behaviour in numerical modelling.
Mukund Deshpande, N, Gite, S, Pradhan, B & Ebraheem Assiri, M 2022, 'Explainable Artificial Intelligence–A New Step towards the Trust in Medical Diagnosis with AI Frameworks: A Review', Computer Modeling in Engineering & Sciences, vol. 133, no. 3, pp. 843-872.
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Machine learning (ML) has emerged as a critical enabling tool in the sciences and industry in recent years. Today’s machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks–thanks to advancements in technique, the availability of enormous databases, and improved computing power. Deep learning models are at the forefront of this advancement. However, because of their nested nonlinear structure, these strong models are termed as “black boxes,” as they provide no information about how they arrive at their conclusions. Such a lack of transparencies may be unacceptable in many applications, such as the medical domain. A lot of emphasis has recently been paid to the development of methods for visualizing, explaining, and interpreting deep learning models. The situation is substantially different in safety-critical applications. The lack of transparency of machine learning techniques may be limiting or even disqualifying issue in this case. Significantly, when single bad decisions can endanger human life and health (e.g., autonomous driving, medical domain) or result in significant monetary losses (e.g., algorithmic trading), depending on an unintelligible data-driven system may not be an option. This lack of transparency is one reason why machine learning in sectors like health is more cautious than in the consumer, e-commerce, or entertainment industries. Explainability is the term introduced in the preceding years. The AI model’s black box nature will become explainable with these frameworks. Especially in the medical domain, diagnosing a particular disease through AI techniques would be less adapted for commercial use. These models’ explainable natures will help them commercially in diagnosis decisions in the medical field. This paper explores the different frameworks for the explainability of AI models in the medical field. The available frameworks are compared with other parameters, and their suitability fo...
Munawar, HS, Hammad, AWA, Waller, ST & Islam, MR 2022, 'Modern Crack Detection for Bridge Infrastructure Maintenance Using Machine Learning', Human-Centric Intelligent Systems, vol. 2, no. 3-4, pp. 95-112.
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AbstractManual investigation of damages incurred to infrastructure is a challenging process, in that it is not only labour-intensive and expensive but also inefficient and error-prone. To automate the process, a method that is based on computer vision for automatically detecting cracks from 2D images is a viable option. Amongst the different methods of deep learning that are commonly used, the convolutional neural network (CNNs) is one that provides the opportunity for end-to-end mapping/learning of image features instead of using the manual suboptimal image feature extraction. Specifically, CNNs do not require human supervision and are more suitable to be used for indoor and outdoor applications requiring image feature extraction and are less influenced by internal and external noise. Additionally, the CNN’s are also computationally efficient since they are based on special convolution layers and pooling operations that enable the full execution of CNN frameworks on several hardware devices. Keeping this in mind, we propose a deep CNN framework that is based on 10 different convolution layers along with a cycle GAN (Generative Adversarial Network) for predicting the crack segmentation pixel by pixel in an end-to-end manner. The methods proposed here include the Deeply Supervised Nets (DSN) and Fully Convolutional Networks (FCN). The use of DSN enables integrated feature supervision for each stage of convolution. Furthermore, the model has been designed intricately for learning and aggregating multi-level and multiscale features while moving from the lower to higher convolutional layers through training. Hence, the architecture in use here is unique from the ones in practice which just use the final convolution layer. In addition, to further refine the predicted results, we have used a guided filter and CRFs (Conditional Random Fields) based methods. The verification step for the proposed framework was carried out with a...
Muniappan, A, Jayaraja, BG, Vignesh, T, Singh, M, Arunkumar, T, Sekar, S, Priyadharshini, TR, Pant, B & Paramasivam, P 2022, 'Artificial Intelligence Optimization of Turning Parameters of Nanoparticle‐Reinforced P/M Alloy Tool', Journal of Nanomaterials, vol. 2022, no. 1, pp. 1-8.
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In this research, the powder metallurgy‐ (P/M‐) based metal matrix composites were prepared to compose the machinability characteristics. Therefore, the Al2024 and boron carbide (B4C) were the base and strengthened reinforcements. During the powder metallurgy process, weight fractions of boron carbide are 4, 8, and 12%; the compaction pressure is 300 to 400 MPa, and the sintering temperature is 420 to 540°C, respectively. These parameters were planned with Taguchi L9 array for achieving the proper design. After the processing, composite specimens were utilized to conduct the turning process. For all the nine specimens, depth of cut, speed, and feed rates were maintained constant with optimal parameters. The surface roughness and material removal rate responses are successfully achieved in the optimal turning parameters. Then, the artificial neural network (ANN) model was implemented to analyze the predicted values with back propagation algorithm. In this ANN, three input layers, 6 and 4 hidden layers, and two outputs were created as per the design. Finally, the minimized surface roughness and maximized material removal rate were achieved at the process parameters like 8 wt. % of boron carbide, 300 MPa of compaction pressure, and 480°C of sintering temperature. All the predicted values are slightly maximum than the experimental values.
Munot, S, Redfern, J, Bray, J, Angell, B, Bauman, A, Coggins, A, Denniss, AR, Ferry, C, Jennings, G, Kovoor, P, Kumar, S, Lai, K, Khanlari, S, Marschner, S, Middleton, P, Nelson, M, Oppermann, I, Semsarian, C, Taylor, L, Vukasovic, M, Vukasovic, M, Ware, S & Chow, CK 2022, 'Abstract 260: The Relation of Country-of-Birth With Willingness to Respond to Out-of-Hospital Cardiac Arrest in Multiethnic Communities of New South Wales (NSW), Australia', Circulation, vol. 146, no. Suppl_1.
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Introduction: Bystander response including cardiopulmonary resuscitation (CPR) is critical to survival in out-of-hospital cardiac arrest (OHCA). Poorer outcomes have been reported in some immigrant communities but there has been less research about bystander response in these communities. Over a third of New South Wales (NSW) residents were born outside Australia. Hypothesis: Country of birth may explain variation in willingness to respond to OHCA. Methods: A survey was conducted between May 2021-May 2022. It employed multiple recruitment approaches including reaching out to 72 organisations and targeting multi-ethnic community organisations, advertising via social media, and leveraging local networks. Data were collected on demographic variables, CPR training, and attitudes towards responding to OHCA. Results: Of the 1267 respondents (average age 49.6 years, 52% female), 60% were born outside Australia; of which 44% (n=332) were from South Asia, 33% (n=246) from East Asia and the remaining 23% from a mix of other regions including north-west Europe, north Africa-middle east. Most immigrant respondents (73%) had lived in Australia for over ten years. Higher rates of previous CPR training were reported in Australian-born participants compared with South Asian-born and East Asian-born (76%, 35%, 47% respectively p<0.001) with current training rates i.e. in last 12 months (16%, 6%, 12% respectively, p=0.003). Higher rates of willingness to perform CPR on someone they did not know, was reported in Australian-born participants compared to South Asian-born and East Asian-born (74%, 63%, 56% respectively, p=<0.001. After adjusting for age, gender...
Naderi, E, Azizivahed, A & Asrari, A 2022, 'A step toward cleaner energy production: A water saving-based optimization approach for economic dispatch in modern power systems', Electric Power Systems Research, vol. 204, pp. 107689-107689.
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Nagy, E, Ibrar, I, Braytee, A & Iván, B 2022, 'Study of Pressure Retarded Osmosis Process in Hollow Fiber Membrane: Cylindrical Model for Description of Energy Production', Energies, vol. 15, no. 10, pp. 3558-3558.
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A new mathematical model was developed to predict the cylindrical effect of the membrane performance in the pressure retarded osmosis process. The cylindrical membrane transport layers (the draw side boundary and the porous membrane) were divided into very thin sublayers with constant mass transport parameters, among others with a constant radius in every sublayer. The obtained second-order differential mass balance equations were solved analytically, with constant parameters written for every sublayer. The algebraic equation system involving 2N equations was then solved for the determinant solution. It was shown that the membrane properties, water permeability (A), salt permeability (B), structural parameter (S) and the operating conditions (inlet draw side solute concentration and draw side mass transfer coefficient) affect the water flux strongly, and thus the membrane performance, due to the cylindrical effect caused by the variable surface and volume of the sublayers. This effect significantly depends on the lumen radius. The lower radius means a larger change in the internal surface/volume of sublayers with ΔR thickness. The predicted results correspond to that of the flat-sheet membrane layer at ro = 10,000 μm. At the end of this manuscript, the calculated mass transfer rates were compared to those measured. It was stated that the curvature effect in using a capillary membrane must not be left out of consideration when applying hollow fiber membrane modules due to their relatively low lumen radius. The presented model provides more precise prediction of the performance in the case of hollow fiber membranes.
Nahar, K & Gill, AQ 2022, 'Integrated identity and access management metamodel and pattern system for secure enterprise architecture', Data & Knowledge Engineering, vol. 140, pp. 102038-102038.
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Identity and access management (IAM) is one of the key components of the secure enterprise architecture for protecting the digital assets of the information systems. The challenge is: How to model an integrated IAM for a secure enterprise architecture to protect digital assets? This research aims to address this question and develops an ontology based integrated IAM metamodel for the secure digital enterprise architecture (EA). Business domain and technology agnostic characteristics of the developed IAM metamodel will allow it to develop IAM models for different types of information systems. Well-known design science research (DSR) methodology was adopted to conduct this research. The developed IAM metamodel is evaluated by using the demonstration method. Furthermore, as a part of the evaluation, a pattern system has been developed, consisting of eight IAM patterns. Each pattern offers a solution to a specific IAM related problem. The outcome of this research indicates that enterprise, IAM and information systems architects and academic researchers can use the proposed IAM metamodel and the pattern system to design and implement situation-specific IAM models within the overall context of a secure EA for information systems.
Naik, D, Ramesh, D, Gandomi, AH & Babu Gorojanam, N 2022, 'Parallel and distributed paradigms for community detection in social networks: A methodological review', Expert Systems with Applications, vol. 187, pp. 115956-115956.
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Community detection in social networks is the process of identifying the cohesive groups of similar nodes. Detection of these groups can be helpful in many applications, such as finding networks of protein interaction in biological networks, finding the users of similar mind for ads and suggestions, finding a shared research field in collaborative networks, analyzing public health, future link prediction in social networks, analyzing criminology, and many more. However, with the increase in the number of profiles and content shared on social media platforms, the analysis is often time-consuming and exhaustive. In order to speed up and optimize the community detection process, parallel processing and Shared/Distributed memory techniques are widely used. Despite community detection has widespread use in social networks, no attempt has ever been made to compile and systematically discuss research efforts on the emerging subject of identifying parallel and distributed methods for community detection in social networks. Most of the surveys described the serial algorithms used for community detection. Our survey work comes under the scope of new design techniques, exciting or novel applications, components or standards, and applications of an educational, transactional, and co-operational nature. This paper accommodates and presents a systematic literature review with state-of-the-art research on the application of parallel processing and Shared/Distributed techniques to determine communities for social network analysis. Advanced search strategy has been performed on several digital libraries for extracting several studies for the review. The systematic search landed in finding 3220 studies, among which 65 relevant studies are selected after conducting various screening phases for further review. The application of parallel computing, shared memory, and distributed memory on the existing community detection methodologies have been discussed thoroughly. More s...
Nama, S, Sharma, S, Saha, AK & Gandomi, AH 2022, 'A quantum mutation-based backtracking search algorithm', Artificial Intelligence Review, vol. 55, no. 4, pp. 3019-3073.
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The exposition of any nature-inspired optimization technique relies firmly upon its executed organized framework. Since the regularly utilized backtracking search algorithm (BSA) is a fixed framework, it is not always appropriate for all difficulty levels of problems and, in this manner, probably does not search the entire search space proficiently. To address this limitation, we propose a modified BSA framework, called gQR-BSA, based on the quasi reflection-based initialization, quantum Gaussian mutations, adaptive parameter execution, and quasi-reflection-based jumping to change the coordinate structure of the BSA. In gQR-BSA, a quantum Gaussian mechanism was developed based on the best population information mechanism to boost the population distribution information. As population distribution data can represent characteristics of a function landscape, gQR-BSA has the ability to distinguish the methodology of the landscape in the quasi-reflection-based jumping. The updated automatically managed parameter control framework is also connected to the proposed algorithm. In every iteration, the quasi-reflection-based jumps aim to jump from local optima and are adaptively modified based on knowledge obtained from offspring to global optimum. Herein, the proposed gQR-BSA was utilized to solve three sets of well-known standards of functions, including unimodal, multimodal, and multimodal fixed dimensions, and to solve three well-known engineering optimization problems. The numerical and experimental results reveal that the algorithm can obtain highly efficient solutions to both benchmark and real-life optimization problems.
Namdarpour, F, Mesbah, M, Gandomi, AH & Assemi, B 2022, 'Using genetic programming on GPS trajectories for travel mode detection', IET Intelligent Transport Systems, vol. 16, no. 1, pp. 99-113.
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AbstractThe widespread and increased use of smartphones, equipped with the global positioning system (GPS), has facilitated the automation of travel data collection. Most studies on travel mode detection that used GPS data have employed hand‐crafted features that may not have the capabilities to detect all complex travel behaviours since their performance is highly dependent on the skills of domain experts and may limit the performance of classifiers. In this study, a genetic programming (GP) approach is proposed to select and construct features for GPS trajectories. GP increased the macro‐average of the F1‐score from 77.3 to 80.0 in feature construction when applied to the GeoLife dataset. It could transform the decision tree into a competitive classifier with support vector machines (SVMs) and neural networks that are both able to extract high‐level features. Simplicity, interpretability, and a relatively lower risk of overfitting allow the proposed model to be readily used for passive travel data collection even on smartphones with limited computational capacities. The model is validated by a second dataset from Australia and New Zealand, which indicated that a decision tree with the GP constructed features as its input has a considerably higher transferability than SVMs and neural networks.
Namisango, F, Kang, K & Beydoun, G 2022, 'How the Structures Provided by Social Media Enable Collaborative Outcomes: A Study of Service Co-creation in Nonprofits.', Inf. Syst. Frontiers, vol. 24, no. 2, pp. 517-535.
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Nan, Y, Huang, X & Guo, YJ 2022, '3-D Millimeter-Wave Helical Imaging', IEEE Transactions on Microwave Theory and Techniques, vol. 70, no. 4, pp. 2499-2511.
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This article proposes a low-cost three-dimensional (3-D) millimeter-wave (MMW) holographic imaging system using helical scanning with multiple receivers to achieve a fast continuous scanning over a large two-dimensional (2-D) cylindrical surface. First, the system geometry and its imaging process based on the back-projection algorithm (BPA) are presented. The corresponding imaging point spread function (PSF) and resolutions are analyzed accordingly. To reduce the computational cost significantly, a novel 3-D helical imaging algorithm is then proposed based on the piecewise constant Doppler (PCD) principle. The slant range difference resulting from the helical scanning can be compensated jointly along angular and vertical directions. The proposed imaging is prototyped using the AWR1843 radar sensor from Texas Instruments (TIs) and a moving platform composed of step motors and a micro-controller unit (MCU). The digital imaging process and the number of the required complex multiplications are also discussed in detail. Finally, simulation and experimental results are provided to validate the accuracy and efficiency of the proposed imaging system.
Nan, Y, Huang, X & Guo, YJ 2022, 'A Panoramic Synthetic Aperture Radar', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13.
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This article proposes a new synthetic aperture radar (SAR), named as panoramic SAR, based on a combination of linear and rotational SARs, by which a large 360° panoramic view of the observed scene can be reconstructed. First, the system geometry and its imaging process based on the back-projection algorithm (BPA) are presented. The combined movement constitutes a 2-D synthetic aperture, and thus higher imaging resolutions can be obtained. The corresponding resolution analysis and the sampling criteria are discussed accordingly. Then, a novel dynamic piecewise compensation (DPC) algorithm, a recursive imaging process, is proposed to reduce the processing complexity significantly. The imaging implementation and the complexity are also studied respectively. Finally, a prototype of panoramic SAR is built based on an frequency-modulated continuous wave (FMCW) radar and a moving platform, and the simulation and experimental results are provided to validate the proposed panoramic SAR principle and the DPC algorithm.
Nan, Y, Huang, X & Guo, YJ 2022, 'An Universal Circular Synthetic Aperture Radar', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15.
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This article presents an universal circular synthetic aperture radar (SAR) (UCSAR) by which the targets to be observed at any radial distance can be imaged, thus making SAR imaging possible in a more general scenario with a circular movement of the radar platform. The UCSAR point spread function (PSF) is firstly analyzed based on the time-domain correlation imaging approach, and thus a three-dimension (3-D) spatial variant PSF of the target can be formulated. The closed-form PSF expressions with single-frequency and frequency-modulated continuous wave (FMCW) transmitted signals are derived respectively to quantify the imaging resolutions, showing that the PSF is a product of a sinc function and a zeroth-order Bessel function when using a wideband FMCW signal. Secondly, a fast UCSAR imaging algorithm and its further simplified version are proposed to reduce the computational cost significantly based on the piecewise constant Doppler (PCD) principle. To quantify the imaging performance, we derive an error function of the slant range approximation for the proposed algorithm, serving as a practical guideline for the UCSAR parameter selection. Finally, the simulation and experimental results are provided to validate the PSF analysis, the fast imaging algorithm, and the implementation of the proposed UCSAR.
Nascimben, M, Wang, Y-K, King, J-T, Jung, T-P, Touryan, J, Lance, BJ & Lin, C-T 2022, 'Alpha Correlates of Practice During Mental Preparation for Motor Imagery', IEEE Transactions on Cognitive and Developmental Systems, vol. 14, no. 1, pp. 146-155.
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IEEE In this study we quantified performance variations of motor imagery (MI)-based brain-computer interface (BCI) systems induced by practice. Two experimental sessions were recorded from ten healthy subjects while playing a BCI-oriented videogame for two weeks. The analysis focused on the exploration of electroencephalographic changes during mental preparation between novice and practiced subjects. EEG changes were quantified using global field power (GFP), dynamic time warping (TW) and mutual information (MutInf): GFP represents the strength of the electric field, TW measures signal similarities and MutInf signals inter-dependency. Each metric was selected to relate insights extracted from mental preparation to the three experimental hypotheses associating practice with BCI performance. Significant results were identified in lower alpha for GFP and upper alpha for TW and MutInf. GFP in lower alpha during mental preparation assessed not only novice vs practiced variations but also “intra-session” differences. Findings suggest that EEG changes during mental preparation provide a quantitative measure of practice level. These metrics extracted before motor intention could be applied to BCI models targeting MI to monitor a user’s degree of training.
Naseri, H, Shokoohi, M, Jahanbakhsh, H, Golroo, A & Gandomi, AH 2022, 'Evolutionary and swarm intelligence algorithms on pavement maintenance and rehabilitation planning', International Journal of Pavement Engineering, vol. 23, no. 13, pp. 4649-4663.
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Maintenance and Rehabilitation (M&R) scheduling is one of the vital aspects of a pavement management system (PMS). This study aims to establish accurate M&R plans for a large-scale pavement network. To this intent, parameters affecting pavement deterioration were identified from the literature, then Random Forest Regression was employed to determine the effective features for pavement deterioration modelling. An accurate pavement deterioration function was generated by applying significant features. The most robust metaheuristic and evolutionary algorithms were selected and adjusted to solve the M&R scheduling optimisation problem, including the Water Cycle Algorithm (WCA), Arithmetic Optimisation Algorithm (AOA), Differential Evolutionary (DE), Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO), and Genetic Algorithm (GA). The performance of the mentioned algorithms was compared to help researchers and decision-makers to select the appropriate algorithm for M&R scheduling optimisation. WCA and AOA showed to have the best performance among the adapted algorithms. Compared to AOA, DE, ACO, PSO, and GA, WCA's objective function was calculated to be 45%, 74%, 74%, 77% and 83% less, while its M&R cost was cheaper by 13%, 16%, 27%, 19%, and 18%, respectively.
Nasir, AA, Tuan, HD, Dutkiewicz, E & Hanzo, L 2022, 'Finite-Resolution Digital Beamforming for Multi-User Millimeter-Wave Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 9, pp. 9647-9662.
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Recent studies have shown that low-resolution analog-to-digital-converters and digital-to-analog-converters (ADCs and DACs) can make fully-digital beamforming more power efficient than its analog or hybrid beamforming counterpart over wide-band millimeter-wave (mmWave) channels. Inspired by this, we propose a computationally efficient fully-digital beamformer relying on low-resolution ADCs/DACs for multi-user mmWave communication networks. Both a generalized (unstructured) beamformer (GB) and a structured zero-forcing beamformer (ZFB) are proposed. For maintaining fairness among all users in the network, specifically tailored objective functions are considered under sum-power constraints, namely that of maximizing the geometric mean (GM) of users' rate and their max-min rate. These computationally challenging beamforming design problems are tackled by developing computationally efficient steep ascent algorithms, which have the radical benefit of relying on a closed-form solution at each iteration. Moreover, to facilitate the employment of low-cost amplifiers at each antenna, the GB design problem subject to the equal-gain transmission constraint is considered, which assigns equal transmit power to each transmit antenna. The proposed algorithms promise a user-rate distribution having a reduced deviation among the user-rates, i.e., improved rate-fairness. Our extensive simulation results show an approximately upto 45% reduction for the GM-rate of a 2-bit ADC (4-bin quantization) compared to the $\infty$-resolution ADC.
Nasir, AA, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2022, 'Low-Resolution RIS-Aided Multiuser MIMO Signaling', IEEE Transactions on Communications, vol. 70, no. 10, pp. 6517-6531.
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A multi-antenna aided base station (BS) supporting several multi-antenna downlink users with the aid of a reconfigurable intelligent surface (RIS) of programmable reflecting elements (PREs) is considered. Low-resolution PREs constrained by a set of sparse discrete values are used for reasons of cost-efficiency. Our challenging objective is to jointly design the beamformers at the BS and the RIS's PREs for improving the throughput of all users by maximizing their geometric-mean, under a variety of different access schemes. This constitutes a computationally challenging problem of mixed continuous-discrete optimization, because each user's throughput is a complicated function of both the continuous-valued beamformer weights and of the discrete-valued PREs. We develop low-complexity algorithms, which iterate by directly evaluating low-complexity closed-form expressions. Our simulation results show the advantages of non-orthogonal multiple access-aided signaling, which allows the users to decode a part of the multi-user interference for enhancing their throughput.
Nasir, AA, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2022, 'Relay-Aided Multi-User OFDM Relying on Joint Wireless Power Transfer and Self-Interference Recycling', IEEE Transactions on Communications, vol. 70, no. 1, pp. 291-305.
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Relay-aided multi-user OFDM is investigated under which multiple sources transmit their signals to a multi-antenna relay during the first relaying stage and then the relay amplifies and forwards the composite signal to all destinations during the second stage. The signal transmission of both stages experience frequency selectivity. The relay is powered both by an energy source through the wireless power transfer as well as by the energy recycled from its own self-interference during the second stage. Accordingly, we jointly design the power allocations both at the multiple source nodes and at a common relay node for maximizing the network's sum-throughput, which poses a large-scale nonconvex problem, regardless whether proper Gaussian signaling (PGS) or improper Gaussian signaling (IGS) is used for signal transmission to the relay. We develop new alternating descent procedures for solving our joint optimization problems, which are based on closed-forms and thus are of very low computational complexity even for large numbers of subcarriers. The results show the superiority of IGS over PGS in terms of both its sum-rate and individual user-rate. Another benefit of IGS over PGS is that the former promises fairer rate distribution across the subcarriers. Moreover, the recycled self-interference also provides a beneficial complementary energy source.
Naveed Arif, M, Waqas, A, Ahmed Butt, F, Mahmood, M, Hussain Khoja, A, Ali, M, Ullah, K, Mujtaba, MA & Kalam, MA 2022, 'Techno-economic assessment of solar water heating systems for sustainable tourism in northern Pakistan', Alexandria Engineering Journal, vol. 61, no. 7, pp. 5485-5499.
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Naveed, A, Ahmad, N, FathollahZadeh Aghdam, R & Menegaki, AN 2022, 'What have we learned from Environmental Kuznets Curve hypothesis? A citation-based systematic literature review and content analysis', Energy Strategy Reviews, vol. 44, pp. 100946-100946.
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Nawaz, W, Elchalakani, M, Karrech, A, Yehia, S, Yang, B & Youssf, O 2022, 'Flexural behavior of all lightweight reinforced concrete beams externally strengthened with CFRP sheets', Construction and Building Materials, vol. 327, pp. 126966-126966.
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Nazari, H, Heirani-Tabasi, A, Esmaeili, E, Kajbafzadeh, A-M, Hassannejad, Z, Boroomand, S, Shahsavari Alavijeh, MH, Mishan, MA, Ahmadi Tafti, SH, Warkiani, ME & Dadgar, N 2022, 'Decellularized human amniotic membrane reinforced by MoS2-Polycaprolactone nanofibers, a novel conductive scaffold for cardiac tissue engineering', Journal of Biomaterials Applications, vol. 36, no. 9, pp. 1527-1539.
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In order to regenerate myocardial tissues with functional characteristics, we need to copy some properties of the myocardium, such as its extracellular matrix and electrical conductivity. In this study, we synthesized nanosheets of Molybdenum disulfide (MoS2), and integrated them into polycaprolactone (PCL) and electrospun on the surface of decellularized human amniotic membrane (DHAM) with the purpose of improving the scaffolds mechanical properties and electrical conductivity. For in vitro studies, we seeded the mouse embryonic cardiac cells, mouse Embryonic Cardiac Cells (mECCs), on the scaffolds and then studied the MoS2 nanocomposites by scanning electron microscopy and Raman spectroscopy. In addition, we characterized the DHAM/PCL and DHAM/PCL-MoS2 by SEM, transmission electron microscopy, water contact angle measurement, electrical conductivity, and tensile test. Besides, we confirmed the scaffolds are biocompatible by 3-(4, 5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide, MTT assay. Furthermore, by means of SEM images, it was shown that mECCs attached to the DHAM/PCL-MoS2 scaffold have more cell aggregations and elongated morphology. Furthermore, through the Real-Time PCR and immunostaining studies, we found out cardiac genes were maturated and upregulated, and they also included GATA-4, c-TnT, NKX 2.5, and alpha-myosin heavy chain in cells cultured on DHAM/PCL-MoS2 scaffold in comparison to DHAM/PCL and DHAM. Therefore, in terms of cardiac tissue engineering, DHAM nanofibrous scaffolds reinforced by PCL-MoS2 can be suggested as a proper candidate.
Nazari, H, Heirani-Tabasi, A, Ghorbani, S, Eyni, H, Razavi Bazaz, S, Khayati, M, Gheidari, F, Moradpour, K, Kehtari, M, Ahmadi Tafti, SM, Ahmadi Tafti, SH & Ebrahimi Warkiani, M 2022, 'Microfluidic-Based Droplets for Advanced Regenerative Medicine: Current Challenges and Future Trends', Biosensors, vol. 12, no. 1, pp. 20-20.
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Microfluidics is a promising approach for the facile and large-scale fabrication of monodispersed droplets for various applications in biomedicine. This technology has demonstrated great potential to address the limitations of regenerative medicine. Microfluidics provides safe, accurate, reliable, and cost-effective methods for encapsulating different stem cells, gametes, biomaterials, biomolecules, reagents, genes, and nanoparticles inside picoliter-sized droplets or droplet-derived microgels for different applications. Moreover, microenvironments made using such droplets can mimic niches of stem cells for cell therapy purposes, simulate native extracellular matrix (ECM) for tissue engineering applications, and remove challenges in cell encapsulation and three-dimensional (3D) culture methods. The fabrication of droplets using microfluidics also provides controllable microenvironments for manipulating gametes, fertilization, and embryo cultures for reproductive medicine. This review focuses on the relevant studies, and the latest progress in applying droplets in stem cell therapy, tissue engineering, reproductive biology, and gene therapy are separately evaluated. In the end, we discuss the challenges ahead in the field of microfluidics-based droplets for advanced regenerative medicine.
Neha, B, Panda, SK, Sahu, PK, Sahoo, KS & Gandomi, AH 2022, 'A Systematic Review on Osmotic Computing', ACM Transactions on Internet of Things, vol. 3, no. 2, pp. 1-30.
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Osmotic computing in association with related computing paradigms (cloud, fog, and edge) emerges as a promising solution for handling bulk of security-critical as well as latency-sensitive data generated by the digital devices. It is a growing research domain that studies deployment, migration, and optimization of applications in the form of microservices across cloud/edge infrastructure. It presents dynamically tailored microservices in technology-centric environments by exploiting edge and cloud platforms. Osmotic computing promotes digital transformation and furnishes benefits to transportation, smart cities, education, and healthcare. In this article, we present a comprehensive analysis of osmotic computing through a systematic literature review approach. To ensure high-quality review, we conduct an advanced search on numerous digital libraries to extracting related studies. The advanced search strategy identifies 99 studies, from which 29 relevant studies are selected for a thorough review. We present a summary of applications in osmotic computing build on their key features. On the basis of the observations, we outline the research challenges for the applications in this research field. Finally, we discuss the security issues resolved and unresolved in osmotic computing.
Nematian, J & Rahimi, I 2022, 'Feasibility study of using renewable energies in Iranian Seas: A comparative study', Renewable Energy, vol. 189, pp. 383-391.
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Nematollahi, B, Hashempour Bakhtiari, P, Talebbeydokhti, N, Rakhshandehroo, GR, Nikoo, MR & Gandomi, AH 2022, 'A Stochastic Conflict Resolution Optimization Model for Flood Management in Detention Basins: Application of Fuzzy Graph Model', Water, vol. 14, no. 5, pp. 774-774.
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Floods are a natural disaster of significant concern because of their considerable damages to people’s livelihood. To this extent, there is a critical need to enhance flood management techniques by establishing proper infrastructure, such as detention basins. Although intelligent models may be adopted for flood management by detention basins, there is a literature gap on the optimum design of such structures while facing flood risks. The presented study filled this research gap by introducing a methodology to obtain the optimum design of detention basins using a stochastic conflict resolution optimization model considering inflow hydrographs uncertainties. This optimization model was developed by minimizing the conditional value-at-risk (CvaR) of flood overtopping, downstream flood damage, and deficit risk of water demand, as well as the deviation of flood overtopping and downstream damage based on non-linear interval number programming (NINP), for four different outlets types via a robust optimization tool, namely the non-dominated sorting genetic algorithm-III (NSGA-III). Conflict resolution was performed using the graph model for conflict resolution (GMCR) technique, enhanced by fuzzy preferences, to comply with the authorities’ priorities. Results indicated that the proposed framework could effectively design optimum detention basins consistent with the regional and hydrological standards.
Nematollahi, B, Nikoo, MR, Gandomi, AH, Talebbeydokhti, N & Rakhshandehroo, GR 2022, 'A Multi-criteria Decision-making Optimization Model for Flood Management in Reservoirs', Water Resources Management, vol. 36, no. 13, pp. 4933-4949.
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Flood management in a reservoir-outlet system is a multi-criterion decision-making (MCDM) issue, in which preventing flood damage and flood overtopping, as well as fulfilling water demands, are often considered essential practices. However, although MCDM models can be used for flood control, there is a knowledge gap in hybrid modeling of the reservoirs and their outlets based on a coupled MCDM and optimization model during the flood. In this paper, an MCDM-optimization model was presented for reservoir systems' optimal designs in flood conditions based on a robust optimization technique, namely multi-objective particle swarm optimization (MOPSO), applying a powerful MCDM tool, so-called complex proportional assessment (COPRAS) for the first time in the literature, considering the weights generated by Shannon Entropy method. The objectives of this optimization model were defined based on the non-linear interval number programming (NINP) technique to optimize the orifice and triangular, rectangular, and proportional weirs specifications. This methodology was applied to a practical reservoir MCDM optimization problem in flood conditions to demonstrate its applicability and efficiency. Results indicated that the proposed framework could successfully and effectually provide the reservoirs and outlets with superior optimal design.
Neshat, M, Majidi Nezhad, M, Mirjalili, S, Piras, G & Garcia, DA 2022, 'Quaternion convolutional long short-term memory neural model with an adaptive decomposition method for wind speed forecasting: North aegean islands case studies', Energy Conversion and Management, vol. 259, pp. 115590-115590.
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Neshat, M, Mirjalili, S, Sergiienko, NY, Esmaeilzadeh, S, Amini, E, Heydari, A & Garcia, DA 2022, 'Layout optimisation of offshore wave energy converters using a novel multi-swarm cooperative algorithm with backtracking strategy: A case study from coasts of Australia', Energy, vol. 239, pp. 122463-122463.
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Neshat, M, Nezhad, MM, Sergiienko, NY, Mirjalili, S, Piras, G & Garcia, DA 2022, 'Wave power forecasting using an effective decomposition-based convolutional Bi-directional model with equilibrium Nelder-Mead optimiser', Energy, vol. 256, pp. 124623-124623.
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Neves, FDO, Ewbank, H, Roveda, JAF, Trianni, A, Marafão, FP & Roveda, SRMM 2022, 'Economic and Production-Related Implications for Industrial Energy Efficiency: A Logistic Regression Analysis on Cross-Cutting Technologies', Energies, vol. 15, no. 4, pp. 1382-1382.
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Increased industrial energy efficiency (EE) has become one of the main environmental actions to mitigate carbon dioxide (CO2) emissions, contributing also to industrial competitiveness, with several implications on the production system and cost management. Unfortunately, literature is currently lacking empirical evidence on the impact of energy efficiency solutions on production. Thus, this work primarily aims at investigating the economic and production-related influence on the reduction in industrial energy consumption, considering the cross-cutting technologies HVAC, motors, lighting systems and air compressor systems. The analysis is performed using data from previous studies that characterized the main EE measures for the cross-cutting technologies. Four logistic models were built to understand how costs and production influence energy efficiency across such cross-cutting technologies. In this way, motivating industries to implement measures to reduce electrical consumption, offering an economic cost–benefit analysis and optimizing industry processes so that the reduction in electricity consumption adds to industrial energy efficiency were the aims of this study. The results of this work show through the adjusted indicators that senior management is mainly responsible for energy savings. The operational measures of each piece of equipment can be oriented in the industry towards a specific maintenance process for each technology, becoming an active procedure in industrial productions to obtain EE. Additionally, maintenance planning and control is essential to the reliability of the reduced energy consumption of cross-cutting technologies. This article concludes with managerial implications and suggestions for future research in this field.
Ng, BYS, Ong, HC, Lau, HLN, Ishak, NS, Elfasakhany, A & Lee, HV 2022, 'Production of sustainable two-stroke engine biolubricant ester base oil from palm fatty acid distillate', Industrial Crops and Products, vol. 175, pp. 114224-114224.
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In this study, palm fatty acid distillate (PFAD) was selected as the feedstock for biolubricant base oil production in two-stroke engine oils’ formulation. PFAD is a low-cost palm refinery by-product with a high free fatty acid (FFA) content (85%). The esterification of PFAD with neopentyl glycol (NPG) was conducted in the presence of a solid acid catalyst (SO42-/Fe2O3/Al2O3) to produce PFAD-NPG ester. Optimization profile indicated that PFAD conversion and PFAD-NPG ester yield were 84% and 82%, respectively, under optimum reaction conditions of 180 °C, 4 h, 2.0 wt% SO42-/Fe2O3/Al2O3 catalyst loading and a 2:1 PFAD to NPG molar ratio. The physicochemical properties of the base oil successfully comply with the Japanese Automotive Standards Organization (JASO) M345:2018 requirements for two-stroke oils in terms of sulfated ash content, kinematic viscosity at 100 °C and flash point. In addition, reusability of solid acid catalyst, SO42-/Fe2O3/Al2O3 was investigated, where PFAD conversion and PFAD-NPG ester yield were found to be excellent at 81% and 80%, respectively, which showed that the catalyst had good consistency after 5 cycles.
Ngo, QT, Phan, KT, Xiang, W, Mahmood, A & Slay, J 2022, 'Two-Tier Cache-Aided Full-Duplex Hybrid Satellite–Terrestrial Communication Networks', IEEE Transactions on Aerospace and Electronic Systems, vol. 58, no. 3, pp. 1753-1765.
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Ngo, T, Indraratna, B & Ferreira, F 2022, 'Influence of synthetic inclusions on the degradation and deformation of ballast under heavy-haul cyclic loading', International Journal of Rail Transportation, vol. 10, no. 4, pp. 413-435.
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This study investigates the benefits of artificial inclusions placed underneath the ballast layer. A series of large-scale cyclic triaxial tests were carried out on ballast with and without these inclusions under 25-tonne and 35-tonne axle loads and frequencies of f = 15 Hz and 25 Hz, using a Process Simulation Prismoidal Triaxial Apparatus. The laboratory results show that a geogrid installed between the ballast and capping layer decreases both deformation and degradation of the aggregates, which can be attributed to enhanced internal confinement and restricted particle movement. Laboratory tests also showed that placing a rubber mat underneath the ballast layer significantly reduced ballast breakage. A numerical model using the discrete element method (DEM) was developed and validated against the experimental observations. The DEM model was utilized to explore the contact forces that developed across the granular assemblies, and to study the interaction between aggregates and the synthetic inclusions from a particle-level perspective.
Nguyen Dang, H-A, Legg, R, Khan, A, Wilkinson, S, Ibbett, N & Doan, A-T 2022, 'Social impact of green roofs', Frontiers in Built Environment, vol. 8, p. 1047335.
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Green roofs are recognised as contributing towards building-related energy efficiency. Since roofs account for 20–25% of all urban surface areas, it is not surprising that such a green system can offer a sustainable solution to decreased energy consumption. The current literature on green roofs mostly focuses on the eco-benefits of such structures. A handful of research papers1 have theorised that as green roofs become more prevalent, there will be associated social outcomes for an urban community. However, empirical work in this space is minimal. This research addresses this gap and contributes to the literature by providing insights into city dwellers’ social experiences when using a green roof space. This study identified a green roof space in central Sydney, Australia: the Alumni Green at the University of Technology Sydney. The roof, containing a garden, a concrete open space and a raised grass area amounting to 1,200 m2, is above parts of the university’s library and classrooms, and is easily accessible by staff, students, and members of the public. Two members of the research team conducted surveys on site. Some green-roof users were also contacted via email. Over 128 individuals began the survey, although after removing responses that were incomplete or containing errors, 104 responses remained. The findings revealed that users, most commonly, relaxed or socialised on the green roof, with exercise a far less frequent activity. Further, those who frequented the green roof once a week or more reported significantly greater social well-being and attachment to place than those who visited less. Likewise, those who visited the green roof for periods of 30 min or more also reported greater social wellbeing. There were no significant differences between frequency and length of use and users’ perspectives on the green roof’s economic, physical, collective...
Nguyen, A, Long Nguyen, C, Gharehbaghi, V, Perera, R, Brown, J, Yu, Y & Kalbkhani, H 2022, 'A computationally efficient crack detection approach based on deep learning assisted by stockwell transform and linear discriminant analysis', Structures, vol. 45, no. Mach Vis Appl 22 2 2011, pp. 1962-1970.
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This paper presents SpeedyNet, a computationally efficient crack detection method. Rather than using a computationally demanding convolutional neural network (CNN), this approach made use of a simple neural network with a shallow architecture augmented by a 2D Stockwell transform for feature transformation and linear discriminant analysis for feature reduction. The approach was employed to classify images with minute cracks under three simulated noisy conditions. Using time–frequency image transformation, feature conditioning and a fast deep learning-based classifier, this method performed better in terms of speed, accuracy and robustness compared to other image classifiers. The performance of SpeedyNet was compared to that of two popular pre-trained CNN models, Xception and GoogleNet, and the results demonstrated that SpeedyNet was superior in both classification accuracy and computational speed. A synthetic efficiency index was then defined for further assessment. Compared to GoogleNet and the Xception models, SpeedyNet enhanced classification efficiency at least sevenfold. Furthermore, SpeedyNet's reliability was demonstrated by its robustness and stability when faced with network parameter and input image uncertainties including batch size, repeatability, data size and image dimensions.
Nguyen, AQ, Nguyen, LN, Johir, MAH, Ngo, HH & Nghiem, LD 2022, 'Linking endogenous decay and sludge bulking in the microbial community to membrane fouling at sub-critical flux', Journal of Membrane Science Letters, vol. 2, no. 1, pp. 100023-100023.
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This study examined membrane fouling and associated microbial taxa in a membrane bioreactor operating at a sub-critical flux condition using next-generation amplicon sequencing. The membrane was operated at a sub-critical flux, thus, fouling was not observed until endogenous decay. The observed fouling could be attributed to endogenous decay which was driven by nutrient deficiency at high sludge age and low food-to-microorganisms ratio (decreasing from 0.15 to 0.09 gBOD/gMLVSS.d). Endogenous decay resulted in a sharp decrease of the number of species and evenness between different species (49.7 and 58.9% compared to the inoculum, respectively). The release of dissolved organic matters and cell debris from endogenous decay as well as the excessive growth of filamentous bacteria, e.g. Thiotrichales were the main contributors to membrane fouling. The relative abundance of Thiotrichales significantly correlated with TMP (Pearson R = 0.996, p-value <0.001), indicating this order's contribution to membrane fouling. Other dominant orders in the mixed liquor after endogenous decay such as Rhizobiales, Burkholderiales, Rhodospirillales and Myxococcales, Flavobacteriales can produce extracellular polymeric substances and aggravating membrane fouling. Fouling layers possess highly similar microbial composition with the mixed liquor, with some filamentous microbial orders, e.g. Corynebacteriales and Oligoflexales showing increased relative abundance by 6.83 and 5.64 folds, respectively.
Nguyen, AQ, Nguyen, LN, McDonald, JA, Nghiem, LD, Leusch, FDL, Neale, PA & Khan, SJ 2022, 'Chiral inversion of 2-arylpropionoic acid (2-APA) enantiomers during simulated biological wastewater treatment', Water Research, vol. 209, pp. 117871-117871.
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Nguyen, AQ, Nguyen, LN, Xu, Z, Luo, W & Nghiem, LD 2022, 'New insights to the difference in microbial composition and interspecies interactions between fouling layer and mixed liquor in a membrane bioreactor', Journal of Membrane Science, vol. 643, pp. 120034-120034.
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This work examined fouling-associated microbial community in a carefully controlled laboratory-scale membrane bioreactor (MBR) at different fouling stages. In agreement with the literature, fouling severity was positively correlated with bound polysaccharide and protein content (indicators) in the mixed liquor. UPGMA clustering analysis with different indices indicated that although the biofouling layer (biofilm) and mixed liquor possessed highly similar microbial identity, important differences between the two communities' structures were also observed. This appears to be the first comprehensive study to apply differential abundance analysis (ANCOM) to identify microbial taxa driven the divergence in microbial structure including Victivallales, Coxiellales, unassigned Microgenomatia and Blastocatellia 11–24 (all presented at <1% abundance). Network analysis also identified Victivallales and Blastocatellia 11–24 among the few key players in the mixed liquor and biofilm community, respectively. Despite their low abundances, key players in both communities positively correlated (Pearson's correlation coefficient >0.6) with fouling indicators, confirming their important contributions to fouling propensity. The biofilm community exhibited a more complex structure with higher level of inter-species interaction and prevalence of positive connections (74.6%) compared to the mixed liquor community (42.2%), reflecting higher stability and synergy between microbial taxa in the biofilm. Results from this comprehensive investigation can support the development of new fouling control strategies.
Nguyen, BP, Nguyen, T, Nguyen, THY & Tran, TD 2022, 'Performance of composite PVD-soil cement column foundation under embankment through plane-strain numerical analysis', International Journal of Geomechanics, vol. 22, no. 8.
Nguyen, B-P, Nguyen, TT, Nguyen, THY & Tran, T-D 2022, 'Performance of Composite PVD–SC Column Foundation under Embankment through Plane-Strain Numerical Analysis', International Journal of Geomechanics, vol. 22, no. 9, p. 04022155.
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The combination of soil-cement (SC) columns and prefabricated vertical drains (PVDs) has indicated great success in improving ground stabilization in recent years; however, there is a lack of proper plane-strain numerical modeling to detail the role of PVDs in improving the performance of the SC column method. This study thus presents a numerical analysis of soft soil ground improved by the coupled PVD-SC column method based on a proposed equivalent plane-strain model considering the combined effects of PVDs and SC columns in the ground. The model is verified by applying it to a test embankment where long PVDs were installed in soft soil in combination with floating SC columns. To investigate the role of PVDs in the composite foundation, the numerical analysis is then conducted for two cases, with and without PVDs. The effects of discharge capacity of PVDs on the SC column behavior are also examined. The results show that the PVDs significantly improve performance of the composite foundation as they considerably reduce both postconstruction settlement and lateral displacement, while increasing the efficiency of soil arching and the bending moment capacity in SC columns. The numerical results obtained from the proposed model are in good agreement with the field data. The current study also shows that the discharge capacity of PVDs should be larger than 20 m3/year to enhance the positive influence of PVDs on the entire performance of the composite foundation.
Nguyen, CT, Van Huynh, N, Chu, NH, Saputra, YM, Hoang, DT, Nguyen, DN, Pham, Q-V, Niyato, D, Dutkiewicz, E & Hwang, W-J 2022, 'Transfer Learning for Wireless Networks: A Comprehensive Survey', Proceedings of the IEEE, vol. 110, no. 8, pp. 1073-1115.
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With outstanding features, machine learning (ML) has become the backbone of numerous applications in wireless networks. However, the conventional ML approaches face many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, can impede the effectiveness and applicability of ML in wireless networks. To address these problems, transfer learning (TL) has recently emerged to be a promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks and valuable experiences accumulated from the past to facilitate the learning of new problems. By doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods' robustness to different wireless environments. This article aims to provide a comprehensive survey on the applications of TL in wireless networks. Particularly, we first provide an overview of TL, including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, signal recognition, security, caching, localization, and human activity recognition, which are all important to next-generation networks, such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.
Nguyen, CT, Walker, PD, Zhou, S & Zhang, N 2022, 'Optimal sizing and energy management of an electric vehicle powertrain equipped with two motors and multi-gear ratios', Mechanism and Machine Theory, vol. 167, pp. 104513-104513.
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Nguyen, D-A, Tran, X-T, Dang, KN & Iacopi, F 2022, 'A low-power, high-accuracy with fully on-chip ternary weight hardware architecture for Deep Spiking Neural Networks', Microprocessors and Microsystems, vol. 90, pp. 104458-104458.
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Recently, Deep Spiking Neural Network (DSNN) has emerged as a promising neuromorphic approach for various AI-based applications, such as image classification, speech recognition, robotic control etc. on edge computing platforms. However, the state-of-the-art offline training algorithms for DSNNs are facing two major challenges. Firstly, many timesteps are required to reach comparable accuracy with traditional frame-based DNNs algorithms. Secondly, extensive memory requirements for weight storage make it impossible to store all the weights on-chip for DSNNs with many layers. Thus the inference process requires continue access to expensive off-chip memory, ultimately leading to performance degradation in terms of throughput and power consumption. In this work, we propose a hardware-friendly training approach for DSNN that allows the weights to be constrained to ternary format, hence reducing the memory footprints and the energy consumption. Software simulations on MNIST and CIFAR10 datasets have shown that our training approach could reach an accuracy of 97% for MNIST (3-layer fully connected networks) and 89.71% for CIFAR10 (VGG16). To demonstrate the energy efficiency of our approach, we have proposed a neural processing module to implement our trained DSNN. When implemented as a fixed, 3-layers fully-connected system, the system has reached at energy efficiency of 74nJ/image with a classification accuracy of 97% for MNIST dataset. We have also considered a scalable design to support more complex network topologies when we integrate the neural processing module with a 3D Network-on-Chip.
Nguyen, HAD & Ha, QP 2022, 'Wireless Sensor Network Dependable Monitoring for Urban Air Quality', IEEE Access, vol. 10, no. 99, pp. 40051-40062.
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Nguyen, H-Q, Foisal, ARM, Tanner, P, Nguyen, T-H, Aberoumand, S, Dau, VT, Dinh, T, Dimitrijev, S, Phan, H-P, Nguyen, N-T & Dao, DV 2022, 'Giant Piezotronic Effect by Photoexcitation–Electronic Coupling in a p-GaN/AlGaN/GaN Heterojunction', ACS Applied Electronic Materials, vol. 4, no. 6, pp. 2648-2655.
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Nguyen, KT, Navidpour, AH, Ahmed, MB, Mojiri, A, Huang, Y & Zhou, JL 2022, 'Adsorption and desorption behavior of arsenite and arsenate at river sediment-water interface', Journal of Environmental Management, vol. 317, pp. 115497-115497.
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The adsorption of inorganic arsenic (As) plays an important role in the mobility and transport of As in the river environment. In this work, the adsorption and desorption of arsenite [As(III)] and arsenate [As(V)] on river sediment were conducted under different pH, initial As concentrations, river water and sediment composition to assess As adsorption behavior and mechanism. Both adsorption kinetics and equilibrium results showed higher adsorption capacity of sediment for As(V) than As(III). Adsorption of As(III) and As(V) on river sediment was favored in acidic to neutral conditions and on finer sediment particles, while sediment organic matter marginally reduced adsorption capacity. In addition, higher adsorption affinity of As(III) and As(V) in river sediment was observed in deionised water than in river water. For the release process, the desorption of both As(III) and As(V) followed nonlinear kinetic models well, showing higher amount of As(III) release from sediment than As(V). Adsorption isotherm was well described by both Langmuir and Freundlich models, demonstrating higher maximum adsorption capacity of As(V) at 298.7 mg/kg than As(III) at 263.3 mg/kg in deionised water, and higher maximum adsorption capacity of As(III) of 234.3 mg/kg than As(V) of 206.2 mg/kg in river water. The XRD showed the changes in the peaks of mineral groups of sediment whilst FTIR results revealed the changes related to surface functional groups before and after adsorption, indicating that Fe-O/Fe-OH, Si(Al)-O, hydroxyl and carboxyl functional groups were predominantly involved in As(III) and As(V) adsorption on sediment surface. XPS analysis evidenced the transformation between these As species in river sediment after adsorption, whilst SEM-EDS revealed higher amount of As(V) in river sediment than As(III) due to the lower signal of Al.
Nguyen, LN, Aditya, L, Vu, HP, Johir, AH, Bennar, L, Ralph, P, Hoang, NB, Zdarta, J & Nghiem, LD 2022, 'Nutrient Removal by Algae-Based Wastewater Treatment', Current Pollution Reports, vol. 8, no. 4, pp. 369-383.
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AbstractAlgae cultivation complements wastewater treatment (WWT) principles as the process uptakes nutrients while assimilates CO2 into biomass. Thus, the application of algae-based WWT is on the upward trajectory as more attention for recovery nutrients and CO2 capture while reducing its economic challenge in the circular economy concept. However, the complexity of wastewater and algal ecological characteristics induces techno-economic challenges for industry implementation. Algae-based WWT relies totally on the ability of algae to uptake and store nutrients in the biomass. Therefore, the removal efficiency is proportional to biomass productivity. This removal mechanism limits algae applications to low nutrient concentration wastewater. The hydraulic retention time (HRT) of algae-based WWT is significantly long (i.e. > 10 days), compared to a few hours in bacteria-based process. Phototrophic algae are the most used process in algae-based WWT studies as well as in pilot-scale trials. Application of phototrophic algae in wastewater faces challenges to supply CO2 and illumination. Collectively, significant landscape is required for illumination. Algae-based WWT has limited organic removals, which require pretreatment of wastewaters before flowing into the algal process. Algae-based WWT can be used in connection with the bacteria-based WWT to remove partial nutrients while capturing CO2. Future research should strive to achieve fast and high growth rate, strong environmental tolerance species, and simple downstream processing and high-value biomass. There is also a clear and urgent need for more systematic analysis of biomass for both carbon credit assessment and economic values to facilitate identification and prioritisation of barriers to lower the cost algae-based WWT. Graphical abstract
Nguyen, LN, Vu, HP, Fu, Q, Abu Hasan Johir, M, Ibrahim, I, Mofijur, M, Labeeuw, L, Pernice, M, Ralph, PJ & Nghiem, LD 2022, 'Synthesis and evaluation of cationic polyacrylamide and polyacrylate flocculants for harvesting freshwater and marine microalgae', Chemical Engineering Journal, vol. 433, pp. 133623-133623.
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This study addresses the challenge of microalgae harvesting through the development of flocculants. Two positively charged cationic polymers including poly[2 (acryloyloxy)ethyl]trimethylammonium chloride (PAETAC) and poly(3 acrylamidopropyl)trimethylammonium chloride (PAmPTAC) were synthesized using the UV-induced radical polymerization, for harvesting both freshwater and marine microalgae. The results show that the synthesized polymers have excellent flocculation performance for both freshwater green microalgae (Chlorella vulgaris) and marine red microalgae (Porphyridium purpureum). PAETAC outperformed PAmPTAC for both Chlorella vulgaris and Porphyridium purpureum microalgae. The optimal PAETAC doses for Chlorella vulgaris and Porphyridium purpureum microalgae were 50 and 4.8 mg/g of dry biomass while the optimal PAmPTAC doses were 252 and 35 mg/g of dry biomass respectively. Additionally, the floc formation with the PAETAC was more stable than PAmPTAC, which supported the dewatering step via sieving. The superior performance can be attributed to the higher molecular weight of the PAETAC polymer when compared to the PAmPTAC polymer. In comparison to commercially available polydiallyldimethylammonium chloride (PolyDADMAC), the newly synthesised PAETAC and PAmPTAC polymers demonstrated superior flocculation efficiency at a lower dose. The findings of this study established a platform technology for designing and synthesising cationic flocculants for use in microalgae harvesting.
Nguyen, NHT, Nguyen, TT & Phan, QT 2022, 'Dynamics and runout distance of saturated particle-fluid mixture flow on a horizontal plane: A coupled VOF-DEM study', Powder Technology, vol. 408, pp. 117759-117759.
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This study investigates the dynamics and runout of particle-water mixture column collapse using a modelling method coupling Discrete Element Method (DEM) and Volume of Fluid (VOF). From the numerical results, we observe similar and distinct responses between the dry and saturated mixture flows. Both exhibit two different flow behaviours for low and high column collapse. However, the presence of water reduces dissipative interactions between particles, hence increasing the mobility of mixture flows compared to their dry counterparts. The reduction of particle interaction forces also weakens the transition from sliding-dominant to inertial-dominant flows with increasing column aspect ratio. Therefore, the runout distance of mixture flows can be described by a single scaling law rather than by two different functions as for the dry flows. Additionally, the impacts of particle density on mixture flows become more significant in which the flows run out less and retain greater height with increasing particle density.
Nguyen, NHT, Perry, S, Bone, D, Le Thanh, H, Xu, M & Nguyen, TT 2022, 'Combination of Images and Point Clouds in a Generative Adversarial Network for Upsampling Crack Point Clouds', IEEE Access, vol. 10, pp. 67198-67209.
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Nguyen, NPT, Sultana, A, Areerachakul, N & Kandasamy, J 2022, 'Evaluating the Field Performance of Permeable Concrete Pavers', Water, vol. 14, no. 14, pp. 2143-2143.
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The benefits of using permeable interlocking concrete pavement systems (PICPs) have not translated into widespread adoption in Australia, where their uptake has been slow. This paper communicates the actual performance of PICPs installed in the field by providing evidence of their long-term efficiency. There are currently no Australian standards for design, specification and installation of PICPs. In this study, field measurements were conducted to determine the infiltration capacity of PICPs in Sydney and Wollongong, New South Wales, applying the single ring infiltrometer test (SRIT) and the stormwater infiltration field test (SWIFT). A strong correlation was found between the results of the two tests in a previous study, which was verified in this study. The long-term performance of PICPs is demonstrated by their high infiltration rates (ranging from 125 mm/h to 25,000 mm/h) measured in this study at field sites under a diverse range of conditions. The influences of conditions such as age of installation, slope and tree cover on infiltration rates were explored.
Nguyen, PM, Do, PT, Pham, YB, Doan, TO, Nguyen, XC, Lee, WK, Nguyen, DD, Vadiveloo, A, Um, M-J & Ngo, HH 2022, 'Roles, mechanism of action, and potential applications of sulfur-oxidizing bacteria for environmental bioremediation', Science of The Total Environment, vol. 852, pp. 158203-158203.
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Sulfur (S) is a crucial component in the environment and living organisms. This work is the first attempt to provide an overview and critical discussion on the roles, mechanisms, and environmental applications of sulfur-oxidizing bacteria (SOB). The findings reveal that key enzymes of SOB embarked on oxidation of sulfide, sulfite, thiosulfate, and elemental S. Conversion of reduced S compounds was oxidatively catalyzed by various enzymes (e.g. sulfide: quinone oxidoreductase, flavocytochrome c-sulfide dehydrogenase, dissimilatory sulfite reductase, heterodisulfide reductase-like proteins). Environmental applications of SOB discussed include detoxifying hydrogen sulfide, soil bioremediation, and wastewater treatment. SOB producing S0 engaged in biological S soil amendments (e.g. saline-alkali soil remediation, the oxidation of sulfide-bearing minerals). Biotreatment of H2S using SOB occurred under both aerobic and anaerobic conditions. Sulfide, nitrate, and sulfamethoxazole were removed through SOB suspension cultures and S0-based carriers. Finally, this work presented future perspectives on SOB development, including S0 recovery, SOB enrichment, field measurement and identification of sulfur compounds, and the development of mathematical simulation.
Nguyen, QA, Vu, HP, McDonald, JA, Nguyen, LN, Leusch, FDL, Neale, PA, Khan, SJ & Nghiem, LD 2022, 'Chiral Inversion of 2-Arylpropionic Acid Enantiomers under Anaerobic Conditions', Environmental Science & Technology, vol. 56, no. 12, pp. 8197-8208.
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This work examined the chiral inversion of 2-arylpropionic acids (2-APAs) under anaerobic conditions and the associated microbial community. The anaerobic condition was simulated by two identical anaerobic digesters. Each digester was fed with the substrate containing 11 either pure (R)- or pure (S)-2-APA enantiomers. Chiral inversion was evidenced by the concentration increase of the other enantiomer in the digestate and the changes in the enantiomeric fraction between the two enantiomers. Both digesters showed similar and poor removal of 2-APAs (≤30%, except for naproxen) and diverse chiral inversion behaviors under anaerobic conditions. Four compounds exhibited (S → R) unidirectional inversion [flurbiprofen, ketoprofen, naproxen, and 2-(4-tert-butylphenyl)propionic acid], and the remaining seven compounds showed bidirectional inversion. Several aerobic and facultative anaerobic bacterial genera (Candidatus Microthrix, Rhodococcus, Mycobacterium, Gordonia, and Sphingobium) were identified in both digesters and predicted to harbor the 2-arylpropionyl-CoA epimerase (enzyme involved in chiral inversion) encoding gene. These genera presented at low abundances, <0.5% in the digester dosed with (R)-2-APAs and <0.2% in the digester dosed with (S)-2-APAs. The low abundances of these genera explain the limited extent of chiral inversion observed in this study.
Nguyen, QD, Afroz, S, Zhang, Y, Kim, T, Li, W & Castel, A 2022, 'Autogenous and total shrinkage of limestone calcined clay cement (LC3) concretes', Construction and Building Materials, vol. 314, pp. 125720-125720.
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In this study, the developments of autogenous and total shrinkage of limestone calcined clay cement (LC3) concretes were investigated. Three concrete grades including 25 MPa, 32 MPa and 45 MPa of both LC3 and general purpose cement (GPC) concretes were considered. Compressive strength and tensile strength were measured until curing of 28 days. In addition, pore size distribution of cementitious pastes was evaluated by using nitrogen adsorption. Several models were used to assess their applicability for LC3 concretes in predicting mechanical properties and shrinkage development. The LC3 concretes showed higher autogenous shrinkage at a later age up to 100 days due to continuous refinement of the pore structure whilst the development of total shrinkage was similar between LC3 and OPC concretes. All models underestimated LC3 concrete autogenous shrinkage and the Bazant B4 model provided the best prediction of total shrinkage development.
Nguyen, TAH, Le, TV, Ngo, HH, Guo, WS, Vu, ND, Tran, TTT, Nguyen, THH, Nguyen, XC, Nguyen, VH & Pham, TT 2022, 'Hybrid use of coal slag and calcined ferralsol as wetland substrate for improving phosphorus removal from wastewater', Chemical Engineering Journal, vol. 428, pp. 132124-132124.
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Nguyen, TG, Phan, TV, Hoang, DT, Nguyen, HH & Le, DT 2022, 'DeepPlace: Deep reinforcement learning for adaptive flow rule placement in Software-Defined IoT Networks', Computer Communications, vol. 181, pp. 156-163.
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In this paper, we propose a novel and adaptive flow rule placement system based on deep reinforcement learning, namely DeepPlace, in Software-Defined Internet of Things (SDIoT) networks. DeepPlace can provide a fine-grained traffic analysis capability while assuring QoS of traffic flows and proactively avoiding the flow-table overflow issue in the data plane. Specifically, we first investigate the traffic forwarding process in an SDIoT network, i.e., routing and flow rule placement tasks. We design a cost function for the routing to set up traffic flow paths in the data plane. Next, we propose an adaptive flow rule placement approach to maximize the number of match-fields in a flow rule at SDN switches. To deal with the dynamics of IoT traffic flows, we model the system operation by using the Markov decision process (MDP) with a continuous action space and formulate its optimization problem. Subsequently, we develop a deep deterministic policy gradient-based algorithm to help the system obtain the optimal policy. The evaluation results demonstrate that DeepPlace can efficiently maintain a significant number of match-fields in a flow rule, i.e., approximately 86% of the maximum level, while minimizing the QoS violation ratio of traffic flows, i.e., 6.7%, in a highly dynamic traffic scenario, which outperforms three other existing solutions, i.e., FlowMan, FlowStat, and DeepMatch.
Nguyen, TH, Loganathan, P, Nguyen, TV, Vigneswaran, S, Ha Nguyen, TH, Tran, HN & Nguyen, QB 2022, 'Arsenic removal by pomelo peel biochar coated with iron', Chemical Engineering Research and Design, vol. 186, pp. 252-265.
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Nguyen, T-H, Nguyen, T, Foisal, ARM, Pham, TA, Dinh, T, Nguyen, H-Q, Streed, EW, Vu, T-H, Fastier-Wooller, J, Duran, PG, Dau, VT, Nguyen, N-T & Dao, DV 2022, 'Ultrasensitive Self-Powered Position-Sensitive Detector Based on n-3C-SiC/p-Si Heterojunctions', ACS Applied Electronic Materials, vol. 4, no. 2, pp. 768-775.
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Nguyen, TH, Ryu, S, Loganathan, P, Kandasamy, J, Nguyen, TV & Vigneswaran, S 2022, 'Arsenic adsorption by low-cost laterite column: Long-term experiments and dynamic column modeling', Process Safety and Environmental Protection, vol. 160, pp. 868-875.
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Arsenic (As) contamination of drinking water supplies is a major concern in many countries due to its large concentration in groundwater and high toxicity. In this study, batch adsorption experiments on a natural laterite adsorbent from Vietnam (NLTT) were firstly conducted, followed by four column adsorption experiments using NLTT working with synthetic water under different experimental conditions (initial arsenate As(V) concentration: 0.1 and 0.5 mg/L; bed height: 0.15 and 0.41 m). Results from the batch equilibrium adsorption study show that all three models - Sips, Langmuir, and Freundlich - fitted the experimental data very well. The Sips and Langmuir maximum adsorption capacities were 0.76 mg/g and 0.58 mg/g, respectively. At an As(V) concentration of 0.5 mg/L, adsorption breakthrough occurred at 28 h and 122 h for column heights of 0.15 m and 0.41 m, respectively. When As(V) concentration fell to 0.1 mg/L, the breakthrough times rose to 144 h and 240 h, respectively. A linear driving force approximation (LDFA) model incorporating the Sips equation was calibrated with data from the equilibrium and kinetic adsorption experiments and one column adsorption experiment (initial concentration: 0.1 mg/L; bed height: 0.15 m). The LDFA model with the calibrated model coefficients could predict the breakthrough curves and adsorption time in the three other column experiments and four household column filters used to treat As contaminated groundwater in Vietnam. The study revealed that application potential for NLTT in column adsorption studies and field trials to remove As(V) is significant despite this study having limited data. Subsequently, refining the model based on simulation of results is cost-effective, saves time and effort, and negates the need for multiple experiments to optimize filter conditions.
Nguyen, TH, Tran, HN, Nguyen, TV, Vigneswaran, S, Trinh, VT, Nguyen, TD, Ha Nguyen, TH, Mai, TN & Chao, H-P 2022, 'Single-step removal of arsenite ions from water through oxidation-coupled adsorption using Mn/Mg/Fe layered double hydroxide as catalyst and adsorbent', Chemosphere, vol. 295, pp. 133370-133370.
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This study developed a layered double hydroxides (Mn/Mg/Fe-LDH) material through a simple co-precipitation method. The Mn/Mg/Fe-LDH oxidized arsenite [As(III)] ions into arsenate [As(V)] anions. The As(III) and oxidized As(V) were then adsorbed on Mn/Mg/Fe-LDH. The adsorption process of arseniate [As(V)] oxyanions by Mn/Mg/Fe-LDH was simultaneously conducted for comparison. Characterization results indicated that (i) the best Mg/Mn/Fe molar ratio was 1/1/1, (ii) the Mn/Mg/Fe-LDH structure was similar to that of hydrotalcite, (iii) the Mn/Mg/Fe-LDH possessed a positively charged surface (pHIEP of 10.15) and low Brunauer-Emmett-Teller surface area (SBET = 75.2 m2/g), and (iv) Fe2+/Fe3+ and Mn2+/Mn3+/Mn4+ coexisted in Mn/Mg/Fe-LDH. The As(III) adsorption process by Mn/Mg/Fe-LDH was similar to that of As(V) under different experimental conditions (initial solutions pH, coexisting foreign anions, contact times, initial As concentrations, temperatures, and desorbing agents). The Langmuir maximum adsorption capacity of Mn/Mg/Fe-LDH to As(III) (56.1 mg/g) was higher than that of As(V) (32.2 mg/g) at pH 7.0 and 25 °C. X-ray photoelectron spectroscopy was applied to identify the oxidation states of As in laden Mn/Mg/Fe-LDH. The key removal mechanism of As(III) by Mn/Mg/Fe-LDH was oxidation-coupled adsorption, and that of As(V) was reduction-coupled adsorption. The As(V) mechanism adsorption mainly involved: (1) the inner-sphere and outer-sphere complexation with OH groups of Mn/Mg/Fe-LDH; and (2) anion exchange with host anions (NO3-) in its interlayer. The primary mechanism adsorption of As(III) was the inner-sphere complexation. The redox reactions made Mn/Mg/Fe-LDH loss its original layer structure after adsorbing As(V) or As(III). The adsorption process was highly irreversible. Mn/Mg/Fe-LDH can decontaminate As from real groundwater samples from 45-92 ppb to 0.35-7.9 ppb (using 1.0 g/L). Therefore, Mn/Mg/Fe-LDH has great potential as a material for removing As.
Nguyen, TK, Nguyen, HH, Tuan, HD & Ngo, HQ 2022, 'Improved Pilot Designs for Enhancing Connectivity in Multicarrier Massive MIMO Systems', IEEE Wireless Communications Letters, vol. 11, no. 5, pp. 1057-1061.
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Nguyen, TN, Sanchez, LFM, Li, J, Fournier, B & Sirivivatnanon, V 2022, 'Correlating alkali-silica reaction (ASR) induced expansion from short-term laboratory testings to long-term field performance: A semi-empirical model', Cement and Concrete Composites, vol. 134, pp. 104817-104817.
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Correlating short-term expansion of concrete specimens in the laboratory and long-term expansion of concrete in the field is crucial to evaluate the reliability of laboratory test methods and essential for the prognosis of alkali-silica reaction (ASR) in concrete infrastructures. In this study, a novel semi-empirical approach is proposed for forecasting ASR-induced expansion of unrestrained concrete in the field using laboratory measurements data. In addition to the use of short-term laboratory expansion data, the model accounts for the effects of alkali leaching, alkali contribution from aggregates, and environmental conditions (i.e., temperature and relative humidity). A comprehensive database from the literature was gathered for the development and calibration of the proposed model. Finally, the model was used for various concrete blocks incorporating different reactive aggregates and exposed to three outdoor conditions in Canada and the USA. Model outcomes show that it is highly promising for forecasting the induced expansion of concrete in the field from the accelerated laboratory tests data. Analysing the modelling results also highlights the importance of alkali leaching and environmental conditions on the correlation between laboratory and field performance.
Nguyen, TT & Indraratna, B 2022, 'Fluidization of soil under increasing seepage flow: an energy perspective through CFD-DEM coupling', Granular Matter, vol. 24, no. 3.
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AbstractIncreasing seepage flow causes soil particles to migrate, i.e., from local piping to complete fluidization, resulting in reduced effectives stress and degraded shear stiffness of the soil foundation. This process has received considerable attention in the past years, however, majority of them concentrate on macro-aspects such as the internal erosion and soil deformation, while there is a lack of fundamental studies addressing the energy transport at micro-scale of fluid-soil systems during soil approaching fluidization. In this regard, the current study presents an assessment of the energy evolution in soil fluidization based on the discrete element method (DEM) coupled with computation fluid dynamics (CFD). In this paper, an upward seepage flow of fluid is modelled by CFD based on the modified Navier–Stokes equations, while soil particles are governed by DEM with their mutual interactions being computed through fluid-particle force models. The energy transformation from the potential state to kinetic forms during fluid flowing is discussed with respect to numerical (CFD-DEM) results and the energy conservation concepts. The results show that majority of the potential energy induced by fluid flows has lost due to frictional mechanisms, while only a small amount of energy is needed to cause the soil to fluidize completely. The contribution of rotational and translational components to the total kinetic energy of particles, and their changing roles during soil fluidization is also presented. The effect of boundary condition on the energy transformation and fluidization of soil is also investigated and discussed. Graphical abstract
Nguyen, TT & Indraratna, B 2022, 'Rail track degradation under mud pumping evaluated through site and laboratory investigations', International Journal of Rail Transportation, vol. 10, no. 1, pp. 44-71.
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This paper presents the results of field and laboratory studies of slurry tracks along the South Coast rail line in NSW, Australia. Site investigations on fouled tracks were followed by a series of laboratory tests to determine the properties of mud fines, and how they can reduce track performance. This study reveals two distinctly different ways of forming slurry tracks, i.e., non-subgrade and subgrade mud pumping, resulting in different characteristics of degraded tracks. More cohesive the fouling materials are, the greater the reduction in hydraulic conductivity (kb) and shear strength (Sb) of the contaminated ballast. When the fouling index FI > 30%, kb drops severely, causing insufficient drainage capacity of the track while the loss of Sb can exceed 22%. Different types of fouling index are also discussed with reference to the field and laboratory data, followed by proposed empirical equations to estimate the values of kb and Sb.
Nguyen, TT, Indraratna, B & Leroueil, S 2022, 'Localized behaviour of fluidized subgrade soil subjected to cyclic loading', Canadian Geotechnical Journal, vol. 59, no. 10, pp. 1844-1849.
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Recent investigations have shown that under adverse cyclic triaxial loading, the upper part of soil specimens can turn into a fluid-like state with increased water content (i.e., fluidization), whereas the lower layers can maintain a relatively high stiffness. This paper aims to gain further insight into this behaviour by monitoring the development in excess pore water pressure (EPWP) at the top and bottom of the test specimens, followed by post-analysis of water content distribution along the specimen. The results show that the EPWP at the uppermost part of the specimen develops rapidly and approaches the zero-effective stress level, whereas the EPWP at the bottom part of the specimen tends to stabilize while undergoing densification. Accompanied with this process is a redistribution of the water content along the specimen height where the water content at the upper soil layer increases to approach the liquid limit while increasing the void ratio.
Nguyen, TT, Indraratna, B & Rujikiatkamjorn, C 2022, 'A numerical approach to modelling biodegradable vertical drains', Environmental Geotechnics, vol. 9, no. 8, pp. 515-523.
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Because of their distinct features such as biodegradability and favourable engineering properties, naturally occurring materials including jute and coconut fibres have been used increasingly in numerous geoengineering applications in recent years. However, these materials can sometimes decompose rapidly when subjected to adverse environmental conditions, resulting in severe degradation of their engineering characteristics and consequently causing damage to the design target. This paper presents a numerical approach where the finite-element method (FEM) is used to estimate the influence that the degradation of natural fibre drains can have on soil consolidation. A subroutine which can describe the reduction in drain discharge capacity over time is incorporated into the FEM model. Different cases including those varying the rate and time-dependent form of biodegradation are examined in this paper. The results of this investigation indicate that the dissipation of excess pore pressure can be hampered significantly if drains decay too early and speedily, particularly when the discharge capacity falls below 0·03 m3/d. Different rates of decay can impose different consolidation responses in the surrounding soft soil. Application of the proposed FEM to compare with laboratory data indicates an acceptable agreement between the predictions and the measurements.
Nguyen, TT, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Nguyen, CT, Zhang, J, Liang, S, Bui, XT & Hoang, NB 2022, 'A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm', Science of The Total Environment, vol. 833, pp. 155066-155066.
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A high-resolution soil moisture prediction method has recently gained its importance in various fields such as forestry, agricultural and land management. However, accurate, robust and non- cost prohibitive spatially monitoring of soil moisture is challenging. In this research, a new approach involving the use of advance machine learning (ML) models, and multi-sensor data fusion including Sentinel-1(S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and ALOS Global Digital Surface Model (ALOS DSM) to predict precisely soil moisture at 10 m spatial resolution across research areas in Australia. The total of 52 predictor variables generated from S1, S2 and ALOS DSM data fusion, including vegetation indices, soil indices, water index, SAR transformation indices, ALOS DSM derived indices like digital model elevation (DEM), slope, and topographic wetness index (TWI). The field soil data from Western Australia was employed. The performance capability of extreme gradient boosting regression (XGBR) together with the genetic algorithm (GA) optimizer for features selection and optimization for soil moisture prediction in bare lands was examined and compared with various scenarios and ML models. The proposed model (the XGBR-GA model) with 21 optimal features obtained from GA was yielded the highest performance (R2 = 0. 891; RMSE = 0.875%) compared to random forest regression (RFR), support vector machine (SVM), and CatBoost gradient boosting regression (CBR). Conclusively, the new approach using the XGBR-GA with features from combination of reliable free-of-charge remotely sensed data from Sentinel and ALOS imagery can effectively estimate the spatial variability of soil moisture. The described framework can further support precision agriculture and drought resilience programs via water use efficiency and smart irrigation management for crop production.
Nguyen, TT, Pham, TD, Nguyen, CT, Delfos, J, Archibald, R, Dang, KB, Hoang, NB, Guo, W & Ngo, HH 2022, 'A novel intelligence approach based active and ensemble learning for agricultural soil organic carbon prediction using multispectral and SAR data fusion', Science of The Total Environment, vol. 804, pp. 150187-150187.
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Monitoring agricultural soil organic carbon (SOC) has played an essential role in sustainable agricultural management. Precise and robust prediction of SOC greatly contributes to carbon neutrality in the agricultural industry. To create more knowledge regarding the ability of remote sensing to monitor carbon soil, this research devises a state-of-the-art low cost machine learning model for quantifying agricultural soil carbon using active and ensemble-based decision tree learning combined with multi-sensor data fusion at a national and world scale. This work explores the use of Sentinel-1 (S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and an innovative machine learning (ML) approach using an integration of active learning for land-use mapping and advanced Extreme Gradient Boosting (XGBoost) for robustness of the SOC estimates. The collected soil samples from a field survey in Western Australia were used for the model validation. The indicators including the coefficient of determination (R2) and root - mean - square - error (RMSE) were applied to evaluate the model's performance. A numerous features computed from optical and SAR data fusion were employed to build and test the proposed model performance. The effectiveness of the proposed machine learning model was assessed by comparing with the two well-known algorithms such as Random Forests (RF) and Support Vector Machine (SVM) to predict agricultural SOC. Results suggest that a combination of S1 and S2 sensors could effectively estimate SOC in farming areas by using ML techniques. Satisfactory accuracy of the proposed XGBoost with optimal features was achieved the highest performance (R2 = 0.870; RMSE = 1.818 tonC/ha) which outperformed RF and SVM. Thus, multi-sensor data fusion combined with the XGBoost lead to the best prediction results for agricultural SOC at 10 m spatial resolution. In short, this new approach could significantly contribute to var...
Nguyen, T-T-D, Bui, X-T, Nguyen, T-T, Hao Ngo, H, Yi Andrew Lin, K, Lin, C, Le, L-T, Dang, B-T, Bui, M-H & Varjani, S 2022, 'Co-culture of microalgae-activated sludge in sequencing batch photobioreactor systems: Effects of natural and artificial lighting on wastewater treatment', Bioresource Technology, vol. 343, pp. 126091-126091.
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Nguyen, TV 2022, 'Personalised assessment of fracture risk: Which tool to use?', Australian Journal of General Practice, vol. 51, no. 3, pp. 189-190.
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Nguyen, XC, Nguyen, TTH, Le, QV, Le, PC, Srivastav, AL, Pham, QB, Nguyen, PM, La, DD, Rene, ER, Ngo, HH, Chang, SW & Nguyen, DD 2022, 'Developing a new approach for design support of subsurface constructed wetland using machine learning algorithms', Journal of Environmental Management, vol. 301, pp. 113868-113868.
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Knowing the effluent quality of treatment systems in advance to enable the design of treatment systems that comply with environmental standards is a realistic strategy. This study aims to develop machine learning - based predictive models for designing the subsurface constructed wetlands (SCW). Data from the SCW literature during the period of 2009-2020 included 618 sets and 10 features. Five algorithms namely, Random forest, Classification and Regression trees, Support vector machines, K-nearest neighbors, and Cubist were compared to determine an optimal algorithm. All nine input features including the influent concentrations, C:N ratio, hydraulic loading rate, height, aeration, flow type, feeding, and filter type were confirmed as relevant features for the predictive algorithms. The comparative result revealed that Cubist is the best algorithm with the lowest RMSE (7.77 and 21.77 mg.L-1 for NH4-N and COD, respectively) corresponding to 84% of the variance in the effluents explained. The coefficient of determination of the Cubist algorithm obtained for NH4-N and COD prediction from the test data were 0.92 and 0.93, respectively. Five case studies of the application of SCW design were also exercised and verified by the prediction model. Finally, a fully developed Cubist algorithm-based design tool for SCW was proposed.
Ni, M, Wang, C, Zhu, T, Yu, S & Liu, W 2022, 'Attacking neural machine translations via hybrid attention learning', Machine Learning, vol. 111, no. 11, pp. 3977-4002.
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AbstractDeep-learning based natural language processing (NLP) models are proven vulnerable to adversarial attacks. However, there is currently insufficient research that studies attacks to neural machine translations (NMTs) and examines the robustness of deep-learning based NMTs. In this paper, we aim to fill this critical research gap. When generating word-level adversarial examples in NLP attacks, there is a conventional trade-off in existing methods between the attacking performance and the amount of perturbations. Although some literature has studied such a trade-off and successfully generated adversarial examples with a reasonable amount of perturbations, it is still challenging to generate highly successful translation attacks while concealing the changes to the texts. To this end, we propose a novel Hybrid Attentive Attack method to locate language-specific and sequence-focused words, and make semantic-aware substitutions to attack NMTs. We evaluate the effectiveness of our attack strategy by attacking three high-performing translation models. The experimental results show that our method achieves the highest attacking performance compared with other existing attacking strategies.
Ni, Q, Ji, JC, Feng, K & Halkon, B 2022, 'A fault information-guided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis', Mechanical Systems and Signal Processing, vol. 164, pp. 108216-108216.
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Being an effective methodology to adaptatively decompose a multi-component signal into a series of amplitude-modulated-frequency-modulated (AMFM) sub-signals with limited bandwidth, the variational mode decomposition (VMD) has received increasing attention in the diagnosis of rolling element bearings. In implementing VMD, an optimal determination of decomposition parameters, including the mode number and bandwidth control parameter, is the pivotal starting point. However, in practical engineering, heavy background noise, abnormal impulses and vibration interferences from other internal components, often bring great challenges in selecting mode number and bandwidth control parameter. These issues may lead to the performance degradation of VMD for bearing fault diagnosis. Therefore, a fault information-guided VMD (FIVMD) method is proposed in this paper for extracting the weak bearing repetitive transient. To minimize the effects of background noise and/or interferences from other components, two nested statistical models based on the fault cyclic information, incorporated with the statistical threshold at a specific significance level, are used to approximately determine the mode number. Then the ratio of fault characteristic amplitude (RFCA) is defined and utilized to identify the optimal bandwidth control parameter, through which the maximum fault information is extracted. Finally, comparisons with the original VMD, empirical mode decomposition (EMD) and local mean decomposition (LMD) are conducted using both simulation and experimental datasets. Successful fault diagnosis of rolling element bearings under complicated operating conditions, including early bearing fault signals in run-to-failure test datasets, signals with impulsive noise and planet bearing signals, demonstrates that the proposed FIVMD is a superior approach in extracting weak bearing repetitive transients.
Ni, W, Liu, W, Zhao, Z, Yuan, X, Sun, Y, Zhang, H, Wang, L, Zhou, M, Yin, P & Xu, J 2022, 'Body Mass Index and Mortality in Chinese Older Adults —New Evidence from a Large Prospective Cohort in China', The Journal of nutrition, health and aging, vol. 26, no. 6, pp. 628-636.
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Ni, W, Zhu, G, Liu, F, Xie, C, Li, W & Zhu, S 2022, 'Rapid profiling of carboxylic acids in reservoir biodegraded crude oils using gas purge microsyringe extraction coupled to comprehensive two-dimensional gas chromatography-mass spectrometry', Fuel, vol. 316, pp. 123312-123312.
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Ni, Z, Zhang, JA, Yang, K, Huang, X & Tsiftsis, TA 2022, 'Multi-Metric Waveform Optimization for Multiple-Input Single-Output Joint Communication and Radar Sensing', IEEE Transactions on Communications, vol. 70, no. 2, pp. 1276-1289.
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Nichols, E, Steinmetz, JD, Vollset, SE, Fukutaki, K, Chalek, J, Abd-Allah, F, Abdoli, A, Abualhasan, A, Abu-Gharbieh, E, Akram, TT, Al Hamad, H, Alahdab, F, Alanezi, FM, Alipour, V, Almustanyir, S, Amu, H, Ansari, I, Arabloo, J, Ashraf, T, Astell-Burt, T, Ayano, G, Ayuso-Mateos, JL, Baig, AA, Barnett, A, Barrow, A, Baune, BT, Béjot, Y, Bezabhe, WMM, Bezabih, YM, Bhagavathula, AS, Bhaskar, S, Bhattacharyya, K, Bijani, A, Biswas, A, Bolla, SR, Boloor, A, Brayne, C, Brenner, H, Burkart, K, Burns, RA, Cámera, LA, Cao, C, Carvalho, F, Castro-de-Araujo, LFS, Catalá-López, F, Cerin, E, Chavan, PP, Cherbuin, N, Chu, D-T, Costa, VM, Couto, RAS, Dadras, O, Dai, X, Dandona, L, Dandona, R, De la Cruz-Góngora, V, Dhamnetiya, D, Dias da Silva, D, Diaz, D, Douiri, A, Edvardsson, D, Ekholuenetale, M, El Sayed, I, El-Jaafary, SI, Eskandari, K, Eskandarieh, S, Esmaeilnejad, S, Fares, J, Faro, A, Farooque, U, Feigin, VL, Feng, X, Fereshtehnejad, S-M, Fernandes, E, Ferrara, P, Filip, I, Fillit, H, Fischer, F, Gaidhane, S, Galluzzo, L, Ghashghaee, A, Ghith, N, Gialluisi, A, Gilani, SA, Glavan, I-R, Gnedovskaya, EV, Golechha, M, Gupta, R, Gupta, VB, Gupta, VK, Haider, MR, Hall, BJ, Hamidi, S, Hanif, A, Hankey, GJ, Haque, S, Hartono, RK, Hasaballah, AI, Hasan, MT, Hassan, A, Hay, SI, Hayat, K, Hegazy, MI, Heidari, G, Heidari-Soureshjani, R, Herteliu, C, Househ, M, Hussain, R, Hwang, B-F, Iacoviello, L, Iavicoli, I, Ilesanmi, OS, Ilic, IM, Ilic, MD, Irvani, SSN, Iso, H, Iwagami, M, Jabbarinejad, R, Jacob, L, Jain, V, Jayapal, SK, Jayawardena, R, Jha, RP, Jonas, JB, Joseph, N, Kalani, R, Kandel, A, Kandel, H, Karch, A, Kasa, AS, Kassie, GM, Keshavarz, P, Khan, MAB, Khatib, MN, Khoja, TAM, Khubchandani, J, Kim, MS, Kim, YJ, Kisa, A, Kisa, S, Kivimäki, M, Koroshetz, WJ, Koyanagi, A, Kumar, GA, Kumar, M, Lak, HM, Leonardi, M, Li, B, Lim, SS, Liu, X, Liu, Y, Logroscino, G, Lorkowski, S, Lucchetti, G, Lutzky Saute, R, Magnani, FG, Malik, AA, Massano, J, Mehndiratta, MM, Menezes, RG, Meretoja, A, Mohajer, B, Mohamed Ibrahim, N, Mohammad, Y, Mohammed, A, Mokdad, AH, Mondello, S, Moni, MAA, Moniruzzaman, M, Mossie, TB, Nagel, G, Naveed, M, Nayak, VC, Neupane Kandel, S, Nguyen, TH, Oancea, B, Otstavnov, N, Otstavnov, SS, Owolabi, MO, Panda-Jonas, S, Pashazadeh Kan, F, Pasovic, M, Patel, UK, Pathak, M, Peres, MFP, Perianayagam, A, Peterson, CB, Phillips, MR & et al. 2022, 'Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019', The Lancet Public Health, vol. 7, no. 2, pp. e105-e125.
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BACKGROUND: Given the projected trends in population ageing and population growth, the number of people with dementia is expected to increase. In addition, strong evidence has emerged supporting the importance of potentially modifiable risk factors for dementia. Characterising the distribution and magnitude of anticipated growth is crucial for public health planning and resource prioritisation. This study aimed to improve on previous forecasts of dementia prevalence by producing country-level estimates and incorporating information on selected risk factors. METHODS: We forecasted the prevalence of dementia attributable to the three dementia risk factors included in the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 (high body-mass index, high fasting plasma glucose, and smoking) from 2019 to 2050, using relative risks and forecasted risk factor prevalence to predict GBD risk-attributable prevalence in 2050 globally and by world region and country. Using linear regression models with education included as an additional predictor, we then forecasted the prevalence of dementia not attributable to GBD risks. To assess the relative contribution of future trends in GBD risk factors, education, population growth, and population ageing, we did a decomposition analysis. FINDINGS: We estimated that the number of people with dementia would increase from 57·4 (95% uncertainty interval 50·4-65·1) million cases globally in 2019 to 152·8 (130·8-175·9) million cases in 2050. Despite large increases in the projected number of people living with dementia, age-standardised both-sex prevalence remained stable between 2019 and 2050 (global percentage change of 0·1% [-7·5 to 10·8]). We estimated that there were more women with dementia than men with dementia globally in 2019 (female-to-male ratio of 1·69 [1·64-1·73]), and we expect this pattern to continue to 2050 (female-to-male ratio of 1·67 [1·52-1·85]). There was geographical heterogeneity in the ...
Nie, X, Zhang, A, Chen, J, Qu, Y & Yu, S 2022, 'Blockchain-Empowered Secure and Privacy-Preserving Health Data Sharing in Edge-Based IoMT', Security and Communication Networks, vol. 2022, pp. 1-16.
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Health data sharing, as a booming demand, enables the patients with similar symptoms to connect with each other and doctors to obtain the medical history of patients. Health data are usually collected from edge-based Internet of medical things (IoMT) with devices such as smart wearable devices, smart watches, and smartphones. Since health data are highly private and have great financial value, adversaries ceaselessly launch diverse attacks to obtain private information. All these issues pose great challenges to health data sharing in edge-based IoMT scenarios. Existing research either lacks comprehensive consideration of privacy and security protection or fails to provide a proper incentive mechanism, which expels users from sharing data. In this study, we propose a novel blockchain-assisted data sharing scheme, which allows secure and privacy-preserving profile matching. A bloom filter with hash functions is designed to verify the authenticity of keyword ciphertext. Key-policy attribute-based encryption (KP-ABE) algorithm and smart contracts are employed to achieve secure profile matching. To incentivize users actively participating in profile matching, we devise an incentive mechanism and construct a two-phase Stackelberg game to address pricing problems for data owners and accessing problems of data requesters. The optimal pricing mechanism is specially designed for encouraging more users to participate in health data sharing and maximizing users’ profit. Moreover, security analysis illustrates that the proposed protocol is capable of satisfying various security goals, while performance evaluation shows high scalability and feasibility of the proposed scheme in edge-based IoMT scenarios.
Nie, X, Zhang, A, Chen, J, Qu, Y & Yu, S 2022, 'Time-Enabled and Verifiable Secure Search for Blockchain-Empowered Electronic Health Record Sharing in IoT', Security and Communication Networks, vol. 2022, pp. 1-15.
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The collection and sharing of electronic health records (EHRs) via the Internet of Things (IoT) can enhance the accuracy of disease diagnosis. However, it is challenging to guarantee the secure search of EHR during the sharing process. The advent of blockchain is a promising solution to address the issues, owing to its remarkable features such as immutability and anonymity. In this paper, we propose a novel blockchain-based secure sharing system over searchable encryption and hidden data structure via IoT devices. EHR ciphertexts of data owners are stored in the interplanetary file system (IPFS). A user with proper access permissions can search for the desired data with the data owner’s time-bound authorization and verify the authenticity of the search result. After that, the data user can access the relevant EHR ciphertext from IPFS using a symmetric key. The scheme jointly uses searchable encryption and smart contract to realize secure search, time control, verifiable keyword search, fast search, and forward privacy in IoT scenarios. Performance analysis and proof demonstrate that the proposed protocol can satisfy the design goals. In addition, performance evaluation shows the high scalability and feasibility of the proposed scheme.
Nimmy, SF, Hussain, OK, Chakrabortty, RK, Hussain, FK & Saberi, M 2022, 'Explainability in supply chain operational risk management: A systematic literature review', Knowledge-Based Systems, vol. 235, pp. 107587-107587.
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It is important to manage operational disruptions to ensure the success of supply chain operations. To achieve this aim, researchers have developed techniques that determine the occurrence of operational risk events which assists supply chain operational risk managers develop plans to manage them by detection/monitoring, mitigation/management, or optimization techniques. Various artificial intelligence (AI) approaches have been used to develop such techniques in the broad activities of operational risk management. However, all of these techniques are black box in their working nature. This means that the chosen technique cannot explain why it has given that output and whether it is correct and free from bias. To address this, researchers argue the need for supply chain management professionals to move towards using explainable AI methods for operational risk management. In this paper, we conduct a systematic literature review on the techniques used to determine operational risks and analyse whether they satisfy the requirement of them being explainable. The findings highlight the shortcomings and inspires directions for future research. From a managerial perspective, the paper encourages risk managers to choose techniques for supply chain operational risk management that can be auditable as this will ensure that the risk managers know why they should take a particular risk management action rather than just what they should do to manage the operational risks.
Nithya, R, Santhi, B, Manikandan, R, Rahimi, M & Gandomi, AH 2022, 'Computer Vision System for Mango Fruit Defect Detection Using Deep Convolutional Neural Network', Foods, vol. 11, no. 21, pp. 3483-3483.
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Machine learning techniques play a significant role in agricultural applications for computerized grading and quality evaluation of fruits. In the agricultural domain, automation improves the quality, productivity, and economic growth of a country. The quality grading of fruits is an essential measure in the export market, especially defect detection of a fruit’s surface. This is especially pertinent for mangoes, which are highly popular in India. However, the manual grading of mango is a time-consuming, inconsistent, and subjective process. Therefore, a computer-assisted grading system has been developed for defect detection in mangoes. Recently, machine learning techniques, such as the deep learning method, have been used to achieve efficient classification results in digital image classification. Specifically, the convolution neural network (CNN) is a deep learning technique that is employed for automated defect detection in mangoes. This study proposes a computer-vision system, which employs CNN, for the classification of quality mangoes. After training and testing the system using a publicly available mango database, the experimental results show that the proposed method acquired an accuracy of 98%.
Niu, K, Guo, Z, Peng, X & Pei, S 2022, 'P-ResUnet: Segmentation of brain tissue with Purified Residual Unet', Computers in Biology and Medicine, vol. 151, no. Pt B, pp. 106294-106294.
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Brain tissue of Magnetic Resonance Imaging is precisely segmented and quantified, which aids in the diagnosis of neurological diseases such as epilepsy, Alzheimer's, and multiple sclerosis. Recently, UNet-like architectures are widely used for medical image segmentation, which achieved promising performance by using the skip connection to fuse the low-level and high-level information. However, In the process of integrating the low-level and high-level information, the non-object information (noise) will be added, which reduces the accuracy of medical image segmentation. Likewise, the same problem also exists in the residual unit. Since the output and input of the residual unit are fused, the non-object information (noise) of the input of the residual unit will be in the integration. To address this challenging problem, in this paper we propose a Purified Residual U-net for the segmentation of brain tissue. This model encodes the image to obtain deep semantic information and purifies the information of low-level features and the residual unit from the image, and acquires the result through a decoder at last. We use the Dilated Pyramid Separate Block (DPSB) as the first block to purify the features for each layer in the encoder without the first layer, which expands the receptive field of the convolution kernel with only a few parameters added. In the first layer, we have explored the best performance achieved with DPB. We find the most non-object information (noise) in the initial image, so it is good for the accuracy to exchange the information to the max degree. We have conducted experiments with the widely used IBSR-18 dataset composed of T-1 weighted MRI volumes from 18 subjects. The results show that compared with some of the cutting-edge methods, our method enhances segmentation performance with the mean dice score reaching 91.093% and the mean Hausdorff distance decreasing to 3.2606.
Niu, K, Lu, Y, Peng, X & Zeng, J 2022, 'Fusion of sequential visits and medical ontology for mortality prediction', Journal of Biomedical Informatics, vol. 127, pp. 104012-104012.
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The goal of mortality prediction task is to predict the future death risk of patients according to their previous Electronic Healthcare Records (EHR). The main challenge of mortality prediction is how to design an accurate and robust predictive model with sequential, multivariate, sparse and irregular EHR data. In addition, the performance of model may be affected by lack of sufficient information of some patients with rare diseases in EHRs. To address these challenges, we propose a model to fuse Sequential visits and Medical Ontology to predict patients' death risk. SeMO not only learns reasonable embeddings for medical concepts from sequential and irregular visits, but also exploits medical ontology to improve the prediction performance. With integration of multivariate features, SeMO learns robust representations of medical codes, mitigating data insufficiency and insightful sequential dependencies among patient's visits. Experimental results on real world datasets prove that the proposed SeMO improves the prediction performance compared with the baseline approaches. Our model achieves an precision of up to 0.975. Compared with RNN, the precision has been improved up to 2.204%.
Nizami, S, Tushar, W, Hossain, MJ, Yuen, C, Saha, T & Poor, HV 2022, 'Transactive energy for low voltage residential networks: A review', Applied Energy, vol. 323, pp. 119556-119556.
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Transactive Energy (TE) is envisaged as an advanced demand response (DR) variant to leverage the flexibility of distributed energy resources (DERs) for enhancing energy balance and network management in modern power systems. However, there have been limited implementations of TE frameworks for low voltage (LV) residential networks to capture the underutilised flexibility potential of DER-equipped residential prosumers. The main purpose of this paper is to identify the rationale behind this gap in light of recent advances in TE-based energy management for residential networks. As such, first, we identify the motivation and significance of the evolution of TE framework from traditional DR schemes by reviewing their relative efficacies in utilising demand-side flexibility of DER-rich residential networks for enhancing energy balance and local network management. Second, we provide an overview of the key components of the TE framework that are essential to facilitate active negotiation and trading of demand-side flexibility in residential networks. Third, we review the state-of-the-art TE methodologies and industry projects that have utilised demand-side flexibility of residential prosumers. Finally, several challenges relevant to TE frameworks in LV residential networks are identified followed by some concluding remarks at the end of the paper.
Norouzian-Maleki, P, Izadbakhsh, H, Saberi, M, Hussain, O, Jahangoshai Rezaee, M & GhanbarTehrani, N 2022, 'An integrated approach to system dynamics and data envelopment analysis for determining efficient policies and forecasting travel demand in an urban transport system', Transportation Letters, vol. 14, no. 2, pp. 157-173.
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Nouhi, B, Jahani, Y, Talatahari, S & Gandomi, AH 2022, 'A swarm optimizer with modified feasible-based mechanism for optimum structure in steel industry', Decision Analytics Journal, vol. 5, pp. 100129-100129.
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Nouhi, B, Khodadadi, N, Azizi, M, Talatahari, S & Gandomi, AH 2022, 'Multi-Objective Material Generation Algorithm (MOMGA) for Optimization Purposes', IEEE Access, vol. 10, pp. 107095-107115.
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Nur Azreena, I, Lau, HLN, Asikin-Mijan, N, Hassan, MA, Izham, SM, Safa Gamal, M, Nor Adira Wan Khalit, W, Arumugam, M, Kennedy, E, Stockenhuber, M & Taufiq-Yap, YH 2022, 'Hydrodeoxygenation of fatty acid over La-modified HZSM5 for premium quality renewable diesel production', Journal of Analytical and Applied Pyrolysis, vol. 161, pp. 105406-105406.
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Nuruzzaman, M, Liu, Y, Ren, J, Rahman, MM, Zhang, H, Hasan Johir, MA, Shon, HK & Naidu, R 2022, 'Capability of Organically Modified Montmorillonite Nanoclay as a Carrier for Imidacloprid Delivery', ACS Agricultural Science & Technology, vol. 2, no. 1, pp. 57-68.
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Organically modified clays have attracted increasing research attention for their various commercial and industrial applications, such as being carriers for pesticide delivery. Besides, the suitability and performance of commercially available organoclays could further promote their applicability. Hence, this study investigated the potential application of a commercially available alkylamine-modified montmorillonite (MMT) nanoclay as a carrier for a widely used insecticide, imidacloprid. X-ray diffraction and thermogravimetric analysis were employed to illustrate the arrangement, orientation, and conformation of surface-modifying agents (SMAs) on MMT nanoclay. It was observed that the clay was modified at an ∼1.0 cation exchange capacity, with the SMAs, especially octadecylamine, arranged in the MMT nanoclay as a bilayer to a pseudo-trilayer or a paraffin monolayer with a tilting angle of ∼25°, which indicated the nanoclay’s ability to adsorb a large amount of imidacloprid. The adsorption-desorption of imidacloprid to MMT nanoclay further confirmed this applicability. A high adsorption capacity (∼85 mg g-1) was observed, with a high reversibility in desorption, showing a hysteresis value of 0.75. Further, the adsorption kinetics and response of the nanoclay to imidacloprid revealed that, initially, a rapid sorption occurred due to a hydrophobic interaction. This was followed by a slower diffusion-controlled sorption due to hydrogen bonding to the internal binding sites. The releasing pattern of imidacloprid from the MMT nanoclay indicated its potential for the preparation of a slow-releasing pesticide formulation where the nanoclay will reduce the instantaneous release of the total amount of pesticide.
Nuvoli, S, Pietroni, N, Cignoni, P, Scateni, R & Tarini, M 2022, 'SkinMixer', ACM Transactions on Graphics, vol. 41, no. 6, pp. 1-15.
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We propose a novel technique to compose new 3D animated models, such as videogame characters, by combining pieces from existing ones. Our method works on production-ready rigged, skinned, and animated 3D models to reassemble new ones. We exploit mix-and-match operations on the skeletons to trigger the automatic creation of a new mesh, linked to the new skeleton by a set of skinning weights and complete with a set of animations. The resulting model preserves the quality of the input meshings (which can be quad-dominant and semi-regular), skinning weights (inducing believable deformation), and animations, featuring coherent movements of the new skeleton. Our method enables content creators to reuse valuable, carefully designed assets by assembling new ready-to-use characters while preserving most of the hand-crafted subtleties of models authored by digital artists. As shown in the accompanying video, it allows for drastically cutting the time needed to obtain the final result.
Nuvoli, S, Pietroni, N, Cignoni, P, Scateni, R & Tarini, M 2022, 'SkinMixer: Blending 3D Animated Models.', ACM Trans. Graph., vol. 41, pp. 250:1-250:1.
O’Brien, K, Sood, S & Shete, R 2022, 'Big Data Approach to Visualising, Analysing and Modelling Company Culture: A New Paradigm and Tool for Exploring Toxic Cultures and the Way We Work', THE INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND BUSINESS ADMINISTRATION, vol. 8, no. 2, pp. 48-61.
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This paper explores the use of big data to measure company culture, good and bad, including toxic culture. Culture is a central factor driving employee experiences and contributing to the “great resignation”. Harnessing the key Artificial Intelligence (AI) technology of neural networks using deep learning methodology for NLP provides the capability to extract cultural meanings from a diverse array of organizational information and cultural artefacts ( texts, images, speech and video) available online. Using big data and AI provides a predictive capability surpassing the value of employee survey instruments of the last century providing a rear view of insights. Big data helps break free from the paradigm of only thinking about culture moving at a glacial pace. An innovative methodology and AI technologies help measure and visually plot the organizational culture trajectory within a company cultural landscape. Cultural values, inclusive of toxicity, have the potential for detection across all forms of communications media. A non-invasive approach using a broad range of open data sources overcomes limitations of the traditional survey instruments and approaches for achieving a culture read. The benefits of the approach and the AI technology are the real-time ingestion of ongoing executive and managerial feedback while entirely sidestepping the issues of survey biases and viable samples. The methodology under study for reading a culture moves well beyond traditional text-centric searches, content analyses, dictionaries and text mining, delivering an understanding of the meanings of words, phrases, sentences or even concepts comprising company culture. Embeddings are an ideal neural network breakthrough technology enabling the computation of text as data through creating a meaningful space in which similar word meanings exist in close proximity. Vector algebra in a multidimensional space helps unpack the cultural nuances and biases pent up within the...
Ocreto, JB, Chen, W-H, Rollon, AP, Chyuan Ong, H, Pétrissans, A, Pétrissans, M & De Luna, MDG 2022, 'Ionic liquid dissolution utilized for biomass conversion into biofuels, value-added chemicals and advanced materials: A comprehensive review', Chemical Engineering Journal, vol. 445, pp. 136733-136733.
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Olszak, CM, Zurada, J & Kozanoglu, DC 2022, 'Introduction to the Business Intelligence & Big Data for Innovative and Sustainable Development of Organizations Mini-track', Proceedings of the Annual Hawaii International Conference on System Sciences, vol. 2022-January, pp. 264-265.
Olszak, CM, Zurada, JM & Cetindamar, D 2022, 'Business Intelligence & Big Data for Innovative and Sustainable Development of Organizations: Special Issue (SI) Editors: Celina M. Olszak, Jozef Zurada, Dilek Cetindamar.', Inf. Syst. Manag., vol. 39, no. 1, pp. 2-2.
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Omar, KR, Fatahi, B & Nguyen, LD 2022, 'Impacts of Pre-contamination Moisture Content on Mechanical Properties of High-Plasticity Clay Contaminated with Used Engine Oil', Journal of Testing and Evaluation, vol. 50, no. 6, pp. 3001-3027.
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ABSTRACT The oil contamination of soils and the remediation techniques to enhance the engineering properties of the ground have been an emerging challenge in the geoenvironmental field. While several studies were conducted to examine the behavior of the contaminated granular soils, little is known about the mechanical properties of the oil-contaminated clays. This paper investigates the impacts of the in situ pre-contamination moisture content (PMC) on the behavior of fine-grained soil contaminated with various levels of used engine oil. Extensive laboratory experiments were performed on sandy clay with different initial moisture conditions and various amounts of used engine oil varying from 0 to 16 %. The experimental results, including the Atterberg limits, linear shrinkage (LS), unconfined compressive strength, shear strength, and small-strain shear modulus in conjunction with microstructural image analysis, were reported and discussed. It is observed that when oil content was increased, both LS and plastic limit (PL) increased while the liquid limit decreased in the contaminated soil. Moreover, the inclusion of engine oil contributed to the reduction in the plasticity index, which was also impacted by the PMC of the soil. An increment in the PL was correlated with a significant decrease in shear strength, shear modulus, and other associated parameters such as friction angle and cohesion. In agreement with the results, a broader range of elasticity and improved stability at the microstructure level was associated with a lower pre-contamination water content (PMC). Overall, this paper shows that knowledge of site moisture levels before contamination is essential to evaluate the implications of contamination by used engine oil.
Oner, O & Khalilpour, K 2022, 'Evaluation of green hydrogen carriers: A multi-criteria decision analysis tool', Renewable and Sustainable Energy Reviews, vol. 168, pp. 112764-112764.
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Ong, Y, Loban, R & Rauno, P 2022, 'The Fight is the Dance: Modding Chinese Martial Arts and Culture into Beat Saber', Journal of Games Criticism, vol. 5, no. Bonus Issue A: Surviving Whiteness in Games.
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The article explores the design process of a Beat Saber mod called Good Bag. In the Good Bag mod level, players perform saber-sword Wushu martial art moves to the rhythm of contemporary Wuxia-influenced (Chinese martial arts fantasy) music. The vocals version of the mod is also commentary on contemporary racism as well as on historical race-relations. The Good Bag project drew upon two expert cultural practitioners, a martial arts Grandmaster and a musician, from the Chinese diaspora community to strongly shape the mod design and output. The article also discusses the limitations of modding martial arts into Beat Saber. The article outlines the project team’s planned approach to further improve the mod via a dual expert and player design approach using both expert opinion and player feedback. In the next stages of the project, the mod project teams plan to access how effective the mod has been in teaching martial arts, as well as how this might influence attitudes in historical and contemporary race relations.
Ong, YR, Cao, S, Lee, SS, Lim, CS, Chen, MM, Kurdkandi, NV, Barzegarkhoo, R & Siwakoti, YP 2022, 'A Dual-Buck-Boost DC–DC/AC Universal Converter', Electronics, vol. 11, no. 13, pp. 1973-1973.
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This paper proposes a universal converter that is capable of operating in three modes for generating positive dc voltage, negative dc voltage, and sinusoidal ac voltage. By controlling the duty-cycle of two half-bridges, an inductor is operated at a high frequency to control the voltage across two film capacitors that constitute a dual-buck-boost converter. Two additional half-bridges operating at a fixed state or line frequency are used to select the mode of operation. Compared to the latest universal converter in the recent literature, the proposed topology has the same switch count while reducing the number of conducting switches for inductor current and reducing the number of switches operating at high frequency. The operation of the proposed dual-buck-boost dc–dc/ac universal converter is analyzed. Experimental results are presented for validation. The power conversion efficiency of the 100 W experimental prototype modeled in PLECS is approximately 98%.
Onggowarsito, C, Feng, A, Mao, S, Nguyen, LN, Xu, J & Fu, Q 2022, 'Water Harvesting Strategies through Solar Steam Generator Systems', ChemSusChem, vol. 15, no. 23, p. e202201543.
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AbstractSolar steam generator (SSG) systems have attracted increasing attention, owing to its simple manufacturing, material abundance, cost‐effectiveness, and environmentally friendly freshwater production. This system relies on photothermic materials and water absorbing substrates for a clean continuous distillation process. To optimize this process, there are factors that are needed to be considered such as selection of solar absorber and water absorbent materials, followed by micro/macro‐structural system design for efficient water evaporation, floating, and filtration capability. In this contribution, we highlight the general interfacial SSG concept, review and compare recent progresses of different SSG systems, as well as discuss important factors on performance optimization. Furthermore, unaddressed challenges such as SSG's cost to performance ratio, filtration of untreatable micropollutants/microorganisms, and the need of standardization testing will be discussed to further advance future SSG studies.
Onggowarsito, C, Feng, A, Mao, S, Zhang, S, Ibrahim, I, Tijing, L, Fu, Q & Ngo, HH 2022, 'Development of an innovative MnO2 nanorod for efficient solar vapor generator', Environmental Functional Materials, vol. 1, no. 2, pp. 196-203.
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Orcesi, A, O'Connor, A, Bastidas-Arteaga, E, Stewart, MG, Imam, B, Kreislova, K, Schoefs, F, Markogiannaki, O, Wu, T, Li, Y, Salman, A, Hawchar, L & Ryan, PC 2022, 'Investigating the Effects of Climate Change on Material Properties and Structural Performance', Structural Engineering International, vol. 32, no. 4, pp. 577-588.
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Ortega-Delgado, B, Palenzuela, P, Altaee, A, Alarcón-Padilla, D-C, Hawari, AH & Zaragoza, G 2022, 'Thermo-economic assessment of forward osmosis as pretreatment to boost the performance and sustainability of multi-effect distillation for seawater desalination', Desalination, vol. 541, pp. 115989-115989.
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Osatiashtiani, A, Orr, SA, Durndell, LJ, García, IC, Merenda, A, Lee, AF & Wilson, K 2022, 'Liquid phase catalytic transfer hydrogenation of ethyl levulinate to γ-valerolactone over ZrO2/SBA-15', Catalysis Science & Technology, vol. 12, no. 18, pp. 5611-5619.
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γ-Valerolactone (GVL) is an important bio-derived platform molecule whose atom- and energy efficient, and scalable, catalytic synthesis is highly desirable.
Ottenhaus, L-M, Li, Z & Crews, K 2022, 'Half hole and full hole dowel embedment Strength: A review of international developments and recommendations for Australian softwoods', Construction and Building Materials, vol. 344, pp. 128130-128130.
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Ouchchen, M, Boutaleb, S, Abia, EH, El Azzab, D, Miftah, A, Dadi, B, Echogdali, FZ, Mamouch, Y, Pradhan, B, Santosh, M & Abioui, M 2022, 'Exploration targeting of copper deposits using staged factor analysis, geochemical mineralization prospectivity index, and fractal model (Western Anti-Atlas, Morocco)', Ore Geology Reviews, vol. 143, pp. 104762-104762.
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Oyelade, ON, Ezugwu, AE, Venter, HS, Mirjalili, S & Gandomi, AH 2022, 'Abnormality classification and localization using dual-branch whole-region-based CNN model with histopathological images', Computers in Biology and Medicine, vol. 149, pp. 105943-105943.
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Pal, PK, Jana, KC, Siwakoti, YP, Majumdar, S & Blaabjerg, F 2022, 'An Active-Neutral-Point-Clamped Switched-Capacitor Multilevel Inverter With Quasi-Resonant Capacitor Charging', IEEE Transactions on Power Electronics, vol. 37, no. 12, pp. 14888-14901.
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Pan, T, Jiang, Z, Han, J, Wen, S, Men, A & Wang, H 2022, 'Taylor saves for later: Disentanglement for video prediction using Taylor representation', Neurocomputing, vol. 472, pp. 166-174.
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Video prediction is a challenging task with wide application prospects in meteorology and robot systems. Existing works fail to trade off short-term and long-term prediction performances and extract robust latent dynamics laws in video frames. We propose a two-branch seq-to-seq deep model to disentangle the Taylor feature and the residual feature in video frames by a novel recurrent prediction module (TaylorCell) and residual module, based on a novel principle for feature separation. TaylorCell can expand the video frames’ high-dimensional features into the finite Taylor series to describe the latent laws. In TaylorCell, we propose the Taylor prediction unit (TPU) and the memory correction unit (MCU). TPU employs the first input frame's derivative information to predict the future frames, avoiding error accumulation. MCU distills all past frames’ information to correct the predicted Taylor feature from TPU. Correspondingly, the residual module extracts the residual feature complementary to the Taylor feature. Due to the characteristic of the Taylor series, our model works better on datasets with short-range spatial dependencies and stable dynamics. On three generalist datasets (Moving MNIST, TaxiBJ, Human 3.6), our model reaches and outperforms the state-of-the-art model in the short-term and long-term forecast, respectively. Ablation experiments demonstrate the contributions of each module in our model.
Pan, Y, Tsang, IW, Chen, W, Niu, G & Sugiyama, M 2022, 'Fast and Robust Rank Aggregation against Model Misspecification', Journal of Machine Learning Research, vol. 23.
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In rank aggregation (RA), a collection of preferences from different users are summarized into a total order under the assumption of homogeneity of users. Model misspecification in RA arises since the homogeneity assumption fails to be satisfied in the complex real-world situation. Existing robust RAs usually resort to an augmentation of the ranking model to account for additional noises, where the collected preferences can be treated as a noisy perturbation of idealized preferences. Since the majority of robust RAs rely on certain perturbation assumptions, they cannot generalize well to agnostic noise-corrupted preferences in the real world. In this paper, we propose CoarsenRank, which possesses robustness against model misspecification. Specifically, the properties of our CoarsenRank are summarized as follows: (1) CoarsenRank is designed for mild model misspecification, which assumes there exist the ideal preferences (consistent with model assumption) that locate in a neighborhood of the actual preferences. (2) CoarsenRank then performs regular RAs over a neighborhood of the preferences instead of the original data set directly. Therefore, CoarsenRank enjoys robustness against model misspecification within a neighborhood. (3) The neighborhood of the data set is defined via their empirical data distributions. Further, we put an exponential prior on the unknown size of the neighborhood, and derive a much-simplified posterior formula for CoarsenRank under particular divergence measures. (4) CoarsenRank is further instantiated to Coarsened Thurstone, Coarsened Bradly-Terry, and Coarsened Plackett-Luce with three popular probability ranking models. Meanwhile, tractable optimization strategies are introduced with regards to each instantiation respectively. In the end, we apply CoarsenRank on four real-world data sets. Experiments show that CoarsenRank is fast and robust, achieving consistent improvements over baseline methods.
Pan, Z, Xiao, Y, Cao, Y, Zhou, L & Chen, W 2022, 'Accurate optical information transmission through thick tissues using zero-frequency modulation and single-pixel detection', Optics and Lasers in Engineering, vol. 158, pp. 107133-107133.
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Pan, Z, Xiao, Y, Cao, Y, Zhou, L & Chen, W 2022, 'Optical data transmission through highly dynamic and turbid water using dynamic scaling factors and single-pixel detector', Optics Express, vol. 30, no. 24, pp. 43480-43480.
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Free-space optical data transmission through non-static scattering media, e.g., dynamic and turbid water, is challenging. In this paper, we propose a new method to realize high-fidelity and high-robustness free-space optical data transmission through highly dynamic and turbid water using a series of dynamic scaling factors to correct light intensities recorded by a single-pixel bucket detector. A fixed reference pattern is utilized to obtain the series of dynamic scaling factors during optical data transmission in free space. To verify the proposed method, different turbidity levels, different strengths of water-flow-induced turbulence and a laser with different wavelengths are studied in optical experiments. It is demonstrated that the proposed scheme is robust against water-flow-induced turbulence and turbid water, and high-fidelity free-space optical information transmission is realized at wavelengths of 658.0 nm and 520.0 nm. The proposed method could shed light on the development of high-fidelity and high-robustness free-space optical data transmission through highly dynamic and turbid water.
Panahi, M, Khosravi, K, Golkarian, A, Roostaei, M, Barzegar, R, Omidvar, E, Rezaie, F, Saco, PM, Sharifi, A, Jun, C, Bateni, SM, Lee, C-W & Lee, S 2022, 'A country-wide assessment of Iran's land subsidence susceptibility using satellite-based InSAR and machine learning', Geocarto International, vol. 37, no. 26, pp. 14065-14087.
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Pandya, SB, Ravichandran, S, Manoharan, P, Jangir, P & Alhelou, HH 2022, 'Multi-Objective Optimization Framework for Optimal Power Flow Problem of Hybrid Power Systems Considering Security Constraints', IEEE Access, vol. 10, pp. 103509-103528.
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Pang, G, Shen, C, Cao, L & Hengel, AVD 2022, 'Deep Learning for Anomaly Detection', ACM Computing Surveys, vol. 54, no. 2, pp. 1-38.
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Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e.,deep anomaly detection, has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.
Panta, J, Zhang, YX, Rider, AN & Wang, J 2022, 'Ozone functionalized graphene nanoplatelets and triblock copolymer hybrids as nanoscale modifiers to enhance the mechanical performance of epoxy adhesives', International Journal of Adhesion and Adhesives, vol. 116, pp. 103135-103135.
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Papanicolaou, M, Parker, AL, Yam, M, Filipe, EC, Wu, SZ, Chitty, JL, Wyllie, K, Tran, E, Mok, E, Nadalini, A, Skhinas, JN, Lucas, MC, Herrmann, D, Nobis, M, Pereira, BA, Law, AMK, Castillo, L, Murphy, KJ, Zaratzian, A, Hastings, JF, Croucher, DR, Lim, E, Oliver, BG, Mora, FV, Parker, BL, Gallego-Ortega, D, Swarbrick, A, O’Toole, S, Timpson, P & Cox, TR 2022, 'Temporal profiling of the breast tumour microenvironment reveals collagen XII as a driver of metastasis', Nature Communications, vol. 13, no. 1, p. 4587.
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AbstractThe tumour stroma, and in particular the extracellular matrix (ECM), is a salient feature of solid tumours that plays a crucial role in shaping their progression. Many desmoplastic tumours including breast cancer involve the significant accumulation of type I collagen. However, recently it has become clear that the precise distribution and organisation of matrix molecules such as collagen I is equally as important in the tumour as their abundance. Cancer-associated fibroblasts (CAFs) coexist within breast cancer tissues and play both pro- and anti-tumourigenic roles through remodelling the ECM. Here, using temporal proteomic profiling of decellularized tumours, we interrogate the evolving matrisome during breast cancer progression. We identify 4 key matrisomal clusters, and pinpoint collagen type XII as a critical component that regulates collagen type I organisation. Through combining our proteomics with single-cell transcriptomics, and genetic manipulation models, we show how CAF-secreted collagen XII alters collagen I organisation to create a pro-invasive microenvironment supporting metastatic dissemination. Finally, we show in patient cohorts that collagen XII may represent an indicator of breast cancer patients at high risk of metastatic relapse.
Park, MJ, Akther, N, Phuntsho, S, Naidu, G, Razmjou, A, An, AK & Shon, HK 2022, 'Development of highly permeable self-standing nanocomposite sulfonated poly ether ketone membrane using covalent organic frameworks', Desalination, vol. 538, pp. 115935-115935.
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This study developed a new symmetric and ultrathin membrane by incorporating Schiff base network-1 (SNW-1), which are covalent organic framework (COF) nanoparticles, as fillers in the sulfonated poly ether ketone (SPEK) matrix to improve forward osmosis (FO) performance. The amine-rich and porous SNW-1 nanoparticles enhanced the surface wettability of the SPEK membranes and offered additional passages for the water molecules' transport, which assisted in the elevation of membrane water flux. The modified membrane loaded with 20 wt% SNW-1 (COF-20) exhibited the best performance with a significantly higher water flux (28.5 L m−2 h−1) and lower specific reverse solute flux (SRSF, 0.05 g L−1) than that of the unmodified SPEK (COF-0) membrane (water flux of 12 L m−2 h−1 and SRSF of 0.16 g L−1) when experimented with deionized water and 1 M Na2SO4 as feed and draw solutions, respectively. The impressive FO performances of nanocomposite SPEK membranes suggest that SNW-1 nanoparticles could be used as fillers for improving the SPEK membrane's performance in the FO application.
Park, MJ, Wang, C, Gonzales, RR, Phuntsho, S, Matsuyama, H, Drioli, E & Shon, HK 2022, 'Fabrication of thin film composite polyamide membrane for water purification via inkjet printing of aqueous and solvent inks', Desalination, vol. 541, pp. 116027-116027.
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Parnell, D, Kardailsky, I, Parnell, J, Badgery, WB & Ingram, L 2022, 'Understanding sheep baa‐haviour: Investigating the relationship between pasture and animal grazing patterns', Grassland Research, vol. 1, no. 3, pp. 143-156.
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AbstractBackgroundGrasslands are the primary source of feed for grazing livestock, and as such, knowledge on how to best manage livestock and grasslands, through the use of spatiotemporal modelling, will assist in the long‐term management of a valuable ecosystem resource.MethodsThis study was conducted over 14 months between March and April 2017 in Orange, NSW, Australia. The study evaluated sheep behaviour in relation to the presence of pasture species, environment and paddock structures, using random forest modelling, to predict sheep location under continuous high (HSR, 13 DSE ha−1) and low (LSR, 7 DSE ha−1) stocking rates.ResultsIn the LSR, significant drivers included water, shade and fence lines (p < 0.01). In the HSR, only fence lines and available biomass were found to be significant (p < 0.01). The presence of green legumes in both stocking rates often increased residency by sheep. Animals spent more time together in the LSR, suggesting that social behaviour played a larger role than pasture quantity and quality in driving grazing behaviours.ConclusionsUnderstanding how pasture type can influence grazing behaviours and also how animal behaviour affects pasture performance and utilisation is important in developing long‐term sustainable management strategies on a paddock scale.
Parsa, K, Hassall, M & Naderpour, M 2022, 'Enhancing Alarm Prioritization in the Alarm Management Lifecycle', IEEE Access, vol. 10, pp. 99-111.
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Despite significant improvements being made in control and safety systems, near-miss incidents and adverse accidents continue to occur in the industry. Indeed, humans have a vital role in process control success or failure due to their responses to abnormal situations and alarms. A broad study on the alarm system performance shows that good rationalization and accurate prioritization of alarms should increase the efficacy of alarm systems and improve operator decision performances. This paper discusses current gaps in alarm prioritization approaches. It then proposes a method based on Graph theory and metrics capabilities to facilitate and improve the alarm prioritization process. The method is developed based on the causal and layer of protection modeling, followed by measuring the graph metrics for prioritization purposes. Finally, the proposed method is evaluated through implementation in a simulated case study. Results show that this approach facilitates similar achievement to the alarm workshop and produces more valuable data to the cascade of abnormal situations in a structured method and shorter time.
Parsajoo, M, Armaghani, DJ & Asteris, PG 2022, 'A precise neuro-fuzzy model enhanced by artificial bee colony techniques for assessment of rock brittleness index', Neural Computing and Applications, vol. 34, no. 4, pp. 3263-3281.
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Pasumarthy, N, Patibanda, R, Tai, YLE, van den Hoven, E, Danaher, J & Khot, RA 2022, 'Gooey Gut Trail: Board Game Play to Understand Human-Microbial Interactions', Proceedings of the ACM on Human-Computer Interaction, vol. 6, no. CHI PLAY, pp. 1-31.
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Our gastrointestinal health is influenced by complex interactions between our gut bacteria and multiple external factors. A wider understanding of these concepts is vital to help make gut-friendly decisions in everyday life; however, its complexity can challenge public understanding if not approached systematically. Research suggests that board games can help to playfully navigate complex subjects. We present Gooey Gut Trail (GGT), a board game to help players understand the multifactorial interactions that influence and sustain gut microbial diversity. Through the embodied enactment of in-game activities, players learn how their habits surrounding diet, physical activity, emotions, and lifestyle influence the gut microbial population. A qualitative field study with 15 participants revealed important facets of our game design that increased participants' awareness, causing them to reflect upon their habits that influence gut health. Drawing upon the study insights, we present five design considerations to aid future playful explorations on nurturing human-microbial relationships.
Patan, R, Kallam, S, Gandomi, AH, Hanne, T & Ramachandran, M 2022, 'Gaussian relevance vector MapReduce-based annealed Glowworm optimization for big medical data scheduling', Journal of the Operational Research Society, vol. 73, no. 10, pp. 2204-2215.
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Patan, R, Manikandan, R, Parameshwaran, R, Perumal, S, Daneshmand, M & Gandomi, AH 2022, 'Blockchain Security Using Merkle Hash Zero Correlation Distinguisher for the IoT in Smart Cities', IEEE Internet of Things Journal, vol. 9, no. 19, pp. 19296-19306.
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Internet of Things (IoT) data is one of the most important assets in business models for offering various ubiquitous and brilliant services. The IoT is provided with the advantage of susceptibility that cybercriminals and other malicious users. Even though smart cities are intended to extend productivity and efficiency, residents and authorities face risks when they avoid cybersecurity. The conventional blockchain methods were introduced to ensure the secure management and examination of the smart city big data. But, the blockchains are found to have computationally high costs, and failed to improve the security, not adequate resource-constrained IoT devices have been designated for smart cities. In order to address these issues, the proposed novel blockchain model called blockchain secured Merkle hash zero correlation distinguisher (BSMH-ZCD) is suitable for IoT devices within the cloud infrastructure. The objective of the BSMH-ZCD method is to enhance security and reduce the run time and computational overhead. Initially, the Merkle hash tree is used to create the hash value with every transaction. Next, the zero correlation distinguisher is applied to perform the data encryption and decryption operation for the ARX block for obtaining proficient secure data access in the IoT devices. Experimental assessment of the proposed BSMH-ZCD method and existing methods are carried out by using the taxi driver data set and Novel Corona Virus 2019 data set with different factors, such as running time, computational complexity, and security with respect to a number of blocks and executions. By using the taxi driver data set, the experimental results reveal that the BSMH-ZCD method performs better with a 19% improvement in security, 20% reduction of computational complexity, and 29% faster running time for IoT compared to existing works.
Patel, V, Jose, L, Philippot, G, Aymonier, C, Inerbaev, T, McCourt, LR, Ruppert, MG, Qi, D, Li, W, Qu, J, Zheng, R, Cairney, J, Yi, J, Vinu, A & Karakoti, AS 2022, 'Fluoride-assisted detection of glutathione by surface Ce3+/Ce4+ engineered nanoceria', Journal of Materials Chemistry B, vol. 10, no. 47, pp. 9855-9868.
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Nanoceria prepared with different Ce3+/Ce4+ ratios show different oxidase mimetic activities. The activity is enhanced selectively in presence of fluoride ions and used for glutathione detection.
Patil, AY, Hegde, C, Savanur, G, Kanakmood, SM, Contractor, AM, Shirashyad, VB, Chivate, RM, Kotturshettar, BB, Mathad, SN, Patil, MB, Soudagar, MEM & Fattah, IMR 2022, 'Biomimicking Nature-Inspired Design Structures—An Experimental and Simulation Approach Using Additive Manufacturing', Biomimetics, vol. 7, no. 4, pp. 186-186.
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Whether it is a plant- or animal-based bio-inspiration design, it has always been able to address one or more product/component optimisation issues. Today’s scientists or engineers look to nature for an optimal, economically viable, long-term solution. Similarly, a proposal is made in this current work to use seven different bio-inspired structures for automotive impact resistance. All seven of these structures are derived from plant and animal species and are intended to be tested for compressive loading to achieve load-bearing capacity. The work may even cater to optimisation techniques to solve the real-time problem using algorithm-based generative shape designs built using CATIA V6 in unit dimension. The samples were optimised with Rhino 7 software and then simulated with ANSYS workbench. To carry out the comparative study, an experimental work of bioprinting in fused deposition modelling (3D printing) was carried out. The goal is to compare the results across all formats and choose the best-performing concept. The results were obtained for compressive load, flexural load, and fatigue load conditions, particularly the number of life cycles, safety factor, damage tolerance, and bi-axiality indicator. When compared to previous research, the results are in good agreement. Because of their multifunctional properties combining soft and high stiffness and lightweight properties of novel materials, novel materials have many potential applications in the medical, aerospace, and automotive sectors.
Patowary, M, Haes Alhelou, H & Panda, G 2022, 'Performance assessment and validation of inverter control current controllers in reduced sensor maximum power point tracking based photovoltaic‐grid tied system', IET Energy Systems Integration, vol. 4, no. 4, pp. 505-517.
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AbstractA relative assessment on conventional and adaptive current controllers used in reduced sensor‐maximum power point tracking (MPPT) based photovoltaic (PV)‐grid tied inverter systems for the improvement of system power quality is suggested. The steady‐state and transients errors produced in the conventional PI and proportional resonant controllers, which are used to generate the references, can be fixed by using an intelligent ADALINE‐LMS adaptive controller; moreover, it helps in reducing the %THD (total harmonic distortion) level measured at different power zones. Also, to track the maximum PV power, which is further integrated to DC‐bus, a reduced sensor‐based technology is added into the circuit that sidesteps the problem of tracking local MPP instead of global MPP and the drawbacks of using current sensors. The use of a reduced sensor‐based MPPT controller confirms extraction of maximum PV power and it guarantees a constant DC‐link voltage under all the possible test conditions. The overall control architectures and system performances, which are tested under different system dynamics, are validated through MATLAB/Simulink as well as experimental findings obtained using the dSPACE RTI 1202 interfacing kit. These experimental results confirm that the adaptive control technique used in reduced sensor‐MPPT based PV‐grid tied inverter systems performs unbeatably with balanced load and grid voltages, less harmonics, quick response time etc. under the operation of linear, non‐linear and transient loads, whereas, conventional controllers are best only for the linear loads.
Paudel, KR, Mehta, M, Shukla, SD, Panth, N, Chellappan, DK, Dua, K & Hansbro, P 2022, 'Advancements in Nanotherapeutics Targeting Senescence in Chronic Obstructive Pulmonary Disease', Nanomedicine, vol. 17, no. 23, pp. 1757-1760.
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Paudel, KR, Mehta, M, Yin, GHS, Yen, LL, Malyla, V, Patel, VK, Panneerselvam, J, Madheswaran, T, MacLoughlin, R, Jha, NK, Gupta, PK, Singh, SK, Gupta, G, Kumar, P, Oliver, BG, Hansbro, PM, Chellappan, DK & Dua, K 2022, 'Berberine-loaded liquid crystalline nanoparticles inhibit non-small cell lung cancer proliferation and migration in vitro', Environmental Science and Pollution Research, vol. 29, no. 31, pp. 46830-46847.
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AbstractNon-small cell lung cancer (NSCLC) is reported to have a high incidence rate and is one of the most prevalent types of cancer contributing towards 85% of all incidences of lung cancer. Berberine is an isoquinoline alkaloid which offers a broad range of therapeutical and pharmacological actions against cancer. However, extremely low water solubility and poor oral bioavailability have largely restricted its therapeutic applications. To overcome these limitations, we formulated berberine-loaded liquid crystalline nanoparticles (LCNs) and investigated their in vitro antiproliferative and antimigratory activity in human lung epithelial cancer cell line (A549). 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT), trypan blue staining, and colony forming assays were used to evaluate the anti-proliferative activity, while scratch wound healing assay and a modified Boyden chamber assay were carried out to determine the anti-migratory activity. We also investigated major proteins associated with lung cancer progression. The developed nanoparticles were found to have an average particle size of 181.3 nm with spherical shape, high entrapment efficiency (75.35%) and have shown sustained release behaviour. The most remarkable findings reported with berberine-loaded LCNs were significant suppression of proliferation, inhibition of colony formation, inhibition of invasion or migration via epithelial mesenchymal transition, and proliferation related proteins associated with cancer progression. Our findings suggest that anti-cancer compounds with the problem of poor solubility and bioavailability can be overcome by formulating them into nanotechnology-based delivery systems for better efficacy. Further in-depth investigations into anti-cancer mechanistic research will expand and strengthen the current findings of berberine-LCNs as a potential NSCLC treatment option.
Paudel, KR, Patel, V, Vishwas, S, Gupta, S, Sharma, S, Chan, Y, Jha, NK, Shrestha, J, Imran, M, Panth, N, Shukla, SD, Jha, SK, Devkota, HP, Warkiani, ME, Singh, SK, Ali, MK, Gupta, G, Chellappan, DK, Hansbro, PM & Dua, K 2022, 'Nutraceuticals and COVID‐19: A mechanistic approach toward attenuating the disease complications', Journal of Food Biochemistry, vol. 46, no. 12, p. e14445.
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Nutraceuticals have emerged as potential compounds to attenuate the COVID-19 complications. Precisely, these food additives strengthen the overall COVID treatment and enhance the immunity of a person. Such compounds have been used at a large scale, in almost every household due to their better affordability and easy access. Therefore, current research is focused on developing newer advanced formulations from potential drug candidates including nutraceuticals with desirable properties viz, affordability, ease of availability, ease of administration, stability under room temperature, and potentially longer shelf-lives. As such, various nutraceutical-based products such as compounds could be promising agents for effectively managing COVID-19 symptoms and complications. Most importantly, regular consumption of such nutraceuticals has been shown to boost the immune system and prevent viral infections. Nutraceuticals such as vitamins, amino acids, flavonoids like curcumin, and probiotics have been studied for their role in the prevention of COVID-19 symptoms such as fever, pain, malaise, and dry cough. In this review, we have critically reviewed the potential of various nutraceutical-based therapeutics for the management of COVID-19. We searched the information relevant to our topic from search engines such as PubMed and Scopus using COVID-19, nutraceuticals, probiotics, and vitamins as a keyword. Any scientific literature published in a language other than English was excluded. PRACTICAL APPLICATIONS: Nutraceuticals possess both nutritional values and medicinal properties. They can aid in the prevention and treatment of diseases, as well as promote physical health and the immune system, normalizing body functions, and improving longevity. Recently, nutraceuticals such as probiotics, vitamins, polyunsaturated fatty acids, trace minerals, and medicinal plants have attracted considerable attention and are widely regarded as potential alternatives to current the...
Pearce, A, Zhang, JA & Xu, R 2022, 'A Combined mmWave Tracking and Classification Framework Using a Camera for Labeling and Supervised Learning', Sensors, vol. 22, no. 22, pp. 8859-8859.
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Millimeter wave (mmWave) radar poses prosperous opportunities surrounding multiple-object tracking and sensing as a unified system. One of the most challenging aspects of exploiting sensing opportunities with mmWave radar is the labeling of mmWave data so that, in turn, a respective model can be designed to achieve the desired tracking and sensing goals. The labeling of mmWave datasets usually involves a domain expert manually associating radar frames with key events of interest. This is a laborious means of labeling mmWave data. This paper presents a framework for training a mmWave radar with a camera as a means of labeling the data and supervising the radar model. The methodology presented in this paper is compared and assessed against existing frameworks that aim to achieve a similar goal. The practicality of the proposed framework is demonstrated through experimentation in varying environmental conditions. The proposed framework is applied to design a mmWave multi-object tracking system that is additionally capable of classifying individual human motion patterns, such as running, walking, and falling. The experimental findings demonstrate a reliably trained radar model that uses a camera for labeling and supervision that can consistently produce high classification accuracy across environments beyond those in which the model was trained against. The research presented in this paper provides a foundation for future research in unified tracking and sensing systems by alleviating the labeling and training challenges associated with designing a mmWave classification model.
Peden, AE, Cullen, P, Francis, KL, Moeller, H, Peden, MM, Ye, P, Tian, M, Zou, Z, Sawyer, SM, Aali, A, Abbasi-Kangevari, Z, Abbasi-Kangevari, M, Abdelmasseh, M, Abdoun, M, Abd-Rabu, R, Abdulah, DM, Abebe, G, Abebe, AM, Abedi, A, Abidi, H, Aboagye, RG, Abubaker Ali, H, Abu-Gharbieh, E, Adane, DE, Adane, TD, Addo, IY, Adewole, OG, Adhikari, S, Adnan, M, Adnani, QES, Afolabi, AAB, Afzal, S, Afzal, MS, Aghdam, ZB, Ahinkorah, BO, Ahmad, AR, Ahmad, T, Ahmad, S, Ahmadi, A, Ahmed, H, Ahmed, MB, Ahmed, A, Ahmed, A, Ahmed, JQ, Ahmed Rashid, T, Aithala, JP, Aji, B, Akhlaghdoust, M, Alahdab, F, Alanezi, FM, Alemayehu, A, Al Hamad, H, Ali, SS, Ali, L, Alimohamadi, Y, Alipour, V, Aljunid, SM, Almidani, L, Almustanyir, S, Altirkawi, KA, Alvis-Zakzuk, NJ, Ameyaw, EK, Amin, TT, Amir-Behghadami, M, Amiri, S, Amiri, H, Anagaw, TF, Andrei, T, Andrei, CL, Anvari, D, Anwar, SL, Anyasodor, AE, Arabloo, J, Arab-Zozani, M, Arja, A, Arulappan, J, Arumugam, A, Aryannejad, A, Asgary, S, Ashraf, T, Athari, SS, Atreya, A, Attia, S, Aujayeb, A, Awedew, AF, Azadnajafabad, S, Azangou-Khyavy, M, Azari, S, Azari Jafari, A, Azizi, H, Azzam, AY, Badiye, AD, Baghcheghi, N, Bagherieh, S, Baig, AA, Bakkannavar, SM, Balta, AB, Banach, M, Banik, PC, Bansal, H, Bardhan, M, Barone-Adesi, F, Barrow, A, Bashiri, A, Baskaran, P, Basu, S, Bayileyegn, NS, Bekel, AA, Bekele, AB, Bendak, S, Bensenor, IM, Berhie, AY, Bhagat, DS, Bhagavathula, AS, Bhardwaj, P, Bhardwaj, N, Bhaskar, S, Bhat, AN, Bhattacharyya, K, Bhutta, ZA, Bibi, S, Bintoro, BS, Bitaraf, S, Bodicha, BBA, Boloor, A, Bouaoud, S, Brown, J, Burkart, K, Butt, NS, Butt, MH, Cámera, LA, Campuzano Rincon, JC, Cao, C, Carvalho, AF, Carvalho, M, Chakraborty, PA, Chandrasekar, EK, Chang, J-C, Charalampous, P, Charan, J, Chattu, VK, Chekole, BM, Chitheer, A, Cho, DY, Chopra, H, Christopher, DJ, Chukwu, IS, Cruz-Martins, N, Dadras, O, Dahlawi, SMA, Dai, X, Damiani, G, Darmstadt, GL, Darvishi Cheshmeh Soltani, R, Darwesh, AM, Das, S, Dastiridou, A, Debela, SA, Dehghan, A, Demeke, GM, Demetriades, AK, Demissie, S, Dessalegn, FN, Desta, AA, Dianatinasab, M, Diao, N, Dias da Silva, D, Diaz, D, Digesa, LE, Diress, M, Djalalinia, S, Doan, LP, Dodangeh, M, Doku, PN, Dongarwar, D, Dsouza, HL, Eini, E, Ekholuenetale, M, Ekundayo, TC, Elagali, AEM, Elbahnasawy, MA, Elhabashy, HR, Elhadi, M, El Sayed Zaki, M & et al. 2022, 'Adolescent transport and unintentional injuries: a systematic analysis using the Global Burden of Disease Study 2019', The Lancet Public Health, vol. 7, no. 8, pp. e657-e669.
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BACKGROUND: Globally, transport and unintentional injuries persist as leading preventable causes of mortality and morbidity for adolescents. We sought to report comprehensive trends in injury-related mortality and morbidity for adolescents aged 10-24 years during the past three decades. METHODS: Using the Global Burden of Disease, Injuries, and Risk Factors 2019 Study, we analysed mortality and disability-adjusted life-years (DALYs) attributed to transport and unintentional injuries for adolescents in 204 countries. Burden is reported in absolute numbers and age-standardised rates per 100 000 population by sex, age group (10-14, 15-19, and 20-24 years), and sociodemographic index (SDI) with 95% uncertainty intervals (UIs). We report percentage changes in deaths and DALYs between 1990 and 2019. FINDINGS: In 2019, 369 061 deaths (of which 214 337 [58%] were transport related) and 31·1 million DALYs (of which 16·2 million [52%] were transport related) among adolescents aged 10-24 years were caused by transport and unintentional injuries combined. If compared with other causes, transport and unintentional injuries combined accounted for 25% of deaths and 14% of DALYs in 2019, and showed little improvement from 1990 when such injuries accounted for 26% of adolescent deaths and 17% of adolescent DALYs. Throughout adolescence, transport and unintentional injury fatality rates increased by age group. The unintentional injury burden was higher among males than females for all injury types, except for injuries related to fire, heat, and hot substances, or to adverse effects of medical treatment. From 1990 to 2019, global mortality rates declined by 34·4% (from 17·5 to 11·5 per 100 000) for transport injuries, and by 47·7% (from 15·9 to 8·3 per 100 000) for unintentional injuries. However, in low-SDI nations the absolute number of deaths increased (by 80·5% to 42 774 for transport injuries and by 39·4% to 31 961 for unintentional injuries). In the high-SDI quintil...
Peellage, WH, Fatahi, B & Rasekh, H 2022, 'Experimental investigation for vibration characteristics of jointed rocks using cyclic triaxial tests', Soil Dynamics and Earthquake Engineering, vol. 160, pp. 107377-107377.
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Peng, M, Tian, Y, Gaudin, C, Zhang, L & Sheng, D 2022, 'Application of a coupled hydro‐mechanical interface model in simulating uplifting problems', International Journal for Numerical and Analytical Methods in Geomechanics, vol. 46, no. 17, pp. 3256-3280.
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AbstractThis paper presents the detailed formulation of a coupled hydro‐mechanical structure‐soil interface and demonstrates its application in simulating uplifting problems. This interface features real‐time prediction of the pore pressure generation and structure‐soil separation, and thus rate dependency and ‘breakaway’ can be modeled without user intervention. Constitutive relations of this interface were derived by considering the coupling between soil skeleton and fluid along the interface. A complete finite element formulation and numerical implementation of the interface is provided based on an eight‐node element. The performance of this interface is demonstrated by simulating lifting a surface footing at varying rates (spanning across undrained, partially drained and drained conditions), compared with existing theoretical solutions, numerical results and experimental data. The good agreement achieved indicates that this interface is capable of modelling uplift at varying rates, which is an extremely challenging topic in offshore engineering. Sensitivity studies were conducted to investigate the parameters affecting uplifting behaviour. A unified backbone curve was established correspondingly, which is shown to be different from existing studies in compression, due to the difference in the mechanism between the two cases.
Peng, S, Cao, L, Zhou, Y, Ouyang, Z, Yang, A, Li, X, Jia, W & Yu, S 2022, 'A survey on deep learning for textual emotion analysis in social networks', Digital Communications and Networks, vol. 8, no. 5, pp. 745-762.
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Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states in texts. Various Deep Learning (DL) methods have developed rapidly, and they have proven to be successful in many fields such as audio, image, and natural language processing. This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research. In this paper, we provide an overview on TEA based on DL methods. After introducing a background for emotion analysis that includes defining emotion, emotion classification methods, and application domains of emotion analysis, we summarize DL technology, and the word/sentence representation learning method. We then categorize existing TEA methods based on text structures and linguistic types: text-oriented monolingual methods, text conversations-oriented monolingual methods, text-oriented cross-linguistic methods, and emoji-oriented cross-linguistic methods. We close by discussing emotion analysis challenges and future research trends. We hope that our survey will assist interested readers in understanding the relationship between TEA and DL methods while also improving TEA development.
Peng, X, Li, Y, Tsang, IW, Zhu, H, Lv, J & Zhou, JT 2022, 'XAI beyond Classification: Interpretable Neural Clustering', Journal of Machine Learning Research, vol. 23, no. -.
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In this paper, we study two challenging problems in explainable AI (XAI) and data clustering. The first is how to directly design a neural network with inherent interpretability, rather than giving post-hoc explanations of a black-box model. The second is implementing discrete k-means with a differentiable neural network that embraces the advantages of parallel computing, online clustering, and clustering-favorable representation learning. To address these two challenges, we design a novel neural network, which is a differentiable reformulation of the vanilla k-means, called inTerpretable nEuraL cLustering (TELL). Our contributions are threefold. First, to the best of our knowledge, most existing XAI works focus on supervised learning paradigms. This work is one of the few XAI studies on unsupervised learning, in particular, data clustering. Second, TELL is an interpretable, or the so-called intrinsically explainable and transparent model. In contrast, most existing XAI studies resort to various means for understanding a black-box model with post-hoc explanations. Third, from the view of data clustering, TELL possesses many properties highly desired by k-means, including but not limited to online clustering, plug-and-play module, parallel computing, and provable convergence. Extensive experiments show that our method achieves superior performance comparing with 14 clustering approaches on three challenging data sets. The source code could be accessed at www.pengxi.me.
Peng, Y, Azeem, M, Li, R, Xing, L, Li, Y, Zhang, Y, Guo, Z, Wang, Q, Ngo, HH, Qu, G & Zhang, Z 2022, 'Zirconium hydroxide nanoparticle encapsulated magnetic biochar composite derived from rice residue: Application for As(III) and As(V) polluted water purification', Journal of Hazardous Materials, vol. 423, pp. 127081-127081.
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Finding a low-cost and suitable adsorbent is still in urgent need for efficient decontamination of As(III) and As(V) elements from the polluted waters. A novel zirconium hydroxide nanoparticle encapsulated magnetic biochar composite (ZBC) derived from rice residue was synthesized for the adsorptive capture of As(III) and As(V) from aqueous solutions. The results revealed that ZBC showed an acceptable magnet separation ability and its surface was encapsulated with lots of hydrous zirconium oxide nanoparticles. Compared to As(III), the adsorption of As(V) onto ZBC was mainly dependent on the pH of the solution. The intraparticle diffusion model described the adsorption process. ZBC showed satisfactory adsorption performances to As(III) and As(V) with the highest adsorption quantity of 107.6 mg/g and 40.8 mg/g at pH 6.5 and 8.5, respectively. The adsorption of As(III) and As(V) on ZBC was almost impervious with the ionic strength while the presence of coexisting ions, especially phosphate, significantly affected the adsorption process. The processes of complexation reaction and electrostatic attraction contributed to the adsorption of As(III) and As(V) onto ZBC. ZBC prepared from kitchen rice residue was found to be a low cost environmentally friendly promising adsorbent with high removal capacity for As(III) and As(V) and could be recycled easily from contaminated waters.
Peng, Y, Lin, X, Zhang, Y, Zhang, W & Qin, L 2022, 'Answering reachability and K-reach queries on large graphs with label constraints.', VLDB J., vol. 31, no. 1, pp. 101-127.
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The purpose of this paper is to examine the problem of label-constrained reachability (LCR) and K-reach (LCKR) queries, which are fundamental in a wide variety of applications using directed edge-labeled graphs. While reachability and K-reach queries have been extensively researched, LCR and LCKR queries are much more challenging due to the fact that the number of potential label-constraint sets is exponential to the size of the labels. We note that existing techniques for LCR queries only build a partial index and that their worse-case query time could be comparable to that of an online breadth-first search (BFS). This paper proposes a new label-constrained 2-hop indexing method with innovative pruning rules and order strategies. Our work demonstrates that the worst query time could be bounded by the number of in-out index entries. Extensive experiments demonstrate that the proposed methods substantially outperform the state-of-the-art approach in terms of the query response time (up to 5 orders of magnitude speedup), index size, and the index construction time. More precisely, the method we present can response LCR queries across billion-scale networks within microseconds on a single machine. We formally define the problem of LCKR queries and discuss critical applications for addressing it. To tackle the difficulties presented by label and hop constraints, an efficient upper and lower bound is suggested based on a search method. Using all of these techniques, extensive experiments on synthetic and real-world networks demonstrate that our algorithm outperforms the baseline by about three to four orders of magnitude while maintaining competitive indexing time and size.
Peng, Y, Liu, Y, Li, M, Liu, H & Guo, YJ 2022, 'Synthesizing Circularly Polarized Multi-Beam Planar Dipole Arrays With Sidelobe and Cross-Polarization Control by Two-Step Element Rotation and Phase Optimization', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4379-4391.
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Perera, D, Wang, Y-K, Lin, C-T, Nguyen, H & Chai, R 2022, 'Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators', Sensors, vol. 22, no. 16, pp. 6230-6230.
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This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger–Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.
Pérez-Escamilla, B, Benrimoj, SI, Martínez-Martínez, F, Gastelurrutia, MÁ, Varas-Doval, R, Musial-Gabrys, K & Garcia-Cardenas, V 2022, 'Using network analysis to explore factors moderating the implementation of a medication review service in community pharmacy', Research in Social and Administrative Pharmacy, vol. 18, no. 3, pp. 2432-2443.
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Background
Implementation factors are hypothesised to moderate the implementation of innovations. Although individual barriers and facilitators have been identified for the implementation of different evidence-based services in pharmacy, relationships between implementation factors are usually not considered.Objectives
To examine how a network of implementation factors and the position of each factor within this network structure influences the implementation of a medication review service in community pharmacy.Methods
A mixed methods approach was used. Medication review with follow-up service was the innovation to be implemented over 12 months in community pharmacies. A network analysis to model relationships between implementation factors was undertaken. Two networks were created.Results
Implementation factors hindering the service implementation with the highest centrality measures were time, motivation, recruitment, individual identification with the organization and personal characteristics of the pharmacists. Three hundred and sixty-nine different interrelationships between implementation factors were identified. Important causal relationships between implementation factors included: workflow-time; characteristics of the pharmacy-time; personal characteristics of the pharmacists-motivation. Implementation factors facilitating the implementation of the service with highest centrality scores were motivation, individual identification with the organization, beliefs, adaptability, recruitment, external support and leadership. Four hundred and fifty-six different interrelationships were identified. The important causal relationships included: motivation-external support; structure-characteristics of the pharmacy; demographics-location of the pharmacy.Conclusion
Network analysis has proven to be a useful technique to explore networks of factors moderating the implementation of a pharmacy service. Relationships were complex ...
Perrier, E 2022, 'The Quantum Governance Stack: Models of Governance for Quantum Information Technologies', Digital Society, vol. 1, no. 3.
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AbstractThe emergence of quantum information technologies with potential application across diverse industrial, consumer and technical domains has thrown into relief the need for practical approaches to their governance. Technology governance must balance multiple objectives including facilitating technological development while meeting legal requirements, normative expectations and managing risks regarding the use of such technology. In this paper, we articulate a variety of idealised governance models and approaches for synthesising these complementary and sometimes competing objectives. We set out a comparative analysis of quantum governance in the context of existing models of technological governance. Using this approach, we develop an actor-instrument model for quantum governance, denoted the ‘quantum governance stack’, across a governance hierarchy from states and governments through to public and private institutions. Our model sets out key characteristics that quantum governance should exhibit at each level in the stack, including identification of stakeholder rights, interests and obligations impacted by quantum technologies and the appropriate instruments by which such impacts are managed. We argue that quantum governance must be responsive based on (a) the state of technology at the time; (b) resource and economic requirements for its development; and (c) assessments and estimates of the near-term and future impacts of such technology. Our work provides a pragmatic introduction to quantum governance by (a) specifying a taxonomy of governance actors and instruments and (b) providing examples of how different stakeholders within the stack might implement governance responses to quantum information technologies. It is intended for use by stakeholders in government, industry, academia and civil society to help inform their governance response to the quantum technology revolution.
Perrier, E, Youssry, A & Ferrie, C 2022, 'QDataSet, quantum datasets for machine learning', Scientific Data, vol. 9, no. 1, p. 582.
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AbstractThe availability of large-scale datasets on which to train, benchmark and test algorithms has been central to the rapid development of machine learning as a discipline. Despite considerable advancements, the field of quantum machine learning has thus far lacked a set of comprehensive large-scale datasets upon which to benchmark the development of algorithms for use in applied and theoretical quantum settings. In this paper, we introduce such a dataset, the QDataSet, a quantum dataset designed specifically to facilitate the training and development of quantum machine learning algorithms. The QDataSet comprises 52 high-quality publicly available datasets derived from simulations of one- and two-qubit systems evolving in the presence and/or absence of noise. The datasets are structured to provide a wealth of information to enable machine learning practitioners to use the QDataSet to solve problems in applied quantum computation, such as quantum control, quantum spectroscopy and tomography. Accompanying the datasets on the associated GitHub repository are a set of workbooks demonstrating the use of the QDataSet in a range of optimisation contexts.
Persson, M, Jackson, E, Duchatel, R, Bramberger, L, McEwen, H, Kearney, P, Findlay, I, Douglas, A, Kobbe, B, Wagener, R, Larsen, M, Faridi, P, Holst, J, Mayall, J, Gedye, C, Hondermarck, H, Horvat, J, Nixon, B, Cartaxo, R, Koschmann, C, Valdes-Mora, F, Ortega, DG, Nazarian, J, Alonso, MM, Hulleman, E, Van der Lugt, J, Vitanza, N, Mueller, S & Dun, M 2022, 'TMIC-06. ANTAGONISM OF DRD2 USING ONC201 INCREASED EXPRESSION OF ANTIGEN PRESENTATION PATHWAY PROTEINS IN DIFFUSE MIDLINE GLIOMA, RECRUITING TUMOR INFILTRATING LYMPHOCYTES IN VIVO', Neuro-Oncology, vol. 24, no. Supplement_7, pp. vii272-vii272.
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Abstract Diffuse midline glioma (DMG) is a high-grade glioma with a median overall survival of 9-11 months. Radiotherapy is the only recognized treatment. The DMG tumor microenvironment (TME) contains few, if any, tumor infiltrating lymphocytes (TILs) or inflammatory cytokines, thus is distinctive of an ‘immunologically cold’ tumor/TME.1 DMG lack the expression of immunosuppressive immune checkpoint proteins, likely explaining the failure of immune checkpoint inhibitors (ICI) tested under clinical trials for DMG patients, and suggestive of an alternative mechanism underpinning the cold TME. 1 Glioblastomas also harbor a cold TME, which can be somewhat explained by T cell lymphopenia, influenced by the sequestration of T cells in the bone marrow (through Beta-arrestin-induced internalization of Sphingosine-1-phosphate receptor 1 [S1PR1]). 2 Dopaminergic activation of Beta-arrestin and hence S1PR1 internalization, is potentially regulated through dopaminergic peripheral nerves in primary and secondary lymphoid organs, regulated by the Dopamine receptor D2 (DRD2), that is highly expressed on T cells. ONC201 is a potent DRD2 antagonist, currently in phase I-III clinical trials for DMG patients, alone and in combination with radiotherapy and the PI3K/AKT inhibitor paxalisib (NCT05009992). Proteomic profiling of DMG patient-derived cells +/-ONC201 showed increased expression of several antigen presenting pathway proteins, including Beta-2-microglobulin (B2M) and HLA class I histocompatibility antigen, A alpha chain (HLA-A). This was confirmed in vivo using SU-DIPG-VI patient-derived xenograft mouse model tissues +/-ONC201 alone, and together with paxalisib. Excitingly, this combination (given orally) promoted the recruitment of TILs to the tumor, revealing novel immunomodulatory effects. In vivo, ONC201 promoted the expression of EMILIN-3, a TGF-β antagonist that is known to inhibit HLA-A/B2M expression, possibl...
Petersen, IR & Dong, D 2022, 'Special section on estimation and control of quantum systems', Annual Reviews in Control, vol. 54, pp. 241-242.
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Petrik, LF, Ngo, HH, Varjani, S, Osseweijer, P, Xevgenos, D, van Loosdrecht, M, Smol, M, Yang, X & Mateo-Sagasta, J 2022, 'From wastewater to resource', One Earth, vol. 5, no. 2, pp. 122-125.
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Pham, DX, Phung, AHT, Nguyen, HD, Bui, TD, Mai, LD, Tran, BNH, Tran, TS, Nguyen, TV & Ho-Pham, LT 2022, 'Trends in colorectal cancer incidence in Ho Chi Minh City, Vietnam (1996–2015): Joinpoint regression and age–period–cohort analyses', Cancer Epidemiology, vol. 77, pp. 102113-102113.
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BACKGROUND: Little is known about the trends in colorectal cancer (CRC) in Vietnam. We aimed to investigate the trends in epidemiology and anatomical subsites of CRC in Ho Chi Minh City, Vietnam. METHODS: Based on the Ho Chi Minh City Cancer Registry data during 1996-2015, we calculated the average annual percent changes (AAPCs) of the age-standardized incidence rates (ASRs) by sex, age groups, and anatomical subsites, using joinpoint regressions analysis. We further performed age-period-cohort (APC) analysis using the United States National Cancer Institute's web-based statistical tool to explore the underlying reason for the incidence trend. RESULTS: Over 20 years the overall ASR of CRC increased from 10.5 to 17.9 per 100,000, a 1.7-fold increase. CRC incidence elevated more rapidly in men (AAPC 4.7, 95%CI 2.2-7.3) than in women (AAPC 2.6, 95%CI 0.6-4.8). The highest and lowest increasing rates of ASRs were observed in the 50-64-year-old age group (AAPC 5.3, 95%CI 2.8-7.9) and < 50-year-old age group (AAPC 1.1, 95%CI -0.7 to 2.9), respectively. Regarding subsites, rectal cancer had the highest rate of increase (AAPC 3.3, 95%CI 1.0-5.7). Furthermore, the APC analysis indicated significant increases in CRC incidence in birth cohorts after 1975 in both genders. CONCLUSIONS: The CRC incidence in Ho Chi Minh City increased, with the more prominent rates being among men and older populations, in rectal subsites, and in people born after 1975. The upward trend of CRC incidence in Ho Chi Minh City may be due to the adoption of a westernized lifestyle.
Pham, HN, Dang, KB, Nguyen, TV, Tran, NC, Ngo, XQ, Nguyen, DA, Phan, TTH, Nguyen, TT, Guo, W & Ngo, HH 2022, 'A new deep learning approach based on bilateral semantic segmentation models for sustainable estuarine wetland ecosystem management', Science of The Total Environment, vol. 838, pp. 155826-155826.
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Pham, T, Faust, O, Koh, JEW, Ciaccio, EJ, Barua, PD, Omar, N, Ng, WL, Ab Mumin, N, Rahmat, K & Acharya, UR 2022, 'Fusion of B‐mode and shear wave elastography ultrasound features for automated detection of axillary lymph node metastasis in breast carcinoma', Expert Systems, vol. 39, no. 5.
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AbstractIn this study, we evaluate and compare the diagnostic performance of ultrasound for non‐invasive axillary lymph node (ALN) metastasis detection. The study was based on fusing shear wave elastography (SWE) and B‐mode ultrasonography (USG) images. These images were subjected to pre‐processing and feature extraction, based on bi‐dimensional empirical mode decomposition and higher order spectra methods. The resulting nonlinear features were ranked according to theirp‐value, which was established with Student'st‐test. The ranked features were used to train and test six classification algorithms with 10‐fold cross‐validation. Initially, we considered B‐mode USG images in isolation. A probabilistic neural network (PNN) classifier was able to discriminate positive from negative cases with an accuracy of 74.77% using 15 features. Subsequently, only SWE images were used and as before, the PNN classifier delivered the best result with an accuracy of 87.85% based on 47 features. Finally, we combined SWE and B‐mode USG images. Again, the PNN classifier delivered the best result with an accuracy of 89.72% based on 71 features. These three tests indicate that SWE images contain more diagnostically relevant information when compared with B‐mode USG. Furthermore, there is scope in fusing SWE and B‐mode USG to improve non‐invasive ALN metastasis detection.
Phan, TC, Pranata, A, Farragher, J, Bryant, A, Nguyen, HT & Chai, R 2022, 'Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain', Sensors, vol. 22, no. 17, pp. 6694-6694.
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This paper proposes an innovative methodology for finding how many lifting techniques people with chronic low back pain (CLBP) can demonstrate with camera data collected from 115 participants. The system employs a feature extraction algorithm to calculate the knee, trunk and hip range of motion in the sagittal plane, Ward’s method, a combination of K-means and Ensemble clustering method for classification algorithm, and Bayesian neural network to validate the result of Ward’s method and the combination of K-means and Ensemble clustering method. The classification results and effect size show that Ward clustering is the optimal method where precision and recall percentages of all clusters are above 90, and the overall accuracy of the Bayesian Neural Network is 97.9%. The statistical analysis reported a significant difference in the range of motion of the knee, hip and trunk between each cluster, F (9, 1136) = 195.67, p < 0.0001. The results of this study suggest that there are four different lifting techniques in people with CLBP. Additionally, the results show that even though the clusters demonstrated similar pain levels, one of the clusters, which uses the least amount of trunk and the most knee movement, demonstrates the lowest pain self-efficacy.
Pietroni, N, Campen, M, Sheffer, A, Cherchi, G, Bommes, D, Gao, X, Scateni, R, Ledoux, F, Remacle, J-F & Livesu, M 2022, 'Hex-Mesh Generation and Processing: a Survey.', CoRR, vol. abs/2202.12670, no. 2, pp. 1-44.
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In this article, we provide a detailed survey of techniques for hexahedral mesh generation. We cover the whole spectrum of alternative approaches to mesh generation, as well as post-processing algorithms for connectivity editing and mesh optimization. For each technique, we highlight capabilities and limitations, also pointing out the associated unsolved challenges. Recent relaxed approaches, aiming to generate not pure-hex but hex-dominant meshes, are also discussed. The required background, pertaining to geometrical as well as combinatorial aspects, is introduced along the way.
Pietroni, N, Dumery, C, Falque, R, Liu, M, Vidal-Calleja, TA & Sorkine-Hornung, O 2022, 'Computational pattern making from 3D garment models.', ACM Trans. Graph., vol. 41, pp. 157:1-157:1.
Pietroni, N, Dumery, C, Guenot-Falque, R, Liu, M, Vidal-Calleja, TA & Sorkine-Hornung, O 2022, 'Computational Pattern Making from 3D Garment Models.', CoRR, vol. abs/2202.10272, no. 4, pp. 1-14.
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We propose a method for computing a sewing pattern of a given 3D garment model. Our algorithm segments an input 3D garment shape into patches and computes their 2D parameterization, resulting in pattern pieces that can be cut out of fabric and sewn together to manufacture the garment. Unlike the general state-of-the-art approaches for surface cutting and flattening, our method explicitly targets garment fabrication. It accounts for the unique properties and constraints of tailoring, such as seam symmetry, the usage of darts, fabric grain alignment, and a flattening distortion measure that models woven fabric deformation, respecting its anisotropic behavior. We bootstrap a recent patch layout approach developed for quadrilateral remeshing and adapt it to the purpose of computational pattern making, ensuring that the deformation of each pattern piece stays within prescribed bounds of cloth stress. While our algorithm can automatically produce the sewing patterns, it is fast enough to admit user input to creatively iterate on the pattern design. Our method can take several target poses of the 3D garment into account and integrate them into the sewing pattern design. We demonstrate results on both skintight and loose garments, showcasing the versatile application possibilities of our approach.
Pileggi, SF 2022, 'Holistic Resilience Index: measuring the expected country resilience to pandemic', Quality & Quantity, vol. 56, no. 6, pp. 4107-4127.
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This study aims to holistically measure the expected resilience of the different countries to a global pandemic like COVID-19. The proposed indicator has been designed looking at the direct and indirect impact of the COVID-19 pandemic on our society at different levels, including health and socio-economic aspects. More concretely, the resulting index has been produced by combining 11 different indicators grouped in five categories. It is actually composed of two sub-indicators that aim to measure the expected resilience according, respectively, to the data available in a given moment and to a period of development. The former sub-indicator depends on the actual values of the underpinning indicators, while the latter takes into account only their variation in a given time. In this paper we address 22 countries among the most affected by COVID-19, looking at recent pre-pandemic data and at the development in the past 20 years. As expected, the combination of the two methods determines contrasting results but also a more comprehensive analysis framework. As part of the lesson learnt, we do expect countries to prioritise the increasing of their holistic resilience to situations of pandemic.
Pingel, NM, Dempsey, J, McClure-Griffiths, NM, Dickey, JM, Jameson, KE, Arce, H, Anglada, G, Bland-Hawthorn, J, Breen, SL, Buckland-Willis, F, Clark, SE, Dawson, JR, Dénes, H, Di Teodoro, EM, For, B-Q, Foster, TJ, Gómez, JF, Imai, H, Joncas, G, Kim, C-G, Lee, M-Y, Lynn, C, Leahy, D, Ma, YK, Marchal, A, McConnell, D, Miville-Deschènes, M-A, Moss, VA, Murray, CE, Nidever, D, Peek, J, Stanimirović, S, Staveley-Smith, L, Tepper-Garcia, T, Tremblay, CD, Uscanga, L, van Loon, JT, Vázquez-Semadeni, E, Allison, JR, Anderson, CS, Ball, L, Bell, M, Bock, DC-J, Bunton, J, Cooray, FR, Cornwell, T, Koribalski, BS, Gupta, N, Hayman, DB, Harvey-Smith, L, Lee-Waddell, K, Ng, A, Phillips, CJ, Voronkov, M, Westmeier, T & Whiting, MT 2022, 'GASKAP-HI pilot survey science I: ASKAP zoom observations of Hi emission in the Small Magellanic Cloud', Publications of the Astronomical Society of Australia, vol. 39, pp. 1-24.
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Abstract We present the most sensitive and detailed view of the neutral hydrogen ( ${\rm H\small I}$ ) emission associated with the Small Magellanic Cloud (SMC), through the combination of data from the Australian Square Kilometre Array Pathfinder (ASKAP) and Parkes (Murriyang), as part of the Galactic Australian Square Kilometre Array Pathfinder (GASKAP) pilot survey. These GASKAP-HI pilot observations, for the first time, reveal ${\rm H\small I}$ in the SMC on similar physical scales as other important tracers of the interstellar medium, such as molecular gas and dust. The resultant image cube possesses an rms noise level of 1.1 K ( $1.6\,\mathrm{mJy\ beam}^{-1}$ ) $\mathrm{per}\ 0.98\,\mathrm{km\ s}^{-1}$ spectral channel with an angular resolution of IEEE Transactions on Power Electronics, vol. 37, no. 9, pp. 10306-10318.
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Pongiannan, RK, Krishnamoorthy, M, Srinivasan, S, M, C, Bharatiraja, C, B, PK, Cherukuri, SK, Babu, TS & Alhelou, HH 2022, 'Cost-Effective Energy Conservation Techniques for Textile Spinning Mills', IEEE Access, vol. 10, pp. 49839-49852.
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Poostchi, H & Piccardi, M 2022, 'BiLSTM-SSVM: Training the BiLSTM with a Structured Hinge Loss for Named-Entity Recognition', IEEE Transactions on Big Data, vol. 8, no. 1, pp. 203-212.
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Building on the achievements of the BiLSTM-CRF in named-entity recognition (NER), this paper introduces the BiLSTM-SSVM, an equivalent neural model where training is performed using a structured hinge loss. The typical loss functions used for evaluating NER are entity-level variants of the F1 score such as the CoNLL and MUC losses. Unfortunately, the common loss function used for training NER - the cross entropy - is only loosely related to the evaluation losses. For this reason, in this paper we propose a training approach for the BiLSTM-CRF that leverages a hinge loss bounding the CoNLL loss from above. In addition, we present a mixed hinge loss that bounds either the CoNLL loss or the Hamming loss based on the density of entity tokens in each sentence. The experimental results over four benchmark languages (English, German, Spanish and Dutch) show that training with the mixed hinge loss has led to small but consistent improvements over the cross entropy across all languages and four different evaluation measures
Poursafar, N, Hossain, MJ & Taghizadeh, S 2022, 'Distributed DC-Bus Signaling Control of Photovoltaic Systems in Islanded DC Microgrid', CSEE Journal of Power and Energy Systems, vol. 8, no. 6, pp. 1741-1750.
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The stability of an islanded DC microgrid (DCMG) is highly dependent on the presence and performance of the backup energy storage system (BESS), due to the lack of main grid support. This condition makes the DCMG vulnerable to the critical situation of absence of the BESS, which could be caused by a fault or being fully charged or flat. This paper presents an enhanced distributed DC-bus signaling control strategy for converters of photovoltaic systems (PVs) to make the islanded DCMG less dependent on the BESS. Unlike a conventional control approach that utilizes PVs to operate in maximum power point tracking (MPPT) mode and the BESS solely regulating DC-bus voltage, the proposed control method maintains DC-bus voltage via intelligently managing output powers of the PVs. The proposed control method continuously monitors DC-bus voltage and regulates the output powers of all the PVs via switching between MPPT mode and voltage regulating mode. Accordingly, if the DC-bus voltage level is less than a predefined maximum level, the PVs work in MPPT mode; otherwise, the PVs work in voltage regulating mode to maintain DC-bus voltage at an acceptable range. Such switching between MPPT and voltage regulating control operations results in protecting the DCMG from unavoidable shutdowns conventionally necessary during the absence of the BESS unit. Moreover, the proposed control method reduces oscillations on the DC-bus voltage during existence of the BESS. The performance and effectiveness of the proposed control strategy are validated through different case studies in MATLAB/Simulink.
Poursafar, N, Taghizadeh, S, J. Hossain, M & M. Guerrero, J 2022, 'An Optimized Distributed Cooperative Control to Improve the Charging Performance of Battery Energy Storage in a Multiphotovoltaic Islanded DC Microgrid', IEEE Systems Journal, vol. 16, no. 1, pp. 1170-1181.
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Pourzeynali, S, Zhu, X, Ghari Zadeh, A, Rashidi, M & Samali, B 2022, 'Simultaneous Identification of Bridge Structural Damage and Moving Loads Using the Explicit Form of Newmark-β Method: Numerical and Experimental Studies', Remote Sensing, vol. 14, no. 1, pp. 119-119.
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Bridge infrastructures are always subjected to degradation because of aging, their environment, and excess loading. Now it has become a worldwide concern that a large proportion of bridge infrastructures require significant maintenance. This compels the engineering community to develop a robust method for condition assessment of the bridge structures. Here, the simultaneous identification of moving loads and structural damage based on the explicit form of the Newmark-β method is proposed. Although there is an extensive attempt to identify moving loads with known structural parameters, or vice versa, their simultaneous identification considering the road roughness has not been studied enough. Furthermore, most of the existing time domain methods are developed for structures under non-moving loads and are commonly formulated by state-space method, thus suffering from the errors of discretization and sampling ratio. This research is believed to be among the few studies on condition assessment of bridge structures under moving vehicles considering factors such as sensor placement, sampling frequency, damage type, measurement noise, vehicle speed, and road surface roughness with numerical and experimental verifications. Results indicate that the method is able to detect damage with at least three sensors, and is not sensitive to sensors location, vehicle speed and road roughness level. Current limitations of the study as well as prospective research developments are discussed in the conclusion.
Pradeep, T, GuhaRay, A, Bardhan, A, Samui, P, Kumar, S & Armaghani, DJ 2022, 'Reliability and Prediction of Embedment Depth of Sheet pile Walls Using Hybrid ANN with Optimization Techniques', Arabian Journal for Science and Engineering, vol. 47, no. 10, pp. 12853-12871.
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Pradhan, B, Jena, R, Talukdar, D, Mohanty, M, Sahu, BK, Raul, AK & Abdul Maulud, KN 2022, 'A New Method to Evaluate Gold Mineralisation-Potential Mapping Using Deep Learning and an Explainable Artificial Intelligence (XAI) Model', Remote Sensing, vol. 14, no. 18, pp. 4486-4486.
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Geoscientists have extensively used machine learning for geological mapping and exploring the mineral prospect of a province. However, the interpretation of results becomes challenging due to the complexity of machine learning models. This study uses a convolutional neural network (CNN) and Shapley additive explanation (SHAP) to estimate potential locations for gold mineralisation in Rengali Province, a tectonised mosaic of volcano-sedimentary sequences juxtaposed at the interface of the Archaean cratonic segment in the north and the Proterozoic granulite provinces of the Eastern Ghats Belt in Eastern India. The objective is to integrate multi-thematic data involving geological, geophysical, mineralogical and geochemical surveys on a 1:50 K scale with the aim of prognosticating gold mineralisation. The available data utilised during the integration include aero-geophysical (aeromagnetic and aerospectrometric), geochemical (national geochemical mapping), ground geophysical (gravity), satellite gravity, remote sensing (multispectral) and National Geomorphology and Lineament Project structural lineament maps obtained from the Geological Survey of India Database. The CNN model has an overall accuracy of 90%. The SHAP values demonstrate that the major contributing factors are, in sequential order, antimony, clay, lead, arsenic content and a magnetic anomaly in CNN modelling. Geochemical pathfinders, including geophysical factors, have high importance, followed by the shear zones in mineralisation mapping. According to the results, the central parts of the study area, including the river valley, have higher gold prospects than the surrounding areas. Gold mineralisation is possibly associated with intermediate metavolcanics along the shear zone, which is later intruded by quartz veins in the northern part of the Rengali Province. This work intends to model known occurrences with respect to multiple themes so that the results can be replicated in surrou...
Pradhan, S, Dyson, LE & Lama, S 2022, 'The nexus between cultural tourism and social entrepreneurship: a pathway to sustainable community development in Nepal', Journal of Heritage Tourism, vol. 17, no. 6, pp. 615-630.
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Cultural tourism offers a pathway to community development and poverty eradication, particularly in developing countries and poor rural communities. In order to ensure that the benefits are spread equitably across the community and that cultural and environmental integrity is maintained over time, active participation of community members supported by outside actors is essential. This paper explores the potential for community-based cultural tourism initiatives in three different regions of Nepal through a series of interviews with 18 experts in the Nepalese tourism industry. The list of tourism programs suggested by the interviewees were interpreted through a community-based entrepreneurship model, focussing on the processes required to produce a sustainable cultural tourism product or service. The research furthers our understanding of the tourism industry in Nepal as well as providing guidance for the implementation of sustainable cultural tourism initiatives using community-based entrepreneurship.
Prakash, K, Ali, M, Siddique, MNI, Karmaker, AK, Macana, CA, Dong, D & Pota, HR 2022, 'Bi-level planning and scheduling of electric vehicle charging stations for peak shaving and congestion management in low voltage distribution networks', Computers and Electrical Engineering, vol. 102, pp. 108235-108235.
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Premkumar, M, Jangir, P, Kumar, C, Sundarsingh Jebaseelan, SDT, Alhelou, HH, Madurai Elavarasan, R & Chen, H 2022, 'Constraint estimation in three‐diode solar photovoltaic model using Gaussian and Cauchy mutation‐based hunger games search optimizer and enhanced Newton–Raphson method', IET Renewable Power Generation, vol. 16, no. 8, pp. 1733-1772.
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AbstractThe reliability of the photovoltaic models is strongly reliant on their parameters, which are primarily determined by the optimization algorithm and the objective function. As a result, obtaining the parameters under different environmental conditions is critical for increasing their performance, reliability and significantly lowering cost. Many optimization techniques are reported to address this problem based on the complexity. As a result, an enhanced version of the recently reported Hunger Games Search Optimizer (HGSO) method called Gaussian and Cauchy Mutation‐based HGSO (GCMHGSO) algorithm for defining the requirements of the Three‐Diode equivalent Model (TDeM) by utilizing multiple representations in the algorithm along with an efficient objective function. The Cauchy mutation increases the exploration ability, and Gaussian mutation increases the exploitation ability of the basic HGSO. Furthermore, an Enhanced Newton–Raphson Method (ENRM) is presented to effectively solve the behaviour of the current–voltage relation of the TDeM. The robust optimization is also considered to demonstrate the impact of the measurement error. Comparing the GCMHGSO‐ENRM to other competitors reveals that the proposed GCMHGSO‐ENRM can accurately find the best solution, and its effectiveness is verified in many statistical parameters. It is found that the GCMHGSO‐ENRM algorithm is stable and robust compared to other competitors.
Prior, DD, Saberi, M, Janjua, NK & Jie, F 2022, 'Can i trust you? incorporating supplier trustworthiness into supplier selection criteria', Enterprise Information Systems, vol. 16, no. 8-9, pp. 1-28.
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Pu, Y, Tang, J, Zeng, T, Hu, Y, Wang, Q, Huang, J, Pan, S, Wang, XC, Li, Y, Hao Ngo, H & Abomohra, A 2022, 'Enhanced energy production and biological treatment of swine wastewater using anaerobic membrane bioreactor: Fouling mechanism and microbial community', Bioresource Technology, vol. 362, pp. 127850-127850.
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Pugalia, S & Cetindamar, D 2022, 'Insights on the glass ceiling for immigrant women entrepreneurs in the technology sector', International Journal of Gender and Entrepreneurship, vol. 14, no. 1, pp. 44-68.
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PurposeTechnology sector is the pivotal element for innovation and economic development of any country. Hence, the present article explores past researches looking into challenges faced by immigrant women entrepreneurs in technology sector and their corresponding response strategies.Design/methodology/approachThis study employs a systematic literature review (SLR) technique to collate all the relevant literature looking into the challenges and strategies from immigrant women entrepreneur's perspective and provide a comprehensive picture. Overall, 49 research articles are included in this SLR.FindingsFindings indicate that immigrant status further escalates the human, financial and network disadvantages faced by women who want to start a technology-based venture.Originality/valueThis paper contributes to the literature by categorizing the barriers and strategies on a 3 × 2 matrix reflecting the origins of the barrier or strategy (taking place at the individual, firm or institutional level) versus the type of the barrier or strategy (arising from being an immigrant woman and being a woman in the technology sector). After underlining the dearth of studies in the literature about the complex phenomenon of immigrant WEs in the technology sector, the paper points out several neglected themes for future research.
Punetha, P & Nimbalkar, S 2022, 'Geotechnical rheological modeling of ballasted railway tracks considering the effect of principal stress rotation', Canadian Geotechnical Journal, vol. 59, no. 10, pp. 1793-1818.
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The rotation of principal stress direction experienced by the soil elements in a railway track substructure during a train passage influences the magnitude of accumulated settlement. However, the existing methods to evaluate the track response under repeated train loads disregard the influence of principal stress rotation (PSR). This article presents a novel approach for assessing the behavior of ballasted railway tracks incorporating the contribution of PSR on track deformation. The proposed technique employs a geotechnical rheological model to evaluate the track behavior, in which the material plasticity is captured through plastic slider elements. The influence of PSR is accounted for by extending an existing constitutive relationship for the slider elements for the substructure layers, which is successfully validated against experimental data reported in the literature. The results reveal that PSR causes significant cumulative deformation in the substructure layers, and disregarding it in the analysis leads to inaccurate predictions. The proposed approach is then applied to an open track-bridge transition with heterogeneous support conditions, in which the differential settlement is found to be largely influenced by PSR. The findings from this study highlight the importance of including the effect of PSR in predictive models for a reliable evaluation of track performance.
Punetha, P & Nimbalkar, S 2022, 'Performance improvement of ballasted railway tracks using three-dimensional cellular geoinclusions', Geotextiles and Geomembranes, vol. 50, no. 6, pp. 1061-1082.
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Punitha, S, Stephan, T & Gandomi, AH 2022, 'A Novel Breast Cancer Diagnosis Scheme With Intelligent Feature and Parameter Selections', Computer Methods and Programs in Biomedicine, vol. 214, pp. 106432-106432.
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BACKGROUND AND OBJECTIVE: Breast cancer is the most commonly occurring cancer among women, which contributes to the global death rate. The key to increasing the survival rate of affected patients is early diagnosis along with appropriate treatments. Manual methods for breast cancer diagnosis fail due to human errors, inaccurate diagnoses, and are time-consuming when demands are high. Intelligent systems based on Artificial Neural Network (ANN) for automated breast cancer diagnosis are powerful due to their strong decision-making capabilities in complicated cases. Artificial Bee Colony, Artificial Immune System, and Bacterial Foraging Optimization are swarm intelligence algorithms that solve combinatorial optimization problems. This paper proposes two novel hybrid Artificial Bee Colony (ABC) optimization algorithms that overcome the demerits of standard ABC algorithms. First, this paper proposes a hybrid ABC approach called HABC, in which the standard ABC optimization is hybridized with a modified clonal selection algorithm of the Artificial Immune System that eliminates the poor exploration capabilities of standard ABC optimization. Further, this paper proposes a novel hybrid Artificial Bee Colony (Hybrid ABC) optimization where the strong explorative capabilities of the chemotaxis phase of the bacterial foraging optimization are integrated with a spiral model-based exploitative phase of the ABC by which the proposed Hybrid ABC overcomes the demerits of poor exploration and exploitation of the standard ABC algorithm. METHODS: In this work, the two proposed hybrid approaches were used in concurrent feature selection and parameter optimization of an ANN model. The proposed algorithm is implemented using various back-propagation algorithms, including resilient back-propagation (HABC-RP and Hybrid ABC-RP), Levenberg Marquart (HABC-LM and Hybrid ABC-LM), and momentum-based gradient descent (HABC-MGD and Hybrid ABC-GD) for parameter tuning of ANN. The Wiscons...
Pynn, EV, Ransom, M, Walker, B, McGinnis, E, Brown, S, Gilberts, R, Trehan, P, Jayasekera, PSA, Veitch, D, Hussain, W, Collins, J, Abbott, RA, Chen, KS & Nixon, J 2022, 'Healing of ExcisionAl wounds on Lower legs by Secondary intention (HEALS) cohort study. Part 1: a multicentre prospective observational cohort study in patients without planned compression', Clinical and Experimental Dermatology, vol. 47, no. 10, pp. 1829-1838.
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Abstract Background There is no agreed treatment pathway following excision of keratinocyte cancer (KC). Compression therapy is considered beneficial for secondary intention healing on the lower leg; however, there is a lack of supportive evidence. To plan a randomized controlled trial (RCT), suitable data are needed. We report a multicentre prospective observational cohort study in this patient population with the intention of informing a future trial design. Aim To estimate the time to healing in wounds healing by secondary intention without planned postoperative compression, following excision of KC on the lower leg; to characterize the patient population, including factors affecting healing; and to assess the incidence of complications. Methods This was a multicentre prospective observational cohort study. Inclusion criteria were age ≥ 18 years with planned excision of KC on the lower leg and healing by secondary intention, an ankle–brachial pressure index (ABPI) of ≥ 0.8; and written informed consent. Exclusion criteria included planned excision with primary closure, skin graft or flap; compression therapy for another indication; planned compression; inability of patient to receive, comply with or tolerate high compression; or a suspected diagnosis other than KC. Results This study recruited 58 patients from 9 secondary care dermatology clinics. In the analysis population (n = 53), mean age was 81 years (range 25–97 years), median time to healin...
Qasim, M, Lee, CK & Zhang, YX 2022, 'An experimental study on interfacial bond strength between hybrid engineered cementitious composite and concrete', Construction and Building Materials, vol. 356, pp. 129299-129299.
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Qi, C, Yin, R, Cheng, J, Xu, Z, Chen, J, Gao, X, Li, G, Nghiem, L & Luo, W 2022, 'Bacterial dynamics for gaseous emission and humification during bio-augmented composting of kitchen waste with lime addition for acidity regulation', Science of The Total Environment, vol. 848, pp. 157653-157653.
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This study investigated the impacts of lime addition and further microbial inoculum on gaseous emission and humification during kitchen waste composting. High-throughput sequencing was integrated with Linear Discriminant Analysis Effect Size (LEfSe) and Functional Annotation of Prokaryotic Taxa (FAPROTAX) to decipher bacterial dynamics in response to different additives. Results showed that lime addition enriched bacteria, such as Taibaiella and Sphingobacterium as biomarkers, to strengthen organic biodegradation toward humification. Furthermore, lime addition facilitated the proliferation of thermophilic bacteria (e.g. Bacillus and Symbiobacterium) for aerobic chemoheterotrophy, leading to enhanced organic decomposition to trigger notable gaseous emission. Such emission profile was further exacerbated by microbial inoculum to lime-regulated condition given the rapid enrichment of bacteria (e.g. Caldicoprobacter and Pusillimonas as biomarkers) for fermentation and denitrification. In addition, microbial inoculum slightly hindered humus formation by narrowing the relative abundance of bacteria for humification. Results from this study show that microbial inoculum to feedstock should be carefully regulated to accelerate composting and avoid excessive gaseous emission.
Qi, Y & Indraratna, B 2022, 'Influence of Rubber Inclusion on the Dynamic Response of Rail Track', Journal of Materials in Civil Engineering, vol. 34, no. 2.
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Qian, H, Li, J, Pan, Y, Zong, Z & Wu, C 2022, 'Numerical derivation of P-I diagrams for shallow buried RC box structures', Tunnelling and Underground Space Technology, vol. 124, pp. 104454-104454.
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To perform quick damage assessment and preliminary blast resistant design, the present study develops Pressure-Impulse (P-I) diagrams for the shallow-buried box structures based on high fidelity numerical modelling. Shock wave propagation in the soil and its interaction with the roof slab and vertical wall are considered in the blast load modelling, both flexure and shear damage of the roof slab under surface blast loads are considered in the P-I diagram. Parametric studies are carried out to investigate the effects of roof span, wall thickness, concrete strength, flexure and shear reinforcement ratio on the P-I diagram. Based on the numerical results, analytical formulae to predict the P-I diagrams for buried box structures are derived. The applicability of the improved P-I diagrams approaches in practical evaluation is illustrated through case studies.
Qian, J, Zhang, Y, Bai, L, Yan, X, Du, Y, Ma, R & Ni, B-J 2022, 'Revealing the mechanisms of polypyrrole (Ppy) enhancing methane production from anaerobic digestion of waste activated sludge (WAS)', Water Research, vol. 226, pp. 119291-119291.
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Anaerobic digestion (AD) is a promising method for treating waste activated sludge (WAS), but the low methane yield limits its large-scale application. The addition of conductive nanomaterials has been demonstrated to enhance the activity of AD via promoting the direct interspecies electron transfer (DIET). In this study, novel conductive polypyrrole (Ppy) was prepared to effectively improve the AD performance of WAS. The results showed that the accumulative methane production was enhanced by 27.83% by Ppy, with both acidogenesis and methanogenesis being efficiently accelerated. The microbial community analysis indicated that the abundance of bacteria associated with acidogenesis process was significantly elevated by Ppy. Further investigation by metatranscriptomics revealed that fadE and fadN genes (to express the key enzymes in fatty acid metabolism) were highly expressed in the Ppy-driven AD, suggesting that Ppy promoted electron generation during acid production. For methanogenesis metabolism, genes related to acetate utilization and CO2 utilization methanogenesis were also up-regulated by Ppy, illustrating that Ppy facilitates the utilization of acetate and electrons by methanogenic archaea, thus potentially promoting the methanogenesis through DIET.
Qiang, X, Du, H, Cao, B, Ma, Y, Li, Z, Lu, J, Zhou, W, Zhao, J & Liu, H 2022, 'Boosting the Lithium Storage of Tin Dioxide Nanotubes by MXene Inks as Conductive Binder', Chemistry Letters, vol. 51, no. 6, pp. 585-589.
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Qin, H, Li, R, Yuan, Y, Wang, G, Qin, L & Zhang, Z 2022, 'Mining Bursting Core in Large Temporal Graph.', Proc. VLDB Endow., vol. 15, no. 13, pp. 3911-3923.
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Temporal graphs are ubiquitous. Mining communities that are bursting in a period of time is essential for seeking real emergency events in temporal graphs. Unfortunately, most previous studies on community mining in temporal networks ignore the bursting patterns of communities. In this paper, we study the problem of seeking bursting communities in a temporal graph. We propose a novel model, called the (1, g)-maximal bursting core, to represent a bursting community in a temporal graph. Specifically, an (1, g)-maximal bursting core is a temporal subgraph in which each node has an average degree no less than (1, g) in a time segment with length no less than (1, g). To compute the (1, g)-maximal bursting core, we first develop a novel dynamic programming algorithm that can reduce time complexity of calculating the segment density from (1, g) ( | T |)2 to (1, g) ( | T |). Then, we propose an efficient updating algorithm which can update the segment density in (1, g) (1, g) time. In addition, we develop an efficient algorithm to enumerate all (1, g)-maximal bursting cores that are not dominated by the others in terms of (1, g) and (1, g). The results of extensive experiments on 9 real-life datasets demonstrate the effectiveness, efficiency and scalability of our algorithms.
Qin, H, Li, R-H, Yuan, Y, Wang, G, Yang, W & Qin, L 2022, 'Periodic Communities Mining in Temporal Networks: Concepts and Algorithms', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 8, pp. 3927-3945.
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Qin, J, Liu, X, Zhong, S, Tian, K & Zhang, J 2022, 'Amorphous CoxOy with nano-flake structure for activated persulfate degradation of p-nitrophenol', Journal of Water Process Engineering, vol. 47, pp. 102776-102776.
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Transition metal oxides can activate peroxymonosulfate (PMS) to produce reactive species to degrade pollutants. In this work, we prepared an amorphous CoxOy with nanosheet structure activator to activate PMS and remove p-nitrophenol (PNP). We discussed the degradation performance of PNP by CoxOy/PMS system under different parameters, under the optimum technological conditions, CoxOy has excellent and rapid catalytic activity. In the presence of different anions (Cl−, HCO3−), humic acids and pH (3–11), CoxOy can still maintain favourable catalytic activity. Active species produced in the CoxOy/PMS system were investigated, Sulfate radical (SO4[rad]–) and singlet oxygen (1O2) dominated the degradation of pollutants. This work demonstrates the favourable activity of amorphous CoxOy for degradation of organic pollutants.
Qin, L, Yang, G, Li, D, Ou, K, Zheng, H, Fu, Q & Sun, Y 2022, 'High area energy density of all-solid-state supercapacitor based on double-network hydrogel with high content of graphene/PANI fiber', Chemical Engineering Journal, vol. 430, pp. 133045-133045.
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In order to improve the energy and power density of all-solid-state supercapacitor, more attention is currently focused on the development of electrodes and electrolyte materials with various chemical structure and compositions. However, current studies rarely report hydrogel electrodes with high content of active materials (i.e. > 20.0 wt%), and study their influence on the performance of supercapacitors. Here, a double-network hydrogel electrode was developed and prepared by blade-coating and 3D printing for application in all-solid-state supercapacitor. Moreover, the hydrogel electrode has an unusually high content (25.0 wt%) of active material, leading to high area specific capacitance (871.4mF/cm2) and area energy density (0.14 mWh/cm2 at 0.27 mW/cm2.). This study opens a new pathway to develop high-performance all-solid-state supercapacitors on large-scale.
Qin, P-Y, Song, L-Z & Guo, YJ 2022, 'Conformal Transmitarrays for Unmanned Aerial Vehicles Aided 6G Networks', IEEE Communications Magazine, vol. 60, no. 1, pp. 14-20.
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Qiu, N, Zhang, J, Yuan, F, Jin, Z, Zhang, Y & Fang, J 2022, 'Mechanical performance of triply periodic minimal surface structures with a novel hybrid gradient fabricated by selective laser melting', Engineering Structures, vol. 263, pp. 114377-114377.
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Triply periodic minimal surface (TPMS) structures have been extensively investigated for their excellent mechanical properties and lightweight potential. In this study, a new hybrid gradient (HG) TPMS structure was proposed by combining geometrically deformed gradient (GDG) and volume fraction gradient (VFG). All designed structures were fabricated by selective laser melting (SLM) using Ti-6Al-4V. They were investigated experimentally in terms of deformation behavior, stress–strain curve and energy absorption. The results demonstrated that the GDG structure can develop a better deformation mode to enhance the energy absorption, while the VFG structure can help to reduce the initial peak force and delay the densification point. Most importantly, the hybridization of these two gradients can both greatly enhance the energy absorption capacity and delay the densification point. Compared with the uniform structures, the hybrid gradient structures can almost double the energy absorption under compression. The results of this research may provide valuable insights for the design of high-performance energy-absorbing structures in the future.
Qiu, Y-X, Wen, D, Qin, L, Li, W, Li, R & Zhang, Y 2022, 'Efficient Shortest Path Counting on Large Road Networks.', Proc. VLDB Endow., vol. 15, no. 10, pp. 2098-2110.
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The shortest path distance and related concepts lay the foundations of many real-world applications in road network analysis. The shortest path count has drawn much research attention in academia, not only as a closeness metric accompanying the shorted distance but also serving as a building block of centrality computation. This paper aims to improve the efficiency of counting the shortest paths between two query vertices on a large road network. We propose a novel index solution by organizing all vertices in a tree structure and propose several optimizations to speed up the index construction. We conduct extensive experiments on 14 realworld networks. Compared with the state-of-the-art solution, we achieve much higher efficiency on both query processing and index construction with a more compact index.
Qu, F, Li, W, Guo, Y, Zhang, S, Zhou, JL & Wang, K 2022, 'Chloride-binding capacity of cement-GGBFS-nanosilica composites under seawater chloride-rich environment', Construction and Building Materials, vol. 342, pp. 127890-127890.
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The effects of granulated blast furnace slag (GGBFS) and nano-silica (NS) on the chloride-binding capacity of cement paste after 6-month exposure to seawater chloride-rich solutions were investigated in this paper. The pH, chloride-binding ratio (CBR), leaching behavior, and phase transformation were investigated by various experimental and analysis methods. Thermodynamic modeling was also used to study the phase assemblages for the Portland cement-GGBFS-NS composites exposed to the NaCl and MgCl2 solutions. It was found that for all cementitious composites, more chlorides were bounded in samples exposed to the salt solutions with sodium ions than that with magnesium ions. Proper additions of GGBFS and NS can enhance the chloride-binding capacity of cementitious composites. The results confirm that the addition of GGBFS can improve the chemical chloride-binding capacity because of the increased amount of chloroaluminate. The increased amount of hydrated gels in the cementitious composites with GGBFS also improved the physical chloride-binding capacity. The addition of NS increased the physical chloride-binding capacity due to the more formation of C-S-H/C-A-S-H gels, while the excessive addition of NS left less aluminum phase available for the formation of chloroaluminate, thus further decreased the chemical chloride-binding capacity. Magnesium ions in solutions increased the amount of chloride in the diffuse layer of C-S-H gels and hydrotalcite. The related results provide novel insight into the influences of GGBFS and NS on the chloride-binding capacity of cementitious composites under chloride-rich environments.
Qu, X, Zou, Z, Su, X, Zhou, P, Wei, W, Wen, S & Wu, D 2022, 'Attend to Where and When: Cascaded Attention Network for Facial Expression Recognition', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 3, pp. 580-592.
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Recognizing human expression in videos is a challenging task due to dynamic changes in facial actions and diverse visual appearances. The key to design a reliable video-based expression recognition system is to extract robust spatial features and make full use of temporal modality characteristics. In this paper, we present a novel network architecture called Cascaded Attention Network (CAN) which is a cascaded spatiotemporal model incorporating with both spatial and temporal attention, tailored to video-level facial expression recognition. The cascaded fundamental model consists of a transfer convolutional network and Bidirectional Long Short-Term Memory (BiLSTM) network. Spatial attention is designed from the facial landmarks since facial expressions depend on the actions of key regions (eyebrows, eyes, nose, and mouth) on the face. Focusing on these key regions can help to decrease the effect of person-specific attributes. Meanwhile, the temporal attention is applied to automatically select the peak of expressions and aggregate the video-level representation. Our proposed CAN achieves the state-of-the-art performance on the three most widely used facial expression datasets: CK+ (99.03%), Oulu-CASIA (88.33%), and MMI (83.55%). Moreover, we conduct an extended experiment on a much more complex wild dataset AFEW and the experimental results further verify the generality of our attention mechanisms.
Qu, Y, Gao, L, Xiang, Y, Shen, S & Yu, S 2022, 'FedTwin: Blockchain-Enabled Adaptive Asynchronous Federated Learning for Digital Twin Networks', IEEE Network, vol. 36, no. 6, pp. 183-190.
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The fast proliferation of digital twin (DT) establishes a direct connection between the physical entity and its deployed digital representation. As markets shift toward mass customization and new service delivery models, the digital representation has become more adaptive and agile by forming digital twin networks (DTN). DTN institutes a real-time single source of truth everywhere. However, there are several issues preventing DTN from further application, which are centralized processing, data falsification, privacy leakage, lack of incentive mechanism, etc. To make DTN better meet the ever-changing demands, we propose a novel blockchain-enabled adaptive asynchronous federated learning (FedTwin) paradigm for privacy-preserving and decentralized DTN. We design Proof-of-Federalism (PoF), which is a tailor-made consensus algorithm for autonomous DTN. In each DT's local training phase, generative adversarial network enhanced differential privacy is used to protect the privacy of local model parameters while a modified Isolation Forest is deployed to filter out the falsified DTs. In the global aggregation phase, an improved Markov decision process is leveraged to select optimal DTs to achieve adaptive asynchronous aggregation while providing a roll-back mechanism to redact the falsified global models. With this paper, we aim to provide insights to the forthcoming researchers and readers in this under-explored domain.
Qu, Y, Xu, C, Gao, L, Xiang, Y & Yu, S 2022, 'FL-SEC: Privacy-Preserving Decentralized Federated Learning Using SignSGD for the Internet of Artificially Intelligent Things', IEEE Internet of Things Magazine, vol. 5, no. 1, pp. 85-90.
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Qu, Z, Lau, CW, Simoff, SJ, Kennedy, PJ, Nguyen, QV & Catchpoole, DR 2022, 'Review of Innovative Immersive Technologies for Healthcare Applications', Innovations in Digital Health, Diagnostics, and Biomarkers, vol. 2, no. 2022, pp. 27-39.
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ABSTRACTImmersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), can connect people using enhanced data visualizations to better involve stakeholders as integral members of the process. Immersive technologies have started to change the research on multidimensional genomic data analysis for disease diagnostics and treatments. Immersive technologies are highlighted in some research for health and clinical needs, especially for precision medicine innovation. The use of immersive technology for genomic data analysis has recently received attention from the research community. Genomic data analytics research seeks to integrate immersive technologies to build more natural human-computer interactions that allow better perception engagements. Immersive technologies, especially VR, help humans perceive the digital world as real and give learning output with lower performance errors and higher accuracy. However, there are limited reviews about immersive technologies used in healthcare and genomic data analysis with specific digital health applications. This paper contributes a comprehensive review of using immersive technologies for digital health applications, including patient-centric applications, medical domain education, and data analysis, especially genomic data visual analytics. We highlight the evolution of a visual analysis using VR as a case study for how immersive technologies step, can by step, move into the genomic data analysis domain. The discussion and conclusion summarize the current immersive technology applications' usability, innovation, and future work in the healthcare domain, and digital health data visual analytics.
Quach, S, Reise, K, McGregor, C, Papaconstantinou, E & Nonoyama, ML 2022, 'A Delphi Survey of Canadian Respiratory Therapists’ Practice Statements on Pediatric Mechanical Ventilation', Respiratory Care, vol. 67, no. 11, pp. 1420-1436.
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BACKGROUND: Pediatric mechanical ventilation practice guidelines are not well established; therefore, the European Society for Paediatric and Neonatal Intensive Care (ESPNIC) developed consensus recommendations on pediatric mechanical ventilation management in 2017. However, the guideline's applicability in different health care settings is unknown. This study aimed to determine the consensus on pediatric mechanical ventilation practices from Canadian respiratory therapists' (RTs) perspectives and consensually validate aspects of the ESPNIC guideline. METHODS: A 3-round modified electronic Delphi survey was conducted; contents were guided by ESPNIC. Participants were RTs with at least 5 years of experience working in standalone pediatric ICUs or units with dedicated pediatric intensive care beds across Canada. Round 1 collected open-text feedback, and subsequent rounds gathered feedback using a 6-point Likert scale. Consensus was defined as ≥ 75% agreement; if consensus was unmet, statements were revised for re-ranking in the subsequent round. RESULTS: Fifty-two RTs from 14 different pediatric facilities participated in at least one of the 3 rounds. Rounds 1, 2, and 3 had a response rate of 80%, 93%, and 96%, respectively. A total of 59 practice statements achieved consensus by the end of round 3, categorized into 10 sections: (1) noninvasive ventilation and high-flow oxygen therapy, (2) tidal volume and inspiratory pressures, (3) breathing frequency and inspiratory times, (4) PEEP and FIO2 , (5) advanced modes of ventilation, (6) weaning, (7) physiological targets, (8) monitoring, (9) general, and (10) equipment adjuncts. Cumulative text feedback guided the formation of the clinical remarks to supplement these practice statements. CONCLUSIONS: This was the first study to survey RTs for their perspectives on the general practice of pediatric mechanical ventilation management in Canada, generally aligning with the ESPNIC guideline. These practice stateme...
Quetglas-Llabrés, MM, Quispe, C, Herrera-Bravo, J, Catarino, MD, Pereira, OR, Cardoso, SM, Dua, K, Chellappan, DK, Pabreja, K, Satija, S, Mehta, M, Sureda, A, Martorell, M, Satmbekova, D, Yeskaliyeva, B, Sharifi-Rad, J, Rasool, N, Butnariu, M, Bagiu, IC, Bagiu, RV, Calina, D & Cho, WC 2022, '[Retracted] Pharmacological Properties of Bergapten: Mechanistic and Therapeutic Aspects', Oxidative Medicine and Cellular Longevity, vol. 2022, no. 1, pp. 1-10.
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Bergapten (BP) or 5‐methoxypsoralen (5‐MOP) is a furocoumarin compound mainly found in bergamot essential oil but also in other citrus essential oils and grapefruit juice. This compound presents antibacterial, anti‐inflammatory, hypolipemic, and anticancer effects and is successfully used as a photosensitizing agent. The present review focuses on the research evidence related to the therapeutic properties of bergapten collected in recent years. Many preclinical and in vitro studies have been evidenced the therapeutic action of BP; however, few clinical trials have been carried out to evaluate its efficacy. These clinical trials with BP are mainly focused on patients suffering from skin disorders such as psoriasis or vitiligo. In these trials, the administration of BP (oral or topical) combined with UV irradiation induces relevant lesion clearance rates. In addition, beneficial effects of bergamot extract were also observed in patients with altered serum lipid profiles and in people with nonalcoholic fatty liver. On the contrary, there are no clinical trials that investigate the possible effects on cancer. Although the bioavailability of BP is lower than that of its 8‐methoxypsoralen (8‐MOP) isomer, it has fewer side effects allowing higher concentrations to be administered. In conclusion, although the use of BP has therapeutic applications on skin disorders as a sensitizing agent and as components of bergamot extract as hypolipemic therapy, more trials are necessary to define the doses and treatment guidelines and its usefulness against other pathologies such as cancer or bacterial infections.
Quevedo, RP, Maciel, DA, Uehara, TDT, Vojtek, M, Rennó, CD, Pradhan, B, Vojteková, J & Pham, QB 2022, 'Consideration of spatial heterogeneity in landslide susceptibility mapping using geographical random forest model', Geocarto International, vol. 37, no. 25, pp. 8190-8213.
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Most previous studies of landslide susceptibility mapping (LSM) have not contemplated spatial heterogeneity and the commonly used models for LSM are aspatial, which could reduce model performance. Therefore, aiming to evaluate the applicability of spatial algorithms to predict landslide susceptibility, the performance of geographical random forest (GRF) was evaluated, in comparison to random forest (RF) and extreme gradient boosting (XGBoost). Based on the results, GRF presented the better performance (AUC = 0.876), followed by RF (AUC = 0.748) and XGBoost (AUC = 0.745). GRF also provided the most suitable susceptibility map. While RF and XGBoost presented almost 50% of the study area as susceptible, the GRF presented more concentrated susceptibility areas spatially, with a reasonable area for moderate (15.55%), high (8.73%) and very-high (2.59%) susceptibility classes. Finally, it can be inferred that spatial assessment may improve model performance, and that spatial models have a great potential for LSM.
Quijano-Baron, J, Carlier, R, Rodriguez, JF, Sandi, SG, Saco, PM, Wen, L & Kuczera, G 2022, 'And we thought the Millennium Drought was bad: Assessing climate variability and change impacts on an Australian dryland wetland using an ecohydrologic emulator', Water Research, vol. 218, pp. 118487-118487.
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Quijano-Baron, J, Saco, PM & Rodriguez, JF 2022, 'Modelling the effects of above and belowground biomass pools on erosion dynamics', CATENA, vol. 213, pp. 106123-106123.
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R., J, Gurunathan, B, K, S, Varjani, S, Ngo, HH & Gnansounou, E 2022, 'Advancements in heavy metals removal from effluents employing nano-adsorbents: Way towards cleaner production', Environmental Research, vol. 203, pp. 111815-111815.
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Due to the development in science field which gives not only benefit but also introducesundesirable pollution to the environment. This pollution is due to poor discharge activities of industrial effluents into the soil and water bodies, surface run off from fields of agricultural lands, dumping of untreated wastes by municipalities, and mining activites, which deteriorates the cardinal virtue of our environment and causes menace to human health and life. Heavy metal(s), a natural constituent on earth's crust and economic important mineral, due to its recalcitrant effects creates heavy metal pollution which affects food chain and also reduces the quality of water. For this, many researchers have performed studies to find efficient methods for wastewater remediation. One of the most promising methods from economic point of view is adsorption, which is simple in design, but leads to use of a wide range of adsorbents and ease of operations. Due to advances in nanotechnology, many nanomaterials were used as adsorbents for wastewater remediation, because of their efficiency. Many researchers have reported that nanoadsorbents are unmitigatedly a fruitful solution to address this world's problem. This review presents a potent view on various classes of nanoadsorbents and their application to wastewater treatment. It provides a bird's eye view of the suitability of different types of nanomaterials for remediation of wastewater and Backspace gives up-to-date information about polymer based and silica-based nanoadsorbents.
Rad, HS, Shiravand, Y, Radfar, P, Ladwa, R, Perry, C, Han, X, Warkiani, ME, Adams, MN, Hughes, BGM, O'Byrne, K & Kulasinghe, A 2022, 'Understanding the tumor microenvironment in head and neck squamous cell carcinoma', Clinical & Translational Immunology, vol. 11, no. 6, p. e1397.
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AbstractHead and neck squamous cell carcinoma (HNSCC) represents a heterogeneous group of tumors. While significant progress has been made using multimodal treatment, the 5‐year survival remains at 50%. Developing effective therapies, such as immunotherapy, will likely lead to better treatment of primary and metastatic disease. However, not all HNSCC tumors respond to immune checkpoint blockade therapy. Understanding the complex cellular composition and interactions of the tumor microenvironment is likely to lead to new knowledge for effective therapies and treatment resistance. In this review, we discuss HNSCC characteristics, predictive biomarkers, factors influencing immunotherapy response, with a focus on the tumor microenvironment.
Rad, MA, Mahmodi, H, Filipe, EC, Cox, TR, Kabakova, I & Tipper, JL 2022, 'Micromechanical characterisation of 3D bioprinted neural cell models using Brillouin microspectroscopy', Bioprinting, vol. 25, pp. e00179-e00179.
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Biofabrication of three-dimensional (3D) in vitro neural cell models that closely mimic the central nervous system (CNS) is an emerging field of research with applications from fundamental biology to regenerative medicine, and far reaching benefits for the economy, healthcare and the ethical use of animals. The micromechanical properties of such models are an important factor dictating the success of modelling outcomes in relation to accurate reproduction of the processes in native tissues. Characterising the micromechanical properties of such models non-destructively and over a prolonged span of time, however, are key challenges. Brillouin microspectroscopy (BM) could provide a solution to this problem since this technology is non-invasive, label-free and is capable of micro-scale 3D imaging. In this work, the micromechanical properties of 3D bioprinted neural cell models consisting of NG 108-15 neuronal cells and Gelatin methacryloyl (GelMA) hydrogels of various concentrations were investigated using BM. We demonstrate changes in the volume-averaged (VA) and local micro-scale mechanical properties of these models over a 7 day period, in which the hydrogel component of the model are found to soften as the cells grow, multiply and form stiffer spheroid-type structures. These findings signify the necessity to resolve in microscopic detail the mechanics of in vitro 3D tissue models and suggest Brillouin microspectroscopy to be a suitable technology to bridge this gap.
Radfar, P, Aboulkheyr Es, H, Salomon, R, Kulasinghe, A, Ramalingam, N, Sarafraz-Yazdi, E, Thiery, JP & Warkiani, ME 2022, 'Single-cell analysis of circulating tumour cells: enabling technologies and clinical applications', Trends in Biotechnology, vol. 40, no. 9, pp. 1041-1060.
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Multimodal analysis of circulating tumour cells (CTCs) has the potential to provide remarkable insight for cancer development and metastasis. CTCs and CTC clusters investigation using single-cell analysis, enables researchers to gain crucial information on metastatic mechanisms and the genomic alterations responsible for drug resistance, empowering treatment, and management of cancer. Despite a plethora of CTC isolation technologies, careful attention to the strengths and weaknesses of each method should be considered in order to isolate these rare cells. Here, we provide an overview of cutting-edge technologies used for single-cell isolation and analysis of CTCs. Additionally, we highlight the biological features, clinical application, and the therapeutic potential of CTCs and CTC clusters using single-cell analysis platforms for cancer management.
Rafique, S, Nizami, MSH, Irshad, UB, Hossain, MJ & Mukhopadhyay, SC 2022, 'A two-stage multi-objective stochastic optimization strategy to minimize cost for electric bus depot operators', Journal of Cleaner Production, vol. 332, pp. 129856-129856.
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Rafique, S, Nizami, MSH, Irshad, UB, Hossain, MJ & Mukhopadhyay, SC 2022, 'EV Scheduling Framework for Peak Demand Management in LV Residential Networks', IEEE Systems Journal, vol. 16, no. 1, pp. 1520-1528.
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Ragazzon, MRP, Messineo, S, Gravdahl, JT, Harcombe, DM & Ruppert, MG 2022, 'The Generalized Lyapunov Demodulator: High-Bandwidth, Low-Noise Amplitude and Phase Estimation', IEEE Open Journal of Control Systems, vol. 1, pp. 69-84.
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Rahimi, I & Nematian, J 2022, 'A Bibliometric Analysis on Optimization Solution Methods Applied to Supply Chain of Solar Energy', Archives of Computational Methods in Engineering, vol. 29, no. 6, pp. 4213-4231.
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Rahimi, I, Gandomi, AH, Deb, K, Chen, F & Nikoo, MR 2022, 'Scheduling by NSGA-II: Review and Bibliometric Analysis', Processes, vol. 10, no. 1, pp. 98-98.
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NSGA-II is an evolutionary multi-objective optimization algorithm that has been applied to a wide variety of search and optimization problems since its publication in 2000. This study presents a review and bibliometric analysis of numerous NSGA-II adaptations in addressing scheduling problems. This paper is divided into two parts. The first part discusses the main ideas of scheduling and different evolutionary computation methods for scheduling and provides a review of different scheduling problems, such as production and personnel scheduling. Moreover, a brief comparison of different evolutionary multi-objective optimization algorithms is provided, followed by a summary of state-of-the-art works on the application of NSGA-II in scheduling. The next part presents a detailed bibliometric analysis focusing on NSGA-II for scheduling applications obtained from the Scopus and Web of Science (WoS) databases based on keyword and network analyses that were conducted to identify the most interesting subject fields. Additionally, several criteria are recognized which may advise scholars to find key gaps in the field and develop new approaches in future works. The final sections present a summary and aims for future studies, along with conclusions and a discussion.
Rahma, ON, Putra, AP, Rahmatillah, A, Putri, YSKA, Fajriaty, ND, Ain, K & Chai, R 2022, 'Electrodermal Activity for Measuring Cognitive and Emotional Stress Level', Journal of Medical Signals & Sensors, vol. 12, no. 2, pp. 155-162.
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Stress can lead to harmful conditions in the body, such as anxiety disorders and depression. One of the promising noninvasive methods, which has been widely used in detecting stress and emotion, is electrodermal activity (EDA). EDA has a tonic and phasic component called skin conductance level and skin conductance response (SCR). However, the components of the EDA cannot be directly extracted and need to be deconvolved to obtain it. The EDA signals were collected from 18 healthy subjects that underwent three sessions – Stroop test with increasing stress levels. The EDA signals were then deconvoluted by using continuous deconvolution analysis (CDA) and convex optimization approach to electrodermal activity (cvxEDA). Four features from the result of the deconvolution process were collected, namely sample average, standard deviation, first absolute difference, and normalized first absolute difference. Those features were used as the input of the classification process using the extreme learning machine (ELM). The output of classification was the stress level; mild, moderate, and severe. The visual of the phasic component using cvxEDA is more precise or smoother than the CDA's result. However, both methods could separate SCR from the original skin conductivity raw and indicate the small peaks from the SCR. The classification process results showed that both CDA and cvxEDA methods with 50 hidden layers in ELM had a high accuracy in classifying the stress level, which was 95.56% and 94.45%, respectively. This study developed a stress level classification method using ELM and the statistical features of SCR. The result showed that EDA could classify the stress level with over 94% accuracy. This system could help people monitor their mental health during overworking, leading to anxiety and depression because of untreated stress.
Rahman, M, Zhao, M, Islam, MS, Dong, K & Saha, SC 2022, 'Numerical study of nano and micro pollutant particle transport and deposition in realistic human lung airways', Powder Technology, vol. 402, pp. 117364-117364.
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Rahman, MM, Arefi, A, Shafiullah, GM, Hettiwatte, S, Azizivahed, A, Muyeen, SM & Islam, MR 2022, 'An Incentivized and Optimized Dynamic Mechanism for Demand Response for Managing Voltage in Distribution Networks', IEEE Access, vol. 10, pp. 96359-96377.
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Rahman, MM, Zhao, M, Islam, MS, Dong, K & Saha, SC 2022, 'Nanoparticle transport and deposition in a heterogeneous human lung airway tree: An efficient one path model for CFD simulations', European Journal of Pharmaceutical Sciences, vol. 177, pp. 106279-106279.
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Ramadan, HS, Hassan, IA & Alhelou, HH 2022, 'Robust control for techno‐economic efficient energy management of fuel cell hybrid electric vehicles', IET Renewable Power Generation, vol. 16, no. 8, pp. 1661-1684.
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AbstractThe design of an efficient techno‐economic autonomous fuel cell hybrid electric vehicle (FCHEV) is a crucial challenge. This paper investigates the design of a near optimal PI controller for an automated FCHEV, where autonomy is expressed as efficient and robust tracking of a given reference speed trajectory without driver's intervention. An impartial comparison is introduced to illustrate the effectiveness of the proposed metaheuristic‐based optimal controllers in enhancing the system dynamic performance. The comprehensive optimization performance indicator is considered as a function of the vehicle dynamic characteristics while determining the optimal controller gains. In this paper, the proposed effective up‐to‐date metaheuristic techniques are the grey wolf optimization (GWO) as well as the artificial bee colony (ABC). Using MATLABTM/Simulink, numerical simulations clearly illustrate the efficiency of near‐optimal gains in the optimized tuning methodologies and the fixed manual one in realizing adequate velocity tracking. The simulation results demonstrate the superiority of both ABC and GWO rather than the manual controller for driving cycles of high acceleration and deceleration levels. In absence of these latter, the manual defined gain controller is considered sufficient. Through a comprehensive sensitivity analysis, the robustness of both metaheuristic‐based controllers is verified under diverse driving cycles of different operation features and nature. Despite GWO results in better dynamic characteristics, the ABC provides more economical feature with about 1.5% compared to manual system in extra urban driving cycle. However, manual‐controller has the minimum fuel cost under the United States driving cycle developed by the environmental protection agency as a New York city cycle (US EPA NYCC) and urban driving cycle (ECE). Ecologically, electric vehicles have an environmentally frie...
Ramadan, HS, Iqbal, M, Becherif, M & Haes Alhelou, H 2022, 'Efficient control and multi‐criteria energy scheduling of renewable‐based utility grid via pareto‐metaheuristic optimizers', IET Renewable Power Generation, vol. 16, no. 6, pp. 1246-1266.
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AbstractBeing a renewable influenced utility grid, the wind integrated hydrothermal system (WHTS) is considered as a future‐proof and contemporary energy system to reduce the dependency on depleting fossil fuels. However, due to intermittent nature of wind energy and complex operational constraints, offering the day‐ahead operation schedule of WHTS with joint awareness of fuel economy and emissions is classified as a complicated multi‐objective optimization problem. The major challenge is to maximize the utilization of clean hydro power to accommodate uncertain wind energy together with reduced dependency on fossils based thermal power. In this context, the paper devises an efficient control and multi‐criteria energy scheduling method, essentially comprised of adaptive repair and volume restriction to maximize the utilization of available hydro power. The pareto‐metaheuristic optimizer based on non‐dominated sorting is exploited for the day‐ahead scheduling of WHTS based utility grid. The most appropriate solution from pareto‐optimal archive is selected using fuzzy‐ranking index. The expected reductions in fuel cost and emissions are reflected using a significant probability scheme. The effectiveness of the proposed approach based on pareto‐metaheuristic is appraised by various case studies. The simulation results provide a suitable day‐ahead generation schedule of WHTS based grid, considering both economics and environmental aspects.
Ramakrishna, VAS, Chamoli, U, Larosa, AG, Mukhopadhyay, SC, Prusty, BG & Diwan, AD 2022, 'Finite element modeling of temporal bone graft changes in XLIF: Quantifying biomechanical effects at adjacent levels', Journal of Orthopaedic Research, vol. 40, no. 6, pp. 1420-1435.
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AbstractExtreme lateral interbody fusion allows for the insertion of a large‐footprint interbody cage while maintaining the presence of natural stabilizing ligaments and the facets. It is unclear how the load‐distribution mechanisms through these structures alter with temporal changes in the bone graft. The aim of this study was to examine the effects of temporal bone graft changes on load distribution among the cage, graft, and surrounding spinal structures using finite element analysis. Thoracolumbosacral spine computed tomography data from an asymptomatic male subject were segmented into anatomical regions of interest and digitally stitched to generate a surface mesh of the lumbar spine (L1‐S1). The interbody cage was inserted into the L4‐L5 region during surface meshing. A volumetric mesh was generated and imported into finite element software for pre‐processing, running nonlinear static solves, and post‐processing. Temporal stiffening was simulated in the graft region with unbonded (Soft Callus, Temporal Stages 1–3, Solid Graft) and bonded (Partial Fusion, Full Fusion) contact. In flexion and extension, cage stress reduced by 20% from the soft callus to solid graft state. Force on the graft was directly related to its stiffness, and load‐share between the cage and graft improved with increasing graft stiffness, regardless of whether contact was fused with the endplates. Fused contact between the cage‐graft complex and the adjacent endplates shifted load‐distribution pathways from the ligaments and facets to the implant, however, these changes did not extend to adjacent levels. These results suggest that once complete fusion is achieved, the existing load paths are seemingly diminished.
Ramia, G, Mitchell, E, Hastings, C, Morris, A & Wilson, S 2022, 'The pandemic and the welfare of international students Abandonment or policy consistency?', AUSTRALIAN UNIVERSITIES REVIEW, vol. 64, no. 1, pp. 17-26.
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In its response to COVID-19 in 2020, the Australian Government excluded international students from the temporary financial assistance it offered most permanent residents. This article examines the status of international student welfare as a policy question before and during the pandemic, and discusses post-pandemic policy implications. It draws on pre- and during-COVID-19 survey data from international students in Sydney and Melbourne. We argue that the pandemic highlighted and exacerbated an existing policy absence, rather than constituting a fresh abandonment of international students. Since the Dawkins changes in the early 1990s, international students have been officially treated in policy as consumers, not as ‘social citizens’. This made many of them vulnerable to socio-economic shocks, given widespread dependence on precarious employment and insecure private income sources. The central policy implication is that, to avoid disproportionate welfare diminutions in future crises, the government needs to align the treatment of international and domestic students.
Ran, H, Sun, L, Cheng, S, Ma, Y, Yan, S, Meng, S, Shi, K & Wen, S 2022, 'A novel cooperative searching architecture for multi‐unmanned aerial vehicles under restricted communication', Asian Journal of Control, vol. 24, no. 2, pp. 510-516.
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AbstractSearching for a distinguishable lost target in a bounded area automatically with unmanned aerial vehicle (UAV) is a fundamental problem in the theory of physical search. This paper studies the problem in detail, presents a new and more realistic Bayesian formula representing the problem by taking communication capability of UAVs into consideration, and focuses on the case of applying a team of multiple cooperative UAVs in the field for the search, where each UAV can autonomously search the area for the target using Bayesian filtering algorithm. The perturbation of the searching performance by the different prior belief probability distribution functions is investigated. Empirical evidence findings illustrate that our approach yields improved accuracy.
Ran, H, Wen, S, Li, Q, Yang, Y, Shi, K, Feng, Y, Zhou, P & Huang, T 2022, 'Memristor-Based Edge Computing of Blaze Block for Image Recognition', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 5, pp. 2121-2131.
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In this article, a novel edge computing system is proposed for image recognition via memristor-based blaze block circuit, which includes a memristive convolutional neural network (MCNN) layer, two single-memristive blaze blocks (SMBBs), four double-memristive blaze blocks (DMBBs), a global Avg-pooling (GAP) layer, and a memristive full connected (MFC) layer. SMBBs and DMBBs mainly utilize the depthwise separable convolution neural network (DwCNN) that can be implemented with a much smaller memristor crossbar (MC). In the backward propagation, we use batch normalization (BN) layers to accelerate the convergence. In the forward propagation, this circuit combines DwCNN layers/CNN layers with nonseparate BN layers, which means that the required number of operational amplifiers is cut by half as long as the greatly reduced power consumption. A diode is added after the rectified linear unit (ReLU) layer to limit the output of the circuit below the threshold voltage Vt of the memristor; thus, the circuit is more stable. Experiments show that the proposed memristor-based circuit achieves an accuracy of 84.38% on the CIFAR-10 data set with advantages in computing resources, calculation time, and power consumption. Experiments also show that, when the number of multistate conductance is 2⁸ and the quantization bit of the data is 8, the circuit can achieve its best balance between power consumption and production cost.
Rana, AK, Thakur, MK, Saini, AK, Mokhta, SK, Moradi, O, Rydzkowski, T, Alsanie, WF, Wang, Q, Grammatikos, S & Thakur, VK 2022, 'Recent developments in microbial degradation of polypropylene: Integrated approaches towards a sustainable environment', Science of The Total Environment, vol. 826, pp. 154056-154056.
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Fossil-fuel-based plastics have many enticing properties, but their production has resulted in significant environmental issues that require immediate attention. Despite the fact that these polymers are manmade, some bacteria can degrade and metabolise them, suggesting that biotechnologies based on the principle of plastic biodegradation could be beneficial. Among different types of plastics, polypropylene (PP), either having low or high density, is one of the most consumed plastics (18.85%). Their debasement under natural conditions is somewhat tricky. Still, their debasement under natural conditions is rather difficult slightly. However, different scientists have still made efforts by employing other microbes such as bacteria, fungi, and guts bacteria of larvae of insects to bio-deteriorate the PP plastic. Pre-irradiation techniques (ultraviolet and gamma irradiations), compatibilizers, and bio-additives (natural fibers, starch, and polylactic acid) have been found to impact percent bio-deterioration of different PP derivatives stronglythe various. The fungal and bacterial study showed that PP macro/microplastic might serve as an energy source and sole carbon during bio-degradation. Generally, gravimetric method or physical characterization techniques such as FTIR, XRD, SEM, etc., are utilized to affirm the bio-degradation of PP plastics-based materials. However, these techniques are not enough to warrant the bio-deterioration of PP. In this regard, a new technique approach that measures the amount of carbon dioxide emitted during bacterial or fungus degradation has also been discussed. In addition, further exploration is needed on novel isolates from plastisphere environments, sub-atomic strategies to describe plastic-debasing microorganisms and improve enzymatic action strategies, and omics-based innovations to speed up plastic waste bio-deterioration.
Rani, P, Mishra, AR, Krishankumar, R, Ravichandran, KS & Gandomi, AH 2022, 'A New Pythagorean Fuzzy Based Decision Framework for Assessing Healthcare Waste Treatment', IEEE Transactions on Engineering Management, vol. 69, no. 6, pp. 2915-2929.
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Rao, P, Ouyang, P, Wu, J, Li, P, Nimbalkar, S & Chen, Q 2022, 'Seismic Stability of Heterogeneous Slopes with Tensile Strength Cutoff Using Discrete-Kinematic Mechanism and a Pseudostatic Approach', International Journal of Geomechanics, vol. 22, no. 12.
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Rao, P, Xiang, Y, Ouyang, P, Nimbalkar, S & Chen, Q 2022, 'Finite Element Analysis of Electro-Thermal Coupling of Sandstone Under Lightning Currents', Geotechnical and Geological Engineering, vol. 40, no. 5, pp. 2593-2604.
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Rao, P-P, Ouyang, P-H, Nimbalkar, S, Chen, Q-S, Wu, Z-L & Cui, J-F 2022, 'Analytical modelling of the mechanical damage of soil induced by lightning strikes capturing electro-thermal, thermo-osmotic, and electro-osmotic effects', Journal of Mountain Science, vol. 19, no. 7, pp. 2027-2043.
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Rasal, AS, Yadav, S, Kashale, AA, Altaee, A & Chang, J-Y 2022, 'Stability of quantum dot-sensitized solar cells: A review and prospects', Nano Energy, vol. 94, pp. 106854-106854.
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Rashid, MI, Benhelal, E, Anderberg, L, Farhang, F, Oliver, T, Rayson, MS & Stockenhuber, M 2022, 'Aqueous carbonation of peridotites for carbon utilisation: a critical review', Environmental Science and Pollution Research, vol. 29, no. 50, pp. 75161-75183.
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Rasouli, H & Fatahi, B 2022, 'Liquefaction and post-liquefaction resistance of sand reinforced with recycled geofibre', Geotextiles and Geomembranes, vol. 50, no. 1, pp. 69-81.
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The present study provides an insight into the effect of recycled carpet fibre on the mechanical response of clean sand as backfill material subjected to monotonic loading and cyclic loading as well as post-liquefaction resistance of both unreinforced and carpet fibre reinforced soils. To achieve these goals, a series of multi-stage soil element tests under cyclic loading event resulting in liquefaction followed by undrained monotonic shearing without excess pore water pressure dissipation as well as a series of monotonic undrained shear test is conducted. All the specimens are isotropically consolidated under a constant effective confining stress of 100 kPa by considering the effect of cyclic stress ratio and carpet fibre content ranging from 0.25% to 0.75%. The obtained results revealed the efficiency of carpet fibre inclusion in increasing the secant shear modulus and ductility of clean sand under monotonic shearing without previous loading history. The impact of carpet fibre inclusion on the trend of cyclic excess pore water pressure generation and cyclic stiffness degradation was minimal. However, adding carpet fibre significantly improved both liquefaction and post-liquefaction resistances of clean sand. The liquefaction resistance of clean sand, at a constant 15 loading cycles, improved by 26.3% when the soil was reinforced with 0.75% recycled carpet fibre. In addition, the initial shear modulus of the liquefied specimen significantly increased by adding recycled carpet fibre.
Rasouli, H, Fatahi, B & Nimbalkar, S 2022, 'Re-liquefaction resistance of lightly cemented sands', Canadian Geotechnical Journal, vol. 59, no. 12, pp. 2085-2101.
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The re-liquefaction resistance of cemented sands under multiple liquefaction events such as pre-shock, main-shock, and after-shock earthquakes is a complex phenomenon because the response may alter due to bond breakage. A series of multistage liquefaction–re-consolidation soil element tests under undrained stress-controlled cyclic loading condition using cyclic triaxial were carried out to assess the liquefaction and re-liquefaction resistance of cemented sands with varying degrees of cementation. Lightly cemented specimens were reconstituted using Sydney sand and high early strength Portland cement with cement content ranging from 0.25% to 1% and unconfined compression strength from 15 to 80 kPa. The results showed that the re-liquefaction resistance of cemented sands with different amounts of cement decreased after the first liquefaction event and then increased for succeeding liquefaction events. While the trend of residual excess pore water pressure ratio and cyclic stiffness degradation index of untreated sand under successive liquefaction events remained consistent, the corresponding responses for cemented sands altered for the second to the fifth liquefaction events. In fact, the residual excess pore water pressure ratio and cyclic stiffness of cemented sand increased and degraded faster during the early cycles of loading for the second to fifth liquefaction events.
Rastpour, A & McGregor, C 2022, 'Predicting Patient Wait Times by Using Highly Deidentified Data in Mental Health Care: Enhanced Machine Learning Approach', JMIR Mental Health, vol. 9, no. 8, pp. e38428-e38428.
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Background Wait times impact patient satisfaction, treatment effectiveness, and the efficiency of care that the patients receive. Wait time prediction in mental health is a complex task and is affected by the difficulty in predicting the required number of treatment sessions for outpatients, high no-show rates, and the possibility of using group treatment sessions. The task of wait time analysis becomes even more challenging if the input data has low utility, which happens when the data is highly deidentified by removing both direct and quasi identifiers. Objective The first aim of this study was to develop machine learning models to predict the wait time from referral to the first appointment for psychiatric outpatients by using real-time data. The second aim was to enhance the performance of these predictive models by utilizing the system’s knowledge while the input data were highly deidentified. The third aim was to identify the factors that drove long wait times, and the fourth aim was to build these models such that they were practical and easy-to-implement (and therefore, attractive to care providers). Methods We analyzed retrospective highly deidentified administrative data from 8 outpatient clinics at Ontario Shores Centre for Mental Health Sciences in Canada by using 6 machine learning methods to predict the first appointment wait time for new outpatients. We used the system’s knowledge to mitigate the low utility of our data. The data included 4187 patients who received care through 30,342 appointments. Results The average wait time varied widely between d...
Ravindran, MXY, Asikin-Mijan, N, Ong, HC, Derawi, D, Yusof, MR, Mastuli, MS, Lee, HV, Wan Mahmood, WNAS, Razali, MS, Abdulkareem Al-Sultan, G & Taufiq-Yap, YH 2022, 'Feasibility of advancing the production of bio-jet fuel via microwave reactor under low reaction temperature', Journal of Analytical and Applied Pyrolysis, vol. 168, pp. 105772-105772.
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Rawat, S, Zhang, YX & Lee, CK 2022, 'Multi-response optimization of hybrid fibre engineered cementitious composite using Grey-Taguchi method and utility concept', Construction and Building Materials, vol. 319, pp. 126040-126040.
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Raza, MA, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2022, 'Statistical Learning-Based Grant-Free Access for Delay-Sensitive Internet of Things Applications', IEEE Transactions on Vehicular Technology, vol. 71, no. 5, pp. 5492-5506.
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Mission-critical Internet-of-Things (IoT) applications require communication interfaces that provide ultra-reliability and low latency. Acquiring knowledge regarding the number of active devices and their latency-reliability requirements becomes essential to optimize resource allocation in heterogeneous networks. Due to the inherent heavy computation overheads, the conventional centralized decision-making approaches result in large latency. The distributed computing and device-level prediction of network parameters can play a significant role in designing mission-critical IoT applications operating in dynamic environments. This paper considers the medium access control (MAC) layer of heterogeneous networks employing a framed-ALOHA-based restricted transmission strategy to enhance reliability. We present a statistical learning-based device-level network exploration mechanism in which end-devices use their transmission history to predict different network parameters. The IoT devices share the learned parameters with the base station (BS) to identify different groups presented in the network. The simulation results show that the mean square error (MSE) in predicting different network parameters can be reduced by increasing the history window size. In this regard, the optimal size of the history window under the given accuracy constraints is also determined. We demonstrate that the proposed device-level network load prediction mechanism is more robust as compared to the BS-centered approach.
Raza, SA, Vitale, J, Tonkin, M, Johnston, B, Billingsley, R, Herse, S & Williams, M-A 2022, 'An in-the-wild study to find type of questions people ask to a social robot providing question-answering service', Intelligent Service Robotics, vol. 15, no. 3, pp. 411-426.
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AbstractThe role of a human assistant, such as receptionist, is to provide specific information to the public. Questions asked by the public are often context dependent and related to the environment where the assistant is situated. Should similar behaviour and questions be expected when a social robot offers the same assistant service to visitors? Would it be sufficient for the robot to answer only service-specific questions, or is it necessary to design the robot to answer more general questions? This paper aims to answer these research questions by investigating the question-asking behaviour of the public when interacting with a question-answering social robot. We conducted the study at a university event that was open to the public. Results demonstrate that almost no participants asked context-specific questions to the robot. Rather, unrelated questions were common and included queries about the robot’s personal preferences, opinions, thoughts and emotional state. This finding contradicts popular belief and common sense expectations from what is otherwise observed during similar human–human interactions. In addition, we found that incorporating non-context-specific questions in a robot’s database increases the success rate of its question-answering system.
Razavi Bazaz, S, Mihandust, A, Salomon, R, Joushani, HAN, Li, W, A. Amiri, H, Mirakhorli, F, Zhand, S, Shrestha, J, Miansari, M, Thierry, B, Jin, D & Ebrahimi Warkiani, M 2022, 'Zigzag microchannel for rigid inertial separation and enrichment (Z-RISE) of cells and particles', Lab on a Chip, vol. 22, no. 21, pp. 4093-4109.
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Overview of the zigzag microchannel for rigid inertial separation and enrichment (Z-RISE). The proposed device has superior performance for particle focusing and separation.
Razmjoo, A, Gandomi, A, Mahlooji, M, Astiaso Garcia, D, Mirjalili, S, Rezvani, A, Ahmadzadeh, S & Memon, S 2022, 'An Investigation of the Policies and Crucial Sectors of Smart Cities Based on IoT Application', Applied Sciences, vol. 12, no. 5, pp. 2672-2672.
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As smart cities (SCs) emerge, the Internet of Things (IoT) is able to simplify more sophisticated and ubiquitous applications employed within these cities. In this regard, we investigate seven predominant sectors including the environment, public transport, utilities, street lighting, waste management, public safety, and smart parking that have a great effect on SC development. Our findings show that for the environment sector, cleaner air and water systems connected to IoT-driven sensors are used to detect the amount of CO2, sulfur oxides, and nitrogen to monitor air quality and to detect water leakage and pH levels. For public transport, IoT systems help traffic management and prevent train delays, for the utilities sector IoT systems are used for reducing overall bills and related costs as well as electricity consumption management. For the street-lighting sector, IoT systems are used for better control of streetlamps and saving energy associated with urban street lighting. For waste management, IoT systems for waste collection and gathering of data regarding the level of waste in the container are effective. In addition, for public safety these systems are important in order to prevent vehicle theft and smartphone loss and to enhance public safety. Finally, IoT systems are effective in reducing congestion in cities and helping drivers to find vacant parking spots using intelligent smart parking.
Razmjoo, A, Gandomi, AH, Pazhoohesh, M, Mirjalili, S & Rezaei, M 2022, 'The key role of clean energy and technology in smart cities development', Energy Strategy Reviews, vol. 44, pp. 100943-100943.
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Razzak, I, Eklund, P & Xu, G 2022, 'Improving healthcare outcomes using multimedia big data analytics', Neural Computing and Applications, vol. 34, no. 17, pp. 15095-15097.
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Razzak, I, Moustafa, N, Mumtaz, S & Xu, G 2022, 'One‐class tensor machine with randomized projection for large‐scale anomaly detection in high‐dimensional and noisy data', International Journal of Intelligent Systems, vol. 37, no. 8, pp. 4515-4536.
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Razzak, I, Xu, G & Khan, MK 2022, 'Guest Editorial: Privacy-Preserving Federated Machine Learning Solutions for Enhanced Security of Critical Energy Infrastructures', IEEE Transactions on Industrial Informatics, vol. 18, no. 5, pp. 3449-3451.
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Razzaq, L, Abbas, MM, Miran, S, Asghar, S, Nawaz, S, Soudagar, MEM, Shaukat, N, Veza, I, Khalil, S, Abdelrahman, A & Kalam, MA 2022, 'Response Surface Methodology and Artificial Neural Networks-Based Yield Optimization of Biodiesel Sourced from Mixture of Palm and Cotton Seed Oil', Sustainability, vol. 14, no. 10, pp. 6130-6130.
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In this present study, cold flow properties of biodiesel produced from palm oil were improved by adding cotton seed oil into palm oil. Three different mixtures of palm and cotton oil were prepared as P50C50, P60C40, and P70C30. Among three oil mixtures, P60C40 was selected for biodiesel production via ultrasound assisted transesterification process. Physiochemical characteristics—including density, viscosity, calorific value, acid value, and oxidation stability—were measured and the free fatty acid composition was determined via GCMS. Response surface methodology (RSM) and artificial neural network (ANN) techniques were utilized for the sake of relation development among operating parameters (reaction time, methanol-to-oil ratio, and catalyst concentration) ultimately optimizing yield of palm–cotton oil sourced biodiesel. Maximum yield of P60C40 biodiesel estimated via RSM and ANN was 96.41% and 96.67% respectively, under operating parameters of reaction time (35 min), M:O molar ratio (47.5 v/v %), and catalyst concentration (1 wt %), but the actual biodiesel yield obtained experimentally was observed 96.32%. The quality of the RSM model was examined by analysis of variance (ANOVA). ANN model statistics exhibit contented values of mean square error (MSE) of 0.0001, mean absolute error (MAE) of 2.1374, and mean absolute deviation (MAD) of 2.5088. RSM and ANN models provided a coefficient of determination (R2) of 0.9560 and a correlation coefficient (R) of 0.9777 respectively.
Razzaq, L, Mujtaba, MA, Shahbaz, MA, Nawaz, S, Mahmood Khan, H, Hussain, A, Ishtiaq, U, Kalam, MA, M. Soudagar, ME, Ismail, KA, Elfasakhany, A & Rizwan, HM 2022, 'Effect of biodiesel-dimethyl carbonate blends on engine performance, combustion and emission characteristics', Alexandria Engineering Journal, vol. 61, no. 7, pp. 5111-5121.
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Rees, N, Thiyagarajan, K, Wickramanayake, S & Kodagoda, S 2022, 'Ground-Penetrating Radar Signal Characterization for Non-destructive Evaluation of Low-Range Concrete Sub-surface Boundary Conditions', IEEE Sensors Letters, vol. 6, no. 4, pp. 1-4.
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Rehman, FU, Liu, Y, Yang, Q, Yang, H, Liu, R, Zhang, D, Muhammad, P, Liu, Y, Hanif, S, Ismail, M, Zheng, M & Shi, B 2022, 'Heme Oxygenase-1 targeting exosomes for temozolomide resistant glioblastoma synergistic therapy', Journal of Controlled Release, vol. 345, pp. 696-708.
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Reja, VK, Varghese, K & Ha, QP 2022, 'Computer vision-based construction progress monitoring', Automation in Construction, vol. 138, pp. 104245-104245.
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Automating the process of construction progress monitoring through computer vision can enable effective control of projects. Systematic classification of available methods and technologies is necessary to structure this complex, multi-stage process. Using the PRISMA framework, relevant studies in the area were identified. The various concepts, tools, technologies, and algorithms reported by these studies were iteratively categorised, developing an integrated process framework for Computer-Vision-Based Construction Progress Monitoring (CV-CPM). This framework comprises: data acquisition and 3D-reconstruction, as-built modelling, and progress assessment. Each stage is discussed in detail, positioning key studies, and concurrently comparing the methods used therein. The four levels of progress monitoring are defined and found to strongly influence all stages of the framework. The need for benchmarking CV-CPM pipelines and components are discussed, and potential research questions within each stage are identified. The relevance of CV-CPM to support emerging areas such as Digital Twin is also discussed.
Ren, H, Lu, W, Xiao, Y, Chang, X, Wang, X, Dong, Z & Fang, D 2022, 'Graph convolutional networks in language and vision: A survey', Knowledge-Based Systems, vol. 251, pp. 109250-109250.
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Graph convolutional networks (GCNs) have a strong ability to learn graph representation and have achieved good performance in a range of applications, including social relationship analysis, biological information processing, natural language processing (NLP), computer vision (CV), and so on. In recent years, the application of GCNs in natural language processing and computer vision has attracted substantial interest from researchers, as a result of which many studies based on GCNs have emerged in the fields of natural language processing and computer vision. However, to the best of our knowledge, a comprehensive survey of GCN application in natural language processing and computer vision has not yet been conducted. Accordingly, this survey presents a comprehensive review of the principles of GCNs and its applications in these two fields. First, we summarize the principles of the two types of GCNs, namely spatial methods and spectral methods. Then we divide GCN applications into two categories: natural language processing and computer vision. Subsequently, we present multiple applications from each category in detail. Finally, we outline the limitations of GCNs and discuss possible future research directions.
Ren, J, Zhang, B, Zhu, X & Li, S 2022, 'Damaged cable identification in cable-stayed bridge from bridge deck strain measurements using support vector machine', Advances in Structural Engineering, vol. 25, no. 4, pp. 754-771.
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A new two-step approach is developed for damaged cable identification in a cable-stayed bridge from deck bending strain responses using Support Vector Machine. A Damaged Cable Identification Machine (DCIM) based on support vector classification is constructed to determine the damaged cable and a Damage Severity Identification Machine (DSIM) based on support vector regression is built to estimate the damage severity. A field cable-stayed bridge with a long-term monitoring system is used to verify the proposed method. The three-dimensional Finite Element Model (FEM) of the cable-stayed bridge is established using ANSYS, and the model is validated using the field testing results, such as the mode shape, natural frequencies and its bending strain responses of the bridge under a moving vehicle. Then the validated FEM is used to simulate the bending strain responses of the longitude deck near the cable anchors when the vehicle is passing over the bridge. Different damage scenarios are simulated for each cable with various severities. Based on damage indexes vector, the training datasets and testing datasets are acquired, including single damaged cable scenarios and double damaged cable scenarios. Eventually, DCIM is trained using Support Vector Classification Machine and DSIM is trained using Support Vector Regression Machine. The testing datasets are input in DCIM and DSIM to check their accuracy and generalization capability. Different noise levels including 5%, 10%, and 20% are considered to study their anti-noise capability. The results show that DCIM and DSIM both have good generalization capability and anti-noise capability.
Ren, P, Xiao, Y, Chang, X, Huang, P-Y, Li, Z, Chen, X & Wang, X 2022, 'A Comprehensive Survey of Neural Architecture Search', ACM Computing Surveys, vol. 54, no. 4, pp. 1-34.
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Deep learning has made substantial breakthroughs in many fields due to its powerful automatic representation capabilities. It has been proven that neural architecture design is crucial to the feature representation of data and the final performance. However, the design of the neural architecture heavily relies on the researchers’ prior knowledge and experience. And due to the limitations of humans’ inherent knowledge, it is difficult for people to jump out of their original thinking paradigm and design an optimal model. Therefore, an intuitive idea would be to reduce human intervention as much as possible and let the algorithm automatically design the neural architecture.Neural Architecture Search(NAS) is just such a revolutionary algorithm, and the related research work is complicated and rich. Therefore, a comprehensive and systematic survey on the NAS is essential. Previously related surveys have begun to classify existing work mainly based on the key components of NAS: search space, search strategy, and evaluation strategy. While this classification method is more intuitive, it is difficult for readers to grasp the challenges and the landmark work involved. Therefore, in this survey, we provide a new perspective: beginning with an overview of the characteristics of the earliest NAS algorithms, summarizing the problems in these early NAS algorithms, and then providing solutions for subsequent related research work. In addition, we conduct a detailed and comprehensive analysis, comparison, and summary of these works. Finally, we provide some possible future research directions.
Ren, P, Xiao, Y, Chang, X, Huang, P-Y, Li, Z, Gupta, BB, Chen, X & Wang, X 2022, 'A Survey of Deep Active Learning', ACM Computing Surveys, vol. 54, no. 9, pp. 1-40.
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Active learning (AL) attempts to maximize a model’s performance gain while annotating the fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize a massive number of parameters if the model is to learn how to extract high-quality features. In recent years, due to the rapid development of internet technology, we have entered an era of information abundance characterized by massive amounts of available data. As a result, DL has attracted significant attention from researchers and has been rapidly developed. Compared with DL, however, researchers have a relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples, meaning that early AL is rarely according the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to a large number of publicly available annotated datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, making it unfeasible in fields that require high levels of expertise (such as speech recognition, information extraction, medical images, etc.). Therefore, AL is gradually coming to receive the attention it is due. It is therefore natural to investigate whether AL can be used to reduce the cost of sample annotation while retaining the powerful learning capabilities of DL. As a result of such investigations, deep active learning (DeepAL) has emerged. Although research on this topic is quite abundant, there has not yet been a comprehensive survey of DeepAL-related works; accordingly, this article aims to fill this gap. We provide a formal classification method for the existing work, along with a comprehensive and systematic overview. In addition, we also analyze and summarize the development of DeepAL from an application perspective. Finally, we discuss the confu...
Ren, S, Guo, B, Cao, L, Li, K, Liu, J & Yu, Z 2022, 'DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction', ACM Transactions on Intelligent Systems and Technology, vol. 13, no. 6, pp. 1-22.
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The prediction of express delivery sequence, i.e., modeling and estimating the volumes of daily incoming and outgoing parcels for delivery, is critical for online business, logistics, and positive customer experience, and specifically for resource allocation optimization and promotional activity arrangement. A precise estimate of consumer delivery requests has to involve sequential factors such as shopping behaviors, weather conditions, events, business campaigns, and their couplings. Despite that various methods have integrated external features to enhance the effects, extant works fail to address complex feature-sequence couplings in the following aspects: weaken the inter-dependencies when processing heterogeneous data and ignore the cumulative and evolving situation of coupling relationships. To address these issues, we propose DeepExpress—a deep-learning-based express delivery sequence prediction model, which extends the classic seq2seq framework to learn feature-sequence couplings. DeepExpress leverages an express delivery seq2seq learning, a carefully designed heterogeneous feature representation, and a novel joint training attention mechanism to adaptively handle heterogeneity issues and capture feature-sequence couplings for accurate prediction. Experimental results on real-world data demonstrate that the proposed method outperforms both shallow and deep baseline models.
Ren, S, Guo, B, Li, K, Wang, Q, Yu, Z & Cao, L 2022, 'CoupledMUTS: Coupled Multivariate Utility Time-Series Representation and Prediction', IEEE Internet of Things Journal, vol. 9, no. 22, pp. 22972-22982.
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Ren, Z, Zhang, X, Huang, Z, Hu, J, Li, Y, Zheng, S, Gao, M, Pan, H & Liu, Y 2022, 'Controllable synthesis of 2D TiH2 nanoflakes with superior catalytic activity for low-temperature hydrogen cycling of NaAlH4', Chemical Engineering Journal, vol. 427, pp. 131546-131546.
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Nanosized titanium compounds are particularly effective in catalyzing hydrogen cycling by NaAlH4. Titanium hydride (TiH2), as a catalyst, is highly interesting since it contributes hydrogen in addition to active Ti. However, it has been challenging to fabricate nanosized TiH2 due to the strong affinity of Ti with oxygen. Herein, TiH2 nanoflakes with a lateral size of ~10 nm and thickness of ~1 nm are successfully synthesized through a novel facile one-pot solvothermal process. In an anhydrous THF solution, LiH reacts with TiCl4 rapidly at 100 °C forming TiH2 and LiCl. The newly formed TiH2 nucleates and grows epitaxially on the graphene surface due to the well-matched lattice parameters, giving rise to the formation of TiH2 nanoflakes. Both theoretical calculations and experiments reveal the generation of Cl· radicals and unsaturated C[dbnd]C bonds when TiCl4 reacts with THF, which promotes the formation of TiH2. The nanoflake-like TiH2 on graphene enables an outstanding hydrogen storage performance of NaAlH4, i.e., full dehydrogenation at 80 °C and hydrogenation at 30 °C and under 100 bar H2, with a practical hydrogen capacity of 4.9 wt%, which has been never reported before.
Rene, ER, Kennes, C, Nghiem, LD & Varjani, S 2022, 'New insights in biodegradation of organic pollutants', Bioresource Technology, vol. 347, pp. 126737-126737.
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Rezaei, J, Golshan, MEH & Alhelou, HH 2022, 'Impacts of integration of very large‐scale photovoltaic power plants on rotor angle and frequency stability of power system', IET Renewable Power Generation, vol. 16, no. 11, pp. 2384-2401.
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AbstractThe increase in penetration of large‐scale renewable generation significantly affects power system characteristics. Thus, it is necessary to assess the steady state and dynamic performance of a power system for different installing scenarios of renewable power plants such as photovoltaic (PV) ones. The paper specifically investigates the impacts of very large‐scale photovoltaic (VLS‐PV) generation on the power system dynamic performance including rotor‐angle and frequency stabilities. For this purpose, a detailed equivalent dynamic model for a multi‐unit VLS‐PV system and corresponding controllers are developed. The paper also proposes a comprehensive set of replacement scenarios comprising different PV penetration, location, and voltage control strategies as well as simulating various large disturbances. To investigate the impacts of VLS‐PV generation on dynamic system performance, a study framework for frequency and rotor‐angle stability involving the modal and transient analysis is presented as well. By using the VLS‐PV detailed model along with other dynamic components of a power system such as synchronous generators, automatic voltage regulators (AVRs), and governors, the stabilities studying instructions are employed for the IEEE 39‐bus and 118‐bus test systems.
Rezazadegan, D, Berkovsky, S, Quiroz, JC, Kocaballi, AB, Wang, Y, Laranjo, L & Coiera, E 2022, 'Symbolic and Statistical Learning Approaches to Speech Summarization: A Scoping Review', Computer Speech & Language, vol. 72, pp. 101305-101305.
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Riayatsyah, TMI, Geumpana, TA, Fattah, IMR & Mahlia, TMI 2022, 'Techno-Economic Analysis of Hybrid Diesel Generators and Renewable Energy for a Remote Island in the Indian Ocean Using HOMER Pro', Sustainability, vol. 14, no. 16, pp. 9846-9846.
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This study is about the electrification of the remote islands in the Indian Ocean that were severely affected by the tsunami in the 2004 earth earthquake. To supply electricity to the islands, two diesel generators with capacities of 110 kW and 60 kW were installed in 2019. The feasibility of using renewable energy to supplement or replace the units in these two generators is investigated in this work. In 2019, two diesel generators with capacities of 110 kW and 60 kW were installed in the islands to supply electricity. This work analyses whether the viability of using renewable energy can be used to supplement or replace these two generators. Among the renewable energy options proposed here are a 100 kW wind turbine, solar PV, a converter, and batteries. As a result, the study’s goal is to perform a techno-economic analysis and optimise the proposed hybrid diesel and renewable energy system for a remote island in the Indian Ocean. The Hybrid Optimisation Model for Electric Renewable (HOMER) Pro software was used for all simulations and optimisation for this analysis. The calculation is based on the current diesel price of USD 0.90 per litre (without subsidy). The study found that renewable alone can contribute to 29.2% of renewable energy fractions based on the most optimised systems. The Net Present Cost (NPC) decreased from USD 1.65 million to USD 1.39 million, and the levelised Cost of Energy (CoE) decreased from 0.292 USD/kWh to 0.246 USD/kWh, respectively. The optimised system’s Internal Rate of Return (IRR) is 14% and Return on Investment (ROI) 10%, with a simple payback period of 6.7 years. This study shows that it would be technically feasible to introduce renewable energy on a remote island in Indonesia, where numerous islands have no access to electricity.
Riayatsyah, TMI, Geumpana, TA, Fattah, IMR, Rizal, S & Mahlia, TMI 2022, 'Techno-Economic Analysis and Optimisation of Campus Grid-Connected Hybrid Renewable Energy System Using HOMER Grid', Sustainability, vol. 14, no. 13, pp. 7735-7735.
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This study aimed to conduct a techno-economic performance and optimisation analysis of grid-connected PV, wind turbines, and battery packs for Syiah Kuala University, situated at the tip of Sumatra island in the tsunami-affected region. The simulation software Hybrid Optimisation Model for Electric Renewables (HOMER) was used to analyse and optimise the renewable energy required by the institution. The methodology began with the location specification, average electric load demand, daily radiation, clearness index, location daily temperature, and system architecture. The results revealed that the energy storage system was initially included in the simulation, but it was later removed in order to save money and optimise the share of renewable energy. Based on the optimisation results, two types of energy sources were chosen for the system, solar PV and wind turbine, which contributed 62% and 20%, respectively. Apart from the renewable energy faction, another reason for the system selection is cost of energy (CoE), which decreased to $0.0446/kWh from $0.060/kWh. In conclusion, the study found that by connecting solar PV and wind turbines to the local grid, this renewable energy system is able to contribute up to 82% of the electricity required. However, the obstacle to implementing renewable energy in Indonesia is the cheap electricity price that is mainly generated using cheap coal, which is abundantly available in the country.
Riayatsyah, TMI, Sebayang, AH, Silitonga, AS, Padli, Y, Fattah, IMR, Kusumo, F, Ong, HC & Mahlia, TMI 2022, 'Current Progress of Jatropha Curcas Commoditisation as Biodiesel Feedstock: A Comprehensive Review', Frontiers in Energy Research, vol. 9, p. 815416.
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This article looks at the national and global actors, social networks, and narratives that have influencedJatropha’sworldwide acceptability as a biofuel crop.Jatropha Curcasis a genus of around 175 succulent shrubs and trees in theEuphorbiaceaefamily (some of which are deciduous, such asJatropha CurcasL.). It’s a drought-tolerant perennial that thrives in poor or marginal soil and produces a large amount of oil per hectare. It is easy to grow, has a fast growth rate, and can generate seeds for up to 50 years.Jatropha Curcashas been developed as a unique and promising tropical plant for augmenting renewable energy sources due to its various benefits. It is deserving of being recognised as the only competitor in terms of concrete and intangible environmental advantages.Jatropha Curcasis a low-cost biodiesel feedstock with good fuel properties and more oil than other species. It is a non-edible oilseed feedstock. Thus it will have no impact on food prices or the food vs fuel debate.Jatropha Curcasemits fewer pollutants than diesel and may be used in diesel engines with equivalent performance.Jatropha Curcasalso makes a substantial contribution to the betterment of rural life. The plant may also provide up to 40% oil yield per seed based on weight. This study looks at the features characteristics ofJatropha Curcasas biodiesel feedstock and performance, and emissions of internal combustion engine that operates on this biodiesel fuel.
Ricafrente, A, Cwiklinski, K, Nguyen, H, Dalton, JP, Tran, N & Donnelly, S 2022, 'Stage-specific miRNAs regulate gene expression associated with growth, development and parasite-host interaction during the intra-mammalian migration of the zoonotic helminth parasite Fasciola hepatica', BMC Genomics, vol. 23, no. 1, p. 419.
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Abstract Background MiRNAs are small non-coding RNAs that post-transcriptionally regulate gene expression in organisms ranging from viruses to mammals. There is great relevance in understanding how miRNAs regulate genes involved in the growth, development, and maturation of the many parasitic worms (helminths) that together afflict more than 2 billion people. Results Here, we describe the miRNAs expressed by each of the predominant intra-mammalian development stages of Fasciola hepatica, a foodborne flatworm that infects a wide range of mammals worldwide, most importantly humans and their livestock. A total of 124 miRNAs were profiled, 72 of which had been previously reported and three of which were conserved miRNA sequences described here for the first time. The remaining 49 miRNAs were novel sequences of which, 31 were conserved with F. gigantica and the remaining 18 were specific to F. hepatica. The newly excysted juveniles express 22 unique miRNAs while the immature liver and mature bile duct stages each express 16 unique miRNAs. We discovered several sequence variant miRNAs (IsomiRs) as well as miRNA clusters that exhibit strict temporal expression paralleling parasite development. Target analysis revealed the close association between miRNA expression and stage-specific changes in the transcriptome; for example, we identified specific miRNAs that target parasite proteases known to be essential for intestinal wall penetration (cathepsin L3). Moreover, we demonstrate that miRNAs fine-tune the expression of genes involved in the metabolic pathways that allow the parasites to move from an aerobic external environment to the anerobic environment of the host....
Rizoiu, M-A, Soen, A, Li, S, Calderon, P, Dong, LJ, Menon, AK & Xie, L 2022, 'Interval-censored Hawkes processes', JOURNAL OF MACHINE LEARNING RESEARCH, vol. 23, pp. 1-84.
Roberts, AGK, Catchpoole, DR & Kennedy, PJ 2022, 'Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability', NAR Genomics and Bioinformatics, vol. 4, no. 1, p. lqab124.
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ABSTRACTThere is increasing evidence that changes in the variability or overall distribution of gene expression are important both in normal biology and in diseases, particularly cancer. Genes whose expression differs in variability or distribution without a difference in mean are ignored by traditional differential expression-based analyses. Using a Bayesian hierarchical model that provides tests for both differential variability and differential distribution for bulk RNA-seq data, we report here an investigation into differential variability and distribution in cancer. Analysis of eight paired tumour–normal datasets from The Cancer Genome Atlas confirms that differential variability and distribution analyses are able to identify cancer-related genes. We further demonstrate that differential variability identifies cancer-related genes that are missed by differential expression analysis, and that differential expression and differential variability identify functionally distinct sets of potentially cancer-related genes. These results suggest that differential variability analysis may provide insights into genetic aspects of cancer that would not be revealed by differential expression, and that differential distribution analysis may allow for more comprehensive identification of cancer-related genes than analyses based on changes in mean or variability alone.
Rodd, J & Castel, A 2022, 'Structural considerations to minimise the risk of horizontal cracks in the wall of circular concrete tanks', Structures, vol. 40, pp. 1091-1106.
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Rodriguez, J, Garcia, C, Mora, A, Flores-Bahamonde, F, Acuna, P, Novak, M, Zhang, Y, Tarisciotti, L, Davari, SA, Zhang, Z, Wang, F, Norambuena, M, Dragicevic, T, Blaabjerg, F, Geyer, T, Kennel, R, Khaburi, DA, Abdelrahem, M, Zhang, Z, Mijatovic, N & Aguilera, RP 2022, 'Latest Advances of Model Predictive Control in Electrical Drives—Part I: Basic Concepts and Advanced Strategies', IEEE Transactions on Power Electronics, vol. 37, no. 4, pp. 3927-3942.
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The application of model predictive control in electrical drives has been studied extensively in the past decade. This article presents what the authors consider the most relevant contributions published in the last years, mainly focusing on three relevant issues: weighting factor calculation when multiple objectives are utilized in the cost function, current/torque harmonic distortion optimization when the power converter switching frequency is reduced, and robustness improvement under parameters uncertainties. Therefore, this article aims to enable readers to have a more precise overview while facilitating their future research work in this exciting area.
Romeijn, T, Behrens, M, Paul, G & Wei, D 2022, 'Experimental analysis of water and slurry flows in gravity-driven helical mineral separators', Powder Technology, vol. 405, pp. 117538-117538.
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Romeijn, T, Behrens, M, Paul, G & Wei, D 2022, 'Instantaneous and long-term mechanical properties of Polyethylene Terephthalate Glycol (PETG) additively manufactured by pellet-based material extrusion', Additive Manufacturing, vol. 59, pp. 103145-103145.
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Polyethylene Terephthalate Glycol (PETG) is a highly popular feedstock for extrusion-based additive manufacturing. While data are available on the instantaneous properties of additively manufactured PETG, few research have been done on forecasting the creep behaviour of additively manufactured PETG while accounting for the material altering effects of ageing. This research article aims to enhance the understanding of both the instantaneous and time-dependent mechanical properties of additively manufactured PETG through a series of tensile, FEA simulations, Dynamic Mechanical Analysis (DMA), and two types of creep experiments. The details of experimental and mathematical calculations of the instantaneous and time-dependent properties of additively manufactured PETG are provided. Nine independent material parameters have been determined including three Young's moduli, three shear moduli and three Poisson's ratios, to fully quantify an orthotropic material model of additively manufactured PETG. The printed material exhibited a Young's modulus that is 86.5% of the theoretically possible value in direction 1, a Young's modulus in direction 2 is 66.0% of the theoretical optimum, and a Young's modulus in direction 3 is within 1% of its theoretical maximum. In addition to reporting the creep behaviour of PETG, the novel application of the Time-Temperature Superposition Principle (TTSP) to additively manufactured PETG has been shown to produce an age-affected creep prediction for up to 3.88 years based on samples aged for 221 h and at 23 °C. The methodology and data models have been found to enable predictions for other ages and temperatures. It was concluded that the application of the TTSP creep methodology was limited by the creep test temperature, 60 °C, after which the material began to behave in a non rheologically-simple manner.
Roohani, I, No, YJ, Zuo, B, Xiang, SD, Lu, Z, Liu, H, Plebanski, M & Zreiqat, H 2022, 'Low-Temperature Synthesis of Hollow β-Tricalcium Phosphate Particles for Bone Tissue Engineering Applications', ACS Biomaterials Science & Engineering, vol. 8, no. 5, pp. 1806-1815.
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Roohani, I, Yeo, GC, Mithieux, SM & Weiss, AS 2022, 'Emerging concepts in bone repair and the premise of soft materials', Current Opinion in Biotechnology, vol. 74, pp. 220-229.
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Roslan, MF, Hannan, MA, Ker, PJ, Mannan, M, Muttaqi, KM & Mahlia, TMI 2022, 'Microgrid control methods toward achieving sustainable energy management: A bibliometric analysis for future directions', Journal of Cleaner Production, vol. 348, pp. 131340-131340.
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Recently, numerous significant advances in control methods have been made in Microgrid development especially in grid-connected mode to ensure a reliable and sustainable operation. The concept of control strategies for inverter systems to ensure proper microgrid integration has sparked a lot of research towards innovation. This review provides a comprehensive overview and analysis of microgrid integrated control methods and energy management systems for both grid-connected and island-based systems. The Scopus database is used to compile a list of the most cited published papers in the field of microgrid control methods and energy management systems, based on predetermined criteria. In the second week of January 2021, the study was performed using the Scopus database. The papers with the most citations were published in 33 different journals from 30 different countries. An 85% of the published articles are based on the control system development and experimental setup whereas 15% are review-based articles. Thus, it can be deduced that this research topic has always been under constant investigation and development in order to enhance the sustainability of microgrid systems in the electric power sector. The paper aims to identify and analyze the highly cited published articles on the respective field to provide future research direction on the microgrid integrated control method and energy management system. The review also underlines numerous factors, issues, challenges, and difficulties that next-generation microgrids must compete with in regards to grid sustainability. Thus, this review will strengthen the scopes and provide context for the development of microgrid integrated control methods and energy management systems in order to achieve an efficient, reliable, cost-effective, and sustainable power supply.
Rout, RK, Hassan, SS, Sheikh, S, Umer, S, Sahoo, KS & Gandomi, AH 2022, 'Feature-extraction and analysis based on spatial distribution of amino acids for SARS-CoV-2 Protein sequences', Computers in Biology and Medicine, vol. 141, pp. 105024-105024.
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BACKGROUND AND OBJECTIVE: The world is currently facing a global emergency due to COVID-19, which requires immediate strategies to strengthen healthcare facilities and prevent further deaths. To achieve effective remedies and solutions, research on different aspects, including the genomic and proteomic level characterizations of SARS-CoV-2, are critical. In this work, the spatial representation/composition and distribution frequency of 20 amino acids across the primary protein sequences of SARS-CoV-2 were examined according to different parameters. METHOD: To identify the spatial distribution of amino acids over the primary protein sequences of SARS-CoV-2, the Hurst exponent and Shannon entropy were applied as parameters to fetch the autocorrelation and amount of information over the spatial representations. The frequency distribution of each amino acid over the protein sequences was also evaluated. In the case of a one-dimensional sequence, the Hurst exponent (HE) was utilized due to its linear relationship with the fractal dimension (D), i.e. D+HE=2, to characterize fractality. Moreover, binary Shannon entropy was considered to measure the uncertainty in a binary sequence then further applied to calculate amino acid conservation in the primary protein sequences. RESULTS AND CONCLUSION: Fourteen (14) SARS-CoV protein sequences were evaluated and compared with 105 SARS-CoV-2 proteins. The simulation results demonstrate the differences in the collected information about the amino acid spatial distribution in the SARS-CoV-2 and SARS-CoV proteins, enabling researchers to distinguish between the two types of CoV. The spatial arrangement of amino acids also reveals similarities and dissimilarities among the important structural proteins, E, M, N and S, which is pivotal to establish an evolutionary tree with other CoV strains.
Roy, NC & Saha, G 2022, 'Heat and Mass Transfer of Dusty Casson Fluid over a Stretching Sheet', Arabian Journal for Science and Engineering, vol. 47, no. 12, pp. 16091-16101.
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Rozali, S, Abd Latif, Z, Adnan, NA, Hussin, Y, Blackburn, A & Pradhan, B 2022, 'Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using and object-based technique', Geocarto International, vol. 37, no. 11, pp. 3247-3264.
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Ruan, W, Jiao, M, Xu, S, Ismail, M, Xie, X, An, Y, Guo, H, Qian, R, Shi, B & Zheng, M 2022, 'Brain-targeted CRISPR/Cas9 nanomedicine for effective glioblastoma therapy', Journal of Controlled Release, vol. 351, pp. 739-751.
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Ruppert, MG, Martin-Jimenez, D, Yong, YK, Ihle, A, Schirmeisen, A, Fleming, AJ & Ebeling, D 2022, 'Experimental analysis of tip vibrations at higher eigenmodes of QPlus sensors for atomic force microscopy', Nanotechnology, vol. 33, no. 18, pp. 185503-185503.
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Abstract QPlus sensors are non-contact atomic force microscope probes constructed from a quartz tuning fork and a tungsten wire with an electrochemically etched tip. These probes are self-sensing and offer an atomic-scale spatial resolution. Therefore, qPlus sensors are routinely used to visualize the chemical structure of adsorbed organic molecules via the so-called bond imaging technique. This is achieved by functionalizing the AFM tip with a single CO molecule and exciting the sensor at the first vertical cantilever resonance mode. Recent work using higher-order resonance modes has also resolved the chemical structure of single organic molecules. However, in these experiments, the image contrast can differ significantly from the conventional bond imaging contrast, which was suspected to be caused by unknown vibrations of the tip. This work investigates the source of these artefacts by using a combination of mechanical simulation and laser vibrometry to characterize a range of sensors with different tip wire geometries. The results show that increased tip mass and length cause increased torsional rotation of the tuning fork beam due to the off-center mounting of the tip wire, and increased flexural vibration of the tip. These undesirable motions cause lateral deflection of the probe tip as it approaches the sample, which is rationalized to be the cause of the different image contrast. The results also provide a guide for future probe development to reduce these issues.
Rush, A, Catchpoole, DR, Reaiche-Miller, G, Gilbert, T, Ng, W, Watson, PH & Byrne, JA 2022, 'What Do Biomedical Researchers Want from Biobanks? Results of an Online Survey', Biopreservation and Biobanking, vol. 20, no. 3, pp. 271-282.
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Aims: The purpose of biobanking is to provide biospecimens and associated data to researchers, yet the perspectives of biobank research users have been under-investigated. This study aimed to ascertain biobank research users' needs and opinions about biobanking services. Methods: An online survey was developed, which requested information about researcher demographics, localities of biobanks accessed, methods of sourcing biospecimens, and opinions on topics including but not limited to, application processes, data availability, access fees, and return of research results. There were 27 multiple choice/check box questions, 4 questions with a 10-point Likert scale, and 8 questions with provision for further comment. A web link for the survey was distributed to researchers in late 2019/early 2020 in four Australian states: New South Wales, Victoria, Western Australia, and South Australia. Results: Respondents were generally satisfied with biobank application processes and the fit for purpose of received biospecimens/data. Nonetheless, most researchers (n = 61/99, 62%) reported creating their own collections owing to gaps in sample availability and a perceived increase in efficiency. Most accessed biobanks (n = 58/74, 78%) were in close proximity (local or intrastate) to the researcher. Most researchers had limited the scope of their research owing to difficulty of obtaining biospecimens (n = 55/86, 64%) and/or data (n = 52/85, 60%), with the top three responses for additional types of data required being 'more long term follow up data,' 'more clinical data,' and 'more linked government data.' The top influence to use a particular biobank was cost, and the most frequently suggested improvement was reduced direct 'cost of obtaining biospecimens.' Conclusion: Biobanks that do not meet the needs of their end-users are unlikely to be optimally utilized or sustainable. This survey provides valuable insights to guide biobanks and other stakeholders, such as devel...
Rutherford, H, Saha Turai, R, Chacon, A, Franklin, DR, Mohammadi, A, Tashima, H, Yamaya, T, Parodi, K, Rosenfeld, AB, Guatelli, S & Safavi-Naeini, M 2022, 'An inception network for positron emission tomography based dose estimation in carbon ion therapy', Physics in Medicine & Biology, vol. 67, no. 19, pp. 194001-194001.
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Abstract Objective. We aim to evaluate a method for estimating 1D physical dose deposition profiles in carbon ion therapy via analysis of dynamic PET images using a deep residual learning convolutional neural network (CNN). The method is validated using Monte Carlo simulations of 12C ion spread-out Bragg peak (SOBP) profiles, and demonstrated with an experimental PET image. Approach. A set of dose deposition and positron annihilation profiles for monoenergetic 12C ion pencil beams in PMMA are first generated using Monte Carlo simulations. From these, a set of random polyenergetic dose and positron annihilation profiles are synthesised and used to train the CNN. Performance is evaluated by generating a second set of simulated 12C ion SOBP profiles (one 116 mm SOBP profile and ten 60 mm SOBP profiles), and using the trained neural network to estimate the dose profile deposited by each beam and the position of the distal edge of the SOBP. Next, the same methods are used to evaluate the network using an experimental PET image, obtained after irradiating a PMMA phantom with a 12C ion beam at QST’s Heavy Ion Medical Accelerator in Chiba facility in Chiba, Japan. The performance of the CNN is compared to that of a recently published iterative technique using the same simulated and experimental 12C SOBP profiles. Main results. The CNN estimated the simulated dose profiles with a mean relative error (MRE) of 0.7% ± 1.0% and the distal edge position with an accuracy of 0.1 mm ± 0.2 mm, and estimate the dose delivered by the experimental 12C ion beam with a MRE of 3.7%, and the distal edge with an accuracy of 1.7 mm. Significance. The CNN was able to produce estimates ...
Rzhevskiy, AS, Kapitannikova, AY, Butnaru, DV, Shpot, EV, Joosse, SA, Zvyagin, AV & Ebrahimi Warkiani, M 2022, 'Liquid Biopsy in Diagnosis and Prognosis of Non-Metastatic Prostate Cancer', Biomedicines, vol. 10, no. 12, pp. 3115-3115.
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Currently, sensitive and specific methods for the detection and prognosis of early stage PCa are lacking. To establish the diagnosis and further identify an appropriate treatment strategy, prostate specific antigen (PSA) blood test followed by tissue biopsy have to be performed. The combination of tests is justified by the lack of a highly sensitive, specific, and safe single test. Tissue biopsy is specific but invasive and may have severe side effects, and therefore is inappropriate for screening of the disease. At the same time, the PSA blood test, which is conventionally used for PCa screening, has low specificity and may be elevated in the case of noncancerous prostate tumors and inflammatory conditions, including benign prostatic hyperplasia and prostatitis. Thus, diverse techniques of liquid biopsy have been investigated to supplement or replace the existing tests of prostate cancer early diagnosis and prognostics. Here, we provide a review on the advances in diagnosis and prognostics of non-metastatic prostate cancer by means of various biomarkers extracted via liquid biopsy, including circulating tumor cells, exosomal miRNAs, and circulating DNAs.
Rzhevskiy, AS, Kapitannikova, AY, Vasilescu, SA, Karashaeva, TA, Razavi Bazaz, S, Taratkin, MS, Enikeev, DV, Lekarev, VY, Shpot, EV, Butnaru, DV, Deyev, SM, Thiery, JP, Zvyagin, AV & Ebrahimi Warkiani, M 2022, 'Isolation of Circulating Tumor Cells from Seminal Fluid of Patients with Prostate Cancer Using Inertial Microfluidics', Cancers, vol. 14, no. 14, pp. 3364-3364.
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Prostate cancer (PCa) diagnosis is primarily based on prostate-specific antigen (PSA) testing and prostate tissue biopsies. However, PSA testing has relatively low specificity, while tissue biopsies are highly invasive and have relatively low sensitivity at early stages of PCa. As an alternative, we developed a technique of liquid biopsy, based on isolation of circulating tumor cells (CTCs) from seminal fluid (SF). The recovery of PCa cells from SF was demonstrated using PCa cell lines, achieving an efficiency and throughput as high as 89% (±3.8%) and 1.7 mL min−1, respectively, while 99% (±0.7%) of sperm cells were disposed of. The introduced approach was further tested in a clinical setting by collecting and processing SF samples of PCa patients. The yield of isolated CTCs measured as high as 613 cells per SF sample in comparison with that of 6 cells from SF of healthy donors, holding significant promise for PCa diagnosis. The correlation analysis of the isolated CTC numbers with the standard prognostic parameters such as Gleason score and PSA serum level showed correlation coefficient values at 0.40 and 0.73, respectively. Taken together, our results show promise in the developed liquid biopsy technique to augment the existing diagnosis and prognosis of PCa.
Saadallah, A, Abdulaaty, O, Büscher, J, Panusch, T, Morik, K & Deuse, J 2022, 'Early Quality Prediction using Deep Learning on Time Series Sensor Data', Procedia CIRP, vol. 107, pp. 611-616.
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Saadallah, A, Büscher, J, Abdulaaty, O, Panusch, T, Deuse, J & Morik, K 2022, 'Explainable Predictive Quality Inspection using Deep Learning in Electronics Manufacturing', Procedia CIRP, vol. 107, pp. 594-599.
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Saberi, M, Kamali, N, Tarnian, F & Sadeghipour, A 2022, 'Investigation Phenol, Flavonoids and Antioxidant Activity Content of Capparis spinosa in Three Natural Habitats of Sistan and Baluchestan Province, Iran', Journal of Rangeland Science, vol. 12, no. 2, pp. 191-204.
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Capparis spinosa L. is a shrub plant that in addition to its forage use, has protective importance to prevent from soil erosion in desert areas and important values in treating many diseases as well. The aim of this study was to investigate the amount of phenol, flavonoids and antioxidant activity in different organs of C. spinosa in Sistan, Iranshahr and Saravan counties, Iran. Morphological traits (number of fruits, wet weight of fruit, dry weight of fruit, fruit diameter, number and length of branches, plant height, leaf length, leaf width and root depth) in each habitat were measured from four individuals of C. spinosa randomly. In order to perform phytochemical tests, different parts of the plant (stem, leaves, flowers, fruits, and roots) were randomly collected from the habitats in the post-flowering stage in June 2019. The total phenol and flavonoid contents of all methanol extracts were measured using the spectrophotometric method and antioxidant activity was determined using the free radical trap method. Data analysis was performed as factorial experiment based on a completely randomized design in four replications. Rresults indicated significant differences between different plant organs (P<0.01) in aspect of the antioxidant activity, the amount of total phenol and flavonoids. Also, there was a significant interaction between plant organs and habitats (P<0.01). The results of the means comparison showed that the highest total phenol and total flavonoids were obtained from the methanol extract of the flower 82.8 mg of quercetin equivalent per gram dry weight and 64.3 mg of gallic acid/g of dry weight in Sistan region, respectively, and the highest antioxidant activity was 15.7% in the fruit in Iranshahr region. According to the results, the obtained methanolic extract of C. spinosa flower and fruit in Sistan natural habitats is recommended to the treatment of diseases as a potential source of natural antioxidants.
Sabetamal, H, Sheng, D & Carter, JP 2022, 'Coupled hydro-mechanical modelling of unsaturated soils; numerical implementation and application to large deformation problems', Computers and Geotechnics, vol. 152, pp. 105044-105044.
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This paper presents coupled hydro-mechanical modelling of unsaturated soil problems by incorporating some advanced numerical and constitutive models in a general-purpose commercial software package, Abaqus. Two different strategies for the interpretation of unsaturated soil behaviour with respect to the soil volume change are considered and the relevant constitutive models are implemented through user-defined subroutines. The first approach that is commonly used in geomechanics considers suction as an additional variable and uses a constitutive model in the space of effective stress–suction assuming that soil compressibility is a function of suction. The second approach treats the constitutive model in the space of effective stress and degree of saturation assuming that the soil compressibility is a function of the degree of saturation. The hydro-mechanical behaviour regarding the change in the degree of saturation caused by both suction and net stress changes is also considered, together with the effect of hydraulic hysteresis. Validation of the implemented algorithms is presented through several benchmark problems. Finally, the application and utility of the implemented procedures are illustrated by simulations of two challenging problems of unsaturated geomechanics, including a slope failure due to seepage and rainfall infiltration as well as cone penetration tests in unsaturated soil. A suitable mesh optimisation scheme is also incorporated to handle finite deformations.
Sadeghian, F, Jahandari, S, Haddad, A, Rasekh, H & Li, J 2022, 'Effects of variations of voltage and pH value on the shear strength of soil and durability of different electrodes and piles during electrokinetic phenomenon', Journal of Rock Mechanics and Geotechnical Engineering, vol. 14, no. 2, pp. 625-636.
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Saha, S, Gayen, A, Gogoi, P, Kundu, B, Paul, GC & Pradhan, B 2022, 'Proposing novel ensemble approach of particle swarm optimized and machine learning algorithms for drought vulnerability mapping in Jharkhand, India', Geocarto International, vol. 37, no. 25, pp. 8004-8035.
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Drought, a natural and very complex climatic hazard, causes impacts on natural and socio-economic environments. This study aims to produce the drought vulnerability map (DVM) considering novel ensemble machine learning algorithms (MLAs) in Jharkhand, India. Forty, drought vulnerability determining factors under the categories of exposure, sensitivity, and adaptive capacity were used. Then, four machine learning and four novel ensemble approaches of particle swarm optimized (PSO) algorithms, named random forest (RF), PSO-RF, multi-layer perceptron (MLP), PSO-MLP, support vector regression (SVM), PSO-MLP, Bagging, and PSO-Bagging, were established for DVMs. The receiver operating characteristic curve (ROC), mean-absolute-error (MAE), root-mean-square-error (RMSE), precision, and K-index were utilized for judging the performance of novel ensemble MLAs. The obtained results show that the PSO-RF had the highest performance with an AUC of 0.874, followed by RF, PSO-MLP, PSO-Bagging, Bagging, MLP, PSO-SVM and SVM, respectively. Produced DVMs would be helpful for policy intervention to minimize drought vulnerability.
Saha, SC, Francis, I, Huang, X & Paul, AR 2022, 'Heat transfer and fluid flow analysis in a realistic 16-generation lung', Physics of Fluids, vol. 34, no. 6, pp. 061906-061906.
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Heat transfer between inhaled hot/cool air and the lung surface within the human respiratory system is an intriguing topic that has not received enough attention. The lung can be considered an in vivo heat exchanger, balancing the inhaled air temperature by lowering the hot air temperature and increasing the cool air temperature. The current work studies the unsteady and incompressible airflow motion and heat transfer during inhalation between the surface of the lungs (37 °C) and the inhaled cool air (25 °C) in one case and inhaled hot air (43 °C) in another. Computerized tomography scan (CT-scan) images of the lung of a 39-year-old male patient were processed to generate the airway geometry consisting of 16 generations. The geometry was further modified in UG NX 12.0, and the mesh generation was carried out using Ansys Meshing. The shear stress transport (SST) k−ω turbulent model was employed in Ansys Fluent 20.2 to model the air/lung convective volume heat transfer utilizing a realistic breathing velocity profile. Temperature streamlines, lung volume temperatures, surface heat flux, and surface temperatures on all 16 generations were produced for both cases during the breathing cycle of 4.75 s. Several conclusions were made by studying and comparing the two cases. First, heat transfer between inhaled hot or cool air and the lung surface mainly occurred in the first few generations. Second, airflow temperature patterns are dependent on the inlet breathing velocity profile. Third, the lung volume temperature change directly correlates with the temperature difference between air and the lung surface. Finally, the surface heat flux strongly depended on the heat transfer coefficient. The density, viscosity, thermal conductivity, and specific heat of hot/cool air affected the Reynolds number, Nusselt number, heat transfer coefficient, and surface heat flux.
Saiprakash, C, Mohapatra, A, Nayak, B, Babu, TS & Alhelou, HH 2022, 'A Novel Benzene Structured Array Configuration for Harnessing Maximum Power From PV Array Under Partial Shading Condition With Reduced Number of Cross Ties', IEEE Access, vol. 10, pp. 129712-129726.
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Sajid, M, Mittal, H, Pare, S & Prasad, M 2022, 'Routing and scheduling optimization for UAV assisted delivery system: A hybrid approach', Applied Soft Computing, vol. 126, pp. 109225-109225.
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This paper proposes a joint-optimization framework for UAV-routing and UAV-route scheduling problems associated with the UAV-assisted delivery system. The mixed-integer linear programming (MILP) models for UAV-routing and UAV-route scheduling problems are proposed considering the effect of incidental processes and the varying payload on travel time. A hybrid genetic and simulated annealing (HGSA) algorithm is proposed for the UAV-routing problem to minimize travel time. In HGSA, genetic algorithm (GA) employs a novel stochastic crossover operator to search for the optimal global position of customers, whereas simulated annealing (SA) utilizes local search operators to avoid the local optima. A UAV-Oriented MinMin (UO-MinMin) algorithm is also proposed to minimize the makespan of the UAV-route scheduling problem. It employs a UAV-oriented view to generate the route-scheduling order with minimal computational efforts without affecting the quality of the makespan. A Monte Carlo simulation-based sensitivity analysis is conducted to evaluate the impact of the hybridization probability of GA and SA in the proposed HGSA algorithm. To assess the performance of the HGSA algorithm, a set P of 24 benchmark instances is adopted and adjusted to meet the constraints of the UAV-Assisted delivery system. The proposed HGSA outperforms the state-of-the-art algorithms such as genetic algorithm (GA), Particle Swarm Optimization & Simulated Annealing algorithm (PSO-SA), Differential Evolution & Simulated Annealing (DE-SA), and Harris-hawks optimization (HHO). For all 24 instances, the aerial routes generated by HGSA have been used to evaluate the effectiveness of the UO-MinMin algorithm for different numbers of UAVs. The proposed UO-MinMin algorithm outperforms the base algorithms such as minimum completion time (MCT) and opportunistic load balancing (OLB).
Saki, M, Abolhasan, M, Lipman, J & Jamalipour, A 2022, 'Mobility Model for Contact-Aware Data Offloading Through Train-to-Train Communications in Rail Networks', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 1, pp. 597-609.
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In this paper, we propose a novel mobility model providing train traffic traces essential for train-to-train communication models. As the proposed mobility model works only based on trip timetables and train timetables are currently available in real-time, the produced mobility traces will be also in real-time. Additionally, as no GPS module is used in this method, our proposed model can provide a practical solution when signal from GPS or Assisted GPS is poor or unavailable such as in urban area or inside tunnels. Furthermore, as we used an energy optimization function, the proposed mobility model will provide a guidance trajectory for trains to have an energy-optimized operation. We also develop an algorithm that can determine the specifications of contacts between trains based on the traffic traces obtained from the mobility model. Such specifications includes duration, rate and location of train contacts used for estimation of data exchange capacity between trains through train-to-train communications. We validate our proposed model using data collected from Sydney Trains of Australia. The results obtained from our proposed model show over 98 percent accuracy in comparison with the real data collected via a GPS module from Sydney Trains.
Sakti, AD, Fauzi, AI, Takeuchi, W, Pradhan, B, Yarime, M, Vega-Garcia, C, Agustina, E, Wibisono, D, Anggraini, TS, Theodora, MO, Ramadhanti, D, Muhammad, MF, Aufaristama, M, Perdana, AMP & Wikantika, K 2022, 'Spatial Prioritization for Wildfire Mitigation by Integrating Heterogeneous Spatial Data: A New Multi-Dimensional Approach for Tropical Rainforests', Remote Sensing, vol. 14, no. 3, pp. 543-543.
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Wildfires drive deforestation that causes various losses. Although many studies have used spatial approaches, a multi-dimensional analysis is required to determine priority areas for mitigation. This study identified priority areas for wildfire mitigation in Indonesia using a multi-dimensional approach including disaster, environmental, historical, and administrative parameters by integrating 20 types of multi-source spatial data. Spatial data were combined to produce susceptibility, carbon stock, and carbon emission models that form the basis for prioritization modelling. The developed priority model was compared with historical deforestation data. Legal aspects were evaluated for oil-palm plantations and mining with respect to their impact on wildfire mitigation. Results showed that 379,516 km2 of forests in Indonesia belong to the high-priority category and most of these are located in Sumatra, Kalimantan, and North Maluku. Historical data suggest that 19.50% of priority areas for wildfire mitigation have experienced deforestation caused by wildfires over the last ten years. Based on legal aspects of land use, 5.2% and 3.9% of high-priority areas for wildfire mitigation are in oil palm and mining areas, respectively. These results can be used to support the determination of high-priority areas for the REDD+ program and the evaluation of land use policies.
Sakti, AD, Rahadianto, MAE, Pradhan, B, Muhammad, HN, Andani, IGA, Sarli, PW, Abdillah, MR, Anggraini, TS, Purnomo, AD, Ridwana, R, Yulianto, F, Manessa, MDM, Fauziyyah, AN, Yayusman, LF & Wikantika, K 2022, 'School Location Analysis by Integrating the Accessibility, Natural and Biological Hazards to Support Equal Access to Education', ISPRS International Journal of Geo-Information, vol. 11, no. 1, pp. 12-12.
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This study proposes a new model for land suitability for educational facilities based on spatial product development to determine the optimal locations for achieving education targets in West Java, Indonesia. Single-aspect approaches, such as accessibility and spatial hazard analyses, have not been widely applied in suitability assessments on the location of educational facilities. Model development was performed based on analyses of the economic value of the land and on the integration of various parameters across three main aspects: accessibility, comfort, and a multi-natural/biohazard (disaster) risk index. Based on the maps of disaster hazards, higher flood-prone areas are found to be in gentle slopes and located in large cities. Higher risks of landslides are spread throughout the study area, while higher levels of earthquake risk are predominantly in the south, close to the active faults and megathrusts present. Presently, many schools are located in very high vulnerability zones (2057 elementary, 572 junior high, 157 senior high, and 313 vocational high schools). The comfort-level map revealed 13,459 schools located in areas with very low and low comfort levels, whereas only 2377 schools are in locations of high or very high comfort levels. Based on the school accessibility map, higher levels are located in the larger cities of West Java, whereas schools with lower accessibility are documented far from these urban areas. In particular, senior high school accessibility is predominant in areas of lower accessibility levels, as there are comparatively fewer facilities available in West Java. Overall, higher levels of suitability are spread throughout West Java. These distribution results revealed an expansion of the availability of schools by area: senior high schools, 303,973.1 ha; vocational high schools, 94,170.51 ha; and junior high schools, 12,981.78 ha. Changes in elementary schools (3936.69 ha) were insignificant, as the current numbe...
Saleem, S, Amin, J, Sharif, M, Mallah, GA, Kadry, S & Gandomi, AH 2022, 'Leukemia segmentation and classification: A comprehensive survey', Computers in Biology and Medicine, vol. 150, pp. 106028-106028.
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Blood is made up of leukocytes (WBCs), erythrocytes (RBCs), and thrombocytes. The ratio of blood cancer diseases is increasing rapidly, among which leukemia is one of the famous cancer which may lead to death. Leukemia cancer is initiated by the unnecessary growth of immature WBCs present in the sponge tissues of bone marrow. It is generally analyzed by etiologists by perceiving slides of blood smear images under a microscope. The morphological features and blood cells count facilitated the etiologists to detect leukemia. Due to the late detection and expensive instruments used for leukemia analysis, the death rate has risen significantly. The fluorescence-based cell sorting technique and manual recounts using a hemocytometer are error-prone and imprecise. Leukemia detection methods consist of pre-processing, segmentation, features extraction, and classification. In this article, recent deep learning methodologies and challenges for leukemia detection are discussed. These methods are helpful to examine the microscopic blood smears images and for the detection of leukemia more accurately.
Salehi, Y, Shafaghat, A & Khabbaz, H 2022, 'A REVIEW ON PERFORMANCE OF STONE COLUMNS AS A GROUND IMPROVEMENT TECHNIQUE: LESSONS LEARNT FROM PAST EXPERIENCES AND PROSPECT FOR FUTURE DEVELOPMENT', Australian Geomechanics Journal, vol. 57, no. 1, pp. 71-91.
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Since the population growth is creating a strong demand for urban development, the need for construction in soft soils is dramatically increasing. Accordingly, ground improvement is an important requirement to avoid problems such as nonuniform settlements, failure due to low bearing capacity or liquefaction. Stone columns are used as one of the ground improvement techniques to stabilize the soil through increasing soil stiffness and shear resistance while decreasing the compressibility and settlement. Predicting the behaviour of a stone column needs to meet technical challenges, particularly in soft cohesive soils. Therefore, the aim of this paper is to make a broad assessment of the performance characteristics of stone columns in clayey soils as a review. In this study, the stone columns behaviour has been studied through analytical, experimental and numerical techniques, and failure modes and design of stone columns and their installation techniques are discussed. Based on previous investigations, it is gathered that in very soft soils, the dry-bottom feed vibro replacement technique is preferred to other methods and usage of geosynthetic encasement is very efficient where insufficient lateral confinement of the soil is problematic. According to past findings, the friction angle of the stone material and the diameter of the column are significant parameters for the design of the bearing capacity of the column. Furthermore, apart from ground improvement benefits, stone columns are used as vertical drains, which can decrease the pore water pressure during earthquakes and therefore mitigate the liquefaction potential. In addition, the cost-effectiveness of using low priced materials instead of aggregates without disturbing the overall performance of stone columns seems to be viable and can be explored further in future. This review can give an enhanced viewpoint to engineers and practitioners considering the use of stone columns in their projects.
Salis, Z, Keen, H, Gallego, B, Nguyen, TV & Sainsbury-Salis, A 2022, 'OP0227 WEIGHT LOSS IS ASSOCIATED WITH REDUCED INCIDENCE AND PROGRESSION OF STRUCTURAL DEFECTS IN KNEE OSTEOARTHRITIS, AS ASSESSED BY RADIOGRAPHY OVER 4 TO 5 YEARS: A PROSPECTIVE MULTI-COHORT STUDY', Annals of the Rheumatic Diseases, vol. 81, no. Suppl 1, pp. 149-150.
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BackgroundOverweight and obesity are associated with greater incidence and progression of the structural defects of knee osteoarthritis, but it is unknown if weight loss is of benefit.ObjectivesTo describe the association between change in body mass index (BMI) and the incidence and progression of structural defects in knee osteoarthritis.MethodsScores from radiographic analyses of knees at baseline and at 4 to 5 years’ follow up were obtained from three independent data sets (the OAI and MOST data sets from the United States from America, and the CHECK data set from the Netherlands). The exposure of interest was change in BMI from baseline to 4 to 5 years’ follow up. To investigate the incidence of structural defects of knee osteoarthritis, we selected a total of 9732 knees (from 5802 participants) that had a Kellgren-Lawrence (KL) grade of knee osteoarthritis at baseline of ‘none’ (0) or ‘doubtful’ (1) (the ‘incidence cohort’), and determined the odds of having a KL grade at follow-up of ‘minimal’ (2), ‘moderate’ (3), or ‘severe’ (4). To investigate progression, we selected a total of 6084 knees (from 3996 participants) that had a KL grade at baseline of ‘minimal’ (2), ‘moderate’ (3), or ‘severe’ (4) (the ‘progression cohort’), and determined the odds of increasing by 1 or more KL grades by follow up. The degradation of three individual structural features of knee osteoarthritis (i.e., joint space narrowing, osteophytes on the femoral surface, and osteophytes on the tibial surface), on both the medial and lateral sides of the knee, were also investigated in both the incidence and progression cohorts. Here, degradation was defined as an increase by 1 or more Osteoarthritis Research Society International (OARSI) grades.Results
Samy Helmy, B & Samy Helmy, A 2022, 'Role of Artificial Intelligence in Mental Wellbeing: Opportunities and Challenges', Journal of Artificial Intelligence, vol. 15, no. 1, pp. 1-8.
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Sanahuja-Embuena, V, Lim, S, Górecki, R, Trzaskus, K, Hélix-Nielsen, C & Kyong Shon, H 2022, 'Enhancing selectivity of novel outer-selective hollow fiber forward osmosis membrane by polymer nanostructures', Chemical Engineering Journal, vol. 433, pp. 133634-133634.
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An ideal forward osmosis (FO) membrane module for osmotic membrane bioreactor (OMBR) application would have high packing density, low reverse solute flux and low fouling propensity. Recently, an outer-selective hollow fiber forward osmosis (HFFO) membrane has been developed to simultaneously improve packing density and reduce fouling propensity. However, a high reverse solute flux of the HFFO membrane still generates a salinity build-up in the reactor and remains the main challenge of this technology. To tackle this problem, we successfully improved the selectivity of an outer-selective HFFO membrane by incorporating a prior developed formulation based on Pluronic® nanostructures containing water selective proteins into the active layer of the membrane. The assimilation of these nanostructures in the membrane resulted in a significant decrease of the specific reverse solute flux from 0.36 ± 0.01 gL-1 to 0.12 ± 0.02 gL-1 with no significant decrease in water flux. Also, urea was selected as a challenging solute to investigate the selectivity of the developed membranes. In comparison with the pristine membranes, membranes containing nanostructures presented a superior rejection of urea from 87.7 ± 2.0 % to 95.2 ± 0.9 %. The developed membranes are able to be used for future OMBR application tests to prove feasibility of the process. Thus, this study can lead to the development of new membranes suitable for efficient and long-term operation in OMBR configurations. Additionally, the nanostructures investigated here can be used for different thin-film composite membranes as an additive to improve membrane selectivity.
Sang, R, Deng, F, Engel, A, Goldys, E & Deng, W 2022, 'Lipid-polymer nanocarrier platform enables X-ray induced photodynamic therapy against human colorectal cancer cells', Biomedicine & Pharmacotherapy, vol. 155, pp. 113837-113837.
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Sansom, TM, Oberst, S, Richter, A, Lai, JCS, Saadatfar, M, Nowotny, M & Evans, TA 2022, 'Low radiodensity μCT scans to reveal detailed morphology of the termite leg and its subgenual organ', Arthropod Structure & Development, vol. 70, pp. 101191-101191.
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Termites sense tiny substrate-borne vibrations through subgenual organs (SGOs) located within their legs' tibiae. Little is known about the SGOs' structure and physical properties. We applied high-resolution (voxel size 0.45 μm) micro-computed tomography (μCT) to Australian termites, Coptotermes lacteus and Nasutitermes exitiosus (Hill) to test two staining techniques. We compared the effectiveness of a single stain of Lugol's iodine solution (LS) to LS followed by Phosphotungstic acid (PTA) solutions (1% and 2%). We then present results of a soldier of Nasutitermes exitiosus combining μCT with LS + 2%PTS stains and scanning electron microscopy to exemplify the visualisation of their SGOs. The termite's SGO due to its approximately oval shape was shown to have a maximum diameter of 60 μm and a minimum of 48 μm, covering 60 ± 4% of the leg's cross-section and 90.4 ± 5% of the residual haemolymph channel. Additionally, the leg and residual haemolymph channel cross-sectional area decreased around the SGO by 33% and 73%, respectively. We hypothesise that this change in cross-sectional area amplifies the vibrations for the SGO. Since SGOs are directly connected to the cuticle, their mechanical properties and the geometric details identified here may enable new approaches to determine how termites sense micro-vibrations.
Santoro, S, Aquino, M, Han Seo, D, Van Der Laan, T, Lee, M, Sung Yun, J, Jun Park, M, Bendavid, A, Kyong Shon, H, Halil Avci, A & Curcio, E 2022, 'Dimensionally controlled graphene-based surfaces for photothermal membrane crystallization', Journal of Colloid and Interface Science, vol. 623, pp. 607-616.
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Membrane-based photothermal crystallization - a pioneering technology for mining valuable minerals from seawater and brines - exploits self-heating nanostructured interfaces to boost water evaporation, so achieving a controlled supersaturation environment that promotes the nucleation and growth of salts. This work explores, for the first time, the use of two-dimensional graphene thin films (2D-G) and three dimensional vertically orientated graphene sheet arrays (3D-G) as potential photothermal membranes applied to the dehydration of sodium chloride, potassium chloride and magnesium sulfate hypersaline solutions, followed by salt crystallization. A systematic study sheds light on the role of vertical alignment of graphene sheets on the interfacial, light absorption and photothermal characteristics of the membrane, impacting on the water evaporation rate and on the crystal size distribution of the investigated salts. Overall, 3D-G facilitates the crystallization of the salts because of superior light-to-heat conversion leading to a 3-fold improvement of the evaporation rate with respect to 2D-G. The exploitation of sunlight graphene-based interfaces is demonstrated as a potential sustainable solution to aqueous wastes valorization via recovery in solid phase of dissolved salts using renewable solar energy.
Saputra, YM, Nguyen, D, Dinh, HT, Pham, Q-V, Dutkiewicz, E & Hwang, W-J 2022, 'Federated Learning Framework with Straggling Mitigation and Privacy-Awareness for AI-based Mobile Application Services', IEEE Transactions on Mobile Computing, vol. PP, no. 99, pp. 1-1.
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This work proposes a novel framework to address straggling and privacy issues for federated learning (FL)-based mobile application services, considering limited computing/communications resources at mobile users (MUs)/mobile application provider (MAP), privacy cost, the rationality and incentive competition among MUs in contributing data to the MAP. Particularly, the MAP first determines a set of the best MUs for the FL process based on MUs' provided information/features. Then, each selected MU can encrypt part of local data and upload the encrypted data to the MAP for an encrypted training process, in addition to the local training process. For that, the selected MU can propose a contract to the MAP according to its expected local and encrypted data. To find optimal contracts that can maximize utilities while maintaining high learning quality of the system, we develop a multi-principal one-agent contract-based problem considering the MUs' privacy cost, the MAP's limited computing resources, and asymmetric information between the MAP and MUs. Experiments with a real-world dataset show that our framework can speed up training time up to 49% and improve prediction accuracy up to 4.6 times while enhancing network's social welfare up to 114% under the privacy cost consideration compared with those of baseline methods.
Saravanakumar, A, Chen, W-H, Arunachalam, KD, Park, Y-K & Chyuan Ong, H 2022, 'Pilot-scale study on downdraft gasification of municipal solid waste with mass and energy balance analysis', Fuel, vol. 315, pp. 123287-123287.
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Sarker, PC, Guo, Y, Lu, H & Zhu, JG 2022, 'Improvement on parameter identification of modified Jiles-Atherton model for iron loss calculation', Journal of Magnetism and Magnetic Materials, vol. 542, pp. 168602-168602.
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The physical behaviour of a magnetic material can be characterized by Jiles-Atherton (J-A) model where some model parameters are generally identified by optimization techniques. For identification of model parameters using optimization techniques, an error criterion based on the error between the measured and calculated magnetic flux density (B) or magnetic field strength (H) is commonly considered where the relative error in the calculation of iron loss is ignored. Consequently, the calculated iron loss from B-H loop sometimes highly differs from its experimental value. In this paper, the error criteria for J-A model's parameter identification are designed as the combination of the relative iron loss error criterion and the general existing error criterion. Furthermore, a modified J-A model is proposed to improve the agreement between experimental and calculated results especially at the low magnetic induction levels by introducing a scaling factor in the anhysteretic magnetization. The proposed modified J-A model and the effectiveness of the error criteria for its parameter identification are tested by comparing calculated results with the experimental results as well as recently works in the literature.
Satpathy, PR, Bhowmik, P, Babu, TS, Sain, C, Sharma, R & Alhelou, HH 2022, 'Performance and Reliability Improvement of Partially Shaded PV Arrays by One-Time Electrical Reconfiguration', IEEE Access, vol. 10, pp. 46911-46935.
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Saxena, A, Chugh, D, Mittal, H, Sajid, M, Chauhan, R, Yafi, E, Cao, J & Prasad, M 2022, 'A Novel Unsupervised Feature Selection Approach Using Genetic Algorithm on Partitioned Data', Advances in Artificial Intelligence and Machine Learning, vol. 02, no. 04, pp. 500-515.
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A novel feature selection approach is presented in this paper. Sammon’s Stress Function transforms the high dimension data to a lower dimension data set. A data set is divided into small partitions. The features are assigned randomly to these partitions. Using GA with Sammon Error as fitness value, a small, desired number of features are selected from every partition. The combination of the reduced subsets of the features from these partitions is again divided into small partitions. After a certain number of iterating the process, a desired small number of features is obtained. For experimental validation, the proposed method has been tested on 11 standard datasets with three classifiers namely, Decision Tree, MLP and KNN. The classification accuracies obtained by the proposed method is highest on most of the considered datasets against the results reported in literature. Moreover, the proposed method selects comparatively less number of features in comparison to considered methods. The optimistic results obtained from the proposed method justify its strength.
Sayem, ASM, Lalbakhsh, A, Esselle, KP, Buckley, JL, O'Flynn, B & Simorangkir, RBVB 2022, 'Flexible Transparent Antennas: Advancements, Challenges, and Prospects', IEEE Open Journal of Antennas and Propagation, vol. 3, pp. 1109-1133.
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Scherer, S, Agrawal, V, Best, G, Cao, C, Cujic, K, Darnley, R, DeBortoli, R, Dexheimer, E, Drozd, B, Garg, R, Higgins, I, Keller, J, Kohanbash, D, Nogueira, L, Pradhan, R, Tatum, M, Viswanathan, VK, Willits, S, Zhao, S, Zhu, H, Abad, D, Angert, T, Armstrong, G, Boirum, R, Dongare, A, Dworman, M, Hu, S, Jaekel, J, Ji, R, Lai, A, Lee, YH, Luong, A, Mangelson, J, Maier, J, Picard, J, Pluckter, K, Saba, A, Saroya, M, Scheide, E, Shoemaker-Trejo, N, Spisak, J, Teza, J, Yang, F, Wilson, A, Zhang, H, Choset, H, Kaess, M, Rowe, A, Singh, S, Zhang, J, Hollinger, GA & Travers, M 2022, 'Resilient and Modular Subterranean Exploration with a Team of Roving and Flying Robots', Field Robotics, vol. 2, no. 1, pp. 678-734.
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Subterranean robot exploration is difficult, with many mobility, communications, and navigation challenges that require an approach with a diverse set of systems, and reliable autonomy. While prior work has demonstrated partial successes in addressing the problem, here we convey a comprehensive approach to address the problem of subterranean exploration in a wide range of tunnel, urban, and cave environments. Our approach is driven by the themes of resiliency and modularity, and we show examples of how these themes influence the design of the different modules. In particular, we detail our approach to artifact detection, pose estimation, coordination, planning, control, and autonomy, and we discuss our performance in the tunnel, urban, and self-organized cave circuits of the DARPA Subterranean Challenge. Our approach led to a winning result in the tunnel circuit, and placing second in the urban circuit event. We convey lessons learned in designing and testing a resilient system for subterranean exploration that can generalize to a large range of operating conditions, and potential improvements for the future.
Schlegl, T, Schlegl, S, Tomaselli, D, West, N & Deuse, J 2022, 'Adaptive similarity search for the retrieval of rare events from large time series databases', Advanced Engineering Informatics, vol. 52, pp. 101629-101629.
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Improving the recall of information retrieval systems for similarity search in time series databases is of great practical importance. In the manufacturing domain, these systems are used to query large databases of manufacturing process data that contain terabytes of time series data from millions of parts. This allows domain experts to identify parts that exhibit specific process faults. In practice, the search often amounts to an iterative query–response cycle in which users define new queries (time series patterns) based on results of previous queries. This is a well-documented phenomenon in information retrieval and not unique to the manufacturing domain. Indexing manufacturing databases to speed up the exploratory search is often not feasible as it may result in an unacceptable reduction in recall. In this paper, we present a novel adaptive search algorithm that refines the query based on relevance feedback provided by the user. Additionally, we propose a mechanism that allows the algorithm to self-adapt to new patterns without requiring any user input. As the search progresses, the algorithm constructs a library of time series patterns that are used to accurately find objects of the target class. Experimental validation of the algorithm on real-world manufacturing data shows, that the recall for the retrieval of fault patterns is considerably higher than that of other state-of-the-art adaptive search algorithms. Additionally, its application to publicly available benchmark data sets shows, that these results are transferable to other domains.
Schlegl, T, Tomaselli, D, Schlegl, S, West, N & Deuse, J 2022, 'Automated search of process control limits for fault detection in time series data', Journal of Process Control, vol. 117, pp. 52-64.
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Manually defined control limits remain a common strategy for quality control in manufacturing due to their ease of deployment on the shop floor compared to more advanced data analysis approaches. Despite their continued importance, there is no systematic method of defining these control limits. However, sub-optimal control limits can lead to undetected faults or cause unnecessary interruption to production. This manuscript presents an algorithm that systematizes this manual process into an efficient search task. We conceptualized the search task as a sequence of sub-problems that are based on the conventional steps taken by process experts when defining control limits. This algorithm can be integrated into an expert tool for shop floor personnel to automate the definition of control limits in annotated time series data. We demonstrate the efficacy of the control limits found by our algorithm by comparing them to those manually defined by process experts in real-world process data from the automotive industry. Furthermore, we show that our algorithm generalizes to traditional time series classification problems and achieves state-of-the-art performance on selected benchmark datasets. Our work is the first effort in automating the otherwise manual definition of control limits for fault detection.
Schwenken, J, Schallow, J, Sollik, D, Richter, R & Deuse, J 2022, 'Identifikation und Prognose dynamischer Engpässe', Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 117, no. 5, pp. 294-299.
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Abstract Eine erhöhte geplante und ungeplante Variabilität innerhalb der Produktion begünstigt das vermehrte Auftreten dynamischer Engpässe. Deren Beherrschung in Form eines zielgerichteten Engpassmanagements gelingt in der Unternehmenspraxis derzeit häufig nur sehr unzureichend und weitestgehend reaktiv. Dieser Beitrag stellt wesentliche Anwenderanforderungen sowie eine Bewertungssystematik und deren Anwendung für ausgewählte Verfahren der Engpassidentifikation und -prognose vor.
Sebayang, AH, Milano, J, Shamsuddin, AH, Alfansuri, M, Silitonga, AS, Kusumo, F, Prahmana, RA, Fayaz, H & Zamri, MFMA 2022, 'Modelling and prediction approach for engine performance and exhaust emission based on artificial intelligence of sterculia foetida biodiesel', Energy Reports, vol. 8, pp. 8333-8345.
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Sterculia foetida derived biodiesel is a potential fuel for a diesel engine. The Sterculia foetida biodiesel required a pre-refining process called degumming and an acid pretreatment process before converting them to methyl ester using the transesterification process. This study blended fuel from Sterculia foetida biodiesel and diesel with different volume ratios (5% to 30% of biodiesel blend with 95% to 70% diesel fuel). Sterculia foetida biodiesel and blended fuels met the ASTM D6751 and EN 14214 standards. The blended fuel is examined to obtain its influences on the performance and emission when operating at a diesel engine (1300 rpm to 2400 rpm). From the outcome, the engine performance of the SFB5 blend shows better performance than diesel fuel in terms of BTE (28.84%) and BSFC (5.86%). Artificial neural networks and extreme learning machines were employed to predict engine performance and exhaust emissions. The developed models gave excellent results, where the coefficient of determination is more than 99% and 98% for BSFC and BTE, respectively. When the engine is operated with SFB5, there is a significant reduction in CO, HC, and smoke opacity emissions by 8.26%, 2.08%, and 3.08%, respectively, and at the same time, an increase in CO2 by 3.53% and NOX by 22.39%. The comparison is made with diesel fuel. The extreme learning machine modelling is powerful for predicting engine performance and exhaust emission compared to artificial neural networks in terms of prediction accuracy. Sterculia foetida biodiesel–diesel blends of 5% show its capability to replace diesel fuel by providing engine peak performance than diesel fuel.
Sedehi, O, Papadimitriou, C & Katafygiotis, LS 2022, 'Hierarchical Bayesian uncertainty quantification of Finite Element models using modal statistical information', Mechanical Systems and Signal Processing, vol. 179, pp. 109296-109296.
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Seethaler, R, Mansour, SZ, Ruppert, MG & Fleming, AJ 2022, 'Piezoelectric benders with strain sensing electrodes: Sensor design for position control and force estimation', Sensors and Actuators A: Physical, vol. 335, pp. 113384-113384.
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Piezoelectric benders are widely used in industrial applications due to their low-cost and compact size. However, due to the large relative size and cost of displacement sensors, bender actuators are often operated in open-loop or with feed-forward control, which can limit positioning accuracy to 20% of full-scale. To improve the positioning accuracy of piezoelectric benders, this article proposes integrating resistive strain gauges into the electrode surface by chemical etching or laser ablation. These strain sensors are then used to measure and control the tip displacement. The proposed sensors are shown to suffer from significant cross-coupling between the actuator voltage and measured signal; however, this can be mitigated by judicious choice of the sensor location and actuator driving scheme. In addition to position sensing, a method is also presented for simultaneous estimation of the contact force between the actuator tip and load. The proposed methods are validated experimentally by controlling the tip position of a piezoelectric bender while simultaneously estimating the force applied to a reference load cell.
Seiler, KM, Palmer, AW & Hill, AJ 2022, 'Flow-Achieving Online Planning and Dispatching for Continuous Transportation With Autonomous Vehicles', IEEE Transactions on Automation Science and Engineering, vol. 19, no. 1, pp. 457-472.
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In large-scale industrial applications, goods must be continuously transported between locations, which in the absence of conveyor systems is by a fleet of individual vehicles. This article introduces flow-achieving scheduling tree (FAST), an online dispatching algorithm that allows vehicles to efficiently operate as a team to maximize the system's throughput while meeting a production schedule. A high-performance model is developed for high-fidelity prediction of vehicle interactions and system performance. It is subsequently optimized using a self-tuning variant of Monte Carlo tree search (MCTS) to make agile dispatch decisions in real time. The method is validated using an open-cut mine site and is shown to outperform a commonly used algorithm in this domain. Note to Practitioners - This article was motivated by the problem of dispatching autonomous haul trucks on open-cut mine sites. The proposed method is suited to any industrial transportation system where a continuous stream of goods must be efficiently transported between the load and unload stations by a potentially heterogeneous fleet of automated vehicles. The system makes decisions in real time while reacting to performance variations and disturbances by using a receding horizon approach. Off-the-shelf software commonly used in this domain is based on heuristics with limited ability to optimize, leading to myopic decision making without taking vehicle interactions into account. Here, flow-achieving scheduling tree (FAST) overcomes this by optimizing over possible schedules and thereby implicitly accounting for knock-on effects. Future work will incorporate additional constraints into the optimization process and validate FAST in other industrial domains.
Sekaran, R, Munnangi, AK, Ramachandran, M & Gandomi, AH 2022, '3D brain slice classification and feature extraction using Deformable Hierarchical Heuristic Model', Computers in Biology and Medicine, vol. 149, pp. 105990-105990.
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Brain tumors are the most frequently occurring and severe type of cancer, with a life expectancy of only a few months in most advanced stages. As a result, planning the best course of therapy is critical to improve a patient's ability to fight cancer and their quality of life. Various imaging modalities, such as computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound imaging, are commonly employed to assess a brain tumor. This research proposes a novel technique for extracting and classifying tumor features in 3D brain slice images. After input images are processed for noise removal, resizing, and smoothening, features of brain tumor are extracted using Volume of Interest (VOI). The extracted features are then classified using the Deformable Hierarchical Heuristic Model-Deep Deconvolutional Residual Network (DHHM-DDRN) based on surfaces, curves, and geometric patterns. Experimental results show that proposed approach obtained an accuracy of 95%, DSC of 83%, precision of 80%, recall of 85%, and F1 score of 55% for classifying brain cancer features.
Senanayake, S & Pradhan, B 2022, 'Predicting soil erosion susceptibility associated with climate change scenarios in the Central Highlands of Sri Lanka', Journal of Environmental Management, vol. 308, pp. 114589-114589.
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Soil erosion hazard is one of the prominent climate hazards that negatively impact countries' economies and livelihood. According to the global climate index, Sri Lanka is ranked among the first ten countries most threatened by climate change during the last three years (2018-2020). However, limited studies were conducted to simulate the impact of the soil erosion vulnerability based on climate scenarios. This study aims to assess and predict soil erosion susceptibility using climate change projected scenarios: Representative Concentration Pathways (RCP) in the Central Highlands of Sri Lanka. The potential of soil erosion susceptibility was predicted to 2040, depending on climate change scenarios, RCP 2.6 and RCP 8.5. Five models: revised universal soil loss (RUSLE), frequency ratio (FR), artificial neural networks (ANN), support vector machine (SVM) and adaptive network-based fuzzy inference system (ANFIS) were selected as widely applied for hazards assessments. Eight geo-environmental factors were selected as inputs to model the soil erosion susceptibility. Results of the five models demonstrate that soil erosion vulnerability (soil erosion rates) will increase 4%-22% compared to the current soil erosion rate (2020). The predictions indicate average soil erosion will increase to 10.50 t/ha/yr and 12.4 t/ha/yr under the RCP 2.6 and RCP 8.5 climate scenario in 2040, respectively. The ANFIS and SVM model predictions showed the highest accuracy (89%) on soil erosion susceptibility for this study area. The soil erosion susceptibility maps provide a good understanding of future soil erosion vulnerability (spatial distribution) and can be utilized to develop climate resilience.
Senanayake, S, Pradhan, B, Alamri, A & Park, H-J 2022, 'A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction', Science of The Total Environment, vol. 845, pp. 157220-157220.
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Rainfall variation causes frequent unexpected disasters all over the world. Increasing rainfall intensity significantly escalates soil erosion and soil erosion related hazards. Forecasting accurate rainfall helps early detection of soil erosion vulnerability and can minimise the damages by taking appropriate measures caused by severe storms, droughts and floods. This study aims to predict soil erosion probability using the deep learning approach: long short-term memory neural network model (LSTM) and revised universal soil loss equation (RUSLE) model. Daily rainfall data were gathered from five agro-meteorological stations in the Central Highlands of Sri Lanka from 1990 to 2021 and fed into the LSTM model simulation. The LSTM model was forecasted with the time-series monthly rainfall data for a long lead time period, rainfall values for next 36 months in each station. Geo-informatics tools were used to create the rainfall erosivity map layer for the year 2024. The RUSLE model prediction indicates the average annual soil erosion over the Highlands will be 11.92 t/ha/yr. Soil erosion susceptibility map suggests around 30 % of the land area will be categorised as moderate to very-high soil erosion susceptible classes. The resulted map layer was validated using past soil erosion map layers developed for 2000, 2010 and 2019. The soil erosion susceptibility map indicates an accuracy of 0.93 with the area under the receiver operator characteristic curve (AUC-ROC), showing a satisfactory prediction performance. These findings will be helpful in policy-level decision making and researchers can further tested different deep learning models with the RUSLE model to enhance the prediction capability of soil erosion probability.
Senanayake, S, Pradhan, B, Huete, A & Brennan, J 2022, 'Spatial modeling of soil erosion hazards and crop diversity change with rainfall variation in the Central Highlands of Sri Lanka', Science of The Total Environment, vol. 806, no. Pt 2, pp. 150405-150405.
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The spatial variation of soil erosion is essential for farming system management and resilience development, specifically in the high climate hazard vulnerable tropical countries like Sri Lanka. This study aimed to investigate climate and human-induced soil erosion through spatial modeling. Remote sensing was used for spatial modeling to detect soil erosion, crop diversity, and rainfall variation. The study employed a time-series analysis of several variables such as rainfall, land-use land-cover (LULC) and crop diversity to detect the spatial variability of soil erosion in farming systems. Rain-use efficiency (RUE) and residual trend analysis (RESTREND) combined with a regression approach were applied to partition the soil erosion due to human and climate-induced land degradation. Results showed that soil erosion has increased from 9.08 Mg/ha/yr to 11.08 Mg/ha/yr from 2000 to 2019 in the Central Highlands of Sri Lanka. The average annual rainfall has increased in the western part of the Central Highlands, and soil erosion hazards such as landslides incidence also increased during this period. However, crop diversity has been decreasing in farming systems, namely wet zone low country (WL1a) and wet zone mid-country (WM1a), in the western part of the Central Highlands. The RUE and RESTREND analyses reveal climate-induced soil erosion is responsible for land degradation in these farming systems and is a threat to sustainable food production in the farming systems of the Central Highlands.
Sepehrirahnama, S & Oberst, S 2022, 'Acoustic Radiation Force and Torque Acting on Asymmetric Objects in Acoustic Bessel Beam of Zeroth Order Within Rayleigh Scattering Limit', Frontiers in Physics, vol. 10.
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Acoustic momentum exchange between objects and the surrounding fluid can be quantified in terms of acoustic radiation force and torque, and depends on several factors including the objects’ geometries. For a one-dimensional plane wave type, the induced torque on the objects with arbitrary shape becomes a function of both, direct polarization and Willis coupling, as a result of shape asymmetry, and has only in-plane components. Here, we investigate, in the Rayleigh scattering limit, the momentum transfer to objects in the non-planar pressure field of an acoustic Bessel beam with axisymmetric wave front. This type of beam is selected since it can be practically realized by an array of transducers that are cylindrically arranged and tilted at the cone angle β which is a proportionality index of the momentum distribution in the transverse and axial propagation directions. The analytical expressions of the radiation force and torque are derived for both symmetric and asymmetric objects. We show the dependence of radiation force and torque on the characteristic parameters β and radial distance from the beam axis. By comparing against the case of a plane travelling plane wave, zero β angle, we demonstrated that the non-planar wavefront of a zeroth order Bessel beam causes an additional radial force and axial torque. We also show that, due to Willis coupling, an asymmetric object experiences greater torques in the θ direction, by minimum of one order of magnitude compared to a plane travelling wave. Further, the components of the partial torques owing to direct polarization and Willis coupling act in the same direction, except for a certain range of cone angle β. Our findings show that a non-planar wavefront, which is quantified by β in the case of a zeroth-order Bessel beam, can be used to con...
Sepehrirahnama, S, Oberst, S, Chiang, YK & Powell, DA 2022, 'Willis Coupling-Induced Acoustic Radiation Force and Torque Reversal', Physical Review Letters, vol. 129, no. 17, p. 174501.
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Acoustic meta-atoms serve as the building blocks of metamaterials, with linear properties designed to achieve functions such as beam steering, cloaking, and focusing. They have also been used to shape the characteristics of incident acoustic fields, which led to the manipulation of acoustic radiation force and torque for development of acoustic tweezers with improved spatial resolution. However, acoustic radiation force and torque also depend on the shape of the object, which strongly affects its scattering properties. We show that by designing linear properties of an object using metamaterial concepts, the nonlinear acoustic effects of radiation force and torque can be controlled. Trapped objects are typically small compared with the wavelength, and are described as particles, inducing monopole and dipole scattering. We extend such models to a polarizability tensor including Willis coupling terms, as a measure of asymmetry, capturing the significance of geometrical features. We apply our model to a three-dimensional, subwavelength meta-atom with maximal Willis coupling, demonstrating that the force and the torque can be reversed relative to an equivalent symmetrical particle. By considering shape asymmetry in the acoustic radiation force and torque, Gorkov's fundamental theory of acoustophoresis is thereby extended. Asymmetrical shapes influence the acoustic fields by shifting the stable trapping location, highlighting a potential for tunable, shape-dependent particle sorting.
Sepehrirahnama, S, Ray Mohapatra, A, Oberst, S, Chiang, YK, Powell, DA & Lim, K-M 2022, 'Acoustofluidics 24: theory and experimental measurements of acoustic interaction force', Lab on a Chip, vol. 22, no. 18, pp. 3290-3313.
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This tutorial review covers theoretical and experimental aspects of acoustic interaction force, as one of the driving forces of acoustophoresis. The non-reciprocity, rotational coupling, viscosity effects, and particle agglomeration are discussed.
Septiadi, WN, Iswari, GA, Sudarsana, PB, Putra, GJP, Febraldo, D, Putra, N & Mahlia, TMI 2022, 'Effect of Al2O3 and TiO2 nano-coated wick on the thermal performance of heat pipe', Journal of Thermal Analysis and Calorimetry, vol. 147, no. 11, pp. 6193-6205.
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A heat pipe is a passive two-phase heat exchanger technology, as a capillary-driven structure that allows heat transport by maintaining temperature difference. Heat pipe performance can be determined from the value of heat resistance, and nanoparticle can be applied to increase heat pipe performance. This research uses Al2O3 and TiO2 as a coating material for the heat pipe. The methods used in this research were by giving the heat pipe a nano-coating treatment using the electrophoretic deposition process and doing a pool boiling experiment by giving the heat pipe some heat loads. The main data of this research are temperature and bubble growth data. Based on the result of the research, the use of nanoparticles can improve heat pipe performance. The temperature difference between the evaporator and condenser area was calculated 2.38 °C on Al2O3 coating and 3.92 °C on TiO2 coating. Al2O3 nanoparticle coating was able to provide a heat transfer coefficient 480% superior to sample without nanoparticle coating, and 174% better than TiO2 nanoparticle coating.
Serbouti, I, Raji, M, Hakdaoui, M, El Kamel, F, Pradhan, B, Gite, S, Alamri, A, Maulud, KNA & Dikshit, A 2022, 'Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers', Remote Sensing, vol. 14, no. 21, pp. 5498-5498.
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In this era of free and open-access satellite and spatial data, modern innovations in cloud computing and machine-learning algorithms (MLAs) are transforming how Earth-observation (EO) datasets are utilized for geological mapping. This study aims to exploit the potentialities of the Google Earth Engine (GEE) cloud platform using powerful MLAs. The proposed method is implemented in three steps: (1) Based on GEE and Sentinel 2A imagery (spectral and textural features), that cover 1283 km2 area, a variety of lithological maps are generated using five supervised classifiers (random forest (RF), support vector machine (SVM), classification and regression tree (CART), minimum distance (MD), naïve Bayes (NB)); (2) the accuracy assessments for each class are performed, by estimating overall accuracy (OA) and kappa coefficient (K) for each classifier; (3) finally, the fusion of classification maps is performed using Dempster–Shafer Theory (DST) for mapping lithological units of the northern part of the complex Paleozoic massif of Rehamna, a large semi-arid region located in the SW of the western Moroccan Meseta. The results were quantitatively compared with existing geological maps, enhanced color composite and validated by field survey investigation. In comparison of individual classifiers, the SVM yields better accuracy of nearly 88%, which was 12% higher than the RF MLA; otherwise, the parametric MLAs produce the weakest lithological maps among other classifiers, with a lower OA of approximately 67%, 54% and 52% for CART, MD and NB, respectively. Noticeably, the highest OA value of 96% is achieved for the proposed approach. Therefore, we conclude that this method allows geoscientists to update previous geological maps and rapidly produce more precise lithological maps, especially for hard-to-reach regions.
Shabani, S, Delshad, M, Sadeghi, R & Alhelou, HH 2022, 'A High Step-Up PWM Non-Isolated DC-DC Converter With Soft Switching Operation', IEEE Access, vol. 10, pp. 37761-37773.
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Shafaghat, A & Khabbaz, H 2022, 'Recent advances and past discoveries on tapered pile foundations: a review', Geomechanics and Geoengineering, vol. 17, no. 2, pp. 455-484.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. The growing tendency to study the behaviour of tapered piles in the last two decades has made it necessary to gain a deeper insight into this specific kind of deep foundation. Tapered piles have been investigated through analytical, experimental, and numerical studies. These piles have revealed different behaviour under various loading conditions. Hence, reviewing and assessing these efforts to comprehend their response can be of great significance. In this paper firstly, it is attempted to go over experimental studies, conducted on tapered piles. Then, the proposed mathematical and numerical solutions, employed to calculate the bearing capacity of single tapered piles, are compared to have a better vision of how these piles behave. In the third section, the optimum tapering angles of tapered piles in loose, medium, and dense sand are discussed. All the efforts are investigated technically to find the advantages, disadvantages, and the research gaps for this specific kind of piles. In addition, another section entitled the directions and ideas for future research on tapered piles is provided comprising the most recent achievements in this area. Moreover, the implementation of tapered piles in a significant project as a case study is discussed.
Shafaghat, A, Khabbaz, H & Fatahi, B 2022, 'Axial and Lateral Efficiency of Tapered Pile Groups in Sand Using Mathematical and Three-Dimensional Numerical Analyses', Journal of Performance of Constructed Facilities, vol. 36, no. 1.
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This study presents a new mathematical equation for calculating the pile group efficiency in cohesionless soil under combined axial and lateral loading conditions, considering the tapering angle effect. Based on the mathematical definition of the pile group efficiency, analytical correlations are developed. The tapering effect is considered by developing a new geometry coefficient for efficiency associated with the shaft vertical bearing component of tapered piles. In addition, a simplified mathematical equation is developed for predicting the group interaction factor as a function of pile spacing, number of piles in the group, diameter of the cylindrical reference pile, tapering angle, and pile slenderness ratio. On the other hand, an array of three-dimensional numerical analyses is performed for modeling same-volume single bored piles and pile groups with various arrangements to capture the accuracy of the proposed mathematical equation. The hardening soil constitutive model is adopted for the modeling of piles in loose sand. Subsequently, the load-displacement diagrams of single piles, as well as pile groups, are obtained. The bearing capacities of straight-sided and tapered bored piles are then calculated and compared using a definite settlement criterion. By computing the various bearing-capacity components, group efficiencies can be attained from both numerical and mathematical analyses. The results indicate an acceptable agreement between both analyses. Finally, the developed equation can predict the pile group efficiency incorporating the tapering angle and other influencing parameters as a novel and simple relationship under simultaneous axial and lateral loading conditions.
Shafapourtehrany, M, Yariyan, P, Özener, H, Pradhan, B & Shabani, F 2022, 'Evaluating the application of K-mean clustering in Earthquake vulnerability mapping of Istanbul, Turkey', International Journal of Disaster Risk Reduction, vol. 79, pp. 103154-103154.
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Performing the most up-to-date and accurate vulnerability assessment is key to an effective earthquake disaster management. In cities like Istanbul (Turkey) with a high rate of urban expansion, the safety of the residents must not be neglected. The challenges in such studies are related to the lack of a training dataset. Some areas are highly prone to earthquakes, however, there have been no earthquakes in those areas recently. This research proposes and tests the ability of the k-mean clustering method to create the training dataset for earthquake vulnerability analysis. Subsequently, the derived sample dataset was used in four state-of-the-art models i.e. Decision Tree (DT), Support Vector Machine (SVM), Self-Organizing Map (SOM) and Logistic Regression (LR) for assessing earthquake vulnerability in Istanbul, Turkey. The multicollinearity among the variables was determined using tolerance (TOL) and variance inflation factor (VIF) which revealed no multicollinearity among the variables. The highest VIF belonged to the “distance to faults” factor. Vulnerability related variables were classified, weighed and using k-mean clustering, a training database was constructed. Then, the standardized variables were keyed in as input alongside the training site maps into DT, SVM, SOM and LR to construct an Earthquake Vulnerability Map (EVM). EVMs were created for all the four samples and graded as very-low, relatively-low, moderate, high, or extremely-high. Several statistical metrics such as Area under the ROC curve (AUC), sensitivity (SST), specificity (SPF), root-mean-squared-errors (RMSE), positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the accuracy of the resultant maps. The highest and lowest AUC prediction rates were 0.962 and 0.912 from the K-means-SOM and K-means-LR models, respectively. The lowest RSME results using the testing dataset (0.329) belonged to K-means-SVM model. The region's most prone vulnerability ...
Shafiei, S, Mihăiţă, A-S, Nguyen, H & Cai, C 2022, 'Integrating data-driven and simulation models to predict traffic state affected by road incidents', Transportation Letters, vol. 14, no. 6, pp. 629-639.
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Shahabuddin, M, Mofijur, M, Rizwanul Fattah, IM, Kalam, MA, Masjuki, HH, Chowdhury, MA & Hossain, N 2022, 'Study on the tribological characteristics of plant oil-based bio-lubricant with automotive liner-piston ring materials', Current Research in Green and Sustainable Chemistry, vol. 5, pp. 100262-100262.
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The development of bio-lubricant is an immerging area of research considering the rapid depletion of petroleum reserve and environmental concern. This study aims to develop non-edible jatropha oil-based bio-lubricant and investigate the tribological properties considering commonly used piston ring-cylinder liner materials of stainless steel and cast iron due to their interaction under lubricated conditions in an internal combustion engine. The bio-lubricant was prepared by blending different percentages of vegetable oil with commercial lubricants. The tribological test was carried out using a Reo-Bicerihigh-frequency reciprocating rig (HFRR) for the duration of 6 h under standard operating conditions. Different properties of bio-lubricants were measured before and after the HFRR test using various analytical instruments. The morphology of the worn material surfaces was examined via Hitachi S-4700 FE-SEM cold field emission high resolution scanning electron microscopy (SEM). The result showed that addition of vegetable oil lubricant up to 7.5% concentration can be compared with commercial lubricant in case of wear rate and coefficient of wear as weight loss reduced significantly. Minimum change in viscosity was observed at the addition of 7.5% bio-lubricant. Surface morphology analysis confirmed less damage of metal surface when tribological analysis were performed at mixed lubricated condition.
Shan, F, He, X, Jahed Armaghani, D, Zhang, P & Sheng, D 2022, 'Success and challenges in predicting TBM penetration rate using recurrent neural networks', Tunnelling and Underground Space Technology, vol. 130, pp. 104728-104728.
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Tunnel Boring Machines (TBMs) have been increasingly used in tunnelling projects. Forecasting future TBM performance would be desirable for project time management and cost control. We aim to use recurrent neural networks to predict the near future TBM penetration rate from historical data. Our datasets are composed of Changsha and Zhengzhou metro lines, with totally different geological conditions. In the experiments, the one-step forecast of TBM penetration rate by the traditional recurrent neural network (RNN) or long short-term memory (LSTM) is relatively accurate, irrespective of the different geological conditions used in training and evaluation. Predicting the next Nth step penetration rate proves to be more challenging and depends on the time to the future or the distance ahead of the TBM cutterhead. There are generally time lags between measured and predicted results. The recursive RNN is then developed to address the lag problems, but to no avail. Alternative methods for predicting future penetration rates are studied, including the penetration rate at the Nth step in the future and the average penetration rate of the next N steps, with the latter being trained by long-input or short-input methods. The average N-step forecast using short inputs provides the best results, and its performance over other alternatives becomes more distinct as the number N increases. We also discuss the possibility of the forecast problem as a quasi-random walk, which means that forecasting penetration rate cannot easily be achieved using low-frequency data with RNNs, and that the accuracy depends on the correlation between the last and predicted steps in the data.
Shanableh, A, Al-Ruzouq, R, Hamad, K, Gibril, MBA, Khalil, MA, Khalifa, I, El Traboulsi, Y, Pradhan, B, Jena, R, Alani, S, Alhosani, M, Stietiya, MH, Al Bardan, M & AL-Mansoori, S 2022, 'Effects of the COVID-19 lockdown and recovery on People's mobility and air quality in the United Arab Emirates using satellite and ground observations', Remote Sensing Applications: Society and Environment, vol. 26, pp. 100757-100757.
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The stringent COVID-19 lockdown measures in 2020 significantly impacted people's mobility and air quality worldwide. This study presents an assessment of the impacts of the lockdown and the subsequent reopening on air quality and people's mobility in the United Arab Emirates (UAE). Google's community mobility reports and UAE's government lockdown measures were used to assess the changes in the mobility patterns. Time-series and statistical analyses of various air pollutants levels (NO2, O3, SO2, PM10, and aerosol optical depth-AOD) obtained from satellite images and ground monitoring stations were used to assess air quality. The levels of pollutants during the initial lockdown (March to June 2020) and the subsequent gradual reopening in 2020 and 2021 were compared with their average levels during 2015-2019. During the lockdown, people's mobility in the workplace, parks, shops and pharmacies, transit stations, and retail and recreation sectors decreased by about 34%-79%. However, the mobility in the residential sector increased by up to 29%. The satellite-based data indicated significant reductions in NO2 (up to 22%), SO2 (up to 17%), and AOD (up to 40%) with small changes in O3 (up to 5%) during the lockdown. Similarly, data from the ground monitoring stations showed significant reductions in NO2 (49% - 57%) and PM10 (19% - 64%); however, the SO2 and O3 levels showed inconsistent trends. The ground and satellite-based air quality levels were positively correlated for NO2, PM10, and AOD. The data also demonstrated significant correlations between the mobility and NO2 and AOD levels during the lockdown and recovery periods. The study documents the impacts of the lockdown on people's mobility and air quality and provides useful data and analyses for researchers, planners, and policymakers relevant to managing risk, mobility, and air quality.
Shannon, A, Dubeau, F, Uysal, M & Özkan, E 2022, 'A Difference Equation Model of Infectious Disease', International Journal Bioautomation, vol. 26, no. 4, pp. 339-352.
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In the context of so much uncertainty with coronavirus variants and official mandate based on seemingly exaggerated predictions of gloom from epidemiologists, it is appropriate to consider a revised model of relative simplicity, because there can be dangers in developing models which endeavour to account for too many variables. Predictions and projections from any such models have to be in the context of relevant contingencies. The model presented here is based on relatively simple second order difference equations. The context here is as important as the content in that in many Western counties where the narrative currently seems more important than the truth, and the results of empirical science are valued more as a shield for politicians than a sword for protection of citizens.
Shao, R, Wu, C & Li, J 2022, 'A comprehensive review on dry concrete: Application, raw material, preparation, mechanical, smart and durability performance', Journal of Building Engineering, vol. 55, pp. 104676-104676.
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Dry concrete, which can be understood literally, is defined as the fresh mixture of concrete having no flowability with a very small slump value. It is a hardened mixture mixed with essentially the same raw materials (cement, aggregate and supplementary cementitious material) but lower water content as compared to conventional concrete. Performance and properties of dry concrete are closely related to the raw materials dosage, preparation technique, curing regimes and curing. At present, the applications of dry concrete products have been expanded to many engineering areas which benefit from their prominent advantages such as fast hardening, high early strength, along with low material and production cost. This paper reviews two most representative dry concrete mixtures, namely roller-compacted concrete (RCC) and dry-cast concrete (DCC), in terms of raw material, preparation method, static/dynamic mechanical behaviour, smart and durability performance, and application. Among them, the static and dynamic mechanical properties, including static strength behaviour and elastic modulus, as well as dynamic responses under seismic and impact loads, are reviewed in detail. In addition, the freeze-thaw resistance, carbonation resistance, permeability, abrasion resistance, fatigue characteristic and volume change which involved in durability investigation of both RCC and DCC are successively elaborated and analysed. Finally, some suggestions and ideas on the further researches of dry concrete are also presented.
Shao, R, Wu, C, Li, J & Liu, Z 2022, 'Development of sustainable steel fibre-reinforced dry ultra-high performance concrete (DUHPC)', Journal of Cleaner Production, vol. 337, pp. 130507-130507.
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Dry concrete technology has been extensively utilized in many engineering fields thanks to its remarkable high early strength, fast construction speed and low production cost. However, its shortcomings such as low flexural tensile strength, poor toughness, and susceptible to crack under stress and temperature also render the safety and service life of concrete structures unable to be effectively ensured. Dry ultra-high performance concrete (DUHPC), a promising building material, has improved mechanical and durability performance, and contributes to economical construction by reducing the cross-section size and improving structural long-term serviceability. In this study, the mechanical performance (such as compressive and indirect tensile behaviour) of fibre-reinforced DUHPC (FR-DUHPC) was experimentally investigated after a benchmark mix proportioning was determined via orthogonal tests. Different steel fibre volume contents (0.5–2.0%) and curing regimes including normal-temperature water curing, moist/steam curing and hot-water bath curing were used to explore their impacts on the mechanical properties of DUHPC. In total, 648 FR-DUHPC samples were fabricated and tested for determining their unit weight, compressive, flexural and split tensile strengths. The samples’ failure modes after bending and split tensile tests were analyzed. The results indicated that the fibre addition exhibited a notable positive effect on the mechanical properties of DUHPC, especially for the enhancement of the flexural and split tensile strengths, along with the improvement of post-cracking behaviour. An evident increase in early strength was found via using moist/steam and hot-water bath curing regime, but the former negatively impacted the development of the long-term strength. 50 °C moist/steam curing temperature was suggested for consolidating the pre-cast DUHPC units based on the microstructure analysis conducted, and the volume content of 1.5% was considered to be the...
Shao, R, Wu, C, Li, J & Liu, Z 2022, 'Investigation on the mechanical characteristics of multiscale mono/hybrid steel fibre-reinforced dry UHPC', Cement and Concrete Composites, vol. 133, pp. 104681-104681.
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Dry ultra-high performance concrete (DUHPC) is a promising building material with better mechanical and durability properties, developed on the basis of retaining the advantages of traditional dry concrete, such as fast hardening speed, high early age strength and rapid demoulding. In the current study, the impacts of multiscale mono and hybrid steel fibre reinforcements on the static mechanical behaviour of DUHPC were further studied based on the benchmark mix ratio and optimal curing regime obtained from the previous study. The experiments carried out included the quasi-static uniaxial compression, four-point bending and split-tensile tests. The mono fibre reinforcement (0.5–2.0 vol. %) comprised of straight steel fibres with the same diameter but different lengths (6, 10 and 13 mm), while the hybrid fibre reinforcement was composed of different combinations of foregoing fibres at a fixed content (1.5 vol. %), which could be further divided into double and ternary hybridization. Test results revealed that compared to control samples without fibre reinforcement, the single addition of any steel fibres improved the static mechanical behaviour of DUHPC, particularly for flexural and split-tensile performance. In the case of fibre hybridization, the replacement of longer fibres with more addition of short (6 mm) ones evidently reduced the flexural toughness and energy absorption capacity of DUHPC upon cracking, whereas the mixtures with hybrid medium (10 mm) and long (13 mm) fibres as well as with hybrid short, medium and long fibres showed better compressive toughness and energy absorption capability. The proposed multivariate regression linear, nonlinear and most of the mixed models could well estimate the compressive, flexural and split-tensile strength values of mono steel fibre-reinforced DUHPC at a given range of fibre length, volume content and curing age. The updated best-fit models containing compressive strength as an additional independent vari...
Shao, Z, Weng, J, Zhang, Y, Wu, Y, Li, M, Weng, J, Luo, W & Yu, S 2022, 'Peripheral-Free Device Pairing by Randomly Switching Power', IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 6, pp. 4240-4254.
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With the growing popularity of the Internet-of-Things (IoT), a massive amount of purpose-specific, heterogeneous, inexpensive devices have been deployed. To allow these devices to perform their duties and collaborate efficiently, designing a secure and dependable communication channel is necessary. Pairing, as the fundamental procedure for establishing a trustworthy communication channel, has received extensive attention from security researchers. Previous secure pairing protocols depend on auxiliary peripherals (e.g. displays, speakers) to share the secret message, while for those products featuring with low-price, manufacturers would probably adopt insecure pairing methods to reduce the cost, so the devices may be subject to various attacks. To mitigate such a situation, we design a peripheral-free secure pairing protocol, termed SwitchPairing. Our protocol only requires users to connect the pre-pairing devices to the same power source, then randomly presses and releases the switch to generate a shared secret. It does not require additional peripherals and can defense eavesdropping and replay attacks innately. We implement a prototype via two CC2640R2F development boards and invite volunteers to participate in the experiments about bench-marking security and usability. The result of our experiments show that our protocol can fulfill the security and efficient requirement of various IoT applications.
Sharari, N, Fatahi, B, Hokmabadi, A & Xu, R 2022, 'Seismic resilience of extra-large LNG tank built on liquefiable soil deposit capturing soil-pile-structure interaction', Bulletin of Earthquake Engineering, vol. 20, no. 7, pp. 3385-3441.
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AbstractAssessment of seismic resilience of critical infrastructure such as liquefied natural gas (LNG) storage tanks, is essential to ensure availability and security of services during and after occurrence of large earthquakes. In many projects, it is preferred to build energy storage facilities in coastal areas for the ease of sea transportation, where weak soils such as soft clay and loose sand with liquefaction potential may be present. In this study, three-dimensional finite element model is implemented to examine the seismic response of a 160,000 m3full containment LNG tank supported by 289 reinforced concrete piles constructed on liquefiable soil overlaying the soft clay deposit. The seismic soil-structure interaction analysis was conducted through direct method in the time domain subjected to the 1999 Chi-Chi and the 1968 Hachinohe earthquakes, scaled to Safe Shutdown Earthquake hazard level for design of LNG tanks. The analyses considered different thicknesses of the liquified soil deposit varying from zero (no liquefaction) to 15 m measured from the ground surface. The key design parameters inspected for the LNG tank include the acceleration profile for both inner and outer tanks, the axial, hoop and shear forces as well as the von Mises stresses in the inner tank wall containing the LNG, in addition to the pile response in terms of lateral displacements, shear forces and bending moments. The results show that the seismic forces generated in the superstructure decreased with increasing the liquefied soil depth. In particular, the von Mises stresses in the inner steel tank exceeded the yield stress for non-liquefied soil deposit, and the elastic–plastic buckling was initiated in the upper section of the tank where plastic deformations were detected as a result of excessive von Mises stresses. However, when soil liquefaction occurred, although von Mises stresses in the inner tank shell remai...
Sharari, N, Fatahi, B, Hokmabadi, AS & Xu, R 2022, 'Impacts of Pile Foundation Arrangement on Seismic Response of LNG Tanks Considering Soil–Foundation–Structure Interaction', Journal of Performance of Constructed Facilities, vol. 36, no. 1.
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Sharbati, R, Ramazi, H, Khoshnoudian, F, Valizadeh, T, Koopialipoor, M & Armaghani, DJ 2022, 'The smooth transition GARCH model for simulation of highly nonstationary earthquake ground motions', Engineering with Computers, vol. 38, no. 2, pp. 1529-1541.
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Sharif, O, Islam, MR, Hasan, MZ, Kabir, MA, Hasan, ME, AlQahtani, SA & Xu, G 2022, 'Analyzing the Impact of Demographic Variables on Spreading and Forecasting COVID-19', Journal of Healthcare Informatics Research, vol. 6, no. 1, pp. 72-90.
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The aim of this study is to analyse the coronavirus disease 2019 (COVID-19) outbreak in Bangladesh. This study investigates the impact of demographic variables on the spread of COVID-19 as well as tries to forecast the COVID-19 infected numbers. First of all, this study uses Fisher's Exact test to investigate the association between the infected groups of COVID-19 and demographical variables. Second, it exploits the ANOVA test to examine significant difference in the mean infected number of COVID-19 cases across the population density, literacy rate, and regions/divisions in Bangladesh. Third, this research predicts the number of infected cases in the epidemic peak region of Bangladesh for the year 2021. As a result, from the Fisher's Exact test, we find a very strong significant association between the population density groups and infected groups of COVID-19. And, from the ANOVA test, we observe a significant difference in the mean infected number of COVID-19 cases across the five different population density groups. Besides, the prediction model shows that the cumulative number of infected cases would be raised to around 500,000 in the most densely region of Bangladesh, Dhaka division.
Sharifi, V, Abdollahi, A, Rashidinejad, M, Heydarian-Forushani, E & Alhelou, HH 2022, 'Integrated Electricity and Natural Gas Demand Response in Flexibility-Based Generation Maintenance Scheduling', IEEE Access, vol. 10, pp. 76021-76030.
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Sharma, P, Beck, D, Murtha, LA, Figtree, G, Boyle, A & Gentile, C 2022, 'Fibulin-3 Deficiency Protects Against Myocardial Injury Following Ischaemia/ Reperfusion in in vitro Cardiac Spheroids', Frontiers in Cardiovascular Medicine, vol. 9, p. 913156.
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Myocardial infarction (MI, or heart attack) is a leading cause of death worldwide. Myocardial ischaemia reperfusion (I/R) injury typical of MI events is also associated with the development of cardiac fibrosis and heart failure in patients. Fibulin-3 is an extracellular matrix component that plays a role in regulating MI response in the heart. In this study, we generated and compared in vitro cardiac spheroids (CSs) from wild type (WT) and fibulin-3 knockout (Fib-3 KO) mice. These were then exposed to pathophysiological changes in oxygen (O2) concentrations to mimic an MI event. We finally measured changes in contractile function, cell death, and mRNA expression levels of cardiovascular disease genes between WT and Fib-3 KO CSs. Our results demonstrated that there are significant differences in growth kinetics and endothelial network formation between WT and Fib-3 KO CSs, however, they respond similarly to changes in O2 concentrations. Fib-3 deficiency resulted in an increase in viability of cells and improvement in contraction frequency and fractional shortening compared to WT I/R CSs. Gene expression analyses demonstrated that Fib-3 deficiency inhibits I/R injury and cardiac fibrosis and promotes angiogenesis in CSs. Altogether, our findings suggest that Fib-3 deficiency makes CSs resistant to I/R injury and associated cardiac fibrosis and helps to improve the vascular network in CSs.
Sharma, P, Gaur, VK, Gupta, S, Varjani, S, Pandey, A, Gnansounou, E, You, S, Ngo, HH & Wong, JWC 2022, 'Trends in mitigation of industrial waste: Global health hazards, environmental implications and waste derived economy for environmental sustainability', Science of The Total Environment, vol. 811, pp. 152357-152357.
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Majority of industries, in order to meet the technological development and consumer demands generate waste. The untreated waste spreads out toxic and harmful substances in the environment which serves as a breeding ground for pathogenic microorganisms thus causing severe health hazards. The three industrial sectors namely food, agriculture, and oil industry are among the primary organic waste producers that affect urban health and economic growth. Conventional treatment generates a significant amount of greenhouse gases which further contributes to global warming. Thus, the use of microbes for utilization of this waste, liberating CO2 offers an indispensable tool. The simultaneous production of value-added products such as bioplastics, biofuels, and biosurfactants increases the economics of the process and contributes to environmental sustainability. This review comprehensively summarized the composition of organic waste generated from the food, agriculture, and oil industry. The linkages between global health hazards of industrial waste and environmental implications have been uncovered. Stare-of-the-art information on their subsequent utilization as a substrate to produce value-added products through bio-routes has been elaborated. The research gaps, economical perspective(s), and future research directions have been identified and discussed to strengthen environmental sustainability.
Sharma, P, Liu Chung Ming, C & Gentile, C 2022, 'In vitro modeling of myocardial ischemia/reperfusion injury with murine or human 3D cardiac spheroids', STAR Protocols, vol. 3, no. 4, pp. 101751-101751.
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Myocardial infarction (MI) is the primary cause of death worldwide, but there are no clinically relevant models to study MI. Here, we describe an ischemia/reperfusion (I/R) injury model typical of MI using mouse or human 3D in vitro cardiac spheroids (CSs). First, we demonstrated the culture and maintenance of CSs. Then, we detailed how to expose CSs to pathophysiological oxygen concentrations to induce I/R injury. The protocol can be used in combination with viability, contractility, and mRNA expression level measurements. For complete details on the use and execution of this protocol, please refer to Sharma et al. (2022).
Sharma, P, Liu Chung Ming, C, Wang, X, Bienvenu, LA, Beck, D, Figtree, G, Boyle, A & Gentile, C 2022, 'Biofabrication of advanced in vitro 3D models to study ischaemic and doxorubicin-induced myocardial damage', Biofabrication, vol. 14, no. 2, pp. 025003-025003.
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Abstract Current preclinical in vitro and in vivo models of cardiac injury typical of myocardial infarction (MI, or heart attack) and drug induced cardiotoxicity mimic only a few aspects of these complex scenarios. This leads to a poor translation of findings from the bench to the bedside. In this study, we biofabricated for the first time advanced in vitro models of MI and doxorubicin (DOX) induced injury by exposing cardiac spheroids (CSs) to pathophysiological changes in oxygen (O2) levels or DOX treatment. Then, contractile function and cell death was analyzed in CSs in control verses I/R and DOX CSs. For a deeper dig into cell death analysis, 3D rendering analyses and mRNA level changes of cardiac damage-related genes were compared in control verses I/R and DOX CSs. Overall, in vitro CSs recapitulated major features typical of the in vivo MI and drug induced cardiac damages, such as adapting intracellular alterations to O2 concentration changes and incubation with cardiotoxic drug, mimicking the contraction frequency and fractional shortening and changes in mRNA expression levels for genes regulating sarcomere structure, calcium transport, cell cycle, cardiac remodelling and signal transduction. Taken together, our study supports the use of I/R and DOX CSs as advanced in vitro models to study MI and DOX-induced cardiac damge by recapitulating their complex in vivo scenario.
Sharma, R, Saqib, M, Lin, CT & Blumenstein, M 2022, 'A Survey on Object Instance Segmentation', SN Computer Science, vol. 3, no. 6, p. 499.
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AbstractIn recent years, instance segmentation has become a key research area in computer vision. This technology has been applied in varied applications such as robotics, healthcare and intelligent driving. Instance segmentation technology not only detects the location of the object but also marks edges for each single instance, which can solve both object detection and semantic segmentation concurrently. Our survey will give a detail introduction to the instance segmentation technology based on deep learning, reinforcement learning and transformers. Further, we will discuss about its development in this field along with the most common datasets used. We will also focus on different challenges and future development scope for instance segmentation. This technology will provide a strong reference for future researchers in our survey paper.
Sharma, S, Dabbiru, L, Hannis, T, Mason, G, Carruth, DW, Doude, M, Goodin, C, Hudson, C, Ozier, S, Ball, JE & Tang, B 2022, 'CaT: CAVS Traversability Dataset for Off-Road Autonomous Driving', IEEE Access, vol. 10, pp. 24759-24768.
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Sharma, V, Hossain, MJ & Mukhopadhyay, S 2022, 'Fault-Tolerant Operation of Bidirectional ZSI-Fed Induction Motor Drive for Vehicular Applications', Energies, vol. 15, no. 19, pp. 6976-6976.
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This paper presents an efficient and fast fault-tolerant control scheme for a bidirectional Z-source inverter (BiZSI)-fed induction-motor drive system for vehicular applications. The proposed strategy aims for the fault detection, localization and diagnosis of the proposed system during switch failures in the inverter module. Generally, power–semiconductor switch failures in inverter modules occur due to open- and short-circuit faults. An efficient modulation scheme is proposed and design specifications are thoroughly derived to obtain high voltage gains across the BiZSI network. A suitably fast detection and diagnosis scheme to isolate the faulty leg and resume the normal operation is discussed in this paper. The control scheme is provided such that the faulty leg is isolated and the motor phase is fed from a redundant leg to resume the operation. A feasible localization algorithm based on experimentally derived values and switching vectors is implemented. In addition, a fast fault diagnosis method based on current estimation and motor speed variation is designed and implemented. Moreover, the most important advantages of the proposed strategy include lower hardware requirements and less harmonic distortion in the output currents. Finally, the simulation and experimental results are presented to validate the feasibility of the theoretical analysis. An extensive performance evaluation of the proposed system with fault ride-through capabilities is performed to prove its suitability for vehicular applications. To validate its merits, the proposed strategy is compared with similar fault-tolerant schemes currently used in the industry.
Shehab, M, Abualigah, L, Shambour, Q, Abu-Hashem, MA, Shambour, MKY, Alsalibi, AI & Gandomi, AH 2022, 'Machine learning in medical applications: A review of state-of-the-art methods', Computers in Biology and Medicine, vol. 145, pp. 105458-105458.
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Applications of machine learning (ML) methods have been used extensively to solve various complex challenges in recent years in various application areas, such as medical, financial, environmental, marketing, security, and industrial applications. ML methods are characterized by their ability to examine many data and discover exciting relationships, provide interpretation, and identify patterns. ML can help enhance the reliability, performance, predictability, and accuracy of diagnostic systems for many diseases. This survey provides a comprehensive review of the use of ML in the medical field highlighting standard technologies and how they affect medical diagnosis. Five major medical applications are deeply discussed, focusing on adapting the ML models to solve the problems in cancer, medical chemistry, brain, medical imaging, and wearable sensors. Finally, this survey provides valuable references and guidance for researchers, practitioners, and decision-makers framing future research and development directions.
Shen, J, Miao, T, Lai, J-F, Chen, X, Li, J & Yu, S 2022, 'IMS: An Identity-Based Many-to-Many Subscription Scheme With Efficient Key Management for Wireless Broadcast Systems', IEEE Transactions on Services Computing, vol. 15, no. 3, pp. 1707-1719.
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Shen, S, Zhu, T, Ye, D, Wang, M, Zuo, X & Zhou, A 2022, 'A novel differentially private advising framework in cloud server environment', Concurrency and Computation: Practice and Experience, vol. 34, no. 7.
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SummaryDue to the rapid development of the cloud computing environment, it is widely accepted that cloud servers are important for users to improve work efficiency. Users need to know servers' capabilities and make optimal decisions on selecting the best available servers for users' tasks. We consider the process of learning servers' capabilities by users as a multiagent reinforcement learning process. The learning speed and efficiency in reinforcement learning can be improved by sharing the learning experience among learning agents which is defined as advising. However, existing advising frameworks are limited by the requirement that during advising all learning agents in a reinforcement learning environment must have exactly the same actions. To address the above limitation, this article proposes a novel differentially private advising framework for multiagent reinforcement learning. Our proposed approach can significantly improve the application of conventional advising frameworks when agents have one different action. The approach can also widen the applicable field of advising and speed up reinforcement learning by triggering more potential advising processes among agents with different actions.
Shen, X, Dong, G, Zheng, Y, Lan, L, Tsang, I & Sun, Q-S 2022, 'Deep Co-Image-Label Hashing for Multi-Label Image Retrieval', IEEE Transactions on Multimedia, vol. 24, pp. 1116-1126.
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Deep supervised hashing has greatly improved retrieval performance with the powerful learning capability of deep neural network. In multi-label image retrieval, existing deep hashing simply indicates whether two images are similar by constructing a similarity matrix. However, it ignores the dependency among multiple labels that has been shown important in multi-label application. To fulfill this gap, this paper proposes Deep Co-Image-Label Hashing (DCILH) to discover label dependency. Specifically, DCILH regards image and label as two views, and maps the two views into a common deep Hamming space. DCILH proposes to learn prototype for each label, and preserve similarity among images, labels, and prototypes. To exploit label dependency, DCILH further employs the label-correlation aware loss on the predicted labels, such that predicted output on positive label is enforced to be larger than that on negative label. Extensive experiments on several multi-label benchmarks demonstrate the proposed DCILH outperforms state-of-the-art deep supervised hashing on large-scale multi-label image retrieval.
Shen, Y, Shen, S, Wu, Z, Zhou, H & Yu, S 2022, 'Signaling game-based availability assessment for edge computing-assisted IoT systems with malware dissemination', Journal of Information Security and Applications, vol. 66, pp. 103140-103140.
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IoT malware dissemination seriously exacerbates the decline of IoT system availability, which deteriorates the users experience. To address the issue, we first predict the optimal IoT malware dissemination strategy based on a signaling game for edge computing-assisted IoT systems. We then develop an algorithm to obtain the solution of the signaling IoT availability assessment game, which is to factually reflect IoT malware dissemination in practice and reasonably express the probability of IoT system nodes being successfully infected by malware. Thus, a state transition diagram of IoT system nodes can be further designed, illustrating intercommunication among all six states during IoT malware dissemination. Upon this state transition diagram, we represent the state transition probability of IoT system nodes in each state utilizing a Markov matrix, and attain the steady-state availability of an IoT system node from reliability theory. Consequently, we deduce metrics to access the steady-state availability of the entire IoT system under typical star-, tree-, and mesh topologies, respectively. We also design the corresponding IoT system availability assessment algorithm from the view of practice. In this manner, an availability assessment mechanism for edge computing-based IoT systems with malware dissemination is constructed. Experiments demonstrate the influence of IoT system features on predicting IoT malware dissemination probability and assessing the steady-state availability of three typical IoT system topologies. Our results can be utilized to lay a theoretical foundation for guiding the implementation of higher availability for edge computing-assisted IoT systems with malware dissemination.
Shenbagamuthuraman, V, Patel, A, Khanna, S, Banerjee, E, Parekh, S, Karthick, C, Ashok, B, Velvizhi, G, Nanthagopal, K & Ong, HC 2022, 'State of art of valorising of diverse potential feedstocks for the production of alcohols and ethers: Current changes and perspectives', Chemosphere, vol. 286, pp. 131587-131587.
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Sheu, A, Bliuc, D, Tran, T, White, CP & Center, JR 2022, 'Fractures in type 2 diabetes confer excess mortality: The Dubbo osteoporosis epidemiology study', Bone, vol. 159, pp. 116373-116373.
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PURPOSE: Diabetes and fractures are both associated with increased mortality, however the effect of the combination is not well-established. We examined the mortality risk following all types of fractures in type 2 diabetes (T2D). METHODS: In the Dubbo Osteoporosis Epidemiology Study (1989-2017), participants were grouped according to T2D and/or incident fracture. Study outcome was all-cause mortality. First incident radiological fragility fracture and incident T2D diagnosis were time-dependent variables. Cox's proportional hazards models quantified mortality risk associated with T2D and incident fracture overall, as well as by fracture site, T2D duration and T2D medication type. RESULTS: In 3618 participants (62% women), 272 had baseline and 179 developed T2D over median 13.0 years (IQR 8.2-19.6). 796 women (56 with T2D) and 240 men (25 with T2D) sustained a fracture. Compared to those without T2D or fracture, mortality risk increased progressively, in T2D without fracture, then no T2D with fracture, and was highest in those with T2D with fracture (adjusted hazard ratio (aHR) (95% CI) for women 2.62 (1.75-3.93) and men 2.61 (1.42-4.81)). Within T2D participants, incident fracture was associated with increased mortality (aHR for women 1.87 (1.10-3.16) and men 2.83 (1.41-5.68)), especially following hip/vertebral fractures in men (aHR 2.97 (1.29-6.83)) and non-hip non-vertebral fractures in women (aHR 2.42 (1.24-4.75)), and in T2D duration >5 years. CONCLUSION: Any fracture in T2D conferred significant excess mortality. Individuals with T2D should be carefully monitored post-fracture, especially if T2D >5 years. Optimising fracture prevention and post-fracture management in T2D is critical and warrants further studies.
Sheu, A, Greenfield, JR, White, CP & Center, JR 2022, 'Assessment and treatment of osteoporosis and fractures in type 2 diabetes', Trends in Endocrinology & Metabolism, vol. 33, no. 5, pp. 333-344.
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Shi, J, Chu, L, Ma, C & Braun, R 2022, 'The Uncertainty Propagation for Carbon Atomic Interactions in Graphene under Resonant Vibration Based on Stochastic Finite Element Model', Materials, vol. 15, no. 10, pp. 3679-3679.
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Graphene is one of the most promising two-dimensional nanomaterials with broad applications in many fields. However, the variations and fluctuations in the material and geometrical properties are challenging issues that require more concern. In order to quantify uncertainty and analyze the impacts of uncertainty, a stochastic finite element model (SFEM) is proposed to propagate uncertainty for carbon atomic interactions under resonant vibration. Compared with the conventional truss or beam finite element models, both carbon atoms and carbon covalent bonds are considered by introducing plane elements. In addition, the determined values of the material and geometrical parameters are expanded into the related interval ranges with uniform probability density distributions. Based on the SFEM, the uncertainty propagation is performed by the Monte Carlo stochastic sampling process, and the resonant frequencies of graphene are provided by finite element computation. Furthermore, the correlation coefficients of characteristic parameters are computed based on the database of SFEM. The vibration modes of graphene with the extreme geometrical values are also provided and analyzed. According to the computed results, the minimum and maximum values of the first resonant frequency are 0.2131 and 16.894 THz, respectively, and the variance is 2.5899 THz. The proposed SFEM is an effective method to propagate uncertainty and analyze the impacts of uncertainty in the carbon atomic interactions of graphene. The work in this paper provides an important supplement to the atomic interaction modeling in nanomaterials.
Shi, J, Fan, K, Yan, L, Fan, Z, Li, F, Wang, G, Liu, H, Liu, P, Yu, H, Li, JJ & Wang, B 2022, 'Cost Effectiveness of Pharmacological Management for Osteoarthritis: A Systematic Review', Applied Health Economics and Health Policy, vol. 20, no. 3, pp. 351-370.
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BACKGROUND AND OBJECTIVE: Osteoarthritis (OA) is a highly prevalent, disabling disease requiring chronic management that is associated with an enormous individual and societal burden. This systematic review provides a global cost-effectiveness evaluation of pharmacological therapy for the management of OA. METHODS: Following Center for Reviews and Dissemination (CRD) guidance, a literature search strategy was undertaken using PubMed, EMBASE, Cochrane Library, Health Technology Assessment (HTA) database, and National Health Service Economic Evaluation database (NHS EED) to identify original articles containing cost-effectiveness evaluation of OA pharmacological treatment published before 4 November 2021. Risk of bias was assessed by two independent reviewers using the Joanna Briggs Institute (JBI) critical appraisal checklist for economic evaluations. The Quality of Health Economic Studies (QHES) instrument was used to assess the reporting quality of included articles. RESULTS: Database searches identified 43 cost-effectiveness analysis studies (CEAs) on pharmacological management of OA that were conducted in 18 countries and four continents, with one study containing multiple continents. A total of four classes of drugs were assessed, including non-steroidal anti-inflammatory drugs (NSAIDs), opioid analgesics, symptomatic slow-acting drugs for osteoarthritis (SYSADOAs), and intra-articular (IA) injections. The methodological approaches of these studies showed substantial heterogeneity. The incremental cost-effectiveness ratios (ICERs) per quality-adjusted life-year (QALY) were (in 2021 US dollars) US$44.40 to US$307,013.56 for NSAIDS, US$11,984.84 to US$128,028.74 for opioids, US$10,930.17 to US$27,799.73 for SYSADOAs, and US$258.36 to US$58,447.97 for IA injections in different continents. The key drivers of cost effectiveness included medical resources, productivity, relative risks, and selected comparators. CONCLUSION: This review showed substantial ...
Shi, J, Li, X, Zhang, S, Sharma, E, Sivakumar, M, Sherchan, SP & Jiang, G 2022, 'Enhanced decay of coronaviruses in sewers with domestic wastewater', Science of The Total Environment, vol. 813, pp. 151919-151919.
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Recent outbreaks caused by coronaviruses and their supposed potential fecal-oral transmission highlight the need for understanding the survival of infectious coronavirus in domestic sewers. To date, the survivability and decay of coronaviruses were predominately studied using small volumes of wastewater (normally 5-30 mL) in vials (in-vial tests). However, real sewers are more complicated than bulk wastewater (wastewater matrix only), in particular the presence of sewer biofilms and different operational conditions. This study investigated the decay of infectious human coronavirus 229E (HCoV-229E) and feline infectious peritonitis virus (FIPV), two typical surrogate coronaviruses, in laboratory-scale reactors mimicking the gravity (GS, gravity-driven sewers) and rising main sewers (RM, pressurized sewers) with and without sewer biofilms. The in-sewer decay of both coronaviruses was greatly enhanced in comparison to those reported in bulk wastewater through in-vial tests. 99% of HCoV-229E and FIPV decayed within 2 h under either GS or RM conditions with biofilms, in contrast to 6-10 h without biofilms. There is limited difference in the decay of HCoV and FIPV in reactors operated as RM or GS, with the T90 and T99 difference of 7-10 min and 14-20 min, respectively. The decay of both coronaviruses in sewer biofilm reactors can be simulated by biphasic first-order kinetic models, with the first-order rate constant 2-4 times higher during the first phase than the second phase. The decay of infectious HCoV and FIPV was significantly faster in the reactors with sewer biofilms than in the reactors without biofilms, suggesting an enhanced decay of these surrogate viruses due to the presence of biofilms and related processes. The mechanism of biofilms in virus adsorption and potential inactivation remains unclear and requires future investigations. The results indicate that the survivability of infectious coronaviruses detected using bulk wastewater overestimated...
Shi, Q, Wu, N, Nguyen, DN, Huang, X, Wang, H & Hanzo, L 2022, 'Low-Complexity Iterative Detection for Dual-Mode Index Modulation in Dispersive Nonlinear Satellite Channels', IEEE Transactions on Communications, vol. 70, no. 2, pp. 1261-1275.
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Shi, X, Chen, Z, Liu, X, Wei, W & Ni, B-J 2022, 'The photochemical behaviors of microplastics through the lens of reactive oxygen species: Photolysis mechanisms and enhancing photo-transformation of pollutants', Science of The Total Environment, vol. 846, pp. 157498-157498.
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The photoaging mechanisms of various polymers have been explored based on the basic autoxidation scheme (BAS) before 10 years ago, however current research verified some defects in the BAS in both thermodynamic and dynamics. These defects are troublesome because they are associated with the hydrogen abstraction which is central to continuously perform the photooxidation process of microplastics. These found indicated that we might wrongly inferred photo-oxidation process of some microplastics. In addition, the important role of reactive oxygen species (ROS) in the type-dependent photoaging process of various microplastics has been revealed recently. In this case, fully and accurately understanding the photoaging mechanisms of different microplastics in environment is a priority to further manage the ecological risk of microplastics. Herein, this review aims to revise and update the degradation process of microplastics based on the revised BAS and in the perspective of ROS. Specifically, the modification of BAS is firstly discussed. The photoaging mechanisms of representative microplastics (i.e., polyethylene, polystyrene and polyethylene terephthalate) are then updated based on the corrected BAS. Additionally, the role of ROS in their photolysis process and the possibility of microplastics as photosensitizers/mediators to regulate the fate of co-existent pollutants are also analyzed. Finally, several perspectives are then proposed to guide future research on the photoaging behaviors of microplastics. This review would pave the way for the understanding of microplastic photoaging and the management of plastic pollution in environments.
Shi, X, Wu, L, Wei, W & Ni, B-J 2022, 'Insights into the microbiomes for medium-chain carboxylic acids production from biowastes through chain elongation', Critical Reviews in Environmental Science and Technology, vol. 52, no. 21, pp. 3787-3812.
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Bioconversion of medium-chain carboxylic acids (MCCAs) from biowastes through anaerobic mixed-culture fermentation is undergoing a revolution in terms of mitigating the lower fossil fuels requirement and increasing biowaste treatment capacity. Benefiting from hydrophobicity and high energy density of MCCA, this high-valuable biofuel exhibits easier separation and wider application than traditional volatile fatty acid products. The biggest bottleneck for efficiently and simultaneously producing MCCAs by mixed-culture fermentation is complicated or even entangled microbial interaction. Therefore, this review aimed to supply guidelines to understand and steer these microbiomes toward the controllable ones. The metabolic pathway of chain elongation and associated cooperating and competing pathways were firstly discussed to understand the primary microbial interaction in mixed-culture fermentation. In an attempt to inspect the overall performance of mixed-culture CE reactor, the typical microbial community and its variation influenced by reactor parameters were also identified. The methods of in-line extraction of MCCAs for relieving toxicity inhibition on microbiome were also summarized regarding the difficulties lied in continuous MCCAs production. Finally, the future research directions of MCCAs production via mixed-culture fermentation would be proposed to help steer these novel bioproduction processes toward full-scale applications.
Shi, X, Xia, Y, Wei, W & Ni, B-J 2022, 'Accelerated spread of antibiotic resistance genes (ARGs) induced by non-antibiotic conditions: Roles and mechanisms', Water Research, vol. 224, pp. 119060-119060.
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The global spread of antibiotic resistance genes (ARGs) has wreaked havoc with the treatment efficiency of antibiotics and, ultimately, anti-microbial chemotherapy, and has been conventionally attributed to the abuse and misuse of antibiotics. However, the ancient ARGs have alterative functions in bacterial physiology and thus they could be co-regulated by non-antibiotic conditions. Recent research has demonstrated that many non-antibiotic chemicals such as microplastics, metallic nanoparticles and non-antibiotic drugs, as well as some non-antibiotic conditions, can accelerate the dissemination of ARGs. These results suggested that the role of antibiotics might have been previously overestimated whereas the effects of non-antibiotic conditions were possibly ignored. Thus, in an attempt to fully understand the fate and behavior of ARGs in the eco-system, it is urgent to critically highlight the role and mechanisms of non-antibiotic chemicals and related environmental factors in the spread of ARGs. To this end, this timely review assessed the evolution of ARGs, especially its function alteration, summarized the non-antibiotic chemicals promoting the spread of ARGs, evaluated the non-antibiotic conditions related to ARG dissemination and analyzed the molecular mechanisms related to spread of ARGs induced by the non-antibiotic factors. Finally, this review then provided several critical perspectives for future research.
Shi, Y, Campbell, D, Yu, X & Li, H 2022, 'Geometry-Guided Street-View Panorama Synthesis From Satellite Imagery', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 12, pp. 10009-10022.
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Shi, Y, Yu, X, Liu, L, Campbell, D, Koniusz, P & Li, H 2022, 'Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PP, no. 3, pp. 1-16.
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We address the problem of ground-to-satellite image geo-localization, that is, estimating the camera latitude, longitude and orientation (azimuth angle) by matching a query image captured at the ground level against a large-scale database with geotagged satellite images. Our prior arts treat the above task as pure image retrieval by selecting the most similar satellite reference image matching the ground-level query image. However, such an approach often produces coarse location estimates because the geotag of the retrieved satellite image only corresponds to the image center while the ground camera can be located at any point within the image. To further consolidate our prior research finding, we present a novel geometry-aware geo-localization method. Our new method is able to achieve the fine-grained location of a query image, up to pixel size precision of the satellite image, once its coarse location and orientation have been determined. Moreover, we propose a new geometry-aware image retrieval pipeline to improve the coarse localization accuracy. Apart from a polar transform in our conference work, this new pipeline also maps satellite image pixels to the ground-level plane in the ground-view via a geometry-constrained projective transform to emphasize informative regions, such as road structures, for cross-view geo-localization. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our newly proposed framework. We also significantly improve the performance of coarse localization results compared to the state-of-the-art in terms of location recalls.
Shi, Y, Zhang, L, Cao, Z, Tanveer, M & Lin, C-T 2022, 'Distributed Semisupervised Fuzzy Regression With Interpolation Consistency Regularization', IEEE Transactions on Fuzzy Systems, vol. 30, no. 8, pp. 3125-3137.
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Recently, distributed semi-supervised learning (DSSL) algorithms have shown their effectiveness in leveraging unlabeled samples over distributed networks with multiple interconnected agents. However, existing DSSL algorithms cannot cope with data uncertainties and may suffer from high computation and communication overhead problems. Hence, we propose a distributed semi-supervised fuzzy regression model, called DSFR to tackle these issues with a two-pronged strategy - first, a structure learning with a distributed fuzzy C-means method (DFCM) that identifies the parameters in the antecedent component of fuzzy if-then rules; and, second, a parameter learning with distributed interpolation consistency regularization (DICR) to obtain the parameters in the consequent component. Since DFCM is both distributed and unsupervised, it can thus extract feature representation from both labeled and unlabeled samples among multiple agents. Meanwhile, DICR expands sample space with interpolated unlabeled instances in a distributed scheme and forces decision boundaries to lie in sparse data areas, thus increasing the models robustness. Both DFCM and DICR are implemented following the alternating direction method of multipliers method. Notably, none of the procedures involve backpropagation, so the model converges very quickly. Further, with the benefit of DFCM and DICR, DSFR is highly scalable to large datasets. Experiments on both artificial and real-world datasets show that this approach yields much lower loss values than the current state-of-the-art DSSL algorithms at a fraction of the computation cost. Our code is available online\footnote{\url{https://github.com/leijiezhang/DSFR}}.
Shi, Z, Cheng, Q, Zhang, JA & Yi Da Xu, R 2022, 'Environment-Robust WiFi-Based Human Activity Recognition Using Enhanced CSI and Deep Learning', IEEE Internet of Things Journal, vol. 9, no. 24, pp. 24643-24654.
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Deep learning has demonstrated its great potential in channel state information (CSI)-based human activity recognition (HAR), and hence has attracted increasing attention in both the industry and academic communities. While promising, most existing high-accuracy methodologies require to retrain their models when applying the previous-trained ones to a new/unseen environment. This issue has limited their practical usabilities. In order to overcome this challenge, this article proposes an innovative scheme, which combines an activity-related feature extraction and enhancement (AFEE) method and matching network (AFEE-MatNet). The proposed scheme is 'one-fits-all,' meaning that the trained model can be directly applied in new/unseen environments without any retraining. We introduce the AFEE method to enhance CSI quality by eliminating noise. Specifically, the approach mitigates environmental noises unrelated to activity while better compressing and preserving the behavior-related information. Moreover, the size of feature signals generated by AFEE are reduced, which in turn significantly shortens the training time. For effective feature extraction, we propose to use the MatNet architecture to learn transferable features shared among source environments. To further improve the recognition performance, we introduce a prediction checking and correction scheme to rectify some classification errors that do not abide by the state transition of human behaviors. Extensive experimental results demonstrate that our proposed AFEE-MatNet significantly outperforms existing state-of-the-art HAR methods, in terms of both recognition accuracy and training time.
Shi, Z, Sun, X, Lei, G, Tian, X, Guo, Y & Zhu, J 2022, 'Multiobjective Optimization of a Five-Phase Bearingless Permanent Magnet Motor Considering Winding Area', IEEE/ASME Transactions on Mechatronics, vol. 27, no. 5, pp. 2657-2666.
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Shi, Z, Zhang, JA, Xu, RY & Cheng, Q 2022, 'Environment-Robust Device-Free Human Activity Recognition With Channel-State-Information Enhancement and One-Shot Learning', IEEE Transactions on Mobile Computing, vol. 21, no. 2, pp. 540-554.
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Shirmard, H, Farahbakhsh, E, Heidari, E, Beiranvand Pour, A, Pradhan, B, Müller, D & Chandra, R 2022, 'A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data', Remote Sensing, vol. 14, no. 4, pp. 819-819.
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Lithological mapping is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region and has implications for mineral prospectivity mapping. This is a challenging task if performed manually, particularly in highly remote areas that require a large number of participants and resources. The combination of machine learning (ML) methods and remote sensing data can provide a quick, low-cost, and accurate approach for mapping lithological units. This study used deep learning via convolutional neural networks and conventional ML methods involving support vector machines and multilayer perceptron to map lithological units of a mineral-rich area in the southeast of Iran. Moreover, we used and compared the efficiency of three different types of multispectral remote-sensing data, including Landsat 8 operational land imager (OLI), advanced spaceborne thermal emission and reflection radiometer (ASTER), and Sentinel-2. The results show that CNNs and conventional ML methods effectively use the respective remote-sensing data in generating an accurate lithological map of the study area. However, the combination of CNNs and ASTER data provides the best performance and the highest accuracy and adaptability with field observations and laboratory analysis results so that almost all the test data are predicted correctly. The framework proposed in this study can be helpful for exploration geologists to create accurate lithological maps in other regions by using various remote-sensing data at a low cost.
Shivakumara, P, Chowdhury, PN, Pal, U, Doermann, D, Ramachandra, R, Lu, T & Blumenstein, M 2022, 'A Knowledge Enforcement Network-Based Approach for Classifying a Photographer’s Images', International Journal of Pattern Recognition and Artificial Intelligence, vol. 36, no. 15, p. 2250046.
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Classification of photos captured by different photographers is an important and challenging problem in knowledge-based and image processing. Monitoring and authenticating images uploaded on social media are essential, and verifying the source is one key piece of evidence. We present a novel framework for classifying photos of different photographers based on the combination of local features and deep learning models. The proposed work uses focused and defocused information in the input images to extract contextual information. The model estimates the weighted gradient and calculates entropy to strengthen context features. The focused and defocused information is fused to estimate cross-covariance and define a linear relationship between them. This relationship results in a feature matrix fed to Knowledge Enforcement Network (KEN) for obtaining representative features. Due to the strong discriminative ability of deep learning models, we employ the lightweight and accurate MobileNetV2. The output of KEN and MobileNetV2 is sent to a classifier for photographer classification. Experimental results of the proposed model on our dataset of 46 photographer classes (46234 images) and publicly available datasets of 41 photographer classes (218303 images) show that the method outperforms the existing techniques by 5%–10% on average. The dataset created for the experimental purpose will be made available upon publication.
Shivakumara, P, Das, A, Raghunandan, KS, Pal, U & Blumenstein, M 2022, 'New Deep Spatio-Structural Features of Handwritten Text Lines for Document Age Classification', International Journal of Pattern Recognition and Artificial Intelligence, vol. 36, no. 09.
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Document age estimation using handwritten text line images is useful for several pattern recognition and artificial intelligence applications such as forged signature verification, writer identification, gender identification, personality traits identification, and fraudulent document identification. This paper presents a novel method for document age classification at the text line level. For segmenting text lines from handwritten document images, the wavelet decomposition is used in a novel way. We explore multiple levels of wavelet decomposition, which introduce blur as the number of levels increases for detecting word components. The detected components are then used for a direction guided-driven growing approach with linearity, and nonlinearity criteria for segmenting text lines. For classification of text line images of different ages, inspired by the observation that, as the age of a document increases, the quality of its image degrades, the proposed method extracts the structural, contrast, and spatial features to study degradations at different wavelet decomposition levels. The specific advantages of DenseNet, namely, strong feature propagation, mitigation of the vanishing gradient problem, reuse of features, and the reduction of the number of parameters motivated us to use DenseNet121 along with a Multi-layer Perceptron (MLP) for the classification of text lines of different ages by feeding features and the original image as input. To demonstrate the efficacy of the proposed model, experiments were conducted on our own as well as standard datasets for both text line segmentation and document age classification. The results show that the proposed method outperforms the existing methods for text line segmentation in terms of precision, recall, F-measure, and document age classification in terms of average classification rate.
Shojaei, M, Shamshirian, A, Monkman, J, Grice, L, Tran, M, Tan, CW, Teo, SM, Rodrigues Rossi, G, McCulloch, TR, Nalos, M, Raei, M, Razavi, A, Ghasemian, R, Gheibi, M, Roozbeh, F, Sly, PD, Spann, KM, Chew, KY, Zhu, Y, Xia, Y, Wells, TJ, Senegaglia, AC, Kuniyoshi, CL, Franck, CL, dos Santos, AFR, Noronha, LD, Motamen, S, Valadan, R, Amjadi, O, Gogna, R, Madan, E, Alizadeh-Navaei, R, Lamperti, L, Zuñiga, F, Nova-Lamperti, E, Labarca, G, Knippenberg, B, Herwanto, V, Wang, Y, Phu, A, Chew, T, Kwan, T, Kim, K, Teoh, S, Pelaia, TM, Kuan, WS, Jee, Y, Iredell, J, O’Byrne, K, Fraser, JF, Davis, MJ, Belz, GT, Warkiani, ME, Gallo, CS, Souza-Fonseca-Guimaraes, F, Nguyen, Q, Mclean, A, Kulasinghe, A, Short, KR & Tang, B 2022, 'IFI27 transcription is an early predictor for COVID-19 outcomes, a multi-cohort observational study', Frontiers in Immunology, vol. 13, p. 1060438.
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PurposeRobust biomarkers that predict disease outcomes amongst COVID-19 patients are necessary for both patient triage and resource prioritisation. Numerous candidate biomarkers have been proposed for COVID-19. However, at present, there is no consensus on the best diagnostic approach to predict outcomes in infected patients. Moreover, it is not clear whether such tools would apply to other potentially pandemic pathogens and therefore of use as stockpile for future pandemic preparedness.MethodsWe conducted a multi-cohort observational study to investigate the biology and the prognostic role of interferon alpha-inducible protein 27 (IFI27) in COVID-19 patients.ResultsWe show that IFI27 is expressed in the respiratory tract of COVID-19 patients and elevated IFI27 expression in the lower respiratory tract is associated with the presence of a high viral load. We further demonstrate that the systemic host response, as measured by blood IFI27 expression, is associated with COVID-19 infection. For clinical outcome prediction (e.g., respiratory failure), IFI27 expression displays a high sensitivity (0.95) and specificity (0.83), outperforming other known predictors of COVID-19 outcomes. Furthermore, IFI27 is upregulated in the blood of infected patients in response to other respiratory viruses. For example, in the pandemic H1N1/09 influenza virus infection, IFI27-like genes were highly upregulated in the blood samples of severely infected patients.ConclusionThese data suggest that prognostic biomarkers targeting the family of IFI27
Shokouhian, B, Aboulkheyr Es, H, Negahdari, B, Tamimi, A, Shahdoust, M, Shpichka, A, Timashev, P, Hassan, M & Vosough, M 2022, 'Hepatogenesis and hepatocarcinogenesis: Alignment of the main signaling pathways', Journal of Cellular Physiology, vol. 237, no. 11, pp. 3984-4000.
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AbstractDevelopment is a symphony of cells differentiation in which different signaling pathways are orchestrated at specific times and periods to form mature and functional cells from undifferentiated cells. The similarity of the gene expression profile in malignant and undifferentiated cells is an interesting topic that has been proposed for many years and gave rise to the differentiation‐therapy concept, which appears a rational insight and should be reconsidered. Hepatocellular carcinoma (HCC), as the sixth common cancer and the third leading cause of cancer death worldwide, is one of the health‐threatening complications in communities where hepatotropic viruses are endemic. Sedentary lifestyle and high intake of calories are other risk factors. HCC is a complex condition in which various dimensions must be addressed, including heterogeneity of cells in the tumor mass, high invasiveness, and underlying diseases that limit the treatment options. Under these restrictions, recognizing, and targeting common signaling pathways during liver development and HCC could expedite to a rational therapeutic approach, reprograming malignant cells to well‐differentiated ones in a functional state. Accordingly, in this review, we highlighted the commonalities of signaling pathways in hepatogenesis and hepatocarcinogenesis, and comprised an update on the current status of targeting these pathways in laboratory studies and clinical trials.
Shokravi, H, Heidarrezaei, M, Shokravi, Z, Ong, HC, Lau, WJ, Din, MFM & Ismail, AF 2022, 'Fourth generation biofuel from genetically modified algal biomass for bioeconomic development', Journal of Biotechnology, vol. 360, pp. 23-36.
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Biofuels from microalgae have promising potential for a sustainable bioeconomy. Algal strains' oil content and biomass yield are the most influential cost drivers in the fourth generation biofuel (FGB) production. Genetic modification is the key to improving oil accumulation and biomass yield, consequently developing the bioeconomy. This paper discusses current practices, new insights, and emerging trends in genetic modification and their bioeconomic impact on FGB production. It was demonstrated that enhancing the oil and biomass yield could significantly improve the probability of economic success and the net present value of the FGB production process. The techno-economic and socioeconomic burden of using genetically modified (GM) strains and the preventive control strategies on the bioeconomy of FGB production is reviewed. It is shown that the fully lined open raceway pond could cost up to 25% more than unlined ponds. The cost of a plastic hoop air-supported greenhouse covering cultivation ponds is estimated to be US 60,000$ /ha. The competitiveness and profitability of large-scale cultivation of GM biomass are significantly locked to techno-economic and socioeconomic drivers. Nonetheless, it necessitates further research and careful long-term follow-up studies to understand the mechanism that affects these parameters the most.
Shokravi, Z, Shokravi, H, Atabani, AE, Lau, WJ, Chyuan, OH & Ismail, AF 2022, 'Impacts of the harvesting process on microalgae fatty acid profiles and lipid yields: Implications for biodiesel production', Renewable and Sustainable Energy Reviews, vol. 161, pp. 112410-112410.
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Shon, HK, Jegatheesan, V, Phuntsho, S, Fujiwara, T, Woo, Y & Yan, B 2022, 'Challenges in environmental science and engineering', Process Safety and Environmental Protection, vol. 168, pp. 300-302.
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Shon, HS, Choi, ES, Cho, Y-S, Cha, EJ, Kang, T-G & Kim, KA 2022, 'Machine learning-based risk factor analysis for periodontal disease from a Korean National Survey', Journal of Biomedical Translational Research, vol. 23, no. 1, pp. 17-28.
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Shrestha, J, Razavi Bazaz, S, Ding, L, Vasilescu, S, Idrees, S, Söderström, B, Hansbro, PM, Ghadiri, M & Ebrahimi Warkiani, M 2022, 'Rapid separation of bacteria from primary nasal samples using inertial microfluidics', Lab on a Chip, vol. 23, no. 1, pp. 146-156.
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Development of an inertial microfluidic device based on a zigzag configuration for rapid separation of bacteria from primary nasal samples.
Shrestha, S, Abbas, SM, Asadnia, M & Esselle, KP 2022, 'Realization of Three Dimensional Printed Multi Layer Wide Band Prototype', IEEE Access, vol. 10, pp. 130944-130954.
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Shrivastava, A, Tolani, K, Pugalia, S & Jain, G 2022, 'Decoding unethical youth buying behaviour: fair trade and unfair means', International Journal of Business and Globalisation, vol. 32, no. 2/3, pp. 287-287.
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Shu, Y, Li, Q, Liu, L & Xu, G 2022, 'Privileged multi-task learning for attribute-aware aesthetic assessment', Pattern Recognition, vol. 132, pp. 108921-108921.
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Aesthetic attributes are crucial for aesthetics because they explicitly present some photo quality cues that a human expert might use to evaluate a photo's aesthetic quality. However, the aesthetic attributes have not been largely and sufficiently exploited for photo aesthetic assessment. In this paper, we propose a novel approach to photo aesthetic assessment with the help of aesthetic attributes. The aesthetic attributes are used as privileged information (PI), which is often available during training phase but unavailable in prediction phase due to the high collection expense. The proposed framework consists of a deep multi-task network as generator and a fully connected network as discriminator. Deep multi-task network learns the aesthetic attributes and score simultaneously to capture their dependencies and extract better feature representations. Specifically, we use ranking constraint in the label space, similarity constraint and prior probabilities loss in the privileged information space to make the output of multi-task network converge to that of ground truth. Adversarial loss is used to identify and distinguish the predicted privileged information of a deep multi-task network from the ground truth PI distribution. Experimental results on two benchmark databases demonstrate the superiority of the proposed method to state-of-the-art.
Shu, Y, Li, Q, Xu, C, Liu, S & Xu, G 2022, 'V-SVR+: Support Vector Regression With Variational Privileged Information', IEEE Transactions on Multimedia, vol. 24, no. 99, pp. 876-889.
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Many regression tasks encounter an asymmetric distribution of information between training and testing phases where the additional information available in training, the so-called privileged information, is often inaccessible in testing. In practice, the privileged information (PI) in training data might be expressed in different formats, such as continuous, ordinal, or binary values. However, most of the existing learning using privileged information (LUPI) paradigms mainly deal with the continuous form of PI, resulting in their incapability to handle variational PI, which motivates this research. Therefore, in this paper, we propose a unified framework to tackle the aforementioned three forms of privileged information systematically. The proposed method (called V-SVR+) integrates the continuous, ordinal, and binary PI into the learning process of support vector regression (SVR) via the proposed three losses. Specifically, for continuous privileged information, we define a linear correcting (slack) function in the privileged information space to estimate the slack variables in the standard SVR method using privileged information. For the ordinal relations of privileged information, we first rank the privileged information. Then, we regard this ordinal privileged information as auxiliary information applied in the learning process of the SVR model. For the binary or Boolean form of privileged information, we infer a probabilistic dependency between the privileged information and labels from the summarized privileged information knowledge. Then, we transfer the privileged information knowledge to constraints and form a constrained optimization problem. We evaluate the proposed method on three applications: music emotion recognition from songs with the help of implicit information about music elements judged by composers; multiple object recognition from images with the help of implicit information about the object's importance conveyed by the list of man...
Sick, N & Bröring, S 2022, 'Exploring the research landscape of convergence from a TIM perspective: A review and research agenda', Technological Forecasting and Social Change, vol. 175, pp. 121321-121321.
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Convergence at the level of science, technology, market or industry can increasingly be witnessed in a number of empirical settings. It is currently seen as one of the most important influence factors on and trigger for developing innovation strategies. This empirical relevance is mirrored by a surge in publications. Therefore, motivated by a highly dynamic but at the same time rather unstructured body of literature, this review offers a systematic and critical analyses of studies related to Technology and Innovation Management (TIM) research that address convergence from a processual perspective. Four major strands can be identified: (1) drivers and patterns of convergence, (2) anticipation of convergence, (3) strategic reactions to convergence, and (4) convergent products. A key finding of this review is that most contributions have been inward oriented, i.e. understanding the dynamics of convergence. A consequence of this inner focus is that the scientific discourse on convergence has to some degree unfolded independently from its theoretical underpinnings. To this end, this review provides a comprehensive framework of convergence research, including current challenges and emerging themes to address these challenges. The resulting research agenda serves as a starting point to inspire future studies of relevance for theory and conceptual development as well as managerial practice.
Siddiki, SYA, Mofijur, M, Kumar, PS, Ahmed, SF, Inayat, A, Kusumo, F, Badruddin, IA, Khan, TMY, Nghiem, LD, Ong, HC & Mahlia, TMI 2022, 'Microalgae biomass as a sustainable source for biofuel, biochemical and biobased value-added products: An integrated biorefinery concept', Fuel, vol. 307, pp. 121782-121782.
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Microalgal biomass has been proved to be a sustainable source for biofuels including bio-oil, biodiesel, bioethanol, biomethane, etc. One of the collateral benefits of integrating the use of microalgal technologies in the industry is microalgae's ability to capture carbon dioxide during the application and biomass production process and consequently reducing carbon dioxide emissions. Although microalgae are a feasible source of biofuel, industrial microalgae applications face energy and cost challenges. To overcome these challenges, researchers have been interested in applying the bio-refinery approach to extract the important components encapsulated in microalgae. This review discusses the key steps of microalgae-based biorefinery including cultivation and harvesting, cell disruption, biofuel and value-added compound extraction along with the detailed technologies associated with each step of biorefinery. This review found that suitable microalgae species are selected based on their carbohydrate, lipid and protein contents and selecting the suitable species are crucial for high-quality biofuel and value-added products production. Microalgae species contain carbohydrates, proteins and lipids in the range of 8% to 69.7%, 5% to 74% and 7% to 65% respectively which proved their ability to be used as a source of value-added commodities in multiple industries including agriculture, animal husbandry, medicine, culinary, and cosmetics. This review suggests that lipid and value-added products from microalgae can be made more economically viable by integrating upstream and downstream processes. Therefore, a systematically integrated genome sequencing and process-scale engineering approach for improving the extraction of lipids and co-products is critical in the development of future microalgal biorefineries.
Siddiq, A, Shukla, N & Pradhan, B 2022, 'Spatio-temporal modelling of dengue fever cases in Saudi Arabia using socio-economic, climatic and environmental factors', Geocarto International, vol. 37, no. 26, pp. 12867-12891.
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Siddiqi, H-U-R, Qamar, A, Shaukat, R, Anwar, Z, Amjad, M, Farooq, M, Abbas, MM, Imran, S, Ali, H, Khan, TMY, Noor, F, Ali, HM, Kalam, MA & Soudagar, MEM 2022, 'Heat transfer and pressure drop characteristics of ZnO/DIW based nanofluids in small diameter compact channels: An experimental study', Case Studies in Thermal Engineering, vol. 39, pp. 102441-102441.
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This experimental study is focused on heat transfer performance and pressure drop characteristics of ZnO/DIW-based nanofluids (NFs) in horizontal mini tubes of different (1.0-2.0 mm) diameters. Different mass concentrations (0.012-0.048 wt %) of nanoparticles (NPs) were tested with varying fluid flow rates (12-24 ml/min) of NFs. The thermal conductivity (TC) and viscosity (VC) of stable NFs were tested at 20-60 °C, at a fixed temperature (40 °C), and concentration of NPs (0.048 wt%) the maximum rise was 18.27% and 20.31%, respectively. The local and average heat transfer coefficients (HTCs) and the pressure gradient were noticed to be directly proportional to volume flow rate of NFs and the mass concentration of NPs. However, an inverse trend was noticed with the test section's diameter. At 0.048 wt % of NPs and 24.0 ml/min flow rate of NFs, the maximum rise in local and average HTCs and pressure gradient was 17.11-11.61% and 13.05-9.79%, and 29.19-12.25%, respectively, in a tube's diameter of 1.0-2.0 mm. The friction factor increased with NP's loading while the same reduced with the fluid flow rate. The corresponding maximum change in the friction factor was 28.85-12.72% for the tubes with 1.0-2.0 mm diameters, respectively, at a 12.0 ml/min flow rate of NFs. The comparison of experimental findings for the HTCs, pressure gradients and friction factors with the standard Shah and Darcy's correlations showed that the observations are in good agreement with the predicted ones.
Siddique, MNI, Hasan, ASMM, Kabir, MA, Prottasha, FZ, Samin, AM, Soumik, SS & Trianni, A 2022, 'Energy management practices, barriers, and drivers in Bangladesh: An exploratory insight from pulp and paper industry', Energy for Sustainable Development, vol. 70, pp. 115-132.
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Industrial energy management is critical to ensuring efficient energy usage, market viability, and greenhouse gas emission reductions. In this study, the aim is to investigate energy management practices in paper and pulp industries (PPI) of Bangladesh. The study identifies several barriers to adopt energy management practices; “Lack of governmental attention”, “Inadequate staff awareness” and “limited access to capital” are identified as the major constraints. In addition, HVAC systems and compressed air systems present higher barriers compared to electrical motor and pump systems while looking at the barriers by technology areas. On the other hand, “Owner's demand”, “Commitment from the top management” and “Pressure from the customers and different non-Governmental organization” are found to be the dominant drivers of energy management in the studied PPI. Energy management practices can enhance the energy efficiency approximately by 4–5 % according to the study. Furthermore, this study reveals that the majority of PPIs are unaware of the energy service companies (ESCO) with the lack of a standardized system for energy audit, as well as a lack of trust and information, appearing to be the obstacles behind this.
Sigari, S & Gandomi, AH 2022, 'Analyzing the past, improving the future: a multiscale opinion tracking model for optimizing business performance', Humanities and Social Sciences Communications, vol. 9, no. 1, p. 341.
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AbstractThe complexity of business decision-making has increased over the years. It is essential for managers to gain a confident understanding of their business environments in order to make successful decisions. With the growth of opinion-rich web resources such as social media, discussion forums, review sites, news corpora, and blogs available on the internet, product and service reviews have become an essential source of information. In a data-driven world, they will improve services and operational insights to achieve real business benefits and help enterprises remain competitive. Despite the prevalence of textual data, few studies have demonstrated the effectiveness of real-time text mining and reporting tools in firms and organizations. To address this aspect of decision-making, we have developed and evaluated an unsupervised learning system to automatically extract and classify topics and their emotion score in text streams. Data were collected from commercial websites, open-access databases, and social networks to train the model. In the experiment, the polarity score was quantified at four different levels: word, sentence, paragraph, and the entire text using Latent Dirichlet Allocation (LDA). Using subjective data mining, we demonstrate how to extract, summarize, and track various aspects of information from the Web and help traditional information retrieval (IR) systems to capture more information. An opinion tracking system presented by our model extracts subjective information, classifies them, and tracks opinions by utilizing location, time, and reviewers’ positions. Using the online-offline data collection technique, we can update the library topic in real-time to provide users with a market opinion tracker. For marketing or economic research, this approach may be useful. In the experiment, the new model is applied to a case study to demonstrate how the business process improves.
Silva, IN, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2022, 'Shear behaviour of subgrade soil with reference to varying initial shear stress and plasticity index', Acta Geotechnica, vol. 17, no. 9, pp. 4207-4216.
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AbstractThe influence of stress anisotropy (K) (i.e. the ratio between effective horizontal and vertical stresses) on the shear behaviour of soils has received significant attention in past studies, but how its influence depends on different values of the plasticity index (PI) has not been properly quantified. In this study, the results of a series of undrained triaxial tests on anisotropically consolidated soil at different values of K are reported, and together with past experimental data, the interactive roles of K and PI on the shear behaviour of soil are rigorously interpreted. The findings indicate that the peak shear strength increases with higher brittleness, whereas the peak excess pore pressure diminishes when the value of K decreases. Moreover, increasing the value of PI up to 35 tends to increase the peak shear strength, but beyond that the influence of PI seems marginal. Based on the findings of this study, empirical equations incorporating PI and K to estimate the undrained shear strength are proposed with acceptable accuracy.
Simachew, B, Khan, B, Guerrero, JM, Padmanaban, S, Mahela, OP & Alhelou, HH 2022, 'Capacity‐basedoptimization using whale optimization technique of a power distribution network', Engineering Reports, vol. 4, no. 1.
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AbstractIn the power distribution network, real power loss and voltage profile management are critical issues. By providing active and reactive power support, both of these issues can be managed. Distributed generation (DG) and capacitor bank (QG) can be utilized to solve these issues. Therefore, this paper utilized optimally placed and sized DG and capacitor (QG) to minimize losses and improve the voltage profile. The overall problem is optimized using an upgraded method of the fitness assignment and solution chasing based on the aggregate approach called multi‐objective whale optimization algorithm (MWOA). Wind and solar photovoltaic sources with biomass are utilized as the DG sources with their probabilistic outputs. The developed method is tested using two practical feeders of Bahir Dar city distribution network, Ethiopia. The results of loss minimization and voltage profile enhancement with MWOA are compared with multi‐objective particle swam optimization (MPSO) with an equal number of iterations to show the superiority of the developed method.
Simeng, B, Zhendong, N, Hui, H, Shi, K, Kun, Y & Yuanchi, M 2022, 'Biomedical Text Classification Method Based on Hypergraph Attention Network', Data Analysis and Knowledge Discovery, vol. 6, no. 11, pp. 13-24.
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Objective This paper proposes a new model integrating tag semantics It uses text level hypergraph and cross attention mechanism to capture the organizational structure and grammatical semantics of literature aiming to improve the classification of biomedical texts Methods First we utilized the fine tuned BioBERT to retrieve vector features from the biomedical texts Then we constructed a text level hypergraph to capture the word order semantics and syntactics of the texts Finally we merged the features of text level hypergraph and labelled semantics through the cross attention mechanism network to finish the text classification Results The experimental results on the PM Sentence dataset show that the proposed model is 2 34 percentage points higher than the baseline model in the comprehensive evaluation of F1 indicators Limitations The experimental dataset needs to be expanded to evaluate the model s performance in other fields Conclusions The newly constructed model improves the classification of biomedical texts and provides effective support for knowledge retrieval and mining 2022 Chinese Academy of Sciences All rights reserved
Singanamalla, SKR & Lin, C-T 2022, 'Spike-Representation of EEG Signals for Performance Enhancement of Brain-Computer Interfaces', Frontiers in Neuroscience, vol. 16, p. 792318.
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Brain-computer interfaces (BCI) relying on electroencephalography (EEG) based neuroimaging mode has shown prospects for real-world usage due to its portability and optional selectivity of fewer channels for compactness. However, noise and artifacts often limit the capacity of BCI systems especially for event-related potentials such as P300 and error-related negativity (ERN), whose biomarkers are present in short time segments at the time-series level. Contrary to EEG, invasive recording is less prone to noise but requires a tedious surgical procedure. But EEG signal is the result of aggregation of neuronal spiking information underneath the scalp surface and transforming the relevant BCI task's EEG signal to spike representation could potentially help improve the BCI performance. In this study, we designed an approach using a spiking neural network (SNN) which is trained using surrogate-gradient descent to generate task-related multi-channel EEG template signals of all classes. The trained model is in turn leveraged to obtain the latent spike representation for each EEG sample. Comparing the classification performance of EEG signal and its spike-representation, the proposed approach enhanced the performance of ERN dataset from 79.22 to 82.27% with naive bayes and for P300 dataset, the accuracy was improved from 67.73 to 69.87% using xGboost. In addition, principal component analysis and correlation metrics were evaluated on both EEG signals and their spike-representation to identify the reason for such improvement.
Singh, A 2022, 'High Voltage Gain Bidirectional DC-DC Converters for Supercapacitor Assisted Electric Vehicles: A Review', CPSS Transactions on Power Electronics and Applications, vol. 7, no. 4, pp. 386-398.
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Integration of supercapacitor alongwith battery in electric vehicles (EVs) improves the life cycle of the battery. Additionally, supercapacitor supplies or absorbs a large amount of instantaneous power during sudden demand such as acceleration or regenerative braking operation, hence also improve the dynamics of the internal power system. However, a major challenge with supercapacitor is that its terminal voltage is low and varies in a wide range during charging and discharging operation. Thus, a high voltage conversion ratio based bidirectional DC-DC converters are required to connect the lower supercapacitor voltage to higher DC-link voltage. The steep voltage conversion ratio with continuous gain based bidirectional DC-DC converters are an integral part of such applications. Various high gain converters exist in the literature such as isolated, non-isolated, cascaded, switched, flying capacitor, and coupled inductor based DC-DC converters, however all these converters have some limitations in context to high gain/high power applications. Therefore, to understand the development of next-generation high voltage gain bidirectional DC-DC converter, this paper focus to comprehensively review and classify various bidirectional step-up DC-DC converters based on their characteristics and voltage-boosting techniques. Further, practical suitability of the reviewed converters and future research directions in switched capacitor converters in context to EVs are also discussed in detail.
Singh, J, Gill, SS, Dogra, M, Sharma, S, Singh, M, Dwivedi, SP, Li, C, Singh, S, Muhammad, S, Salah, B & Shamseldin, MA 2022, 'Effect of Ranque-Hilsch Vortex Tube Cooling to Enhance the Surface-Topography and Tool-Wear in Sustainable Turning of Al-5.6Zn-2.5Mg-1.6Cu-0.23Cr-T6 Aerospace Alloy', Materials, vol. 15, no. 16, pp. 5681-5681.
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The aerospace metal cutting industry’s search for environmentally friendly practices that do not compromise machining performance is well known. One of the major objectives is the reduction in use of cutting fluids, which play a major role in containing the harsh effects of severe heat generated during machining. Machining performance and product quality can be improved by controlling heat during machining. The purpose of this study was to determine the effectiveness of various environmentally friendly metalworking fluid (MF) strategies for the sustainable turning of aerospace aluminum alloy (Al-5.6Zn-2.5Mg-1.6Cu-0.23Cr-T6) for automotive, marine, and aerospace industrial applications. The SEM images were analyzed for worn tool surfaces and machined surfaces. Under dry conditions, heat does not dissipate well, and will enter the workpiece due to the absence of coolant. This causes extreme damage beneath a turned workpiece. Thus, at 10 µm, a drop in microhardness of approximately 20% can be observed. A similar observation was made in a Ranque-Hilsch vortex tube (RHVT) and in compressed air; however, the drop in hardness was relatively low compared to dry conditions. This evaluation of microhardness indicated a heat-based attention in the turned workpiece, and thus, the heat-based effect was found to be lowest in RHVT and compressed air compared to dry conditions. Results showed that RHVT reduces temperature up to 10%, surface roughness 13%, and tool wear 20% compared to dry turning. Overall, RHVT was identified as more effective environmentally friendly cooling strategy than dry and compressed air for the turning of aluminum alloy 7075-T6.
Singh, K, Afzal, MU & Esselle, KP 2022, 'Accurate optimization technique for phase-gradient metasurfaces used in compact near-field meta-steering systems', Scientific Reports, vol. 12, no. 1.
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AbstractNear-Field Meta-Steering (NFMS) is a constantly evolving and progressively emerging novel antenna beam-steering technology that involves an elegant assembly of a base antenna and a pair of Phase-Gradient Metasurfaces (PGMs) placed in the near-field region of the antenna aperture. The upper PGM in an NFMS system receives an oblique incidence from the lower PGM at all times, a fact that is ignored in the traditional design process of upper metasurfaces. This work proposes an accurate optimization method for metasurfaces in NFMS systems to reduce signal leakage by suppressing the grating lobes and side lobes that are innate artifacts of beam-steering. We detail the design and optimization approach for both upper and lower metasurface. Compared to the conventionally optimized compact 2D steering system, the proposed system exhibits higher directivity and lower side-lobe and grating lobe levels within the entire scanning range. The broadside directivity is 1.4 dB higher, and the side-lobe level is 4 dB lower in comparison. The beam-steering patterns for the proposed 2D compact design are experimentally validated, and the measured and predicted results are in excellent concurrence. The versatile compatibility of truncated PGMs with a low gain antenna makes it a compelling technology for wireless backhaul mesh networks and future antenna hardware.
Singh, M 2022, 'Development of a portable Universal Testing Machine (UTM) compatible with 3D laser-confocal microscope for thin materials', Advances in Industrial and Manufacturing Engineering, vol. 4, pp. 100069-100069.
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The tensile test always delivers an in-depth understanding of true stress-strain relationship. However, it is not easy for the researchers to understand and evaluate the tensile properties of micro-specimens. This paper presents a research work aiming at the design and manufacturing of a small universal test machine (UTM) for measuring the mechanical properties of the miniaturised samples. The newly developed machine is sensitive to small loads and permits to obtain the stress-strain curves for thin materials. This portable UTM consists of a stepper motor, a load cell, a linear variable differential transformer (LVDT), a load cell amplifier and a data acquisition system. Copper based small and thin (50 μm) tensile test samples were tested on this machine at room temperature, and the calculated results were compared with the test results derived from a commercial UTM (METEX - 1 kN) to justify the validation of the developed apparatus. The obtained mechanical properties are in good agreement with the values obtained from a commercial UTM. To confirm the possibility of in-situ micro-observation, the surface roughness analysis has been conducted on the developed apparatus for pure copper foils under 3D laser-confocal microscope. Finally, it is concluded that this kind of testing apparatus could be manufactured within a manageable budget.
Singh, M, Pujar, GV, Kumar, SA, Bhagyalalitha, M, Akshatha, HS, Abuhaija, B, Alsoud, AR, Abualigah, L, Beeraka, NM & Gandomi, AH 2022, 'Evolution of Machine Learning in Tuberculosis Diagnosis: A Review of Deep Learning-Based Medical Applications', Electronics, vol. 11, no. 17, pp. 2634-2634.
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Tuberculosis (TB) is an infectious disease that has been a major menace to human health globally, causing millions of deaths yearly. Well-timed diagnosis and treatment are an arch to full recovery of the patient. Computer-aided diagnosis (CAD) has been a hopeful choice for TB diagnosis. Many CAD approaches using machine learning have been applied for TB diagnosis, specific to the artificial intelligence (AI) domain, which has led to the resurgence of AI in the medical field. Deep learning (DL), a major branch of AI, provides bigger room for diagnosing deadly TB disease. This review is focused on the limitations of conventional TB diagnostics and a broad description of various machine learning algorithms and their applications in TB diagnosis. Furthermore, various deep learning methods integrated with other systems such as neuro-fuzzy logic, genetic algorithm, and artificial immune systems are discussed. Finally, multiple state-of-the-art tools such as CAD4TB, Lunit INSIGHT, qXR, and InferRead DR Chest are summarized to view AI-assisted future aspects in TB diagnosis.
Singh, M, Sharma, S, Muniappan, A, Pimenov, DY, Wojciechowski, S, Jha, K, Dwivedi, SP, Li, C, Królczyk, JB, Walczak, D & Nguyen, TVT 2022, 'In Situ Micro-Observation of Surface Roughness and Fracture Mechanism in Metal Microforming of Thin Copper Sheets with Newly Developed Compact Testing Apparatus', Materials, vol. 15, no. 4, pp. 1368-1368.
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A better understanding of material deformation behaviours with changes in size is crucial to the design and operation of metal microforming processes. In order to facilitate the investigation of size effects, material deformation behaviours needed to be determined directly from material characterizations. This study was aimed at the design and manufacture of a compact universal testing machine (UTM) compatible with a 3D laser-confocal microscope to observe the deformation behaviour of materials in real-time. In this study, uniaxial micro tensile testing was conducted on three different thin (0.05 mm, 0.1 mm, and 0.3 mm) copper specimens with characteristic dimensions at micro scales. Micro tensile experimental runs were carried out on copper specimens with varying grain sizes on the newly developed apparatus under a 3D laser-confocal microscope. Microscale experiments under 3D laser-confocal microscope provided not only a method to observe the microstructure of materials, but also a novel way to observe the early stages of fracture mechanisms. From real-time examination using the newly developed compact testing apparatus, we discovered that fracture behaviour was mostly brought about by the concave surface formed by free surface roughening. Findings with high stability were discovered while moving with the sample grasped along the drive screw in the graphical plot of a crosshead’s displacement against time. Our results also showed very low mechanical noise (detected during the displacement of the crosshead), which indicated that there were no additional effects on the machine, such as vibrations or shifts in speed that could influence performance. The engineering stress-strain plots of the pure copper-tests with various thicknesses or samples depicted a level of stress necessary to initiate plastic flowing inside the material. From these results, we observed that strength and ductility declined with decreasing thickness. The influence of thickne...
Singh, NK, Gope, S, Koley, C, Dawn, S & Alhelou, HH 2022, 'Optimal Bidding Strategy for Social Welfare Maximization in Wind Farm Integrated Deregulated Power System Using Artificial Gorilla Troops Optimizer Algorithm', IEEE Access, vol. 10, pp. 71450-71461.
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Singh, R, Kaur, J, Gupta, K, Singh, M, Kanaoujiya, R & Kaur, N 2022, 'Recent advances and applications of polymeric materials in healthcare sector and COVID-19 management', Materials Today: Proceedings, vol. 62, pp. 2878-2882.
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The coronavirus disease pandemic is considered at its worst and all nations are collectively fighting to improve global public health. In this outlook, polymers and their related materials (including plastics) are the primary sources in the manufacturing of medical and personal protective equipment. Plastics can be mass-produced, economical, and sterilized, which makes them an inevitable material in the medical and healthcare sector. Along with plastics, antibacterial and antiviral coatings, polymeric nanomaterials and nanocomposites, and functional polymers have become excellent materials for COIVD-19. This review centres on the applications of polymer materials in managing the COVID-19 outbreak. Moreover, the utilization of plastics with its healthcare applications are reviewed. Apart from this, major challenges and future directions of these materials have also been discussed. This review will help aspiring researchers to develop the basic understanding of polymeric materials currently employed in medical sector.
Singh, R, Ullah, S, Rao, N, Singh, M, Patra, I, Darko, DA, Issac, CPJ, Esmaeilzadeh-Salestani, K, Kanaoujiya, R & Vijayan, V 2022, 'Synthesis of Three‐Dimensional Reduced‐Graphene Oxide from Graphene Oxide', Journal of Nanomaterials, vol. 2022, no. 1, pp. 1-18.
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Carbon materials and their allotropes have been involved significantly in our daily lives. Zero‐dimensional (0D) fullerenes, one‐dimensional (1D) carbon materials, and two‐dimensional (2D) graphene materials have distinctive properties and thus received immense attention from the early 2000s. To meet the growing demand for these materials in applications like energy storage, electrochemical catalysis, and environmental remediation, the special category, i.e., three‐dimensional (3D) structures assembled from graphene sheets, has been developed. Graphene oxide is a chemically altered graphene, the desired building block for 3D graphene matter (i.e., 3D graphene macrostructures). A simple synthesis route and pore morphologies make 3D reduced‐graphene oxide (rGO) a major candidate for the 3D graphene group. To obtain target‐specific 3D rGO, its synthesis mechanism plays an important role. Hence, in this article, we will discuss the general mechanism for 3D rGO synthesis, vital procedures for fabricating advanced 3D rGO, and important aspects controlling the growth of 3D rGO.
Singh, SK, Taylor, RW, Pradhan, B, Shirzadi, A & Pham, BT 2022, 'Predicting sustainable arsenic mitigation using machine learning techniques', Ecotoxicology and Environmental Safety, vol. 232, pp. 113271-113271.
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This study evaluates state-of-the-art machine learning models in predicting the most sustainable arsenic mitigation preference. A Gaussian distribution-based Naïve Bayes (NB) classifier scored the highest Area Under the Curve (AUC) of the Receiver Operating Characteristic curve (0.82), followed by Nu Support Vector Classification (0.80), and K-Neighbors (0.79). Ensemble classifiers scored higher than 70% AUC, with Random Forest being the top performer (0.77), and Decision Tree model ranked fourth with an AUC of 0.77. The multilayer perceptron model also achieved high performance (AUC=0.75). Most linear classifiers underperformed, with the Ridge classifier at the top (AUC=0.73) and perceptron at the bottom (AUC=0.57). A Bernoulli distribution-based Naïve Bayes classifier was the poorest model (AUC=0.50). The Gaussian NB was also the most robust ML model with the slightest variation of Kappa score on training (0.58) and test data (0.64). The results suggest that nonlinear or ensemble classifiers could more accurately understand the complex relationships of socio-environmental data and help develop accurate and robust prediction models of sustainable arsenic mitigation. Furthermore, Gaussian NB is the best option when data is scarce.
Singhania, RR, Guo, W, de Souza Vendenberghe, LP, Mannina, G & Kim, S-H 2022, 'Bioresource technology for bioenergy, bioproducts & environmental sustainability', Bioresource Technology, vol. 347, pp. 126736-126736.
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Sirivivatnanon, V, Xue, C & Khatri, R 2022, 'Service-Life Design of Low-Carbon Concrete Containing Fly Ash and Slag under Marine Tidal Conditions', ACI Materials Journal, vol. 119, no. 6.
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The use of blended cements enables the production of concretes with low embodied carbon and improved resistance to chloride penetration compared to General Purpose (GP) cement concrete. This paper reports the chloride diffusion characteristics in terms of apparent diffusion coefficient (Da), surface chloride concentration (Cs), and corresponding aging factors (a & b) of these low carbon concrete (LCC) derived from up to 9-year long-term exposure of small reinforced concrete slabs in both laboratory-simulated and field marine tidal conditions. LCC with either 30% fly ash or 50% slag provides slightly to significantly lower 28-day compressive strength than GP cement concrete at the same water/binder ratio but significantly better resistance to chloride penetration. The long-term chloride profile necessary to determine the concrete cover where chloride threshold is reached can be determined with the Da.t0, Cs.t0 and corresponding age factors a and b where t0 is the one-year time of exposure. The improved resistance to chloride penetration by use of fly ash and slag as cement replacement was largely due to their intrinsic influence on the microstructure of the concrete. The results highlight the difference in chloride penetration arises from the change in test methods thus the importance of calibration when data obtained from laboratory concrete were used as input for service life design.
Siwal, SS, Sheoran, K, Saini, AK, Vo, D-VN, Wang, Q & Thakur, VK 2022, 'Advanced thermochemical conversion technologies used for energy generation: Advancement and prospects', Fuel, vol. 321, pp. 124107-124107.
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The commercial conquest of the ethanol industry has raised curiosity within operations that transform biomass into biofuels. The energy production from biomass, bioenergy, is an outlook conception to substitute fossil fuels in the coming days, as it is productive, pure, and carbon dioxide neutral. Biomass may be combusted instantly to cause heat and power and employ advanced thermochemical techniques. It can be restored within bio-fuels in solid, liquid, and gas constitutions that may be utilized additionally towards heat and energy production. Here, in this review article, we have discussed the properties of biomass fuels, sustainability attention towards energy production from biomass along with different types of wastes to energy generation, and the advanced thermochemical conversion technologies that can be used for energy production from wastes. In the last, we have compared the advantages and drawbacks of these technologies and concluded our article with current challenges and future perspectives in this field.
Skarding, J, Hellmich, M, Gabrys, B & Musial, K 2022, 'A Robust Comparative Analysis of Graph Neural Networks on Dynamic Link Prediction', IEEE Access, vol. 10, no. 99, pp. 64146-64160.
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Graph neural networks (GNNs) are rapidly becoming the dominant way to learn on graph-structured data. Link prediction is a near-universal benchmark for new GNN models. Many advanced models such as Dynamic graph neural networks (DGNNs) specifically target dynamic graphs. However, these models, particularly DGNNs, are rarely compared to each other or existing heuristics. Different works evaluate their models in different ways, thus one cannot compare evaluation metrics and their results directly. Motivated by this, we perform a comprehensive comparison study. We compare link prediction heuristics, GNNs, discrete DGNNs, and continuous DGNNs on the dynamic link prediction task. In total we summarize the results of over 3200 experimental runs (≈ 1.5 years of computation time). We find that simple link prediction heuristics perform better than GNNs and DGNNs, different sliding window sizes greatly affect performance, and of all examined graph neural networks, that DGNNs consistently outperform static GNNs. This work is a continuation of our previous work, a foundation of dynamic networks and theoretical review of DGNNs. In combination with our survey, we provide both a theoretical and empirical comparison of DGNNs.
Skjöldebrand, C, Tipper, JL, Hatto, P, Bryant, M, Hall, RM & Persson, C 2022, 'Current status and future potential of wear-resistant coatings and articulating surfaces for hip and knee implants', Materials Today Bio, vol. 15, pp. 100270-100270.
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Hip and knee joint replacements are common and largely successful procedures that utilise implants to restore mobility and relieve pain for patients suffering from e.g. osteoarthritis. However, metallic ions and particles released from both the bearing surfaces and non-articulating interfaces, as in modular components, can cause hypersensitivity and local tissue necrosis, while particles originating from a polymer component have been associated with aseptic loosening and osteolysis. Implant coatings have the potential to improve properties compared to both bulk metal and ceramic alternatives. Ceramic coatings have the potential to increase scratch resistance, enhance wettability and reduce wear of the articulating surfaces compared to the metallic substrate, whilst maintaining overall toughness of the implant ensuring a lower risk of catastrophic failure of the device compared to use of a bulk ceramic. Coatings can also act as barriers to inhibit ion release from the underlying material caused by corrosion. This review aims to provide a comprehensive overview of wear-resistant coatings for joint replacements - both those that are in current clinical use as well as those under investigation for future use. While the majority of coatings belong predominantly in the latter group, a few coated implants have been successfully marketed and are available for clinical use in specific applications. Commercially available coatings for implants include titanium nitride (TiN), titanium niobium nitride (TiNbN), oxidized zirconium (OxZr) and zirconium nitride (ZrN) based coatings, whereas current research is focused not only on these, but also on diamond-like-carbon (DLC), silicon nitride (SiN), chromium nitride (CrN) and tantalum-based coatings (TaN and TaO). The coating materials referred to above that are still at the research stage have been shown to be non-cytotoxic and to reduce wear in a laboratory setting. However, the adhesion of implant coatings remains a m...
Smit, R, Awadallah, M, Bagheri, S & Surawski, NC 2022, 'Real-world emission factors for SUVs using on-board emission testing and geo-computation', Transportation Research Part D: Transport and Environment, vol. 107, pp. 103286-103286.
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A Portable Emissions Measurement System (PEMS) was used to measure emissions of five sports utility vehicles (SUVs) in a wide range of real-world driving conditions. The program included testing of fuel quality, coast-down and emissions in start, hot running and extended idling conditions. Geo-computation methods were used to add critical information (road gradient) to the PEMS data. Results from this study are generally in good agreement with international PEMS data. Hot running NOx emission factors are on average seven times higher than the type-approval limit for diesel SUVs, and they reach about 2100 and 400 mg/km in urban conditions for NOx and NO2, respectively. They are 7 (NOx) and 4 (NO2) times higher than current emission factors in COPERT Australia. COPERT Australia emission algorithms for CO2 are well behaved and the PEMS data suggest an update is not required. COPERT Australia emission algorithms should be revised for diesel SUVs (NOx, NO2) and petrol SUVs (CO, THC, NO2) to ensure accurate estimation of vehicle emissions at fleet level. Inclusion of logistic regression is proposed for future COPERT updates.
Smit, R, Chu-Van, T, Suara, K & Brown, RJ 2022, 'Comparing an energy-based ship emissions model with AIS and on-board emissions testing', Atmospheric Environment: X, vol. 16, pp. 100192-100192.
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Smith, CM & Hutvagner, G 2022, 'A comparative analysis of single cell small RNA sequencing data reveals heterogeneous isomiR expression and regulation', Scientific Reports, vol. 12, no. 1.
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AbstractMicroRNAs (miRNAs) are non-coding small RNAs which play a critical role in the regulation of gene expression in cells. It is known that miRNAs are often expressed as multiple isoforms, called isomiRs, which may have alternative regulatory functions. Despite the recent development of several single cell small RNA sequencing protocols, these methods have not been leveraged to investigate isomiR expression and regulation to better understand their role on a single cell level. Here we integrate sequencing data from three independent studies and find substantial differences in isomiR composition that suggest that cell autonomous mechanisms may drive isomiR processing. We also find evidence of altered regulatory functions of different classes of isomiRs, when compared to their respective wild-type miRNA, which supports a biological role for many of the isomiRs that are expressed.
Son, DB, Binh, TH, Vo, HK, Nguyen, BM, Binh, HTT & Yu, S 2022, 'Value-based reinforcement learning approaches for task offloading in Delay Constrained Vehicular Edge Computing', Engineering Applications of Artificial Intelligence, vol. 113, pp. 104898-104898.
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In the age of booming information technology, human-being has witnessed the need for new paradigms with both high computational capability and low latency. A potential solution is Vehicular Edge Computing (VEC). Previous work proposed a Fuzzy Deep Q-Network in Offloading scheme (FDQO) that combines Fuzzy rules and Deep Q-Network (DQN) to improve DQN's early performance by using Fuzzy Controller (FC). However, we notice that frequent usage of FC can hinder the future growth performance of model. One way to overcome this issue is to remove Fuzzy Controller entirely. We introduced an algorithm called baseline DQN (b-DQN), represented by its two variants Static baseline DQN (Sb-DQN) and Dynamic baseline DQN (Db-DQN), to modify the exploration rate base on the average rewards of closest observations. Our findings confirm that these baseline DQN algorithms surpass traditional DQN models in terms of average Quality of Experience (QoE) in 100 time slots by about 6%, but still suffer from poor early performance (such as in the first 5 time slots). Here, we introduce baseline FDQO (b-FDQO). This algorithm has a strategy to modify the Fuzzy Logic usage instead of removing it entirely while still observing the rewards to modify the exploration rate. It brings a higher average QoE in the first 5 time slots compared to other non-fuzzy-logic algorithms by at least 55.12%, prevent the model from getting too bad result over all time slots, while having the late performance as good as that of b-DQN.
Song, D, Zhang, W, Ren, T & Chang, X 2022, 'Editorial paper for pattern recognition letters VSI on multi-view representation learning and multi-modal information representation', Pattern Recognition Letters, vol. 159, pp. 165-166.
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Song, F, Li, L, You, I, Yu, S & Zhang, H 2022, 'Optimizing High-Speed Mobile Networks with Smart Collaborative Theory', IEEE Wireless Communications, vol. 29, no. 3, pp. 48-54.
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Although the vision of cognitive and intelligent Internet of Things is fascinating, it is still challenging to maximize its advantages in high-speed mobile networks. In this article, critical obstacles, including inflexible interactions, unreliable connections, and inefficient computations, are focused to establish a better communication solution. First, a concept named mobile-aware resource sharing (MARS) is proposed with straightforward motivation based on smart collaborative theory. Second, inspired by moving velocity and processing capacity, the design details and corresponding optimizations are analyzed from multiple per-spectives. Third, the implementation procedures are introduced to establish slight cooperations, stable associations, and strong virtualizations by considering specific features of fog, edge, and cloud zones. Fourth, both the functionality and performance are validated and discussed in a complex environment. The comparison results illustrate desirable improvements in critical latency and available bandwidth aspects. We expect MARS will be beneficial for both information and transportation communities within the smart city.
Song, K, Li, Z, Li, L, Zhao, X, Deng, M, Zhou, X, Xu, Y, Peng, L, Li, R & Wang, Q 2022, 'Methane production from peroxymonosulfate pretreated algae biomass: Insights into microbial mechanisms, microcystin detoxification and heavy metal partitioning behavior', Science of The Total Environment, vol. 834, pp. 155500-155500.
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This study investigated the methane production potential of algal biomass by anerobic digestion with the addition of peroxymonosulfate (PMS), the removal of microcystin were analyzed and discussed. The microcystin concentration in the collected algal sludge was 1.20 μg/L in the liquid phase and 1393 μg/g in the algal sludge before anaerobic fermentation. The microcystin concentration decreased to 0.20-0.35 μg/L in the liquid phase and 4.16-11.51 μg/g in the sludge phase after 60 days of digestion. The initial PMS dose and residue microcystin concentration could be simulated with a logarithmic decay model (R2 > 0.87). Anaerobic digestion could recover energy from algal source in the form of methane gas, which was not affected in the presence of microcystin, and the microcystin removal rate was >99%. Digestion decreased the total contents of Cd and Zn in the liquid phase and increased the total contents of Cr and Pb in the liquid phase. The microbial community and function prediction results indicated that the PMS0.1 system had the highest methane production, which was attributed to the high abundance of Mechanosaeta (40.52%). This study provides insights into microbial mechanisms, microcystin detoxification and the heavy metal partitioning behavior of the algal biomass during methane production.
Song, L-Z, Qin, P-Y, Zhu, H & Du, J 2022, 'Wideband Conformal Transmitarrays for E-Band Multi-Beam Applications', IEEE Transactions on Antennas and Propagation, vol. 70, no. 11, pp. 10417-10425.
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Wideband conformal transmitarrays at E-band are developed for multi-beam applications in this paper. A triple-layer element with double split rings is presented for wideband transmissions, achieving a 360° continuous phase variation range at 74 GHz with less than 2.3-dB transmission loss. A comprehensive design methodology of multi-beam conformal transmitarrays is demonstrated for various platforms with different curvatures. To validate the theoretical analysis, conformal transmitarrays with two different curvatures are designed, fabricated, and measured. Multiple radiation beams are realized between ±30° and ±45° for the two prototypes, respectively. Good agreement is obtained between simulation and measurement. The 3-dB gain bandwidths are 30% from 66.5 GHz to 90 GHz, and 27.8% from 68 GHz to 90 GHz for the two designs, respectively, covering the entire E-band.
Song, L-Z, Wang, X & Qin, P-Y 2022, 'Single-Feed Multibeam Conformal Transmitarrays With Phase and Amplitude Modulations', IEEE Antennas and Wireless Propagation Letters, vol. 21, no. 8, pp. 1669-1673.
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Single-feed multibeam conformal transmitarrays using a superposition method are presented in this letter. The arrays consist of ultrathin Huygens elements with independent amplitude and phase manipulations. Three cylindrical conformal transmitarrays with dual-beam radiation patterns are designed at 10 GHz, producing dual beams at ±30°, +30°, and -20°, +30° and -10°, respectively. As an experimental validation, the prototype with symmetrical dual beams is fabricated and measured. Two beams at +29° and -28° along the H-plane are achieved with a measured 18.29 dBi peak gain. The gain difference between the two beams is 0.13 dB. Good agreement between simulation and measurement is observed.
Song, Y, Lu, J, Liu, A, Lu, H & Zhang, G 2022, 'A Segment-Based Drift Adaptation Method for Data Streams', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 9, pp. 4876-4889.
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In concept drift adaptation, we aim to design a blind or an informed strategy to update our best predictor for future data at each time point. However, existing informed drift adaptation methods need to wait for an entire batch of data to detect drift and then update the predictor (if drift is detected), which causes adaptation delay. To overcome the adaptation delay, we propose a sequentially updated statistic, called drift-gradient to quantify the increase of distributional discrepancy when every new instance arrives. Based on drift-gradient, a segment-based drift adaptation (SEGA) method is developed to online update our best predictor. Drift-gradient is defined on a segment in the training set. It can precisely quantify the increase of distributional discrepancy between the old segment and the newest segment when only one new instance is available at each time point. A lower value of drift-gradient on the old segment represents that the distribution of the new instance is closer to the distribution of the old segment. Based on the drift-gradient, SEGA retrains our best predictors with the segments that have the minimum drift-gradient when every new instance arrives. SEGA has been validated by extensive experiments on both synthetic and real-world, classification and regression data streams. The experimental results show that SEGA outperforms competitive blind and informed drift adaptation methods.
Song, Y, Zhang, Z, Wu, J, Wang, Y, Zhao, L & Huang, S 2022, 'A Right Invariant Extended Kalman Filter for Object Based SLAM', IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 1316-1323.
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With the recent advance of deep learning based object recognition and estimation, it is possible to consider object level SLAM where the pose of each object is estimated in the SLAM process. In this letter, based on a novel Lie group structure, a right invariant extended Kalman filter (RI-EKF) for object based SLAM is proposed. The observability analysis shows that the proposed algorithm automatically maintains the correct unobservable subspace, while standard EKF (Std-EKF) based SLAM algorithm does not. This results in a better consistency for the proposed algorithm comparing to Std-EKF. Finally, simulations and real world experiments validate not only the consistency and accuracy of the proposed algorithm, but also the practicability of the proposed RI-EKF for object based SLAM problem. The MATLAB code of the algorithm is made publicly available.
Song, Z, Ji, J, Zhang, R & Cao, L 2022, 'Development of a test equipment for rating front to rear-end collisions based on C-NCAP-2018', International Journal of Crashworthiness, vol. 27, no. 2, pp. 522-532.
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Autonomous emergency braking (AEB) systems play an important role in reducing front to rear-end collisions. To evaluate the performance of AEB systems, different countries have recently published their own new car assessment programs (NCAPs). This study firstly develops a set of test equipment to evaluate the performance of AEB systems in field tests according to the China New Car Assessment Program (C-NCAP-2018). Then, the test accuracy of the AEB test equipment is debugged and verified by comparing the test results with those from IIHS. Finally, field tests are performed to evaluate the AEB systems performance on collision avoidance speeds in CCRs test scenarios and actuation time of warning function and braking of five vehicles using the developed test equipment. Additionally, some underlying causes are discussed for the trade-off between driving comfort and braking effectiveness. The field tests confirm that the developed equipment can effectively evaluate the performance of AEB systems and thus improve the active safety technology for vehicles.
Song, Z, Lu, J, Yao, Y & Zhang, J 2022, 'Self-Supervised Depth Completion From Direct Visual-LiDAR Odometry in Autonomous Driving', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 11654-11665.
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Soomro, MHAA, Indraratna, B & Karekal, S 2022, 'Critical shear strain and sliding potential of rock joint under cyclic loading', Transportation Geotechnics, vol. 32, pp. 100708-100708.
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A new concept of critical shear strain ετcritical of rock joint under cyclic loading is presented, and the role of ετcritical in evaluating the sliding potential of rock joint is highlighted. A series of cyclic triaxial tests was conducted on a cylindrical rock joint specimen with a replicated rough surface representing a joint roughness coefficient JRC value of 12.6 oriented at 60° with respect to the horizontal plane. The experimental results indicate that the onset of instability of rock joint is suppressed with increase in confining pressure and number of loading cycles N until the normalized shear deformation increases beyond a threshold value of ετcritical. Generally, the critical strain of most rock types is considered in the proximity of 1% under small strain conditions [36–37], however, in this study, the critical strain concept is extended to the domain of rock joints, and a semi-empirical model to more rigorously quantify the critical shear strain (ετcritical) of rock joint is suggested considering the effect of joint roughness coefficient JRC, cyclic loading amplitude, and the number of loading cycles N. Also, a rational classification of Joint Sliding Potential (JSP) based on the ετcritical and normalized total shear strain εθN of rock joint is proposed to characterize the cyclic loading induced sliding instability of a rock discontinuity.
Soomro, WA, Guo, Y, Lu, H, Jin, J, Shen, B & Zhu, J 2022, 'Experimental Setup for Measurement of AC Loss in HTS under Rotating Magnetic Field', Energies, vol. 15, no. 21, pp. 7857-7857.
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High-temperature superconducting materials have shown great potential for the design of large-scale industry applications. However, they are complicated under AC conditions, resulting in penalties such as power loss or AC loss. This loss has to be considered in order to design reliable and efficient superconducting devices. Furthermore, when superconductors are used in rotating machines, they may be exposed to rotating magnetic fields, which is critical for the design of such machines. Existing AC loss measuring techniques are limited to measuring under one-dimensional AC magnetic fields or transport currents. Therefore, it is essential to develop and investigate robust experimental techniques to investigate the loss mechanism in HTS machines. In this paper, a new and novel experimental technique has been presented to measure AC loss in rotating magnetic field conditions. The loss under rotating magnetic fields is measured and compared by numerical modeling methods, and the results show a strong correlation with the numerical modeling and show the effectiveness of the experimental setup.
Soomro, WA, Guo, Y, Lu, H, Zhu, J, Jin, J & Shen, B 2022, 'Three-Dimensional Numerical Characterization of High-Temperature Superconductor Bulks Subjected to Rotating Magnetic Fields', Energies, vol. 15, no. 9, pp. 3186-3186.
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High-temperature superconductor (HTS) bulks have shown very promising potential for industrial applications due to the ability to trap much higher magnetic fields compared to traditional permanent magnets. In rotating electrical machines, the magnetic field is a combination of alternating and rotating fields. On the contrary, all studies on electromagnetic characterization of HTS presented in the literature so far have only focused on alternating AC magnetic fields and alternating AC loss due to the unavailability of robust experimental techniques and analytical models. This paper presents a numerical investigation on the characterization of HTS bulks subjected to rotating magnetic fields showing AC loss, current density distribution in three-dimensional axes, and trapped field analysis. A three-dimensional numerical model has been developed using H-formulation based on finite element analysis. An HTS cubic sample is magnetized and demagnetized with two-dimensional magnetic flux density vectors rotating in circular orientation around the XOY, XOZ, and YOZ planes.
Spindler, KP, Imrey, PB, Yalcin, S, Beck, GJ, Calbrese, G, Cox, CL, Fadale, PD, Farrow, L, Fitch, R, Flanigan, D, Fleming, BC, Hulstyn, MJ, Jones, MH, Kaeding, C, Katz, JN, Kriz, P, Magnussen, R, McErlean, E, Melgaard, C, Owens, BD, Saluan, P, Strnad, G, Winalski, CS & Wright, R 2022, 'Design Features and Rationale of the BEAR-MOON (Bridge-Enhanced ACL Restoration Multicenter Orthopaedic Outcomes Network) Randomized Clinical Trial', Orthopaedic Journal of Sports Medicine, vol. 10, no. 1, pp. 232596712110654-232596712110654.
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Background: BEAR (bridge-enhanced anterior cruciate ligament [ACL] restoration), a paradigm-shifting technology to heal midsubstance ACL tears, has been demonstrated to be effective in a single-center 2:1 randomized controlled trial (RCT) versus hamstring ACL reconstruction. Widespread dissemination of BEAR into clinical practice should also be informed by a multicenter RCT to demonstrate exportability and compare efficacy with bone--patellar tendon–bone (BPTB) ACL reconstruction, another clinically standard treatment. Purpose: To present the design and initial preparation of a multicenter RCT of BEAR versus BPTB ACL reconstruction (the BEAR: Multicenter Orthopaedic Outcomes Network [BEAR-MOON] trial). Design and analytic issues in planning the complex BEAR-MOON trial, involving the US National Institute of Arthritis and Musculoskeletal and Skin Diseases, the US Food and Drug Administration, the BEAR implant manufacturer, a data and safety monitoring board, and institutional review boards, can usefully inform both clinicians on the trial’s strengths and limitations and future investigators on planning of complex orthopaedic studies. Study Design: Clinical trial. Methods: We describe the distinctive clinical, methodological, and operational challenges of comparing the innovative BEAR procedure with the well-established BPTB operation, and we outline the clinical motivation, experimental setting, study design, surgical challenges, rehabilitation, outcome measures, and planned analysis of the BEAR-MOON trial. Results: BEAR-MOON is a 6-center, 12-surgeon, 200-patient randomized, partially blinded, noninferiority RCT comparing BEAR with BPTB ACL reconstruction for trea...
Squires, AD, Gao, X, Du, J, Han, Z, Seo, DH, Cooper, JS, Murdock, AT, Lam, SKH, Zhang, T & van der Laan, T 2022, 'Electrically tuneable terahertz metasurface enabled by a graphene/gold bilayer structure', Communications Materials, vol. 3, no. 1, pp. 1-9.
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AbstractReconfigurable terahertz electronics devices with high tuneability are pivotal for next-generation high speed wireless communication and sensing technologies. Significant challenges exist for realizing these devices, particularly on the design of smart metastructures that can manipulate electromagnetic radiation at the terahertz frequencies and the fabrication of devices with effective tuneability and reconfigurability. Here, we incorporate graphene into a graphene/gold bilayer superimposed metamaterial structure, which enables efficient electrical tuning of terahertz waves. A 0.2 THz frequency-selective absorber is designed and experimentally developed using this graphene/gold bilayer metamaterial approach. The device demonstrates 16 dB amplitude tuning at 0.2 THz resonance and over 95% broadband modulation at just 6 V bias voltage while maintaining a benchmark high-quality factor resonance performance. The design and fabrication methods presented can be readily applied to produce a myriad of tuneable terahertz devices required for high-speed, reconfigurable THz wireless communication and sensing technologies.
Srivastava, A, Yetemen, O, Saco, PM, Rodriguez, JF, Kumari, N & Chun, KP 2022, 'Influence of orographic precipitation on coevolving landforms and vegetation in semi‐arid ecosystems', Earth Surface Processes and Landforms, vol. 47, no. 12, pp. 2846-2862.
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AbstractTopography affects the intensity and spatial distribution of precipitation due to orographic lifting mechanisms and, in turn, influences the prevailing climate and vegetation distribution. Previous modelling studies on the impact of orographic precipitation on landform evolution have considered bare soil conditions. However, research on the effect of changes in precipitation regimes induced by elevation gradients (particularly in aspect‐controlled semi‐arid ecosystems) on landform patterns, trying to understand feedbacks and consequences for coevolving vegetation, has been limited. In this study, the Channel–Hillslope Integrated Landscape Development (CHILD) landscape evolution model coupled with the vegetation dynamics Bucket Grassland Model (BGM) is used to analyse the coevolution of semi‐arid landform–vegetation ecosystems. The CHILD+BGM model is run under different combinations of precipitation and solar radiation settings. Three precipitation settings, including uniform, elevation control, and orographic control on precipitation, are considered in combination with spatially uniform and spatially varied radiation settings. Based on the results, elevation control, aspect, and drainage network are identified as the major drivers of the distribution of vegetation cover on the landscapes. Further, the combination of orographic precipitation and spatially varied solar radiation created the highest asymmetry in the landscape and divide migration due to the emergence of gentler slopes on the windward than the leeward sides of the domain. The modelling outcomes from this study indicate that aspect control of solar radiation in combination with orographic precipitation plays a key role in the generation of topographic asymmetry in semi‐arid ecosystems.
Stewart, MG 2022, 'Reliability-based design and robustness for blast-resistant design of RC buildings', Advances in Structural Engineering, vol. 25, no. 7, pp. 1402-1412.
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Explosive blasts from accidental or malevolent sources constitute an extreme event resulting in abnormal loads on buildings and other structures. A reinforced concrete (RC) multistorey building is assumed to be attacked by a terrorist vehicle-borne improvised device. Structural reliabilities are calculated for each RC column in the multistorey building exposed directly to the blast event. The probability of progressive collapse for the building is then estimated using system reliability analysis comprising of ground floor columns exposed to the explosive blast. The RC columns are designed according to United States blast-resistant design standard based on (i) threat dependent and (ii) alternate path design methods. The effects of threat dependent and alternate path design methods on column sizing, column reliability, and building collapse probability are investigated by conservatively assuming that collapse occurs if one or more columns fail. The robustness is also dependent on the location of the explosive. It was also found that a threat-dependent design appears to be more effective than the alternate path method in reducing building collapse risks.
Stewart, MG 2022, 'Simplified calculation of airblast variability and reliability-based design load factors for spherical air burst and hemispherical surface burst explosions', International Journal of Protective Structures, vol. 13, no. 2, pp. 144-160.
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There can be significant uncertainty and variability with explosive blast loading. Standards and codes of practice are underpinned by reliability-based principles, and there is little reason not to apply these to explosive blast loading. This paper develops a simplified approach where regression equations may be used to predict the probabilistic model of airblast variability and associated reliability-based design load factors (or RBDFs) for all combinations of range, explosive mass and model errors. These models are applicable to (i) hemispherical surface bursts, and (ii) spherical free-air bursts. The benefit of this simplified approach is that the equations can be easily programed into a spreadsheet, computer code or other numerical methods. There is no need for any Monte-Carlo or other probabilistic calculations. Examples then illustrate how model error, range and explosive mass uncertainty and variability affect the variability of pressure and impulse, which in turn affect the damage assessment of residential construction.
Stewart, MG 2022, 'Simplified reliability-based load design factors for explosive blast loading, weapons effects, and its application to collateral damage estimation', The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, vol. 19, no. 3, pp. 385-401.
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The paper describes a simplified approach to quantifying a reliability-based design load factor (RBDF) for the variability of explosive blast loading. The user can select range and explosive mass variability and model errors to derive RBDFs for pressure and impulse. These algorithms may be easily programmed into a spreadsheet, computer code, or other numerical method. There is a need by military planners to increase the predictive accuracy of collateral damage estimation (CDE) to ensure maximum damage to the target while minimizing harm to nearby civilians. This present paper uses the CDE damage criterion adopted by the USA and NATO to assess damage and safety risks and recommend safe collateral damage distances. Hence, the present paper utilizes RBDFs to simulate collateral damage risks to a hypothetical reinforced concrete residential building from a 2000 lb bomb using the 99th percentile of blast loads, engineering models, and Monte Carlo simulation analysis that considers variabilities of load and resistance. It was found that CDE is sensitive to airblast model errors and variability of structural resistance. It is recommended that these considerations be incorporated into CDE methodology since existing CDE methodology may be non-conservative, resulting in higher risks of collateral damage.
Stewart, MG 2022, 'Systems thinking averts apocalypses now and in the future: why we should always look on the bright side of life', Civil Engineering and Environmental Systems, vol. 39, no. 3, pp. 188-204.
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Systems thinking and analysis from the civil and environmental engineering communities have been highly successful in mitigating the effects of natural and man-made hazards. Not surprisingly, the United Nations Human Development Index shows steady improvement for every nation since its implementation in 1990. The world has never been healthier, wealthier, or more educated than at the present. Climate change and sustainability remain as significant challenges to be faced. It will be shown, though, that economic and life-safety losses from climate change are often exaggerated and do not reflect wealth creation, human capital, and new improved technologies. There is an urgent need for systems-led approaches and there is a proud record of accomplishments in the past that should equally as well translate into the future. This paper will discuss these issues, as well as briefly describe the importance of systems engineering in dealing with new and emerging threats, as well as the political imperative. The paper will also highlight that there is much to be optimistic about the future, and in the ability of systems thinking to meet any challenges. And to quote Monty Python we should try to ‘Always Look on the Bright Side of Life’.
Stratton‐Powell, AA, Williams, S, Tipper, JL, Redmond, AC & Brockett, CL 2022, 'Mixed material wear particle isolation from periprosthetic tissue surrounding total joint replacements', Journal of Biomedical Materials Research Part B: Applied Biomaterials, vol. 110, no. 10, pp. 2276-2289.
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AbstractSubmicron‐sized wear particles are generally accepted as a potential cause of aseptic loosening when produced in sufficient volumes. With the accelerating use of increasingly wear‐resistant biomaterials, identifying such particles and evaluating their biological response is becoming more challenging. Highly sensitive wear particle isolation methods have been developed but these methods cannot isolate the complete spectrum of particle types present in individual tissue samples. Two established techniques were modified to create one novel method to isolate both high‐ and low‐density materials from periprosthetic tissue samples. Ten total hip replacement and eight total knee replacement tissue samples were processed. All particle types were characterized using high resolution scanning electron microscopy. UHMWPE and a range of high‐density materials were isolated from all tissue samples, including: polymethylmethacrylate, zirconium dioxide, titanium alloy, cobalt chromium alloy and stainless steel. This feasibility study demonstrates the coexistence of mixed particle types in periprosthetic tissues and provides researchers with high‐resolution images of clinically relevant wear particles that could be used as a reference for future in vitro biological response studies.
Stricker, R, Meth, M, Postler, L, Edmunds, C, Ferrie, C, Blatt, R, Schindler, P, Monz, T, Kueng, R & Ringbauer, M 2022, 'Experimental Single-Setting Quantum State Tomography', PRX Quantum, vol. 3, no. 4, p. 040310.
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Quantum computers solve ever more complex tasks using steadily growing system sizes. Characterizing these quantum systems is vital, yet becoming increasingly challenging. The gold-standard method for this task is quantum state tomography (QST), capable of fully reconstructing a quantum state without prior knowledge. The measurement and classical computing costs, however, increase exponentially with the number of constituents (e.g., qubits) - a daunting bottleneck given the scale of existing and near-term quantum devices. Here, we demonstrate a scalable and practical QST approach that only uses a single measurement setting, namely symmetric informationally complete (SIC) positive operator-valued measures (POVMs). We implement these nonorthogonal measurements on an ion trap quantum processor by utilizing additional energy levels within each ion - without requiring ancillary ions to assist in measurements. More precisely, we locally map the SIC POVM to orthogonal states embedded in a higher-dimensional system, which we read out using repeated in-sequence detections, thereby providing full tomographic information in every shot. Combining this SIC tomography with the recently developed randomized measurement toolbox ('classical shadows') proves to be a powerful combination. SIC tomography alleviates the need for choosing measurement settings at random ('derandomization'), while classical shadows enable the estimation of arbitrary polynomial functions of the density matrix orders of magnitudes faster than standard methods. The latter enables in-depth entanglement characterization, which we experimentally showcase on a five-qubit absolutely maximally entangled state. Moreover, the fact that the full tomography information is available in every shot enables online QST in real time (i.e., while the experiment is running). We demonstrate this on an eight-qubit entangled state (which has 28⋅28-1=65535 degrees of freedom), as well as for fast state identification. ...
Su, G, Mohd Zulkifli, NW, Ong, HC, Ibrahim, S, Bu, Q & Zhu, R 2022, 'Pyrolysis of oil palm wastes for bioenergy in Malaysia: A review', Renewable and Sustainable Energy Reviews, vol. 164, pp. 112554-112554.
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Su, G, Ong, HC, Cheah, MY, Chen, W-H, Lam, SS & Huang, Y 2022, 'Microwave-assisted pyrolysis technology for bioenergy recovery: Mechanism, performance, and prospect', Fuel, vol. 326, pp. 124983-124983.
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The emergence of bioenergy provides a solution to the environment and energy crises caused by the indiscriminate use of fossil fuels. Pyrolysis technology has broad application prospects in bioenergy production and waste disposal, providing a solid guarantee for the sustainable development of human beings and the environment. As an endothermic process, pyrolysis relies on external heat as an energy source. The introduction of microwave provides a different energy source for the pyrolysis process and exhibits a different pyrolysis performance due to its unique energy transfer mechanism. Conventional pyrolysis is conducive to the formation of bio-oil, whereas microwave-assisted pyrolysis can improve the composition of bio-oil and the surface properties of biochar. This article focuses on the advantages and limitations of microwave-assisted and conventional pyrolysis modes. Special attention is given to the differences in product distribution and properties and the economic feasibility of the two pyrolysis modes.
Su, G, Ong, HC, Fattah, IMR, Ok, YS, Jang, J-H & Wang, C-T 2022, 'State-of-the-art of the pyrolysis and co-pyrolysis of food waste: Progress and challenges', Science of The Total Environment, vol. 809, pp. 151170-151170.
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The continuous growth of population and the steady improvement of people's living standards have accelerated the generation of massive food waste. Untreated food waste has great potential to harm the environment and human health due to bad odor release, bacterial leaching, and virus transmission. However, the application of traditional disposal techniques like composting, landfilling, animal feeding, and anaerobic digestion are difficult to ease the environmental burdens because of problems such as large land occupation, virus transmission, hazardous gas emissions, and poor efficiency. Pyrolysis is a practical and promising route to reduce the environmental burden by converting food waste into bioenergy. This paper aims to analyze the characteristics of food waste, introduce the production of biofuels from conventional and advanced pyrolysis of food waste, and provide a basis for scientific disposal and sustainable management of food waste. The review shows that co-pyrolysis and catalytic pyrolysis significantly impact the pyrolysis process and product characteristics. The addition of tire waste promotes the synthesis of hydrocarbons and inhibits the formation of oxygenated compounds efficiently. The application of calcium oxide (CaO) exhibits good performance in the increment of bio-oil yield and hydrocarbon content. Based on this literature review, pyrolysis can be considered as the optimal technique for dealing with food waste and producing valuable products.
Su, G, Ong, HC, Gan, YY, Chen, W-H, Chong, CT & Ok, YS 2022, 'Co-pyrolysis of microalgae and other biomass wastes for the production of high-quality bio-oil: Progress and prospective', Bioresource Technology, vol. 344, pp. 126096-126096.
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Su, G, Ong, HC, Mofijur, M, Mahlia, TMI & Ok, YS 2022, 'Pyrolysis of waste oils for the production of biofuels: A critical review', Journal of Hazardous Materials, vol. 424, no. Pt B, pp. 127396-127396.
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The application of waste oils as pyrolysis feedstocks to produce high-grade biofuels is receiving extensive attention, which will diversify energy supplies and address environmental challenges caused by waste oils treatment and fossil fuel combustion. Waste oils are the optimal raw materials to produce biofuels due to their high hydrogen and volatile matter content. However, traditional disposal methods such as gasification, transesterification, hydrotreating, solvent extraction, and membrane technology are difficult to achieve satisfactory effects owing to shortcomings like enormous energy demand, long process time, high operational cost, and hazardous material pollution. The usage of clean and safe pyrolysis technology can break through the current predicament. The bio-oil produced by the conventional pyrolysis of waste oils has a high yield and HHV with great potential to replace fossil fuel, but contains a high acid value of about 120 mg KOH/g. Nevertheless, the application of CaO and NaOH can significantly decrease the acid value of bio-oil to close to zero. Additionally, the addition of coexisting bifunctional catalyst, SBA-15@MgO@Zn in particular, can simultaneously reduce the acid value and positively influence the yield and quality of bio-oil. Moreover, co-pyrolysis with plastic waste can effectively save energy and time, and improve bio-oil yield and quality. Consequently, this paper presents a critical and comprehensive review of the production of biofuels using conventional and advanced pyrolysis of waste oils.
Su, G, Ong, HC, Mohd Zulkifli, NW, Ibrahim, S, Chen, WH, Chong, CT & Ok, YS 2022, 'Valorization of animal manure via pyrolysis for bioenergy: A review', Journal of Cleaner Production, vol. 343, pp. 130965-130965.
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Su, Y, Gu, Y, Wang, Z, Zhang, Y, Qin, J & Yu, G 2022, 'Efficient subhypergraph matching based on hyperedge features', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Subasinghage, K, Gunawardane, K & Kularatna, N 2022, 'Supercapacitor-Assisted Low-Dropout Regulator Technique for Low Output Ripple DC–DC Conversion', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 3, no. 4, pp. 1028-1037.
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Sudhagar, S, Kumar, SS, Premkumar, IJI, Vijayan, V, .Venkatesh, R, Rajkumar, S & Singh, M 2022, 'UV- and visible-light-driven TiO2/La2O3 and TiO2/Al2O3 nanocatalysts: synthesis and enhanced photocatalytic activity', Applied Physics A, vol. 128, no. 4, p. 282.
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The hydrothermal method was used to make the anatase phase of TiO2 nanoparticles, TiO2/La2O3 and TiO2/Al2O3 composites. FTIR spectroscopy, X-ray diffraction (XRD), UV–Vis absorption spectroscopy and scanning electron microscopy (SEM) were used to investigate the crystal structure, shape, and optical characteristics of TiO2, TiO2/La2O3 and TiO2/Al2O3 nanomaterials. The photocatalytic studies were comparatively analyzed by degrading the textile dyes methylene blue (MB) and crystal violet (CV). The degradation process was carried out in both UV light and visible light irradiation. The efficiency achieved by TiO2, TiO2/La2O3 and TiO2/Al2O3 was 87, 95, 45% and 80, 92, 29%, respectively, for MB and CV dye under UV light while under visible light it is 34, 27, 84%, and 29, 24, 81%, respectively, for MB and CV dye.
Suleman, SB, Hai, FI, Mukhtar, H, Duong, HC & Ansari, AJ 2022, 'Influence of operating parameters and membrane fouling on nutrient transport by FO membrane', Journal of Water Process Engineering, vol. 47, pp. 102699-102699.
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Sulthana, R, Taqui, SN, Syed, UT, Khan, TMY, Khadar, SDA, Mokashi, I, Shahapurkar, K, Kalam, MA, Murthy, HCA & Syed, AA 2022, 'Adsorption of Crystal Violet Dye from Aqueous Solution using Industrial Pepper Seed Spent: Equilibrium, Thermodynamic, and Kinetic Studies', Adsorption Science & Technology, vol. 2022, pp. 1-20.
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The economic viability of adsorbing crystal violet (CV) using pepper seed spent (PSS) as a biosorbent in an aqueous solution has been studied. A parametrical investigation was conducted considering parameters like initial concentration of dye, time of contact, pH value, and temperature variation. The analysis of experimental data obtained was carried out by evaluating with the isotherms of Freundlich, Sips, Tempkin, Jovanovic, Brouers–Sotolongo, Toth, Vieth–Sladek, Radke–Prausnitz, Langmuir, and Redlich–Peterson. The adsorption kinetics were studied by implementing the Dumwald-Wagner, Weber-Morris, pseudo-first-order, pseudo-second-order, film diffusion, and Avrami models. The experimental value of adsorption capacity ([Formula: see text]) was observed to be quite close to the Jovanovic isotherm adsorption capacity ([Formula: see text]) at ([Formula: see text]), coefficient of correlation of 0.945. The data validation was found to conform to that of pseudo-second-order and Avrami kinetic models. The adsorption process was specified as a spontaneous and endothermic process owing to the thermodynamic parametrical values of [Formula: see text], [Formula: see text], and [Formula: see text]. The value of [Formula: see text] is an indicator of the process’s physical nature. The adsorption of CV to the PSS was authenticated from infrared spectroscopy and scanning electron microscopy images. The interactions of the CV-PSS system have been discussed, and the observations noted suggest PSS as a feasible adsorbent to extract CV from an aqueous solution.
Sun, C, Du, Q, Zhang, X, Wang, Z, Zheng, J, Wu, Q, Li, Z, Long, T, Guo, W & Ngo, HH 2022, 'Role of spent coffee ground biochar in an anaerobic membrane bioreactor for treating synthetic swine wastewater', Journal of Water Process Engineering, vol. 49, pp. 102981-102981.
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Using anaerobic membrane bioreactors (AnMBRs) to treat swine wastewater is an effective method to recover bioenergy. However, due to the inhibitory effect of high concentrations of organic matter and ammonia nitrogen on microbial activities in swine wastewater, some problems are evident such as low recovery efficiency and serious membrane fouling. In this study, biochar prepared from spent coffee grounds (SCG-BC) was added to AnMBR to investigate its effect on the operation process. Results reported that methane yield rose from 0.227 LCH4/g-CODremoved to 0.267 LCH4/g-CODremoved along with a reduction in CO2 being produced at 35.25 % after adding SCG-BC. It confirmed that in-situ biogas upgrading was achieved. As well, the total volatile fatty acids declined to a low concentration of 194.87 ± 51.82 mg/L while pH remained steadily at 7.70 ± 0.31. Adding SCG-BC reduced irreversible membrane fouling by 34.69 %. Microbial community analysis showed that SCG-BC increased the relative abundance of methanogenic archaea, especially Methanosarcina (from 1.47 % to 8.03 %). Also, Anaerolinea and Methanosaeta participating in direct interspecies electron transfer were enriched onto biochar. They acted together to enhance the biogas production. It can be concluded that AnMBR with SCG-BC addition has good application prospects in recovering bioenergy from wastewater.
Sun, C, Ni, W, Bu, Z & Wang, X 2022, 'Energy Minimization for Intelligent Reflecting Surface-Assisted Mobile Edge Computing', IEEE Transactions on Wireless Communications, vol. 21, no. 8, pp. 6329-6344.
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Intelligent reflecting surface (IRS) has been increasingly considered in mobile edge computing (MEC), assisting smart terminals (STs) in offloading computationally-intense tasks to base stations (BSs). This paper presents a new IRS-assisted MEC framework, which jointly optimizes the local CPU frequencies of the STs, the receive beamformers of the BS, the ST offloading schedules, and the IRS phase configuration, to minimize the energy consumption of the STs. To this end, we reveal that the optimal CPU frequency is time-invariant for each ST. Under flat-fading channels, the IRS phases and the receive beamformers of the BS can be then decoupled from the offloading schedules. Based on this structure, we develop an alternating optimization to solve the IRS phase configuration and the receive beamformers, and then exploit the Lagrange duality method to solve the offloading schedules. We prove that the overall algorithm is guaranteed to compute a stationary point solution for the problem of interest with a low complexity. Under frequency-selective channels, we also develop a new alternating optimization algorithm to minimize the energy consumption, where manifold optimization is leveraged to effectively solve the IRS phase shifts. Numerical results show that the proposed algorithms are superior to existing techniques in terms of energy efficiency under both flat-fading and frequency-selective channels.
Sun, G, Wang, Y, Luo, Q & Li, Q 2022, 'Vibration-based damage identification in composite plates using 3D-DIC and wavelet analysis', Mechanical Systems and Signal Processing, vol. 173, pp. 108890-108890.
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High-speed three-dimensional digital image correlation (3D-DIC) techniques can acquire full-field vibration responses under a single excitation, which is not restricted by the number of degrees of freedom (DOFs) in measurement compared with accelerometers and laser Doppler vibrometers (LDVs). Therefore, 3D-DIC exhibits compelling capacity in dynamic testing, especially for nondestructive damage detection. Based on experimental modal analysis (EMA), this study presents a damage detection method without using a baseline model, which combines the advantages of 3D-DIC with two-dimensional continuous wavelet transform (2D-CWT) for damage detection and recognition. The carbon fiber reinforced plastic (CFRP) composite plates with prefabricated artificial damages, including spatial notches and local crack, were investigated. The mode shapes of damaged CFRP composite plates were obtained by singular value decomposition (SVD) from the matrix of frequency response functions (FRFs). Then the mode shapes were analyzed by using wavelet transform, and the damage index was derived from the wavelet coefficients. For comparison, two other damage indices, namely mode shape curvature and polynomial fitting difference, were also derived. It is found that 3D-DIC could provide different measurement DOFs in line with the damage form for post-processing; and the Mexican-hat wavelet analysis can accurately detect the location and size of damage by suppressing the effects of measurement noise.
Sun, G, Wei, Y, Huo, X, Luo, Q & Li, Q 2022, 'On quasi-static large deflection of single lap joints under transverse loading', Thin-Walled Structures, vol. 170, pp. 108572-108572.
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Sun, J, Peng, Z, Zhu, Z-R, Fu, W, Dai, X & Ni, B-J 2022, 'The atmospheric microplastics deposition contributes to microplastic pollution in urban waters', Water Research, vol. 225, pp. 119116-119116.
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Identifying and understanding the potential sources delivering microplastics into the urban water environment is imperative for microplastic pollution control. However, how atmospheric deposition contributes to microplastic pollution in the urban water environment is unclear. Therefore, this study investigated the contribution of atmospheric deposition to microplastic pollution in urban waters based on the analysis of the atmospheric deposition characteristics in the urban area. The results showed that microplastic deposition fluxes during wet weather and dry weather varied from 1.1 × 103±0.06×103 to 3.5 × 103±0.3 × 103 particles/m2/day and 0.91×103±0.09×103 to 1.6 × 103±0.1 × 103 particles/m2/day, respectively. The microplastics deposition flux showed moderate to strong correlations to atmospheric particulate matter concentrations, especially the PM2.5 concentration (R2 = 0.76-0.93), suggesting the regularly monitored PM2.5 concentration might be served as an indicator for microplastics deposition flux estimation. The deposited microplastics were mainly transparent fragments with an average size of 51-67 μm. Polyethylene and polypropylene were the most abundant plastic polymer, followed by polyethylene terephthalate and polyamide. The comparison of microplastics collected during different weather conditions suggested that rain events could increase microplastics deposition fluxes when air quality conditions are similar. Particularly, rains promoted the deposition of fibrous microplastics as well as smaller microplastics. The estimated daily microplastics deposition in the whole city region suggested more microplastics were deposited in summer and winter. The total quantity of microplastics deposited in the urban environment could reach 1.7-12 times of those discharged from treated wastewater. Among them, 10% would directly deposit to urban waters in the studied city region, while the others may also enter the urban waters through runoff. The results of...
Sun, J, Zhao, L, Wen, S & Wang, Y 2022, 'Memristive Circuit Design of Nonassociative Learning under Different Emotional Stimuli', Electronics, vol. 11, no. 23, pp. 3851-3851.
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Most memristor-based circuits only consider the mechanism of nonassociative learning, and the effect of emotion on nonassociative learning is ignored. In this paper, a memristive circuit that can realize nonassociative learning under different emotional stimuli is designed. The designed circuit consists of stimulus judgment module, habituation module, sensitization module, emotion module. When different stimuli are applied, habituation or sensitisation is formed based on the intensity and nature of the stimuli. In addition, the influence of emotion on nonassociative is considered. Different emotional stimuli will affect the speed of habituation formation and strong negative stimuli will lead to sensitization. The simulation results on PSPICE show that the circuit can simulate the above complex biological mechanism. The memristive circuit of nonassociative learning under different emotional stimuli provides some references for brain-like systems.
Sun, KQ, Zhang, N, Zhu, QX & Liu, X 2022, 'Exact and approximate solutions for free vibrations of continuous partial-interaction composite beams', Steel and Composite Structures, vol. 44, no. 4, pp. 531-543.
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An exact dynamic analytical method for free vibrations of continuous partial-interaction composite beams is proposed based on the Timoshenko beam theory. The main advantage of this method is that the independent shear deformations and rotary inertia of sub-beams are considered, which is more in line with the reality. Therefore, the accuracy of eigenfrequencies obtained by this method is significantly improved, especially for higher order modes, compared to the existing methods where the rotary angles of both sub-beams are assumed to be equal irrespective of the differences in the shear stiffness of each sub-beam. Furthermore, the solutions obtained by the proposed method are exact owing to no introduction of approximated displacement and force fields in the derivation. In addition, an exact analytical solution for the case of simply supported is obtained. Based on this, an approximate expression for the fundamental frequency of continuous partial-interaction composite beams is also proposed, which is useful for practical engineering applications. Finally, the practicability and effectiveness of the proposed method and the approximate expression are explored using numerical and experimental examples; The influence factors including the interfacial interaction, shear modulus ratio, span-to-depth ratio, and side-to-main span length ratio on the eigenfrequencies are presented and discussed in detail.
Sun, KQ, Zhang, N, Zhu, QX & Liu, X 2022, 'Exact and approximate solutions for free vibrations of continuous partial-interaction composite beams', STEEL AND COMPOSITE STRUCTURES, vol. 44, no. 4, pp. 517-529.
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An exact dynamic analytical method for free vibrations of continuous partial-interaction composite beams is proposed based on the Timoshenko beam theory. The main advantage of this method is that the independent shear deformations and rotary inertia of sub-beams are considered, which is more in line with the reality. Therefore, the accuracy of eigenfrequencies obtained by this method is significantly improved, especially for higher order modes, compared to the existing methods where the rotary angles of both sub-beams are assumed to be equal irrespective of the differences in the shear stiffness of each sub-beam. Furthermore, the solutions obtained by the proposed method are exact owing to no introduction of approximated displacement and force fields in the derivation. In addition, an exact analytical solution for the case of simply supported is obtained. Based on this, an approximate expression for the fundamental frequency of continuous partial-interaction composite beams is also proposed, which is useful for practical engineering applications. Finally, the practicability and effectiveness of the proposed method and the approximate expression are explored using numerical and experimental examples; The influence factors including the interfacial interaction, shear modulus ratio, span-to-depth ratio, and side-to-main span length ratio on the eigenfrequencies are presented and discussed in detail.
Sun, N, Dou, P, Zhai, W, He, H, Nghiem, LD, Vatanpour, V, Zhang, Y, Liu, C & He, T 2022, 'Polyethylene separator supported thin-film composite forward osmosis membranes for concentrating lithium enriched brine', Water Research, vol. 216, pp. 118297-118297.
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To extract lithium from salt lake brine involves a process of separation and concentration. After separating lithium from brine, the lithium ion concentration is generally a few hundred mg/L which is far below the required 20-30 g/L (as Li+) before precipitation as lithium carbonate. The concentration step of a lithium enriched brine is crucial but highly energy-intensive. Spontaneous forward osmosis (FO) technology offers the possibility for concentrating lithium ions with low energy. Because the concentrating process involves both feed and draw solution with very high salinity, it is highly desirable to have a high performance FO membrane with a low structural parameter as well as a high rejection to ions. In this work, thin polyethylene separator supported FO (PE-FO) membranes were prepared and post-treated stepwise with benzyl alcohol (BA) and hydraulic compaction. The effect of the post-treatment on the FO performance was systematically analyzed. Excellent FO performance was achieved: the water flux and reverse salt flux selectivity were 66.3 LMH and 5.25 L/g, respectively, when the active layer is oriented towards the 0.5 M NaCl draw solution with deionized water as the feed. To the best of our knowledge, this FO flux is the highest ever reported in the open literature under similar test conditions. Applied in concentrating lithium enriched brine, the membrane showed superior water flux using saturated MgCl2 as draw solution. A new FO model was established to simulate the water flux during the concentration process with good agreement with the experimental results. The promising results using PE-FO membrane for lithium enrichment opens a new frontier for the potential application of FO membranes.
Sun, R, Chen, C, Wang, X, Zhang, Y & Wang, X 2022, 'Stable Community Detection in Signed Social Networks', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 10, pp. 5051-5055.
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IEEE Community detection is one of the most fundamental problems in social network analysis, while most existing research focuses on unsigned graphs. In real applications, social networks involve not only positive relationships but also negative ones. It is important to exploit the signed information to identify more stable communities. In this paper, we propose a novel model, named stable k-core, to measure the stability of a community in signed graphs. The stable k-core model not only emphasizes user engagement, but also eliminates unstable structures. We show that the problem of finding the maximum stable k-core is NP-hard. To scale for large graphs, novel pruning strategies and searching methods are proposed. We conduct extensive experiments on 6 real-world signed networks to verify the efficiency and effectiveness of proposed model and techniques.
Sun, S, Hou, Y-N, Wei, W, Sharif, HMA, Huang, C, Ni, B-J, Li, H, Song, Y, Lu, C, Han, Y & Guo, J 2022, 'Perturbation of clopyralid on bio-denitrification and nitrite accumulation: Long-term performance and biological mechanism', Environmental Science and Ecotechnology, vol. 9, pp. 100144-100144.
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The contaminant of herbicide clopyralid (3,6-dichloro-2- pyridine-carboxylic acid, CLP) poses a potential threat to the ecological system. However, there is a general lack of research devoted to the perturbation of CLP to the bio-denitrification process, and its biological response mechanism remains unclear. Herein, long-term exposure to CLP was systematically investigated to explore its influences on denitrification performance and dynamic microbial responses. Results showed that low-concentration of CLP (<15 mg/L) caused severe nitrite accumulation initially, while higher concentrations (35-60 mg/L) of CLP had no further effect after long-term acclimation. The mechanistic study demonstrated that CLP reduced nitrite reductase (NIR) activity and inhibited metabolic activity (carbon metabolism and nitrogen metabolism) by causing oxidative stress and membrane damage, resulting in nitrite accumulation. However, after more than 80 days of acclimation, almost no nitrite accumulation was found at 60 mg/L CLP. It was proposed that the secretion of extracellular polymeric substances (EPS) increased from 75.03 mg/g VSS at 15 mg/L CLP to 109.97 mg/g VSS at 60 mg/L CLP, which strengthened the protection of microbial cells and improved NIR activity and metabolic activities. Additionally, the biodiversity and richness of the microbial community experienced a U-shaped process. The relative abundance of denitrification- and carbon metabolism-associated microorganisms decreased initially and then recovered with the enrichment of microorganisms related to the secretion of EPS and N-acyl-homoserine lactones (AHLs). These microorganisms protected microbe from toxic substances and regulated their interactions among inter- and intra-species. This study revealed the biological response mechanism of denitrification after successive exposure to CLP and provided proper guidance for analyzing and treating herbicide-containing wastewater.
Sun, X, Feng, L, Zhu, Z, Lei, G, Diao, K, Guo, Y & Zhu, J 2022, 'Optimal Design of Terminal Sliding Mode Controller for Direct Torque Control of SRMs', IEEE Transactions on Transportation Electrification, vol. 8, no. 1, pp. 1445-1453.
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A nonsingular terminal sliding mode controller (NTSMC) based on a direct torque control is presented for a switched reluctance motor (SRM) in this paper. To guarantee dynamic stability, the nonsingular terminal sliding mode based on an improved reaching law is employed to design the speed controller. The torque ripple of the system can be suppressed, and the disturbance caused by uncertainties like load disturbance and parameter perturbation can be suppressed by the proposed NTSMC. Moreover, the gray wolf optimization algorithm is applied to automatically adjust the parameters of the controllers and the value of given flux, thereby acquiring a satisfactory result. The NTSMC is validated by both simulation and experimental results with a six-phase 12/10 SRM. Compared with PI and conventional sliding mode control, NTSMC improves the convergence rate of state and exhibits better performance in torque ripple reduction and anti-disturbance ability. The robustness and dynamic performance of the system can be ensured.
Sun, X, Tang, X, Tian, X, Lei, G, Guo, Y & Zhu, J 2022, 'Sensorless Control With Fault-Tolerant Ability for Switched Reluctance Motors', IEEE Transactions on Energy Conversion, vol. 37, no. 2, pp. 1272-1281.
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Sun, X, Zhang, Y, Lei, G, Guo, Y & Zhu, J 2022, 'An Improved Deadbeat Predictive Stator Flux Control With Reduced-Order Disturbance Observer for In-Wheel PMSMs', IEEE/ASME Transactions on Mechatronics, vol. 27, no. 2, pp. 690-700.
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In this paper, an improved deadbeat predictive stator flux control (DPSFC) based on disturbance observer is proposed to improve the control performance of in-wheel permanent magnet synchronous motors (PMSMs) with parameter mismatch and disturbance. First, the sensitivity of conventional deadbeat predictive current control to the parameter variation, including flux linkage, stator resistance and stator inductance, is analyzed. Then, a reduced-order observer based on additional disturbance state variables is designed to predict the future stator flux and observe the system disturbance caused by parameter mismatch. The proposed DPSFC method is able to enhance the robustness of the drive performance effectively via the compensations of one-step delay and stator voltage. Finally, the performance of the proposed control method is validated by simulations and experiments on a prototype of an in-wheel PMSM drive.
Sun, Y, Yan, D, Wu, Y, Shih, F-Y, Zhang, C, Luo, H, Lin, S-H & Liu, Y 2022, 'Fabrication of Twin-Free Ultrathin NH2-MIL-125(Ti) Membrane with c-Preferred Orientation Using Transition-Metal Trichalcogenides as Titanium Source', ACS Materials Letters, vol. 4, no. 1, pp. 55-60.
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Sun, Z, Chen, Y, Zheng, J, Jiang, S, Dong, W, Li, X, Li, Y & E, S 2022, 'Temperature‐Dependent Electromagnetic Microwave Absorbing Characteristics of Stretchable Polyurethane Composite Foams with Ultrawide Bandwidth', Advanced Engineering Materials, vol. 24, no. 7, pp. 2101489-2101489.
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With the rapid development of current electronic equipment, electromagnetic microwave absorption (EMA) materials with higher design requirements under special circumstances have attracted great attention. Herein, a flexible polyurethane composite foam assisted by coral‐like CNT@Fe3O4/graphene nanocomposites is fabricated by a facile solvothermal and moisture self‐foaming method. The composite foam with 15 wt% nanofillers exhibits an outstanding temperature cycle stability with gradually improved EMA performance at elevated temperature and excellent resilience stability with a tensile strength of 4.81 MPa. An enhanced minimum reflection loss (RLmin) of −59.44 dB at 10.38 GHz with a thickness of 2.28 mm is achieved, while the ultrawide absorption bandwidth of 6.09 GHz nearly covers the full X‐band. This benefits from interface impedance matching, dielectric and magnetic dual losses, and multiple reflections depending on the interior open microcellular structures. It is expected to become a promising thermally tunable microwave absorber in harsh environments.
Surakasi, R, Sekhar, KC, Kavitha, E, Singh, M & Singh, B 2022, 'Evaluation of physico-thermal properties of TiO2–water mixture dispersed with MWCNTs', Nanotechnology for Environmental Engineering, vol. 7, no. 1, pp. 325-331.
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Carbon nanotube-based solar thermal fluids are being studied in this field for their thermo-physical characteristics. Using TiO2 and water as the basic fluids in 100:0, 75:25, and 50:50 ratios, nano-fluids were generated. The MWCNTs were dispersed in these three base fluids at a weight fraction of 0.25, and 0.5% and 0.75%, respectively. For the duration of two months, the zeta potential variation is studied to determine the stability of dispersion. The hot disk method and Anton Paar viscometer were used to measure thermal conductivity and dynamic viscosity. Adding MWCNTs to the basic fluids resulted in a 12–21% increase in thermal conductivity. Although viscosity was shown to increase in the 50–70 °C range, it was found to have a less impact at higher temperatures.
Swain, S, Altaee, A, Saxena, M & Samal, AK 2022, 'A comprehensive study on heterogeneous single atom catalysis: Current progress, and challenges☆', Coordination Chemistry Reviews, vol. 470, pp. 214710-214710.
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Syed-Ab-Rahman, SF, Hesamian, MH & Prasad, M 2022, 'Citrus disease detection and classification using end-to-end anchor-based deep learning model', Applied Intelligence, vol. 52, no. 1, pp. 927-938.
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Plant diseases are the primary issue that reduces agricultural yield and production, causing significant economic losses and instability in the food supply. In plants, citrus is a fruit crop of great economic importance, produced and typically grown in about 140 countries. However, citrus cultivation is widely affected by various factors, including pests and diseases, resulted in significant yield and quality losses. In recent years, computer vision and machine learning have been widely used in plant disease detection and classification, which present opportunities for early disease detection and bring improvements in the field of agriculture. Early and accurate detection of plant diseases is crucial to reducing the disease’s spread and damage to the crop. Therefore, this paper employs a two-stage deep CNN model for plant disease detection and citrus diseases classification using leaf images. The proposed model consists of two main stages; (a) proposing the potential target diseased areas using a region proposal network; (b) classification of the most likely target area to the corresponding disease class using a classifier. The proposed model delivers 94.37% accuracy in detection and an average precision of 95.8%. The findings demonstrate that the proposed model identifies and distinguishes between the three different citrus diseases, namely citrus black spot, citrus bacterial canker and Huanglongbing. The proposed model serves as a useful decision support tool for growers and farmers to recognize and classify citrus diseases.
Sylvester, DE, Chen, Y, Grima, N, Saletta, F, Padhye, B, Bennetts, B, Wright, D, Krivanek, M, Graf, N, Zhou, L, Catchpoole, D, Kirk, J, Latchoumanin, O, Qiao, L, Ballinger, M, Thomas, D, Jamieson, R, Dalla‐Pozza, L & Byrne, JA 2022, 'Rare germline variants in childhood cancer patients suspected of genetic predisposition to cancer', Genes, Chromosomes and Cancer, vol. 61, no. 2, pp. 81-93.
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AbstractIdentification of cancer‐predisposing germline variants in childhood cancer patients is important for therapeutic decisions, disease surveillance and risk assessment for patients, and potentially, also for family members. We investigated the spectrum and prevalence of pathogenic germline variants in selected childhood cancer patients with features suggestive of genetic predisposition to cancer. Germline DNA was subjected to exome sequencing to filter variants in 1048 genes of interest including 176 known cancer predisposition genes (CPGs). An enrichment burden analysis compared rare deleterious germline CPG variants in the patient cohort with those in a healthy aged control population. A subset of predicted deleterious variants in novel candidate CPGs was investigated further by examining matched tumor samples, and the functional impact of AXIN1 variants was analyzed in cultured cells. Twenty‐two pathogenic/likely pathogenic (P/LP) germline variants detected in 13 CPGs were identified in 19 of 76 patients (25.0%). Unclear association with the diagnosed cancer types was observed in 11 of 19 patients carrying P/LP CPG variants. The burden of rare deleterious germline variants in autosomal dominant CPGs was significantly higher in study patients versus healthy aged controls. A novel AXIN1 frameshift variant (Ser321fs) may impact the regulation of β‐catenin levels. Selection of childhood cancer patients for germline testing based on features suggestive of an underlying genetic predisposition could help to identify carriers of clinically relevant germline CPG variants, and streamline the integration of germline genomic testing in the pediatric oncology clinic.
Tabandeh, A & Hossain, MJ 2022, 'Hybrid Scenario-IGDT-Based Congestion Management Considering Uncertain Demand Response Firms and Wind Farms', IEEE Systems Journal, vol. 16, no. 2, pp. 3108-3119.
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Demand response resources (DRRs) have been recently introduced as one of the most economic tools of congestion alleviation in power systems. Nevertheless, the severe uncertainty of multiple DRRs constituting a demand response firm (DRF) is an indispensable issue to be considered for the resilient operation of future power systems. Consequently, the information gap decision theory (IGDT) technique is utilized for addressing the uncertainty of consumers’ participation in demand response programs. This article presents a novel hybrid scenario-IGDT-based framework, designated as SIGDT, for corrective transmission congestion management (CM) in the presence of large-scale uncertain wind farms and DRFs as well as the uncertainty of conventional generating units. A reliability network modeling of a repairable N-component wind turbine (WT) is presented, considering failure and repair rates of turbines’ components, and then the uncertainty of wind farms’ generation is handled using the scenario-based approach. The proposed framework is applied to the IEEE-reliability test system (RTS) system to demonstrate its accuracy and capability. The results discuss the impact of failure and repair rates of WTs components on the proposed SIGDT-based CM and emphasize the application of the proposed framework for decision-makers to ensure the optimal operation of power systems under uncertainties of DRFs, wind farms, and conventional units.
Tabandeh, A, Hossain, MJ & Li, L 2022, 'Integrated multi-stage and multi-zone distribution network expansion planning with renewable energy sources and hydrogen refuelling stations for fuel cell vehicles', Applied Energy, vol. 319, pp. 119242-119242.
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In line with the growing pressures on implementing zero-carbon emission policies and the implementation of hydrogen in the transport sector, energy markets are experiencing inevitable transformation and interactions. Since fuel cell electric vehicles have been attracting considerable attention, the production and supply of renewable hydrogen through hydrogen refuelling stations (HRSs) are of great importance. The growing energy demand, inappropriate siting and sizing of HRSs, and high penetration of distributed renewable energy sources (RESs) make power distribution network planning very challenging. This paper proposes an integrated multi-stage and multi-zone expansion planning framework to coordinate the investment and scheduling of HRSs, wind and solar energy sources, and the distribution network. To model the green HRSs, water electrolysers powered by renewable electricity and storage tanks to locally produce and store hydrogen are suggested. The objective of the proposed problem is to minimise the investment, operation, emissions, and maintenance costs of network's assets, RESs, and HRSs. The RESs expansion scheme comprises the installation of two different groups; the former pertains to distributed renewable sources installed over the network while the latter is integrated into HRSs. Case studies are conducted on 6-node and real Australian 100-node distribution networks. The results show the effectiveness of the proposed model in terms of optimal timing, sizing, location, and operational schedules of HRSs, RESs, and distribution network's assets.
Tabandeh, M, Cheng, CK, Centi, G, Show, PL, Chen, W-H, Ling, TC, Ong, HC, Ng, E-P, Juan, JC & Lam, SS 2022, 'Recent advancement in deoxygenation of fatty acids via homogeneous catalysis for biofuel production', Molecular Catalysis, vol. 523, pp. 111207-111207.
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© 2020 Elsevier B.V. Fuel-like hydrocarbons (also known as biofuel) isolated from the deoxygenation of fatty acids present different advantages as compared with fossil fuels. In particular, the homogeneous and heterogeneous catalytic deoxygenation methods have been the center of attention during recent years. Although catalytic deoxygenation of fatty acids via heterogeneous catalysis has been widely investigated, there is a high demand to review the progress in using the homogeneous catalysis pathways. Among the various homogeneous pathways, radical-based reactions and transition metal catalysis demonstrate the most promising results in the decarboxylation and decarbonylation processes. It is shown that radical-based reactions are more active in decarboxylation meanwhile the transition metal catalysts are rather selective to decarbonylation of fatty acids. Besides, the reaction conditions and type of catalysts are capable of enhancing biofuel production. Homogenous catalysis provides the huge potential for commercializing viability of biofuel via deoxygenation of fatty acids.
Taee, AA, Hosseini, S, Khushaba, RN, Zia, T, Lin, C-T & Al-Jumaily, A 2022, 'Deep Learning Inspired Feature Engineering for Classifying Tremor Severity', IEEE Access, vol. 10, pp. 105377-105386.
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Bio-signals pattern recognition systems can be impacted by several factors with a potential to limit their associated performance and clinical translation. Among these factors, selecting the optimum feature extraction method, that can effectively exploit the interaction between the temporal and spatial information, is the most prominent. Despite the potential of deep learning (DL) models for extracting temporal, spatial, or temporal-spatial information, they are typically restricted by their need for a large amount of training data. The deep wavelet scattering transform (WST) is a relatively recent advancement within the DL literature to replace expensive convolution neural networks models with computationally less demanding methods. However, while some studies have used WST to extract features from biological signals, it has not been investigated before for electromyogram (EMG) and electroencephalogram (EEG) signals feature extraction. To investigate the hypothesis of the usefulness of WST for processing EMG and EEG signals, this study used a tremor dataset collected by the authors from people with tremor disorders. Specifically, the proposed work achieved three goals: (a) study the performance of extracting features from low-density EMG signals (8 channels), using the WST approach, (b) study the effect of extracting the features from high-density EEG signals (33 channels), using WST and study its robustness against changing the spatial and temporal aspects of classification accuracy, and (c) classify tremor severity using the WST method and compare the results with other well-known feature extraction approaches. The classification error rates were significantly reduced (maximum of nearly 12%) compared with other feature sets.
Taghikhah, F, Borevitz, J, Costanza, R & Voinov, A 2022, 'DAESim: A dynamic agro-ecosystem simulation model for natural capital assessment', Ecological Modelling, vol. 468, pp. 109930-109930.
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Taghikhah, F, Voinov, A, Filatova, T & Polhill, JG 2022, 'Machine-assisted agent-based modeling: Opening the black box', Journal of Computational Science, vol. 64, pp. 101854-101854.
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Tahmassebi, A, Motamedi, M, Alavi, AH & Gandomi, AH 2022, 'An explainable prediction framework for engineering problems: case studies in reinforced concrete members modeling', Engineering Computations, vol. 39, no. 2, pp. 609-626.
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PurposeEngineering design and operational decisions depend largely on deep understanding of applications that requires assumptions for simplification of the problems in order to find proper solutions. Cutting-edge machine learning algorithms can be used as one of the emerging tools to simplify this process. In this paper, we propose a novel scalable and interpretable machine learning framework to automate this process and fill the current gap.Design/methodology/approachThe essential principles of the proposed pipeline are mainly (1) scalability, (2) interpretibility and (3) robust probabilistic performance across engineering problems. The lack of interpretibility of complex machine learning models prevents their use in various problems including engineering computation assessments. Many consumers of machine learning models would not trust the results if they cannot understand the method. Thus, the SHapley Additive exPlanations (SHAP) approach is employed to interpret the developed machine learning models.FindingsThe proposed framework can be applied to a variety of engineering problems including seismic damage assessment of structures. The performance of the proposed framework is investigated using two case studies of failure identification in reinforcement concrete (RC) columns and shear walls. In addition, the reproducibility, reliability and generalizability of the results were validated and the results of the framework were compared to the benchmark studies. The results of the proposed framework outperformed the benchmark results with high statistical significance.Originality/valueAlt...
Tan, M, Gao, Q, Fu, Y, Xu, Y, Hao, D, Ni, B-J & Wang, Q 2022, 'Fabrication of visible-light-active Fe-2MI film electrode for simultaneous removal of Cr(VI) and phenol', Materials Science in Semiconductor Processing, vol. 151, pp. 107013-107013.
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In this study, a new coordination polymer of Fe-2-methylimidazole (Fe-2MI) was successfully loaded onto F-doped tin oxide (FTO) via a one-pot solvothermal method, using Fe(NO3)3 and 2MI as raw materials. For X-ray diffraction patterns (XRD) and scanning electron microscope (SEM) analysis, it can be deduced that Fe-2MI assembles were evenly dispersed on the surface of FTO, and were partially encapsulated by tiny FeOOH particles. The as-prepared Fe-2MI film electrode was used as photoanode with titanium sheet (Ti) as the cathode. Simultaneous photoelectrocatalytic (PEC) removal of phenol and Cr(VI) can be effortlessly accomplished under noticeable light illumination. Meanwhile, the impact of initial Fe state was also investigated. Fe-2MI prepared with Fe(III) behaved better PEC performance than that with Fe(II). Furthermore, the Fe-2MI photoanodes were optimized by adjusting the initial concentration of Fe(III) and 2MI precursors. Besides, the application conditions were optimized at acidic pHs and 2.5 V bias voltage. Cr(VI) can be completely reduced with 80% removal of phenol after 5 h PEC reaction. After 5 successive cyclic runs, stable photocatalytic performance can still be observed. Therefore, Fe-2MI coordination polymer can be a promising candidate for preparing visible-light-active photoanode in the application of environmental remediation.
Tan, S, Zhong, X, Tian, Z & Dong, Q 2022, 'Sneaking Through Security: Mutating Live Network Traffic to Evade Learning-Based NIDS', IEEE Transactions on Network and Service Management, vol. 19, no. 3, pp. 2295-2308.
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Machine learning based network intrusion system (NIDS) is known to be vulnerable to evasions. Attackers conceal intrusion activities to make them undetected. Researching evasion techniques contributes to evaluating and increasing the robustness of NIDS. Previous evasion approaches modify feature values or packets of an offline network trace as a whole. However, in real scenarios, attackers are constrained to manipulate only outbound packets on the fly. To bridge this assumption gap, we present the first evasion solution for live network traffic against learning based NIDSs. The solution consists of three components: a devised Kalman filter based algorithm to predicate the feature values of live flows, a set of formally constructed atomic packet mutation operators, and a proposed Strength Enhanced Deep Q-learning (SE-DQN) to determine effective mutation operators on outbound packets according to the predicted features. A defense scheme based on adaptive decision threshold adjustment is also provided. Experimental evaluation is presented on various NIDS classifiers and cyber attacks. Results show that SE-DQN achieves an evasion rate of at least 64.2% on most classifiers and even more than 90% on certain ones, and it is three times faster than DQN on learning mutation policy. The defense scheme shows an improvement of at least 76.4% on recall measurement.
Tang, J, Pu, Y, Huang, J, Pan, S, Wang, XC, Hu, Y, Ngo, HH, Li, Y & Abomohra, A 2022, 'Caproic acid production through lactate-based chain elongation: Effect of lactate-to-acetate ratio and substrate loading', Environmental Technology & Innovation, vol. 28, pp. 102918-102918.
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Substrate properties play a key role in promoting the caproate yield through lactate-based carbon chain elongation pathway. In the present study, the effect of lactate-to-acetate (LA/AA) carbon ratio (from 0.5 to 5.0) and substrate loading (in terms of substrate/inoculum ratio within the range 20–180 mmol-C/g-VSS) on caproate fermentation was investigated. Results showed that both caproate content and yield increased by increasing the LA/AA ratio up to 3.0, then decreased at higher ratios due to activation of acrylate pathway and dispersion of carbon flux at elevated lactate content. At the optimal LA/AA carbon ratio of 3.0, substrate loading lower than 100 mmol-C/g-VSS was beneficial for efficient substrate utilization with low caproate selectivity, while higher substrate-to-inoculums (S/I) ratio led to incomplete substrate utilization and dispersed carbon flow, which finally reduced the caproate yield. Thus, the highest caproate yield of 0.42 g-COD/g-COD and selectivity of 49.5% were recorded at LA/AA and S/I ratio ratios of 3.0 and 100 mmol-C/g-VSS, respectively. The present study further depictures the novel approach for caproate production with lactate.
Tang, T & Li, J 2022, 'Comparative studies on the high-performance compression of SARS-CoV-2 genome collections', Briefings in Functional Genomics, vol. 21, no. 2, pp. 103-112.
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Abstract The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is fast mutating worldwide. The mutated strains have been timely sequenced by worldwide labs, accumulating a huge amount of viral genome sequences open to public for biomedicine research such as mRNA vaccine design and drug recommendation. It is inefficient to transmit the millions of genome sequences without compression. In this study, we benchmark the performance of reference-free and reference-based compression algorithms on SARS-CoV-2 genome collections extracted from NCBI. Experimental results show that reference-based two-level compression is the most suitable approach to the compression, achieving the best compression ratio 1019.33-fold for compressing 132 372 genomes and 949.73-fold for compressing 416 238 genomes. This enormous file size reduction and efficient decompression have enabled a 5-min download and decompression of $10^5$ SARS-CoV-2 genomes. As compression on datasets containing such big numbers of genomes has been explored seldom before, our comparative analysis of the state-of-the-art compression algorithms provides practical guidance for the selection of compression tools and their parameters such as reference genomes to compress viral genome databases with similar characteristics. We also suggested a genome clustering approach using multiple references for a better compression. It is anticipated that the increased availability of SARS-CoV-2 genome datasets will make biomedicine research more productive.
Tang, T, Hutvagner, G, Wang, W & Li, J 2022, 'Simultaneous compression of multiple error-corrected short-read sets for faster data transmission and betterde novoassemblies', Briefings in Functional Genomics, vol. 21, no. 5, pp. 387-398.
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AbstractNext-Generation Sequencing has produced incredible amounts of short-reads sequence data for de novo genome assembly over the last decades. For efficient transmission of these huge datasets, high-performance compression algorithms have been intensively studied. As both the de novo assembly and error correction methods utilize the overlaps between reads data, a concern is that the will the sequencing errors bring up negative effects on genome assemblies also affect the compression of the NGS data. This work addresses two problems: how current error correction algorithms can enable the compression algorithms to make the sequence data much more compact, and whether the sequence-modified reads by the error-correction algorithms will lead to quality improvement for de novo contig assembly. As multiple sets of short reads are often produced by a single biomedical project in practice, we propose a graph-based method to reorder the files in the collection of multiple sets and then compress them simultaneously for a further compression improvement after error correction. We use examples to illustrate that accurate error correction algorithms can significantly reduce the number of mismatched nucleotides in the reference-free compression, hence can greatly improve the compression performance. Extensive test on practical collections of multiple short-read sets does confirm that the compression performance on the error-corrected data (with unchanged size) significantly outperforms that on the original data, and that the file reordering idea contributes furthermore. The error correction on the original reads has also resulted in quality improvements of the genome assemblies, sometimes remarkably. However, it is still an open question that how to combine appropriate error correction methods with an assembly algorithm so that the assembly performance can be always significantly improved.
Tang, Y, Wang, H, Zhan, X, Luo, X, Zhou, Y, Zhou, H, Yan, Q, Sui, Y & Keung, J 2022, 'A Systematical Study on Application Performance Management Libraries for Apps', IEEE Transactions on Software Engineering, vol. 48, no. 8, pp. 3044-3065.
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Being able to automatically detect the performance issues in apps will significantly improve their quality as well as having a positive influence on user satisfaction. Although app developers have been exploiting application performance management (APM)tools to capture these potential performance issues, most of them do not fully understand the internals of these APM tools and the effect on their apps, such as security risks, etc. To fill this gap, in this paper, we conduct the first systematic study on APMs for apps by scrutinizing 25 widely-used APMs for Android apps and develop a framework named APMHunter for exploring the usage of APMs inAndroid apps. Using APMHunter, we conduct a large-scale empirical study on 500,000 Android apps to explore the usage patterns ofAPMs and discover the potential misuses of APMs. We obtain two major findings: 1) some APMs still employ deprecated permissions and approaches, which leads to APM malfunction as expected; 2) inappropriate APMs utilization will cause privacy leakages. Thus, our study suggests that both APM vendors and developers should design and use APMs scrupulously
Tang, Z, Li, W, Peng, Q, Tam, VWY & Wang, K 2022, 'Study on the failure mechanism of geopolymeric recycled concrete using digital image correlation method', Journal of Sustainable Cement-Based Materials, vol. 11, no. 2, pp. 113-126.
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In this study, an experimental investigation was conducted to understand the failure mechanism of geopolymeric recycled aggregate concrete (GRAC) under compression. GRAC specimens with different recycled aggregate (RA) replacement ratios were prepared and tested. A digital image correlation (DIC) system was used to monitor the displacement field and strain distribution over the surface of the specimen. The results revealed that RA replacement adversely affected the mechanical properties of geopolymeric concrete, including compressive strength, elastic modulus, and splitting tensile strength. For all the specimens, cracks mainly initiated near the interfacial transition zones, and usually nucleated around natural aggregate (NA) rather than RA. As observed from the final crack patterns, it was more frequent for the RA that cracks passed through the aggregate particles, in comparison with the NA. The location of strain concentration region detected by the DIC method was closely consistent with that of the formed macro cracks.
Tanko, D, Barua, PD, Dogan, S, Tuncer, T, Palmer, E, Ciaccio, EJ & Acharya, UR 2022, 'EPSPatNet86: eight-pointed star pattern learning network for detection ADHD disorder using EEG signals', Physiological Measurement, vol. 43, no. 3, pp. 035002-035002.
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Abstract Objective. The main objective of this work is to present a hand-modelled one-dimensional signal classification system to detect Attention-Deficit Hyperactivity Disorder (ADHD) disorder using electroencephalography (EEG) signals. Approach. A novel handcrafted feature extraction method is presented in this research. Our proposed method uses a directed graph and an eight-pointed star pattern (EPSPat). Also, tunable q wavelet transforms (TQWT), wavelet packet decomposition (WPD), statistical extractor, iterative Chi2 (IChi2) selector, and the k-nearest neighbors (kNN) classifier have been utilized to develop the EPSPat based learning model. This network uses two wavelet decomposition methods (TQWT and WPD), and 85 wavelet coefficient bands are extracted. The proposed EPSPat and statistical feature creator generate features from the 85 wavelet coefficient bands and the original EEG signal. The learning network is termed EPSPatNet86. The main purpose of the presented EPSPatNet86 is to detect abnormalities of the EEG signals. Therefore, 85 wavelet subbands have been generated to extract features. The created 86 feature vectors have been evaluated using the Chi2 selector and the kNN classifier in the loss value calculation phase. The final features vector is created by employing a minimum loss-valued eight feature vectors. The IChi2 selector selects the best feature vector, which is fed to the kNN classifier. An EEG signal dataset has been used to demonstrate the presented model’s EEG signal classification ability. We have used an ADHD EEG dataset since ADHD is a commonly seen brain-related ailment. Main results. Our developed EPSPatNet86 model can detect the ADHD EEG signals with 97.19% and 87.60% accuracy using 10-fold cross and subject-wise validations, respectively. Significance.
Tanveer, M, Lin, C-T & Kumar Singh, A 2022, 'Guest Editorial Advanced Machine Learning Algorithms for Biomedical Data and Imaging', IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 10, pp. 4809-4813.
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Tao, G, Ouyang, Q, Lei, D, Chen, Q, Nimbalkar, S, Bai, L & Zhu, Z 2022, 'NMR-Based Measurement of AWRC and Prediction of Shear Strength of Unsaturated Soils', International Journal of Geomechanics, vol. 22, no. 9.
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Tao, M, Lu, D, Shi, Y & Wu, C 2022, 'Utilization and life cycle assessment of low activity solid waste as cementitious materials: A case study of titanium slag and granulated blast furnace slag', Science of The Total Environment, vol. 849, pp. 157797-157797.
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Tao, X, Gong, X, Zhang, X, Yan, S & Adak, C 2022, 'Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey', IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-21.
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Tao, X, Zhang, D, Ma, W, Hou, Z, Lu, Z & Adak, C 2022, 'Unsupervised Anomaly Detection for Surface Defects With Dual-Siamese Network', IEEE Transactions on Industrial Informatics, vol. 18, no. 11, pp. 7707-7717.
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Unsupervised anomaly detection in real industrial scenarios is challenging since the small amount of defect-free images contain limited discriminative information, and anomaly defects are unpredictable. Although nowadays image reconstruction-based methods are widely being used in various anomaly detection applications, they cannot effectively learn semantic representation, which leads to imperfect reconstruction. In this article, anomaly detection is formulated as a joint problem of feature reconstruction and inpainting in the dual-siamese framework. The proposed approach forces the network to model the feature distribution from the normal area and capture the semantic context for discriminating normal and abnormal areas. It first uses a Siamese architecture to capture discriminative features of defect-free samples and its corresponding defective samples generated by the defect random generation module. A dense feature fusion module is then employed to obtain the dense feature representation of dual input. The second Siamese network is proposed to reconstruct and inpaint the dual-dense features of the previous stage. Compared to the existing methods that mostly employ single image reconstruction, it is beneficial to simultaneously reconstruct and inpaint the information of dense discriminative features. The experimental results on the MVTec AD datasets and some major real industrial datasets demonstrate that our method achieves state-of-the-art inspection accuracy.
Taşcı, B, Acharya, MR, Datta Barua, P, Metehan Yildiz, A, Veysel Gun, M, Keles, T, Dogan, S & Tuncer, T 2022, 'A new lateral geniculate nucleus pattern-based environmental sound classification using a new large sound dataset', Applied Acoustics, vol. 196, pp. 108897-108897.
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Tashima, T, Takashima, H, Schell, AW, Tran, TT, Aharonovich, I & Takeuchi, S 2022, 'Hybrid device of hexagonal boron nitride nanoflakes with defect centres and a nano-fibre Bragg cavity', Scientific Reports, vol. 12, no. 1, pp. 1-7.
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AbstractSolid-state quantum emitters coupled with a single mode fibre are of interest for photonic and quantum applications. In this context, nanofibre Bragg cavities (NFBCs), which are microcavities fabricated in an optical nanofibre, are promising devices because they can efficiently couple photons emitted from the quantum emitters to the single mode fibre. Recently, we have realized a hybrid device of an NFBC and a single colloidal CdSe/ZnS quantum dot. However, colloidal quantum dots exhibit inherent photo-bleaching. Thus, it is desired to couple an NFBC with hexagonal boron nitride (hBN) as stable quantum emitters. In this work, we realize a hybrid system of an NFBC and ensemble defect centres in hBN nanoflakes. In this experiment, we fabricate NFBCs with a quality factor of 807 and a resonant wavelength at around 573 nm, which matches well with the fluorescent wavelength of the hBN, using helium-focused ion beam (FIB) system. We also develop a manipulation system to place hBN nanoflakes on a cavity region of the NFBCs and realize a hybrid device with an NFBC. By exciting the nanoflakes via an objective lens and collecting the fluorescence through the NFBC, we observe a sharp emission peak at the resonant wavelength of the NFBC.
Tavakoli, J & Tipper, JL 2022, 'Detailed mechanical characterization of the transition zone: New insight into the integration between the annulus and nucleus of the intervertebral disc', Acta Biomaterialia, vol. 143, pp. 87-99.
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The Nucleus Pulposus (NP) and Annulus Fibrous (AF) are two primary regions of the intervertebral disc (IVD). The interface between the AF and NP, where the gradual transition in structure and type of fibers are observed, is known as the Transition Zone (TZ). Recent structural studies have shown that the TZ contains organized fibers that appear to connect the NP to the AF. However, the mechanical characteristics of the TZ are yet to be explored. The current study aimed to investigate the mechanical properties of the TZ at the anterolateral (AL) and posterolateral (PL) regions in both radial and circumferential directions of loading using ovine IVDs (N = 28). Young's and toe moduli, maximum stress, failure strain, strain at maximum stress, and toughness were calculated mechanical parameters. The findings from this study revealed that the mechanical properties of the TZ, including young's modulus (p = 0.001), failure strain (p < 0.001), strain at maximum stress (p = 0.002), toughness (p = 0.027), and toe modulus (p = 0.005), were significantly lower for the PL compared to the AL region. Maximum stress was not significantly different between the PL and AL regions (p = 0.164). We found that maximum stress (p = 0.002), failure strain (p < 0.001), and toughness (p = 0.001) were significantly different in different loading directions. No significant differences for modulus (young's; p = 0.169 and toe; p = 0.352) and strain at maximum stress (p = 0.727) were found between the radial and circumferential loading directions. STATEMENT OF SIGNIFICANCE: To date there has not been a study that has investigated the mechanical characterization of the annulus (AF)-nucleus (NP) interface (transition zone; TZ) in the intervertebral disc (IVD), nor is it known whether the posterolateral (PL) and anterolateral (AL) regions of the TZ exhibit different mechanical properties. Accordingly, the TZ mechanical properties have been rarely used in the development of computational IVD...
Tekle, BH, Al‐Deen, S, Anwar‐Us‐Saadat, M, Willans, N, Zhang, Y & Lee, CK 2022, 'Use of maturity method to estimate early age compressive strength of slab in cold weather', Structural Concrete, vol. 23, no. 2, pp. 1176-1190.
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AbstractAccurate estimation of the in situ strength of concrete at early age is very important as it provides the necessary information required to start subsequent construction operations. Overestimation of the strength may cause serious safety hazards and underestimation may lead to unnecessary costly delays. This study investigates the performance of the maturity method in estimating the strength of in situ concrete subjected to cold weather at early age. Instrumented concrete slabs were subjected to cold weather conditions at early ages and their strengths were measured using drilled core samples from the slab. Sensors embedded in the slabs measured the temperature in the concrete which was used to estimate the strength using the maturity method. The measured core strengths at 24 and 72 h after casting are then compared with the estimated strengths using the maturity method and its performance is evaluated. The core strengths are also compared with the strength of standard cylinders cured at the same condition as the slabs. More than 250 cylinders from two slab thicknesses and four batches of concrete were used in the experiments to obtain statistically significant experimental data. The results show that the maturity method performed much better than the standard cylinder strength. On average the standard cylinder strength underestimated the core strength by more than 40% while the maturity method overestimated the strength by less than 10% with a lower variation.
Telikani, A, Gandomi, AH, Choo, K-KR & Shen, J 2022, 'A Cost-Sensitive Deep Learning-Based Approach for Network Traffic Classification', IEEE Transactions on Network and Service Management, vol. 19, no. 1, pp. 661-670.
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Telikani, A, Tahmassebi, A, Banzhaf, W & Gandomi, AH 2022, 'Evolutionary Machine Learning: A Survey', ACM Computing Surveys, vol. 54, no. 8, pp. 1-35.
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Evolutionary Computation (EC) approaches are inspired by nature and solve optimization problems in a stochastic manner. They can offer a reliable and effective approach to address complex problems in real-world applications. EC algorithms have recently been used to improve the performance of Machine Learning (ML) models and the quality of their results. Evolutionary approaches can be used in all three parts of ML: preprocessing (e.g., feature selection and resampling), learning (e.g., parameter setting, membership functions, and neural network topology), and postprocessing (e.g., rule optimization, decision tree/support vectors pruning, and ensemble learning). This article investigates the role of EC algorithms in solving different ML challenges. We do not provide a comprehensive review of evolutionary ML approaches here; instead, we discuss how EC algorithms can contribute to ML by addressing conventional challenges of the artificial intelligence and ML communities. We look at the contributions of EC to ML in nine sub-fields: feature selection, resampling, classifiers, neural networks, reinforcement learning, clustering, association rule mining, and ensemble methods. For each category, we discuss evolutionary machine learning in terms of three aspects: problem formulation, search mechanisms, and fitness value computation. We also consider open issues and challenges that should be addressed in future work.
Teymouri, D, Sedehi, O, Katafygiotis, LS & Papadimitriou, C 2022, 'A Bayesian Expectation-Maximization (BEM) methodology for joint input-state estimation and virtual sensing of structures', Mechanical Systems and Signal Processing, vol. 169, pp. 108602-108602.
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Thambiliyagodage, C, Usgodaarachchi, L, Jayanetti, M, Liyanaarachchi, C, Kandanapitiye, M & Vigneswaran, S 2022, 'Efficient Visible-Light Photocatalysis and Antibacterial Activity of TiO2-Fe3C-Fe-Fe3O4/Graphitic Carbon Composites Fabricated by Catalytic Graphitization of Sucrose Using Natural Ilmenite', ACS Omega, vol. 7, no. 29, pp. 25403-25421.
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Thiyagarajan, K, Kodagoda, S, Luu, M, Duggan-Harper, T, Ritchie, D, Prentice, K & Martin, J 2022, 'Intelligent Guide Robots for People who are Blind or have Low Vision: A Review', Vision Rehabilitation International, vol. 13, no. 1, pp. 1-15.
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Abstract According to the World Health Organization, at least 2.2 billion people worldwide are blind or have some degree of near or distance vision impairment. Guide Dogs and other existing mobility aids are valuable tools to support travel and independence for these individuals. Nevertheless, the process of training dogs to be effective mobility aids is expensive, time-consuming, and requires highly specialised expertise. With technology making significant inroads into modern society, intelligent robots that are powered by smart sensing and advanced artificial intelligence present an opportunity to improve the mobility of those with blindness or low vision where access to a Guide Dog is not available. However, developing robust and effective robotic mobility aids with static and dynamic settings for use in both indoor and outdoor environments has remained a difficult task to accomplish. This article compares the capabilities of both existing robotic mobility aids, and existing cutting-edge robotic technologies, against a set of fundamental functional mobility aid characteristics with the aim of proposing a minimum viable product to support people who are blind or have low vision in everyday mobility and navigation tasks. The functional qualities identified and examined were sensing and interpretation, obstacle avoidance and object targeting, fluid and adaptable movement and navigation, interlink of device interface and sensing-interpretation unit, device interface to user, form factor and design. The outcomes of our study indicate that while existing robotic aids are limited in scope and focused on a narrow range of functions, existing cutting-edge robotic technologies have the potential to combine to create a fully-functioning robot guide. We propose a minimum viable product using these technologies as the next step towards a fully-functioning robot guide supporting the mobility and navigation of people ...
Thomas, SR, Yang, W, Morgan, DJ, Davies, TE, Li, JJ, Fischer, RA, Huang, J, Dimitratos, N & Casini, A 2022, 'Bottom‐up Synthesis of Water‐Soluble Gold Nanoparticles Stabilized by N‐Heterocyclic Carbenes: From Structural Characterization to Applications', Chemistry – A European Journal, vol. 28, no. 56, p. e202201575.
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AbstractN‐heterocyclic carbenes (NHCs) have become attractive ligands for functionalizing gold nanoparticle surfaces with applications ranging from catalysis to biomedicine. Despite their great potential, NHC stabilized gold colloids (NHC@AuNPs) are still scarcely explored and further efforts should be conducted to improve their design and functionalization. Here, the ‘bottom‐up’ synthesis of two water‐soluble gold nanoparticles (AuNP‐1 and AuNP‐2) stabilized by hydrophilic mono‐ and bidentate NHC ligands is reported together with their characterization by various spectroscopic and analytical methods. The NPs showed key differences likely to be due to the selected NHC ligand systems. Transmission electron microscopy (TEM) images showed small quasi‐spherical and faceted NHC@AuNPs of similar particle size (ca. 2.3–2.6 nm) and narrow particle size distribution, but the colloids featured different ratios of Au(I)/Au(0) by X‐ray photoelectron spectroscopy (XPS). Furthermore, the NHC@AuNPs were supported on titania and fully characterized. The new NPs were studied for their catalytic activity towards the reduction of nitrophenol substrates, the reduction of resazurin and for their photothermal efficiency. Initial results on their application in photothermal therapy (PTT) were obtained in human cancer cells in vitro. The aforementioned reactions represent important model reactions towards wastewater remediation, bioorthogonal transformations and cancer treatment.
Tian, H, Zhu, T & Zhou, W 2022, 'Fairness and privacy preservation for facial images: GAN-based methods', Computers & Security, vol. 122, pp. 102902-102902.
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Tian, H, Zhu, T, Liu, W & Zhou, W 2022, 'Image fairness in deep learning: problems, models, and challenges', Neural Computing and Applications, vol. 34, no. 15, pp. 12875-12893.
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AbstractIn recent years, it has been revealed that machine learning models can produce discriminatory predictions. Hence, fairness protection has come to play a pivotal role in machine learning. In the past, most studies on fairness protection have used traditional machine learning methods to enforce fairness. However, these studies focus on low dimensional inputs, such as numerical inputs, whereas more recent deep learning technologies have encouraged fairness protection with image inputs through deep model methods. These approaches involve various object functions and structural designs that break the spurious correlations between targets and sensitive features. With these connections broken, we are left with fairer predictions. To better understand the proposed methods and encourage further development in the field, this paper summarizes fairness protection methods in terms of three aspects: the problem settings, the models, and the challenges. Through this survey, we hope to reveal research trends in the field, discover the fundamentals of enforcing fairness, and summarize the main challenges to producing fairer models.
Tian, J, Anderson, GE, Hancock, PJ, Miller-Jones, JCA, Sokolowski, M, Rowlinson, A, Williams, A, Morgan, J, Hurley-Walker, N, Kaplan, DL, Murphy, T, Tingay, SJ, Johnston-Hollitt, M, Bannister, KW, Bell, ME & Meyers, BW 2022, 'Early-time searches for coherent radio emission from short GRBs with the Murchison Widefield Array', Publications of the Astronomical Society of Australia, vol. 39.
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AbstractMany short gamma-ray bursts (GRBs) originate from binary neutron star mergers, and there are several theories that predict the production of coherent, prompt radio signals either prior, during, or shortly following the merger, as well as persistent pulsar-like emission from the spin-down of a magnetar remnant. Here we present a low frequency (170–200 MHz) search for coherent radio emission associated with nine short GRBs detected by theSwiftand/orFermisatellites using the Murchison Widefield Array (MWA) rapid-response observing mode. The MWA began observing these events within 30–60 s of their high-energy detection, enabling us to capture any dispersion delayed signals emitted by short GRBs for a typical range of redshifts. We conducted transient searches at the GRB positions on timescales of 5 s, 30 s, and 2 min, resulting in the most constraining flux density limits on any associated transient of 0.42, 0.29, and 0.084 Jy, respectively. We also searched for dispersed signals at a temporal and spectral resolution of 0.5 s and 1.28 MHz, but none were detected. However, the fluence limit of 80–100 Jy ms derived for GRB 190627A is the most stringent to date for a short GRB. Assuming the formation of a stable magnetar for this GRB, we compared the fluence and persistent emission limits to short GRB coherent emission models, placing constraints on key parameters including the radio emission efficiency of the nearly merged neutron stars ($\epsilon_r\lesssim10^{-4}$), the fraction of magnetic energy in the GRB jet (Monthly Notices of the Royal Astronomical Society, vol. 514, no. 2, pp. 2756-2768.
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ABSTRACT We present a low-frequency (170–200 MHz) search for prompt radio emission associated with the long GRB 210419A using the rapid-response mode of the Murchison Widefield Array (MWA), triggering observations with the Voltage Capture System for the first time. The MWA began observing GRB 210419A within 89 s of its detection by Swift, enabling us to capture any dispersion delayed signal emitted by this gamma-ray burst (GRB) for a typical range of redshifts. We conducted a standard single pulse search with a temporal and spectral resolution of $100\, \mu$s and 10 kHz over a broad range of dispersion measures from 1 to $5000\, \text{pc}\, \text{cm}^{-3}$, but none were detected. However, fluence upper limits of 77–224 Jy ms derived over a pulse width of 0.5–10 ms and a redshift of 0.6 < z < 4 are some of the most stringent at low radio frequencies. We compared these fluence limits to the GRB jet–interstellar medium interaction model, placing constraints on the fraction of magnetic energy (ϵB ≲ [0.05–0.1]). We also searched for signals during the X-ray flaring activity of GRB 210419A on minute time-scales in the image domain and found no emission, resulting in an intensity upper limit of $0.57\, \text{Jy}\, \text{beam}^{-1}$, corresponding to a constraint of ϵB ≲ 10−3. Our non-detection could imply that GRB 210419A was at a high redshift, there was not enough magnetic energy for low-frequency emission, or the radio waves did not escape from the GRB environment.
Tian, Y, Li, Q, Wu, D, Chen, X & Gao, W 2022, 'Nonlinear dynamic stability analysis of clamped and simply supported organic solar cells via the third-order shear deformation plate theory', Engineering Structures, vol. 252, pp. 113616-113616.
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This study presents a semi-analytical framework to explore the nonlinear dynamic buckling characteristics of the imperfect organic solar cell (OSC) with clamped and simply supported restrains grounded on the third-order shear deformation plate theory (TSDT). The exerting axial displacement loading is divided into infinite and finite durations. Moreover, the spatially dependent Winker-Pasternak elastic foundation and damping effect are incorporated in the current analysis. Combining the von Kármán nonlinearity, the governing equations are derived with the aid of the Airy stress function. By applying the Galerkin method and fourth-order Runge–Kutta procedure, the dynamic buckling critical condition of the OSC subjected to these two kinds of loadings is determined by Budiansky–Hutchinson (B-H) criterion. Based on the numerical results, the influence of some critical parameters, including the geometrical dimension, boundary condition, loading configuration, initial imperfection, damping ratio, and elastic foundation coefficient are investigated. Not limited to the solar cell, this method is also suitable to the dynamic buckling behaviours of other laminated plates.
Tian, Z, Li, S & Li, Y 2022, 'Enhanced sensing performance of cement-based composites achieved via magnetically aligned nickel particle network', Composites Communications, vol. 29, pp. 101006-101006.
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Tian, Z, Li, Y, Li, S, Vute, S & Ji, J 2022, 'Influence of particle morphology and concentration on the piezoresistivity of cement-based sensors with magneto-aligned nickel fillers', Measurement, vol. 187, pp. 110194-110194.
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Cement-based sensors with magneto-aligned nickel fillers have the proven capability to significantly enhance piezoresistivity compared with the sensors with randomized fillers. In this paper, the influence of particle morphology and concentration of nickel particles on the piezoresistive and mechanical properties of cement-based sensors, treated with and without magnetic field intervention, are investigated experimentally. Five categories of nickel particles with different average diameters are type N50 (50 nm), N500 (0.5 μm), F(1 μm × 20 μm flake), T (5 μm) and U (25 μm). The obtained results indicate that the application of magnetic field enhances most of the piezoresistive performance and yields best piezoresistivity for the samples with type T nickel powder. Anisotropic piezoresistivity can be achieved under a very low filler content (0.1 vol%) in N50 nano-scale nickel powder and cement composite, followed by the N500 and T nickel particles in 5 vol% content. Small particles with lower content have similar piezoresistive performance to the samples with large particles and higher concentration. One half of the samples can achieve high giant gauge factor (GF) of over 500, two-thirds of which are aligned by magnetic field with anisotropic piezoresistive property. Samples with 5 vol% type T nickel content has the highest GF value, followed by the sample with 5 vol% type F nickel flakes and 10 vol% type U nickel powder. It is also found that mechanical strength decreases with the increase of particle concentration.
Tianqing, Z, Zhou, W, Ye, D, Cheng, Z & Li, J 2022, 'Resource Allocation in IoT Edge Computing via Concurrent Federated Reinforcement Learning', IEEE Internet of Things Journal, vol. 9, no. 2, pp. 1414-1426.
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Tiwary, K, Patro, SK, Gandomi, AH & Sahoo, KS 2022, 'Model updating using causal information: a case study in coupled slab', Structural and Multidisciplinary Optimization, vol. 65, no. 2.
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AbstractProblems like improper sampling (sampling on unnecessary variables) and undefined prior distribution (or taking random priors) often occur in model updating. Any such limitations on model parameters can lead to lower accuracy and higher experimental costs (due to more iterations) of structural optimisation. In this work, we explored the effective dimensionality of the model updating problem by leveraging the causal information. In order to utilise the causal structure between the parameters, we used Causal Bayesian Optimisation (CBO), a recent variant of Bayesian Optimisation, to integrate observational and intervention data. We also employed generative models to generate synthetic observational data, which helps in creating a better prior for surrogate models. This case study of a coupled slab structure in a recreational building resulted in the modal updated frequencies which were extracted from the finite element of the structure and compared to measured frequencies from ambient vibration tests found in the literature. The results of mode shapes between experimental and predicted values were also compared using modal assurance criterion (MAC) percentages. The updated frequency and MAC number that was obtained using the proposed model was found in least number of iterations (impacts experimental budget) as compared to previous approaches which optimise the same parameters using same data. This also shows how the causal information has impact on experimental budget.
Tomasz, G, Piotr, P, Robert, S, Michał, M, Wojciech, S & Piotr, SW 2022, 'Application verification of blast mitigation through the use of thuja hedges', International Journal of Protective Structures, vol. 13, no. 2, pp. 363-378.
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Nowadays, large gatherings of people, such as open-air concerts, outdoor-sport events, trade fairs, etc., are often attracted by the terrorists. Recently, an interesting passive alternative way of securing such events against terrorist threats appeared in the scientific literature, in which the tree hedges mitigation potential against blast waves were studied. Despite comprehensive studies regarding selected species of hedge trees, the real application outlines were reported to be still missing for those barriers. Our study verified the mitigation potential of thuja in field tests for (i) several distances behind the hedge and for (ii) several positions along the hedge wall. The explosives of 5 kg trinitrotoluene with a rectangular shape were used in four detonations. Six pressure pencil gauges were registering the overpressure histories. A high-speed camera was recording the in-plane deformation of the hedge wall, the motion of selected points on the height of the wall was plotted. For each position, the reduction of overpressure peak and overpressure impulse were obtained in reference to their counterparts for the position without a hedge. The maximal overpressure peak reductions obtained were 14% for case (i) (differing distances from the explosive) and 22% for case (ii) (differing positions along the hedge wall). The experiments' outcomes showed the safest position behind the thuja wall and the actual benefit from using them in the public application if the terrorist acts would happen.
Tong, C-X, Dong, Z-L, Sun, Q, Zhang, S, Zheng, J-X & Sheng, D 2022, 'On compression behavior and particle breakage of carbonate silty sands', Engineering Geology, vol. 297, pp. 106492-106492.
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Tong, C-X, Zhai, M-Y, Li, H-C, Zhang, S & Sheng, D 2022, 'Particle breakage of granular soils: changing critical state line and constitutive modelling', Acta Geotechnica, vol. 17, no. 3, pp. 755-768.
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Tong, HL, Quiroz, JC, Kocaballi, AB, Ijaz, K, Coiera, E, Chow, CK & Laranjo, L 2022, 'A personalized mobile app for physical activity: An experimental mixed-methods study', DIGITAL HEALTH, vol. 8, pp. 205520762211150-205520762211150.
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Objectives To investigate the feasibility of the be.well app and its personalization approach which regularly considers users’ preferences, amongst university students. Methods We conducted a mixed-methods, pre-post experiment, where participants used the app for 2 months. Eligibility criteria included: age 18–34 years; owning an iPhone with Internet access; and fluency in English. Usability was assessed by a validated questionnaire; engagement metrics were reported. Changes in physical activity were assessed by comparing the difference in daily step count between baseline and 2 months. Interviews were conducted to assess acceptability; thematic analysis was conducted. Results Twenty-three participants were enrolled in the study (mean age = 21.9 years, 71.4% women). The mean usability score was 5.6 ± 0.8 out of 7. The median daily engagement time was 2 minutes. Eighteen out of 23 participants used the app in the last month of the study. Qualitative data revealed that people liked the personalized activity suggestion feature as it was actionable and promoted user autonomy. Some users also expressed privacy concerns if they had to provide a lot of personal data to receive highly personalized features. Daily step count increased after 2 months of the intervention (median difference = 1953 steps/day, p-value <.001, 95% CI 782 to 3112). Conclusions Incorporating users’ preferences in personalized advice provided by a physical activity app was considered feasible and acceptable, with preliminary support for its positive effects on daily step count. Future randomized studies with longer follow up are warranted to determine the effectiveness of personalized mobile apps in promoting physical activity.
Torkaman, H, Zeraatkar, E, Deyhimi, N, Alhelou, HH & Siano, P 2022, 'Rearrangement Method of Reducing Fault Location Error in Tied Uncompleted Parallel Lines', IEEE Access, vol. 10, pp. 51862-51872.
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Tran, D-T, Pham, T-D, Dang, V-C, Pham, T-D, Nguyen, M-V, Dang, N-M, Ha, M-N, Nguyen, V-N & Nghiem, LD 2022, 'A facile technique to prepare MgO-biochar nanocomposites for cationic and anionic nutrient removal', Journal of Water Process Engineering, vol. 47, pp. 102702-102702.
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Tran, K, Standen, M, Kim, J, Bowman, D, Richer, T, Akella, A & Lin, C-T 2022, 'Cascaded Reinforcement Learning Agents for Large Action Spaces in Autonomous Penetration Testing', Applied Sciences, vol. 12, no. 21, pp. 11265-11265.
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Organised attacks on a computer system to test existing defences, i.e., penetration testing, have been used extensively to evaluate network security. However, penetration testing is a time-consuming process. Additionally, establishing a strategy that resembles a real cyber-attack typically requires in-depth knowledge of the cybersecurity domain. This paper presents a novel architecture, named deep cascaded reinforcement learning agents, or CRLA, that addresses large discrete action spaces in an autonomous penetration testing simulator, where the number of actions exponentially increases with the complexity of the designed cybersecurity network. Employing an algebraic action decomposition strategy, CRLA is shown to find the optimal attack policy in scenarios with large action spaces faster and more stably than a conventional deep Q-learning agent, which is commonly used as a method for applying artificial intelligence to autonomous penetration testing.
Tran, T, Bliuc, D, Ho-Le, T, Abrahamsen, B, van den Bergh, JP, Chen, W, Eisman, JA, Geusens, P, Hansen, L, Vestergaard, P, Nguyen, TV, Blank, RD & Center, JR 2022, 'Association of Multimorbidity and Excess Mortality After Fractures Among Danish Adults', JAMA Network Open, vol. 5, no. 10, pp. e2235856-e2235856.
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ImportanceLimited knowledge about interactions among health disorders impedes optimal patient care. Because comorbidities are common among patients 50 years and older with fractures, these fractures provide a useful setting for studying interactions among disorders.ObjectiveTo define multimorbidity clusters at the time of fracture and quantify the interaction between multimorbidity and fracture in association with postfracture excess mortality.Design, Setting, and ParticipantsThis nationwide cohort study included 307 870 adults in Denmark born on or before January 1, 1951, who had an incident low-trauma fracture between January 1, 2001, and December 31, 2014, and were followed up through December 31, 2016. Data were analyzed from February 1 to March 31, 2022.Main Outcomes and MeasuresFracture and 32 predefined chronic diseases recorded within 5 years before the index fracture were identified from the Danish National Hospital Discharge Register. Death was ascertained from the Danish Register on Causes of Death. Latent class analysis was conducted to identify multimorbidity clusters. Relative survival analysis was used to quantify excess mortality associated with the combination of multimorbidity and fractures at specific sites.ResultsAmong the 307 870 participants identified with incident fractures, 95 372 were men (31.0%; mean [SD] age at fracture, 72.3 [11.2] years) and 212 498 were women (69.0%; mean [SD] age at fracture, 74.9 [11.2] years). During a median of 6.5 (IQR, 3.0-11.0) years of follow-up, 41 017 men (43.0%) and 81 727 women (38.5%) died. Almost half of ...
Truong, DQ, Loganathan, P, Tran, LM, Vu, DL, Nguyen, TV, Vigneswaran, S & Naidu, G 2022, 'Removing ammonium from contaminated water using Purolite C100E: batch, column, and household filter studies', Environmental Science and Pollution Research, vol. 29, no. 12, pp. 16959-16972.
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Tuan Tran, H, Lin, C, Bui, X-T, Ky Nguyen, M, Dan Thanh Cao, N, Mukhtar, H, Giang Hoang, H, Varjani, S, Hao Ngo, H & Nghiem, LD 2022, 'Phthalates in the environment: characteristics, fate and transport, and advanced wastewater treatment technologies', Bioresource Technology, vol. 344, pp. 126249-126249.
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Tuan, HD, Nasir, AA, Ngo, HQ, Dutkiewicz, E & Poor, HV 2022, 'Scalable User Rate and Energy-Efficiency Optimization in Cell-Free Massive MIMO', IEEE Transactions on Communications, vol. 70, no. 9, pp. 6050-6065.
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This paper considers a cell-free massive multiple-input multiple-output network (cfm-MIMO) with a massive number of access points (APs) distributed across an area to deliver information to multiple users. Based on only local channel state information, conjugate beamforming is used under both proper and improper Gaussian signalings. To accomplish the mission of cfm-MIMO in providing fair service to all users, the problem of power allocation to maximize the geometric mean (GM) of users' rates (GM-rate) is considered. A new scalable algorithm, which iterates linear-complex closed-form expressions and thus is practical regardless of the scale of the network, is developed for its solution. The problem of quality-of-service (QoS) aware network energy-efficiency is also addressed via maximizing the ratio of the GM-rate and the total power consumption, which is also addressed by iterating linear-complex closed-form expressions. Intensive simulations are provided to demonstrate the ability of the GM-rate based optimization to achieve multiple targets such as a uniform QoS, a good sum rate, and a fair power allocation to the APs.
Tucho, A, Indraratna, B & Ngo, T 2022, 'Stress-deformation analysis of rail substructure under moving wheel load', Transportation Geotechnics, vol. 36, pp. 100805-100805.
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Tunji Oloyede, C, Olatayo Jekayinfa, S, Olanrewaju Alade, A, Ogunkunle, O, Timothy Laseinde, O, Oyejide Adebayo, A, Veza, I & Rizwanul Fattah, IM 2022, 'Potential Heterogeneous Catalysts from Three Biogenic Residues toward Sustainable Biodiesel Production: Synthesis and Characterization', ChemistrySelect, vol. 7, no. 48.
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AbstractThe cost and difficulty in the preparation of synthetic heterogeneous base catalysts is the main barrier to their use. Today, the majority of these catalysts are derived from biomass resources. This study aimed at developing and characterizing these catalysts from three biogenic residues for biodiesel production without catalyst support. The EDS indicated the variation of Na, K, Mg, and Ca, having aggregates of 67.45, 83.15, and 76.85 % in calcined‐ periwinkle shell‐ash (CPWSA), ‐melon seed‐husk ash (CMSHA) and ‐locust bean pod ash (CLBPA), respectively. XRD revealed the presence of sodium oxide (Na2O), calcium oxide (CaO), potassium oxide (K2O), and magnesium oxide (MgO) in the catalysts at 800 °C. The FTIR showed the presence of C=O, C−H, and O−H bonds in the catalyst samples. The basicity values of CPWSA, CMSHA, and CLBPA are 11.65, 10.41, and 11.62, respectively. The developed catalysts were used to synthesize biodiesel from palm kernel oil.
Turner, BD & Spadari, M 2022, 'Mass stabilisation and leaching characteristics of organotins from contaminated dredged sediments', International Journal of Environmental Science and Technology, vol. 19, no. 8, pp. 7425-7436.
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Uddin Murad, MA, Cetindamar, D & Chakraborty, S 2022, 'Identifying the Key Big Data Analytics Capabilities in Bangladesh’s Healthcare Sector', Sustainability, vol. 14, no. 12, pp. 7077-7077.
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The study explores the crucial big data analytics capabilities (BDAC) for healthcare in Bangladesh. After a rigorous and extensive literature review, we list a wide range of BDAC and empirically examine their applicability in Bangladesh’s healthcare sector by consulting 51 experts with ample domain knowledge. The study adopted the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method. Findings highlighted 11 key BDAC, such as using advanced analytical techniques that could be critical in managing big data in the healthcare sector. The paper ends with a summary and puts forward suggestions for future studies.
Uddin, MB, Chow, CM, Ling, SH & Su, SW 2022, 'A generalized algorithm for the automatic diagnosis of sleep apnea from per-sample encoding of airflow and oximetry', Physiological Measurement, vol. 43, no. 6, pp. 065004-065004.
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Abstract Objective. Sleep apnea is a common sleep breathing disorder that can significantly decrease sleep quality and have major health consequences. It is diagnosed based on the apnea hypopnea index (AHI). This study explored a novel, generalized algorithm for the automatic diagnosis of sleep apnea employing airflow (AF) and oximetry (SpO2) signals. Approach. Of the 988 polysomnography records, 45 were randomly selected for developing the automatic algorithm and the remainder 943 for validating purposes. The algorithm detects apnea events by a per-sample encoding process applied to the peak excursion of AF signal. Hypopnea events were detected from the per-sample encoding of AF and SpO2 with an adjustment to time lag in SpO2. Total recording time was automatically processed and optimized for computation of total sleep time (TST). Total number of detected events and computed TST were used to estimate AHI. The estimated AHI was validated against the scored data from the Sleep Heart Health Study. Main results. Intraclass correlation coefficient of 0.94 was obtained between estimated and scored AHIs. The diagnostic accuracies were 93.5%, 92.4%, and 96.6% for AHI cut-off values of ≥5, ≥15, and ≥30 respectively. The overall accuracy for the combined severity categories (normal, mild, moderate, and severe) and kappa were 83.4% and 0.77 respectively. Significance. This new automatic technique was found to be superior to the other existing methods and can be applied to any portable sleep devices especially for home sleep apnea tests.
Ullah, MA, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 2022, 'Low-profile dual-band pixelated defected ground antenna for multistandard IoT devices', Scientific Reports, vol. 12, no. 1, p. 11479.
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AbstractA low-profile dual-band pixelated defected ground antenna has been proposed at 3.5 GHz and 5.8 GHz bands. This work presents a flexible design guide for achieving single-band and dual-band antenna using pixelated defected ground (PDG). The unique pixelated defected ground has been designed using the binary particle swarm optimization (BPSO) algorithm. Computer Simulation Technology Microwave Studio incorporated with Matlab has been utilized in the antenna design process. The PDG configuration provides freedom of exploration to achieve the desired antenna performance. Compact antenna design can be achieved by making the best use of designated design space on the defected ground (DG) plane. Further, a V-shaped transfer function based on BPSO with fast convergence allows us to efficiently implement the PDG technique. In the design procedure, pixelization is applied to a small rectangular region of the ground plane. The square pixels on the designated defected ground area of the antenna have been formed using a binary bit string, consisting of 512 bits taken during each iteration of the algorithm. The PDG method is concerned with the shape of the DG and does not rely on the geometrical dimension analysis used in traditional defected ground antennas. Initially, three single band antennas have been designed at 3.5 GHz, 5.2 GHz and 5.8 GHz using PDG technique. Finally, same PDG area has been used to design a dual-band antenna at 3.5 GHz and 5.8 GHz. The proposed antenna exhibits almost omnidirectional radiation performance with nearly 90% efficiency. It also shows dual radiation pattern property with similar patterns having different polarizations at each operational band. The antenna is fabricated on a ROGERS RO4003 substrate with 1.52 mm thickness. Reflection coefficient and radiation patterns are measured to validate its performance. The simulated and measured results of the antenna are closely correlated. The propos...
Ullah, MA, Keshavarz, R, Abolhasan, M, Lipman, J, Esselle, KP & Shariati, N 2022, 'A Review on Antenna Technologies for Ambient RF Energy Harvesting and Wireless Power Transfer: Designs, Challenges and Applications', IEEE Access, vol. 10, pp. 17231-17267.
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Radio frequency energy harvesting (RFEH) and wireless power transmission (WPT) are two emerging alternative energy technologies that have the potential to offer wireless energy delivery in the future. One of the key components of RFEH or WPT system is the receiving antenna. The receiving antenna's performance has a considerable impact on the power delivery capability of an RFEH or WPT system. This paper provides a well-rounded review of recent advancements of receiving antennas for RFEH and WPT. Antennas discussed in this paper are categorized as low-profile antennas, multi-band antennas, circularly polarized antennas, and array antennas. A number of contemporary antennas from each category are presented, compared, and discussed with particular emphasis on design approach and performance. Current design and fabrication challenges, future development, open research issues of the antennas and visions for RFEH and WPT are also discussed in this review.
Ung, HTT, Leu, BT, Tran, HTH, Nguyen, LN, Nghiem, LD, Hoang, NB, Pham, HT & Duong, HC 2022, 'Combining flowform cascade with constructed wetland to enhance domestic wastewater treatment', Environmental Technology & Innovation, vol. 27, pp. 102537-102537.
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This study reports the performance of a new combined flowform cascade (FC) and constructed wetland (CW) system to enhance nitrogen removal and biological degradation of urban wastewater. A series of 8 FC units at the flow rate of 200 L/h could markedly increase the dissolved oxygen level in the wastewater from the initial value of 0.2 mg/L to 5.6 mg/L, thus providing suitable aerobic condition in the front zone of the CW for nitrification and biodegradation of organic contaminants. The results demonstrate that the combined FC/CW system could achieve the sequence of aerobic and anoxic conditions for nitrification and denitrification, respectively. By using a series of FC units for aeration, the CW system could enhance the removal of total nitrogen from 49.4% to 71.2% and biochemical oxygen demand from 80.9% to 86.1% when the hydraulic loading rate was 31.25 m3/m2⋅day. On the other hand, the FC units exerted negligible effects on the phosphate and total suspended solid removals of the CW system. Thus, the combined FC/CW process exhibited phosphate and total suspended solid removals comparable to those of the CW alone.
Unhelkar, B, Joshi, S, Sharma, M, Prakash, S, Mani, AK & Prasad, M 2022, 'Enhancing supply chain performance using RFID technology and decision support systems in the industry 4.0–A systematic literature review', International Journal of Information Management Data Insights, vol. 2, no. 2, pp. 100084-100084.
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Supply Chain processes are continuously marred by myriad factors including varying demands, changing routes, major disruptions, and compliance issues. Therefore, supply chains require monitoring and ongoing optimization. Data science uses real-time data to provide analytical insights, leading to automation and improved decision making. RFID is an ideal technology to source big data, particularly in supply chains, because RFID tags are consumed across supply chain process, which includes scanning raw materials, completing products, transporting goods, and storing products, with accuracy and speed. This study carries out a systematic literature review of research articles published during the timeline (2000-2021) that discuss the role of RFID technology in developing decision support systems that optimize supply chains in light of Industry 4.0. Furthermore, the study offers recommendations on operational efficiency of supply chains while reducing the costs of implementing the RFID technology. The core contribution of this paper is its analysis and evaluation of various RFID implementation methods in supply chains with the aim of saving time effectively and achieving cost efficiencies.
Usgodaarachchi, L, Thambiliyagodage, C, Wijesekera, R, Vigneswaran, S & Kandanapitiye, M 2022, 'Fabrication of TiO2 Spheres and a Visible Light Active α-Fe2O3/TiO2-Rutile/TiO2-Anatase Heterogeneous Photocatalyst from Natural Ilmenite', ACS Omega, vol. 7, no. 31, pp. 27617-27637.
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High-purity (98.8%, TiO2) rutile nanoparticles were successfully synthesized using ilmenite sand as the initial titanium source. This novel synthesis method was cost-effective and straightforward due to the absence of the traditional gravity, magnetic, electrostatic separation, ball milling, and smelting processes. Synthesized TiO2 nanoparticles were 99% pure. Also, highly corrosive environmentally hazardous acid leachate generated during the leaching process of ilmenite sand was effectively converted into a highly efficient visible light active photocatalyst. The prepared photocatalyst system consists of anatase-TiO2/rutile-TiO2/Fe2O3 (TF-800), rutile-TiO2/Fe2TiO5 (TFTO-800), and anatase-TiO2/Fe3O4 (TF-450) nanocomposites, respectively. The pseudo-second-order adsorption rate of the TF-800 ternary nanocomposite was 0.126 g mg-1 min-1 in dark conditions, and a 0.044 min-1 visible light initial photodegradation rate was exhibited. The TFTO-800 binary nanocomposite adsorbed methylene blue (MB) following pseudo-second-order adsorption (0.224 g mg-1 min-1) in the dark, and the rate constant for photodegradation of MB in visible light was 0.006 min-1. The prepared TF-450 nanocomposite did not display excellent adsorptive and photocatalytic performances throughout the experiment period. The synthesized TF-800 and TFTO-800 were able to degrade 93.1 and 49.8% of a 100 mL, 10 ppm MB dye solution within 180 min, respectively.
Uzair, M 2022, 'Vehicular Wireless Communication Standards: Challenges and Comparison', INTERNATIONAL JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING SYSTEMS, vol. 13, no. 5, pp. 379-397.
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Uzair, M, Eskandari, M, Li, L & Zhu, J 2022, 'Machine Learning Based Protection Scheme for Low Voltage AC Microgrids', Energies, vol. 15, no. 24, pp. 9397-9397.
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The microgrid (MG) is a popular concept to handle the high penetration of distributed energy resources, such as renewable and energy storage systems, into electric grids. However, the integration of inverter-interfaced distributed generation units (IIDGs) imposes control and protection challenges. Fault identification, classification and isolation are major concerns with IIDGs-based active MGs where IIDGs reveal arbitrary impedance and thus different fault characteristics. Moreover, bidirectional complex power flow creates extra difficulties for fault analysis. This makes the conventional methods inefficient, and a new paradigm in protection schemes is needed for IIDGs-dominated MGs. In this paper, a machine-learning (ML)-based protection technique is developed for IIDG-based AC MGs by extracting unique and novel features for detecting and classifying symmetrical and unsymmetrical faults. Different signals, namely, 400 samples, for wide variations in operating conditions of an MG are obtained through electromagnetic transient simulations in DIgSILENT PowerFactory. After retrieving and pre-processing the signals, 10 different feature extraction techniques, including new peaks metric and max factor, are applied to obtain 100 features. They are ranked using the Kruskal–Wallis H-Test to identify the best performing features, apart from estimating predictor importance for ensemble ML classification. The top 18 features are used as input to train 35 classification learners. Random Forest (RF) outperformed all other ML classifiers for fault detection and fault type classification with faulted phase identification. Compared to previous methods, the results show better performance of the proposed method.
Vaghani, A, Sood, K & Yu, S 2022, 'Security and QoS issues in blockchain enabled next-generation smart logistic networks: A tutorial', Blockchain: Research and Applications, vol. 3, no. 3, pp. 100082-100082.
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The blockchain-enabled smart logistics market is expected to grow worth USD 1620 billion and at a compound annual growth rate of 62.4%. Smart logistics ensures intelligence infrastructure, logistics automation, real-time analysis of supply chain data synchronization of the logistics process, cost transparency, unbroken shipment tracking all the way down to the transportation route, etc. In the smart logistics domain, significant advancement and growth of the Internet of Things (IoT) sensors are evident. However, the connectivity of IoT systems, including Tactile Internet, without proper safeguards creates vulnerabilities that can still be deliberately or inadvertently cause disruption. In view of this, we primarily notice two key issues. Firstly, the logistics domain can be compromised by a variety of natural or man-made activities, which eventually affect the overall network security. Secondly, there are thousands of entities in the supply chain network that use extensive machine-learning algorithms in many scenarios, and they require high-power computational resources. From these two challenges, we note that the first concern can be addressed by adding blockchain to IoT logistic networks. The second issue can be addressed using 6G. This will support 1-μs latency communications, support seamless computing at the edges of networks, and autonomously predict the best optimal location for edge computing. Motivated by this, we have highlighted motivational examples to show the necessity to integrate 6G and blockchain in smart logistic networks. Then, we have proposed a 6G and blockchain-enabled smart logistic high-level framework. We have presented the key intrinsic issues of this framework mainly from the security and resource management context. In this paper, recent state-of-the-art advances in blockchain enabled next-generation smart logistic networks are analyzed. We have also examined why 6G and not 5G would be compatible with the smart network. We ha...
Vali, M, Salimifard, K, Gandomi, AH & Chaussalet, TJ 2022, 'Application of job shop scheduling approach in green patient flow optimization using a hybrid swarm intelligence', Computers & Industrial Engineering, vol. 172, pp. 108603-108603.
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With the increasing demand for hospital services amidst the COVID-19 pandemic, allocation of limited public resources and management of healthcare services are of paramount importance. In the field of patient flow scheduling, previous research primarily focused on classical-based objective functions, while ignoring environmental-based objective functions. This study presents a flexible job shop scheduling problem to optimize patient flow and, thereby, minimize the total carbon footprint, as the sustainability-based objective function. Since flexible job shop scheduling is an NP-hard problem, a metaheuristic optimization algorithm, called Chaotic Salp Swarm Algorithm Enhanced with Opposition-Based Learning and Sine Cosine (CSSAOS), was developed. The proposed algorithm integrates the Salp Swarm Algorithm (SSA) with chaotic maps to update the position of followers, the sine cosine algorithm to update the leader position, and opposition-based learning for a better exploration of the search space. generating more accurate solutions. The proposed method was successfully applied in a real-world case study and demonstrated better performance than other well-known metaheuristic algorithms, including differential evolution, genetic algorithm, grasshopper optimization algorithm, SSA based on opposition-based learning, quantum evolutionary SSA, and whale optimization algorithm. In addition, it was found that the proposed method is scalable to different sizes and complexities.
Vali, M, Salimifard, K, Gandomi, AH & Chaussalet, TJ 2022, 'Care process optimization in a cardiovascular hospital: an integration of simulation–optimization and data mining', Annals of Operations Research, vol. 318, no. 1, pp. 685-712.
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AbstractTo provide health services, hospitals consume electrical power and contribute to the CO2 emission. This paper aims to develop a modelling approach to optimize hospital services while reducing CO2 emissions. To capture treatment processes and the production of carbon dioxide, a hybrid method of data mining and simulation–optimization techniques is proposed. Different clustering algorithms are used to categorize patients. Using quality indicators, clustering methods are evaluated to find the best cluster sets, and then patients are categorized accordingly. Discrete-event simulation is applied to each patient category to estimate performance measures such as number of patients being served, waiting times, and length of stay, as well as the amount of CO2 emission. To optimize performance measures of patient flow, metaheuristic searches have been used. The dataset of Bushehr Heart Hospital is considered as a case study. Based on K-means, K-medoid, Hierarchical clustering, and Fuzzy C-means clustering methods, patients are categorized into two groups of high-risk and low-risk patients. The number of patients being served, total waiting time, length of stay, and CO2 emitted during care processes are improved for both groups. The proposed hybrid method is an effective method for hospitals to categorize patients based on care processes. The problems and the proposed solution approach reported in this study could be applicable to other hospitals, worldwide to help both optimize the patient flow and minimize the environmental consequences of care services.
Valipour, M, Yousefi, S, Jahangoshai Rezaee, M & Saberi, M 2022, 'A clustering-based approach for prioritizing health, safety and environment risks integrating fuzzy C-means and hybrid decision-making methods', Stochastic Environmental Research and Risk Assessment, vol. 36, no. 3, pp. 919-938.
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Van Huynh, N, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2022, 'Joint Coding and Scheduling Optimization for Distributed Learning Over Wireless Edge Networks', IEEE Journal on Selected Areas in Communications, vol. 40, no. 2, pp. 484-498.
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Unlike theoretical analysis of distributed learning (DL) in the literature, DL over wireless edge networks faces the inherent dynamics/uncertainty of wireless connections and edge nodes, making DL less efficient or even inapplicable under the highly dynamic wireless edge networks. This article addresses these problems by leveraging recent advances in coded computing and the deep dueling neural network architecture. By introducing coded structures/redundancy, a distributed learning task can be completed without waiting for straggling nodes. Unlike conventional coded computing that only optimizes the code structure, coded distributed learning over the wireless edge also requires to optimize the selection/scheduling of wireless edge nodes with heterogeneous connections, computing capability, and straggling effects. However, even neglecting the aforementioned dynamics/uncertainty, the resulting joint optimization of coding and scheduling to minimize the distributed learning time turns out to be NP-hard. To tackle this and to account for the dynamics and uncertainty of wireless connections and edge nodes, we reformulate the problem as a Markov Decision Process and design a novel deep reinforcement learning algorithm that employs the deep dueling neural network architecture to find the jointly optimal coding scheme and the best set of edge nodes for different learning tasks without explicit information about the wireless environment and edge nodes’ straggling parameters. Simulations show that the proposed framework reduces the average learning delay in wireless edge computing up to 66% compared with other DL approaches. The jointly optimal framework in this article is also applicable to any distributed learning scheme with heterogeneous and uncertain computing nodes.
Van Huynh, N, Nguyen, DN, Hoang, DT, Vu, TX, Dutkiewicz, E & Chatzinotas, S 2022, 'Defeating Super-Reactive Jammers With Deception Strategy: Modeling, Signal Detection, and Performance Analysis', IEEE Transactions on Wireless Communications, vol. 21, no. 9, pp. 7374-7390.
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This paper develops a novel framework to defeat a super-reactive jammer, one of the most difficult jamming attacks to deal with in practice. Specifically, the jammer has an unlimited power budget and is equipped with the self-interference suppression capability to simultaneously attack and listen to the transmitter’s activities. Consequently, dealing with super-reactive jammers is very challenging. Thus, we introduce a smart deception mechanism to attract the jammer to continuously attack the channel and then leverage jamming signals to transmit data based on the ambient backscatter communication technology. To detect the backscattered signals, the maximum likelihood detector can be adopted. However, this method is notorious for its high computational complexity and requires the model of the current propagation environment as well as channel state information. Hence, we propose a deep learning-based detector that can dynamically adapt to any channels and noise distributions. With a Long Short-Term Memory network, our detector can learn the received signals’ dependencies to achieve a performance close to that of the optimal maximum likelihood detector. Through simulation and theoretical results, we demonstrate that with our approaches, the more power the jammer uses to attack the channel, the better bit error rate performance the transmitter can achieve.
Van Nguyen, L, Phung, MD & Ha, QP 2022, 'Game Theory-Based Optimal Cooperative Path Planning for Multiple UAVs', IEEE Access, vol. 10, pp. 108034-108045.
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Van, CN, Tran Thanh, H, Nguyen, TN & Li, J 2022, 'Numerical investigation of the influence of casting techniques on fiber orientation distribution in ECC', Frontiers of Structural and Civil Engineering, vol. 16, no. 11, pp. 1424-1435.
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AbstractEngineered cementitious composites (ECC), also known as bendable concrete, were developed based on engineering the interactions between fibers and cementitious matrix. The orientation of fibers, in this regard, is one of the major factors influencing the ductile behavior of this material. In this study, fiber orientation distributions in ECC beams influenced by different casting techniques are evaluated via numerical modeling of the casting process. Two casting directions and two casting positions of the funnel outlet with beam specimens are modeled using a particle-based smoothed particle hydrodynamics (SPH) method. In this SPH approach, fresh mortar and fiber are discretized by separated mortar and fiber particles, which smoothly interact in the computational domain of SPH. The movement of fiber particles is monitored during the casting simulation. Then, the fiber orientations at different sections of specimens are determined after the fresh ECC stops flowing in the formwork. The simulation results show a significant impact of the casting direction on fiber orientation distributions along the longitudinal wall of beams, which eventually influence the flexural strength of beams. In addition, casting positions show negligible influences on the orientation distribution of fibers in the short ECC beam, except under the pouring position.
Vanda, S, Nikoo, MR, Hashempour Bakhtiari, P, Al-Wardy, M, Franklin Adamowski, J, Šimůnek, J & Gandomi, AH 2022, 'Reservoir operation under accidental MTBE pollution: A graph-based conflict resolution framework considering spatial-temporal-quantitative uncertainties', Journal of Hydrology, vol. 605, pp. 127313-127313.
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Given the hazardous effects of sudden dam reservoir contamination — as might occur upon the intrusion of the fuel additive methyl tert-butyl ether (MTBE) — the contaminant's effect on the quality of allocated waters requires careful study. Employed to determine optimal reservoir operational rules in the case of sudden MTBE pollution, a risk-based simulation–optimization model was developed to simultaneously minimize unsatisfied water demand, the risk of violations of water quality standards, and the reservoir recovery time. Risks were assessed by considering various often-neglected pollution scenarios as a combination of location, quantity, and the season of pollution intrusion. The appropriateness of operational rules proved to depend upon thermal conditions and MTBE intrusion properties, confirming the necessity of considering location-quantity-season uncertainties. Social and regional conditions also occupied a dominant role in determining the level of satisfaction achieved under different water allocation strategies. Accordingly, by considering environmental conditions and local rules, a graph model for conflict resolution was established and used to reach a set of compromise operational rules. The developed framework could serve as a guide for water utilities to determine efficient reservoir operational rules after a sudden contaminant intrusion.
Varjani, S, Shahbeig, H, Popat, K, Patel, Z, Vyas, S, Shah, AV, Barceló, D, Hao Ngo, H, Sonne, C, Shiung Lam, S, Aghbashlo, M & Tabatabaei, M 2022, 'Sustainable management of municipal solid waste through waste-to-energy technologies', Bioresource Technology, vol. 355, pp. 127247-127247.
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Increasing municipal solid waste (MSW) generation and environmental concerns have sparked global interest in waste valorization through various waste-to-energy (WtE) to generate renewable energy sources and reduce dependency on fossil-derived fuels and chemicals. These technologies are vital for implementing the envisioned global 'bioeconomy' through biorefineries. In light of that, a detailed overview of WtE technologies with their benefits and drawbacks is provided in this paper. Additionally, the biorefinery concept for waste management and sustainable energy generation is discussed. The identification of appropriate WtE technology for energy recovery continues to be a significant challenge. So, in order to effectively apply WtE technologies in the burgeoning bioeconomy, this review provides a comprehensive overview of the existing scenario for sustainable MSW management along with the bottlenecks and perspectives.
Vasanthkumar, P, Senthilkumar, N, Rao, KS, Metwally, ASM, Fattah, IMR, Shaafi, T & Murugan, VS 2022, 'Improving energy consumption prediction for residential buildings using Modified Wild Horse Optimization with Deep Learning model', Chemosphere, vol. 308, no. Pt 1, pp. 136277-136277.
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The consumption of a significant quantity of energy in buildings has been linked to the emergence of environmental problems that can have unfavourable effects on people. The prediction of energy consumption is widely regarded as an effective method for the conservation of energy and the improvement of decision-making processes for the purpose of lowering energy use. When it comes to the generation of positive results in prediction tasks, the Machine Learning (ML) technique can be considered the most appropriate and applicable strategy. This article presents a Modified Wild Horse Optimization with Deep Learning approach for Energy Consumption Prediction (MWHODL-ECP) model in residential buildings. The MWHODL-ECP method that has been provided places an emphasis on providing an up-to-date and precise forecast of the amount of energy that residential buildings consume. The MWHODL-ECP algorithm goes through several phases of data preprocessing in order to achieve this goal. These steps include merging and cleaning the data, converting and normalising the data, and converting the data. A model known as deep belief network (DBN) is used here for the purpose of predicting energy consumption. In the end, the MWHO algorithm is utilised for the hyperparameter tuning procedure. The results of the experiments demonstrated that the MWHODL-ECP approach is superior to other existing DL models in terms of its performance. The MWHODL-ECP model has improved its performance, with effective prediction results of MSE-1.10, RMSE-1.05, MAE-0.41, R-squared-96.28, and Training time-1.23.
Vayghan, SS, Salmani, M, Ghasemkhani, N, Pradhan, B & Alamri, A 2022, 'Artificial intelligence techniques in extracting building and tree footprints using aerial imagery and LiDAR data', Geocarto International, vol. 37, no. 10, pp. 2967-2995.
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Vazquez, S, Zafra, E, Aguilera, RP, Geyer, T, Leon, JI & Franquelo, LG 2022, 'Prediction Model With Harmonic Load Current Components for FCS-MPC of an Uninterruptible Power Supply', IEEE Transactions on Power Electronics, vol. 37, no. 1, pp. 322-331.
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A finite control set model predictive control (FCS-MPC) strategy consists of a prediction model, a cost function and an optimization algorithm. Consequently, the performance of the FCS-MPC depends on the proper design of these three elements. This article assesses the influence of the prediction model of an uninterruptible power supply (UPS). Since the load connected to the voltage source inverter (VSI) affects the dynamic of the system state variables, the load dynamic should be included in the system model. This makes the design of the prediction model a challenge because the load connected to the VSI is generally unknown. To deal with this uncertainty, this work proposes an augmented prediction model based on a state observer that includes as many harmonic components as necessary to accurately represent the output current. The performance of the FCS-MPC for a UPS is evaluated in a laboratory prototype using the proposed and the conventional prediction models. Experimental results show that the proposed solution provides a more accurate representation of the output current, improving the system performance.
Veitch, D, Mani, SK, Cao, Y & Barford, P 2022, 'iHorology: Lowering the Barrier to Microsecond-Level Internet Time', IEEE/ACM Transactions on Networking, vol. 30, no. 6, pp. 2544-2558.
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High accuracy, synchronized clocks are essential to a growing number of Internet applications. Standard protocols and their associated server infrastructure typically enable client clocks to synchronize to the order of tens of milliseconds. We address one of the key challenges to high precision Internet timekeeping - the intrinsic contribution to clock error of underlying path asymmetry between client and time server, a fundamental barrier to microsecond level accuracy. We first exploit results of a unique measurement study to reliably quantify asymmetry by taking routing changes into account for the first time, and then to infer the impacts on timing. We then describe three approaches to addressing the path asymmetry problem: LBBE, SBBE and K-SBBE, each based on timestamp exchange with multiple servers, with the goal of tightening bounds on asymmetry for each client. We explore their capabilities and limitations through simulation and model-based argument. We show that substantial improvements are possible, and discuss whether, and how, the goal of microsecond accuracy might be attained.
Verma, M, Sreejeth, M, Singh, M, Babu, TS & Alhelou, HH 2022, 'Chaotic Mapping Based Advanced Aquila Optimizer With Single Stage Evolutionary Algorithm', IEEE Access, vol. 10, pp. 89153-89169.
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Verma, S, Wang, C, Zhu, L & Liu, W 2022, 'Attn-HybridNet: Improving Discriminability of Hybrid Features With Attention Fusion', IEEE Transactions on Cybernetics, vol. 52, no. 7, pp. 6567-6578.
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Veza, I, Afzal, A, Mujtaba, MA, Tuan Hoang, A, Balasubramanian, D, Sekar, M, Fattah, IMR, Soudagar, MEM, EL-Seesy, AI, Djamari, DW, Hananto, AL, Putra, NR & Tamaldin, N 2022, 'Review of artificial neural networks for gasoline, diesel and homogeneous charge compression ignition engine', Alexandria Engineering Journal, vol. 61, no. 11, pp. 8363-8391.
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Veza, I, Idris, M & Fattah, IMR 2022, 'Circular economy, energy transition, and role of hydrogen', Mechanical Engineering for Society and Industry, vol. 2, no. 2, pp. 54-56.
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Circularity is not a new concept. Activities such as reuse or recycling have been around for centuries. Today, an urgent solution to tackle the increasing harmful emissions resulting in severe climate changes is being proposed and investigated. This is because a link between industry and the environment is critically important for business. A more sustainable socio-technical system is therefore urgently needed. There has been a rapid growth of academic articles on the circular economy. The circular economy concept has been considered a solution to many of today’s challenges, including resource scarcity and waste generation.
Veza, I, Irianto, Panchal, H, Paristiawan, PA, Idris, M, Fattah, IMR, Putra, NR & Silambarasan, R 2022, 'Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms', Results in Engineering, vol. 16, pp. 100688-100688.
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The conventional experimental methods to determine biomass heating value are laborious and costly. Numerous correlations to estimate biomass' higher heating values have been proposed using proximate analysis. Recently, the utilisation of artificial neural network (ANN) has been extensively applied to predict HHV. However, most studies of ANN to estimate the biomass’ HHV only use one algorithm to train a small number of biomass datasets. The specific objective of this study is to predict the HHV of 350 samples of biomass from the proximate analysis by developing an ANN model which was trained with 11 different algorithms. This study fills a gap in the research on how to predict the HHV of biomass using numerous ANN training algorithms utilising sizeable biomass datasets. Results show that the ANN trained with Levenberg-Marquardt gave the highest accuracy. The Levenberg–Marquardt algorithm shows the best fit giving the highest R and R2 values and the lowest MAD, MSE, RMSE and MAPE. Compared with previous biomass HHV prediction studies, the ANN model developed in this study provides improved prediction accuracy with higher R2 and lower RMSE. Results from this study have also indicated that the Levenberg-Marquardt should be the first-choice supervised algorithm for feedforward-backpropagation.
Veza, I, Zainuddin, Z, Tamaldin, N, Idris, M, Irianto, I & Fattah, IMR 2022, 'Effect of palm oil biodiesel blends (B10 and B20) on physical and mechanical properties of nitrile rubber elastomer', Results in Engineering, vol. 16, pp. 100787-100787.
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Vo, HNP, Nguyen, TMH, Ngo, HH, Guo, W & Shukla, P 2022, 'Biochar sorption of perfluoroalkyl substances (PFASs) in aqueous film-forming foams-impacted groundwater: Effects of PFASs properties and groundwater chemistry', Chemosphere, vol. 286, no. Pt 1, pp. 131622-131622.
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The widespread use of per- and polyfluoroalkyl substances (PFASs)-related products such as aqueous film-forming foams (AFFF) has led to increasing contamination of groundwater systems. The concentration of PFASs in AFFF-impacted groundwater can be several orders of magnitude higher than the drinking water standard. There is a need for a sustainable and effective sorbent to remove PFASs from groundwater. This work aims to investigate the sorption of PFASs in groundwater by biochar column. The specific objectives are to understand the influences of PFASs properties and groundwater chemistry to PFASs sorption by biochar. The PFASs-spiked Milli-Q water (including 19 PFASs) and four aqueous film-forming foams (AFFF)-impacted groundwater were used. The partitioning coefficients (log Kd) of long chain PFASs ranged from 0.77 to 4.63 while for short chain PFASs they remained below 0.68. For long chain PFASs (C ≥ 7), log Kd increased by 0.5 and 0.8 for each CF2 moiety of PFCAs and PFSAs, respectively. Dissolved organic matter (DOM) was the most influential factor in PFASs sorption over pH, salinity, and specific ultraviolet absorbance (SUVA). DOM contained hydrophobic compounds and metal ions which can form DOM-PFASs complexes to provide more sorption sites for PFASs. The finding is useful for executing PFASs remediation by biochar filtration column, especially legacy long chain PFASs, for groundwater remediation.
Vo, NNY, Vu, QT, Vu, NH, Vu, TA, Mach, BD & Xu, G 2022, 'Domain-specific NLP system to support learning path and curriculum design at tech universities', Computers and Education: Artificial Intelligence, vol. 3, pp. 100042-100042.
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Vo, NNY, Xu, G & Le, DA 2022, 'Causal inference for the impact of economic policy on financial and labour markets amid the COVID-19 pandemic', Web Intelligence, vol. 20, no. 1, pp. 1-19.
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The COVID-19 pandemic has turned the world upside down since the beginning of 2020, leaving most nations worldwide in both health crises and economic recession. Governments have been continually responding with multiple support policies to help people and businesses overcoming the current situation, from “Containment”, “Health” to “Economic” policies, and from local and national supports to international aids. Although the pandemic damage is still not under control, it is essential to have an early investigation to analyze whether these measures have taken effects on the early economic recovery in each nation, and which kinds of measures have made bigger impacts on reducing such negative downturn. Therefore, we conducted a time series based causal inference analysis to measure the effectiveness of these policies, specifically focusing on the “Economic support” policy on the financial markets for 80 countries and on the United States and Australia labour markets. Our results identified initial positive causal relationships between these policies and the market, providing a perspective for policymakers and other stakeholders.
Vosoughi Kurdkandi, N, Husev, O, Matiushkin, O, Vinnikov, D, Siwakoti, YP & Lee, SS 2022, 'Novel Family of Flying Inductor-Based Single-Stage Buck–Boost Inverters', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 5, pp. 6020-6032.
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Single-phase buck-boost inverters are very popular nowadays due to the wide input voltage range regulation capability. This feature is mostly demanded by photovoltaic (PV), fuel cell, or battery storage applications. In this study, four new structures from the family of flying inductor (FI)-based inverters are presented. Performance in a wide range of input dc voltages and the ability to increase the voltage in single-power processing stage is one of the features of the proposed structures. Three of the four structures are common ground and completely bypass the parasitic capacitors which make them attractive for PV application. Nonuse of electrolytic capacitors in the proposed structures helps to increase the life of the converter and also there is no unpleasant inrush current of charging electrolytic capacitors in these structures. The operation modes are fully explained, as well as the theoretical analysis and design of passive elements have been done. Finally, the simulation and 2 kW laboratory circuit for the proposed Type-III structure are performed and the results are investigated.
Vosoughi, F, Nikoo, MR, Rakhshandehroo, G, Adamowski, JF & Gandomi, AH 2022, 'Downstream semi-circular obstacles' influence on floods arising from the failure of dams with different levels of reservoir silting', Physics of Fluids, vol. 34, no. 1, pp. 013312-013312.
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Dam-break wave propagation in a debris flood event is strongly influenced by accumulated reservoir-bound sediment and downstream obstacles. For instance, the Brumadinho dam disaster in Brazil in 2019 released 12 × 106 m3 of mud and iron tailings and inflicted 270 casualties. The present work was motivated by the apparent lack of experimental or numerical studies on silted-up reservoir dam-breaks with downstream semi-circular obstacles. Accordingly, 24 dam-break scenarios with different reservoir sediment depths and with or without obstacles were observed experimentally and verified numerically. Multiphase flood waves were filmed, and sediment depths, water levels, and values of front wave celerity were measured to improve our scientific understanding of shock wave propagation over an abruptly changing topography. Original data generated in this study is available online in the public repository and may be used for practical purposes. The strength of OpenFOAM software in estimating such a complex phenomenon was assessed using two approaches: volume of fluid (VOF) and Eulerian. An acceptable agreement was attained between numerical and experimental records (errors ranged from 1 to 13.6%), with the Eulerian outperforming the VOF method in estimating both sediment depth and water level profiles. This difference was most notable when more than half of the reservoir depth was initially filled by sediment (≥0.15 m), particularly in bumpy bed scenarios.
Vosoughi, F, Nikoo, MR, Rakhshandehroo, G, Alamdari, N, Gandomi, AH & Al-Wardy, M 2022, 'The application of Bayesian model averaging based on artificial intelligent models in estimating multiphase shock flood waves', Neural Computing and Applications, vol. 34, no. 22, pp. 20411-20429.
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Vranken, L, Wyers, CE, Van der Velde, RY, Janzing, HMJ, Kaarsemakers, S, Driessen, J, Eisman, J, Center, JR, Nguyen, TV, Tran, T, Bliuc, D, Geusens, P & van den Bergh, JP 2022, 'Association between incident falls and subsequent fractures in patients attending the fracture liaison service after an index fracture: a 3-year prospective observational cohort study', BMJ Open, vol. 12, no. 7, pp. e058983-e058983.
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ObjectivesTo evaluate the risk of subsequent fractures in patients who attended the Fracture Liaison Service (FLS), with and without incident falls after the index fracture.DesignA 3-year prospective observational cohort study.SettingAn outpatient FLS in the Netherlands.ParticipantsPatients aged 50+ years with a recent clinical fracture.Outcome measuresIncident falls and subsequent fractures.ResultsThe study included 488 patients (71.9% women, mean age: 64.6±8.6 years). During the 3-year follow-up, 959 falls had been ascertained in 296 patients (60.7%) (ie, fallers), and 60 subsequent fractures were ascertained in 53 patients (10.9%). Of the fractures, 47 (78.3%) were fall related, of which 25 (53.2%) were sustained at the first fall incident at a median of 34 weeks. An incident fall was associated with an approximately 9-fold (HR: 8.6, 95% CI 3.1 to 23.8) increase in the risk of subsequent fractures.ConclusionThese data suggest that subsequent fractures among patients on treatment prescribed in an FLS setting are common, and that an incident fall is a strong predictor of subsequent fracture risk. Immediate attention for fall risk could be beneficial in an FLS model of care.Trial registration numberNL45707.072.13.
Vu, HP, Nguyen, LN, Wang, Q, Ngo, HH, Liu, Q, Zhang, X & Nghiem, LD 2022, 'Hydrogen sulphide management in anaerobic digestion: A critical review on input control, process regulation, and post-treatment', Bioresource Technology, vol. 346, pp. 126634-126634.
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Hydrogen sulphide (H2S) in biogas is a problematic impurity that can inhibit methanogenesis and cause equipment corrosion. This review discusses technologies to remove H2S during anaerobic digestion (AD) via: input control, process regulation, and post-treatment. Post-treatment technologies (e.g. biotrickling filters and scrubbers) are mature with >95% removal efficiency but they do not mitigate H2S toxicity to methanogens within the AD. Input control (i.e. substrate pretreatment via chemical addition) reduces sulphur input into AD via sulphur precipitation. However, available results showed <75% of H2S removal efficiency. Microaeration to regulate AD condition is a promising alternative for controlling H2S formation. Microaeration, or the use of oxygen to regulate the redox potential at around -250 mV, has been demonstrated at pilot and full scale with >95% H2S reduction, stable methane production, and low operational cost. Further adaptation of microaeration relies on a comprehensive design framework and exchange operational experience for eliminating the risk of over-aeration.
Vu, L, Cao, VL, Nguyen, QU, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2022, 'Learning Latent Representation for IoT Anomaly Detection', IEEE Transactions on Cybernetics, vol. 52, no. 5, pp. 3769-3782.
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Vu, MT, Nguyen, LN, Ibrahim, I, Abu Hasan Johir, M, Bich Hoang, N, Zhang, X & Nghiem, LD 2022, 'Nutrient recovery from digested sludge centrate using alkali metals from steel-making slag', Chemical Engineering Journal, vol. 450, pp. 138186-138186.
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Results in this study highlighted the potential of nutrient recovery from anaerobically digested sludge centrate using calcium and other alkali metals from steel-making slag. Up to 96% phosphate and 71% ammonia could be recovered from sludge centrate at the optimal conditions. Mass balance calculation confirmed precipitation and volatilisation as the main mechanisms for phosphorus and ammonia recovery, respectively. Morphology and elemental analysis of obtained precipitates confirmed that phosphorus was recovered in the form of dicalcium phosphate dihydrate (CaHPO4·2H2O). The results also showed that sludge centrate pre-treatment by sand filtration and forward osmosis (FO) enrichment was essential to achieve high nutrient recovery. Sand filtration pre-treatment decreased the total suspended solid of sludge centrate by eightfold, leading to mitigated membrane fouling and reduced nutrient loss during FO pre-concentration. The production of slag liquor with high calcium and alkaline content from steel-making slag for nutrient recovery was demonstrated. Slag liquor with high pH increased ammonia recovery significantly, but only enhanced phosphate recovery slightly. Phosphate recovery was more dependent on the initial Ca:PO4 molar ratio than the final pH. The process demonstrated in this study has potential and significant practical implications to nutrient recovery from wastewater and beneficial use of steel-making slag.
Vu, MT, Nguyen, LN, Mofijur, M, Johir, MAH, Ngo, HH, Mahlia, TMI & Nghiem, LD 2022, 'Simultaneous nutrient recovery and algal biomass production from anaerobically digested sludge centrate using a membrane photobioreactor', Bioresource Technology, vol. 343, pp. 126069-126069.
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This study aims to evaluate the performance of C. vulgaris microalgae to simultaneously recover nutrients from sludge centrate and produce biomass in a membrane photobioreactor (MPR). Microalgae growth and nutrient removal were evaluated at two different nutrient loading rates (sludge centrate). The results show that C. vulgaris microalgae could thrive in sludge centrate. Nutrient loading has an indiscernible impact on biomass growth and a notable impact on nutrient removal efficiency. Nutrient removal increased as the nutrient loading rate decreased and hydraulic retention time increased. There was no membrane fouling observed in the MPR and the membrane water flux was fully restored by backwashing using only water. However, the membrane permeability varies with the hydraulic retention time (HRT) and biomass concentration in the reactor. Longer HRT offers higher permeability. Therefore, it is recommended to operate the MPR system in lower HRT to improve the membrane resistance and energy consumption.
Vu, T-H, Nguyen, HT, Fastier-Wooller, JW, Tran, C-D, Nguyen, T-H, Nguyen, H-Q, Nguyen, T, Nguyen, T-K, Dinh, T, Bui, TT, Zhong, Y, Phan, H-P, Nguyen, N-T, Dao, DV & Dau, VT 2022, 'Enhanced Electrohydrodynamics for Electrospinning a Highly Sensitive Flexible Fiber-Based Piezoelectric Sensor', ACS Applied Electronic Materials, vol. 4, no. 3, pp. 1301-1310.
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Wagstyl, D, Borggräfe, T, Oberdiek, S, Wagener, L & Deuse, J 2022, 'Digitale Kollaborationsplattform zur verteilten, agilen Planung im Produktentstehungsprozess', Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 117, no. 12, pp. 879-883.
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Abstract Aktuelle Planungsprojekte zur Produktionssystemgestaltung erfordern die Integration zahlreicher Akteure. Zur erfolgreichen, verteilten Kollaboration ist eine geeignete digitale Plattform erforderlich. Die in diesem Beitrag vorgestellte Plattformarchitektur stellt eine aufgabenorientierte Bereitstellung der Planungsdaten vor und zeigt die Verknüpfung mit einem Workflow-Management zur unternehmensinternen Ablauforganisation auf. Die Kollaboration auf der digitalen Plattform wird auf Konzeptebene mit einer agilen Projektplanung und mit einem flexiblen Visualisierungskonzept unterstützt. Abschließend zeigt dieser Beitrag eine exemplarische Integration der IT-Lösung auf und führt Indikatoren ein, welche die Akzeptanz im industriellen Mittelstand erfassen.
Wali, SB, Hannan, MA, Ker, PJ, Rahman, MSA, Mansor, M, Muttaqi, KM, Mahlia, TMI & Begum, RA 2022, 'Grid-connected lithium-ion battery energy storage system: A bibliometric analysis for emerging future directions', Journal of Cleaner Production, vol. 334, pp. 130272-130272.
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The lithium-ion battery energy storage systems (ESS) have fuelled a lot of research and development due to numerous important advancements in the integration and development over the last decade. The main purpose of the presented bibliometric analysis is to provide the current research trends and impacts along with the comprehensive review in the field of the grid-connected lithium-ion battery (LIB) ESS within the year 2010–2021. The research has been performed using refined keyword searches on grid-connected LIB ESS in the Scopus database and the data of the top 100 highly cited articles were extracted. The research trend has shown that the grid-connected LIB ESS literature has increased substantially between 2016 and 2021, compared to the period 2010–2015. The bibliometric analysis consists of detailed keyword co-occurrence network analysis, co-authorship map, distribution of articles over countries, journals, research types, and subject categories. The evaluation of highly cited articles identifies numerous aspects, including methodologies and systems, issues, and challenges, to determine existing constraints and research gaps. The process of deciding, developing, and evaluating the highly cited articles, is expected to contribute to a methodical foundation for potential progress of grid-connected LIB ESS, as well as identify emerging pathways for future researchers. This study may act as a guideline providing future directions towards improving energy efficiency, environmental sustainability, reliability, and flexibility of the LIB ESS integrated power system.
Walker, P, Li, T, Khonasty, R, Ponnanna, KM, Kuo, A, Zhao, L & Huang, S 2022, 'Proof of concept study for using UR10 robot to help total hip replacement', The International Journal of Medical Robotics and Computer Assisted Surgery, vol. 18, no. 2, p. e2359.
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AbstractBackgroundThe demand for total hip replacement (THR) for treating osteoarthritis has grown substantially worldwide. The existing robotic systems used in THR are invasive and costly. This study aims to develop a less‐invasive and low‐cost robotic system to assist THR surgery.MethodsA preliminary robotic reaming system was developed based on a UR10 robot equipped with a reamer to cut acetabulum. A novel approach was proposed to cut through a 5 mm hole in femur such that the operation is less invasive to the patients.ResultsThe average error of the cutting hemisphere by the robotic reaming system is 0.1182 mm which is smaller than the average result reaming by hand (0.1301 mm).ConclusionThe robotic reaming can help make THR procedures less invasive and more accurate. Moreover, the system is expected to be significantly less expensive than the robotic systems available in the market at present.
Wan Mahari, WA, Kee, SH, Foong, SY, Amelia, TSM, Bhubalan, K, Man, M, Yang, Y, Ong, HC, Vithanage, M, Lam, SS & Sonne, C 2022, 'Generating alternative fuel and bioplastics from medical plastic waste and waste frying oil using microwave co-pyrolysis combined with microbial fermentation', Renewable and Sustainable Energy Reviews, vol. 153, pp. 111790-111790.
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In the present study, microwave co-pyrolysis (MCP) was used to simultaneously convert medical plastic waste (MPW) and waste frying oil (WFO) into liquid oil products. The MCP process demonstrated a faster heating rate (24 °C/min) and shorter process time (20 min) compared to conventional pyrolysis techniques converting MPW and WFO into liquid oil (≥80 wt%). The MCP reduced the oxygen content from 25.7 to 9.82 wt% in liquid oil encompassing light aliphatic hydrocarbons ranging from C10 to C28, generating a novel sustainable liquid fuel. The liquid having a high carbon content (approximately 77.1 wt%) and low carbon to nitrogen ratio (27.9) is a suitable energy feedstock for polyhydroxyalkanoate (PHA) bioplastic production in the form of poly3-hydroxybutyrate [P(3HB)]. The liquid oil acted as an energy source for the growth of Bacillus sp. During microbial fermentation, yielding approximately 11% (w/w) P(3HB). Bioplastics are biodegradable, biocompatible with humans and non-toxic to marine organisms, representing a valuable additive in the production of cosmetics, detergents, and as medical scaffolds for tissue engineering. The results indicate the promising upcycling of waste products by this approach through pyrolytic biorefinery into value-added fuel and bioplastic products, being important for the future sustainable production of renewable resources.
Wan, S, Gao, Z, Zhang, H, Xiaojun, C, Chen, C & Tefas, A 2022, 'Editorial paper for Pattern Recognition Letters VSI on cross model understanding for visual question answering', Pattern Recognition Letters, vol. 160, pp. 9-10.
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Wan, S, Pan, S, Zhong, P, Chang, X, Yang, J & Gong, C 2022, 'Dual Interactive Graph Convolutional Networks for Hyperspectral Image Classification', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-14.
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Recently, graph convolutional network (GCN) has progressed significantly and gained increasing attention in hyperspectral image (HSI) classification due to its impressive representation power. However, existing GCN-based methods do not give full consideration to the multiscale spatial information, since the convolution operations are governed by fixed neighborhood. As a result, their performances can be limited, particularly in the regions with diverse land cover appearances. In this article, we develop a new dual interactive GCN (DIGCN) which introduces the dual GCN branches to capture spatial information at different scales. More significantly, the dual interactive module is embedded across the GCN branches, so that the correlation of multiscale spatial information can be leveraged to refine the graph information. To be concrete, the edge information contained in one GCN branch can be refined by incorporating the feature representations from the other branch. Analogously, improved feature representations can be generated in one GCN branch by fusing the edge information from the other branch. As such, the refined graph information can help enhance the representation power of the model. Furthermore, to avoid the negative effects of the manually constructed graph, our proposed model adaptively learns a discriminative region-induced graph, which also accelerates the convolution operation. We comprehensively evaluate the proposed method on four commonly used HSI benchmark data sets, and the state-of-the-art results can be achieved when compared with several typical HSI classification methods.
Wang, B, Liu, W, Li, JJ, Chai, S, Xing, D, Yu, H, Zhang, Y, Yan, W, Xu, Z, Zhao, B, Du, Y & Jiang, Q 2022, 'A low dose cell therapy system for treating osteoarthritis: In vivo study and in vitro mechanistic investigations', Bioactive Materials, vol. 7, pp. 478-490.
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Mesenchymal stem cells (MSCs) can be effective in alleviating the progression of osteoarthritis (OA). However, low MSC retention and survival at the injection site frequently require high doses of cells and/or repeated injections, which are not economically viable and create additional risks of complications. In this study, we produced MSC-laden microcarriers in spinner flask culture as cell delivery vehicles. These microcarriers containing a low initial dose of MSCs administered through a single injection in a rat anterior cruciate ligament (ACL) transection model of OA achieved similar reparative effects as repeated high doses of MSCs, as evaluated through imaging and histological analyses. Mechanistic investigations were conducted using a co-culture model involving human primary chondrocytes grown in monolayer, together with MSCs grown either within 3D constructs or as a monolayer. Co-culture supernatants subjected to secretome analysis showed significant decrease of inflammatory factors in the 3D group. RNA-seq of co-cultured MSCs and chondrocytes using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed processes relating to early chondrogenesis and increased extracellular matrix interactions in MSCs of the 3D group, as well as phenotypic maintenance in the co-cultured chondrocytes. The cell delivery platform we investigated may be effective in reducing the cell dose and injection frequency required for therapeutic applications.
Wang, B, Lu, J, Li, T, Yan, Z & Zhang, G 2022, 'A quantile fusion methodology for deep forecasting', Neurocomputing, vol. 483, pp. 286-298.
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Wang, C, Ji, J, Miao, Z & Zhou, J 2022, 'Udwadia-Kalaba approach based distributed consensus control for multi-mobile robot systems with communication delays', Journal of the Franklin Institute, vol. 359, no. 14, pp. 7283-7306.
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In this paper, a distributed consensus algorithm for multi-mobile robot systems (MMRSs) with communication delays is proposed based on the Udwadia-Kalaba (UK) approach. The key feature of the proposed algorithm is that the consensus requirement is configured as a second-order constraint, and then a concise and explicit equation of motion for the constrained mechanical systems is formulated. Furthermore, the necessary and sufficient conditions for achieving the consensus of MMRSs with or without communication delays are developed under the network topology possessing a directed spanning tree. Finally, some numerical simulations are performed to verify the validity of the proposed consensus algorithm.
Wang, C, Lu, W, Peng, S, Qu, Y, Wang, G & Yu, S 2022, 'Modeling on Energy-Efficiency Computation Offloading Using Probabilistic Action Generating', IEEE Internet of Things Journal, vol. 9, no. 20, pp. 20681-20692.
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Wireless-powered mobile-edge computing (MEC) emerges as a crucial component in the Internet of Things (IoTs). It can cope with the fundamental performance limitations of low-power networks, such as wireless sensor networks or mobile networks. Although computation offloading and resource allocation in MEC have been studied with different optimization objectives, performance optimization in larger-scale systems still needs to be further improved. More importantly, energy efficiency is also a key issue as well as computation offloading and resource allocation for wireless-powered MEC. In this article, we investigate the joint optimization of computation rate and energy consumption under limited resources, and propose an online offloading model to search for the asymptotically optimal offloading and resource allocation strategy. First, the joint optimization problem is modeled as a mixed integer programming (MIP) problem. Second, a deep reinforcement learning (DRL)-based method, energy efficiency computation offloading using probabilistic action generating (ECOPG), is designed to generate the joint optimization policy for computation offloading and resource allocation. Finally, to avoid the curse of dimensionality in large network scales, an action exploration mechanism based on probability is introduced to accelerate the convergence rate by targeted sampling and dynamic experience replay. The experimental results demonstrate that the proposed methods significantly outperform other DRL-based methods in energy consumption, and gain better computation rate and execution efficiency at the same time. With the expansion of the network scale, the improvements become more apparent.
Wang, C, Pan, S, Yu, CP, Hu, R, Long, G & Zhang, C 2022, 'Deep neighbor-aware embedding for node clustering in attributed graphs', Pattern Recognition, vol. 122, pp. 108230-108230.
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Node clustering aims to partition the vertices in a graph into multiple groups or communities. Existing studies have mostly focused on developing deep learning approaches to learn a latent representation of nodes, based on which simple clustering methods like k-means are applied. These two-step frameworks for node clustering are difficult to manipulate and usually lead to suboptimal performance, mainly because the graph embedding is not goal-directed, i.e., designed for the specific clustering task. In this paper, we propose a clustering-directed deep learning approach, Deep Neighbor-aware Embedded Node Clustering (DNENC for short) for clustering graph data. Our method focuses on attributed graphs to sufficiently explore the two sides of information in graphs. It encodes the topological structure and node content in a graph into a compact representation via a neighbor-aware graph autoencoder, which progressively absorbs information from neighbors via a convolutional or attentional encoder. Multiple neighbor-aware encoders are stacked to build a deep architecture followed by an inner-product decoder for reconstructing the graph structure. Furthermore, soft labels are generated to supervise a self-training process, which iteratively refines the node clustering results. The self-training process is jointly learned and optimized with the graph embedding in a unified framework, to benefit both components mutually. Experimental results compared with state-of-the-art algorithms demonstrate the good performance of our framework.
Wang, C, Park, MJ, Gonzales, RR, Phuntsho, S, Matsuyama, H, Drioli, E & Shon, HK 2022, 'Novel organic solvent nanofiltration membrane based on inkjet printing-assisted layer-by-layer assembly', Journal of Membrane Science, vol. 655, pp. 120582-120582.
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Novel layer-by-layer (LBL) organic solvent nanofiltration (OSN) membrane was developed via inkjet printing of polyethyleneimine (PEI) and single walled carbon nanotube (SWCNT) on a polyketone (PK) membrane surface, followed by post-treatment using three different cross-linking agents: glutaraldehyde (GA), (±)-epichlorohydrin (ECH) and trimesoyl chloride (TMC). The effects of PEI and SWCNT concentrations, bilayer numbers, and cross-linking conditions in the formation of the selective layers were evaluated in terms of membrane OSN performances. PEI concentration of 10.0 g/L and SWCNT concentration of 1.0 g/L with eight cycles of printing bilayers were chosen as optimal conditions. GA cross-linking was found to give the best membrane performance, and thus GA was considered as the best cross-linking agent for inkjet-printed LBL membrane modification among the three kinds of cross-linkers. The (PEI/SWCNT)8-GA exhibited Rose Bengal (RB) rejection over 99% with high organic solvent permeances. Compared to the cross-linking time, cross-linking agent concentration was found to have a greater effect on the membrane modification in terms of rejection performance. Moreover, the inkjet-printed LBL membrane showed negligible changes in membrane weight and OSN performance after immersion in different organic solvents over a period of three weeks, indicating its high mechanical and chemical stability. Finally, the possible applications of our printed LBL membranes in the pharmaceutical and hemp industries were evaluated. Overall, our work could further develop inkjet printing method for LBL OSN membrane fabrications.
Wang, C, Park, MJ, Seo, DH, Phuntsho, S, Gonzales, RR, Matsuyama, H, Drioli, E & Shon, HK 2022, 'Inkjet printed polyelectrolyte multilayer membrane using a polyketone support for organic solvent nanofiltration', Journal of Membrane Science, vol. 642, pp. 119943-119943.
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This study investigates the inkjet printing technique as an efficient way to fabricate polyelectrolyte multilayer membranes (PEM) for organic solvent nanofiltrtaion (OSN). Polyethyleneimine (PEI) and poly(sodium 4-styrene sulfonate) (PSS) were used as polycation and polyanion, respectively. Single walled carbon nanotube (SWCNT) was incorporated into membranes to enhance the membrane physical and chemical stability. The polyketone (PK) membrane served as substrate for OSN because of its organic solvent resistance property in nature. The effects of numbers of bilayer, polyelectrolyte concentration, and the cross-linking condition on the membrane OSN performances were evaluated. The best OSN performance was achieved with 10 bilayers of polyelectrolytes printing, noted as (PEI/PSS-CNT)10. The (PEI/PSS-CNT)10 membrane exhibited ethanol, methanol, IPA and acetone permeances of 2.52, 4.21, 1.21 and 4.75 L m−2 h−1 bar−1, respectively, along with good dye rejection rate (Rose Bengal (RB) rejection >98%). Moreover, the inkjet printed OSN membrane was found to be stable after soaking in different organic solvents for two weeks. The membrane weights and the performances exhibited negligible changes. The 12 h continuous filtration tests also confirmed the membrane stability property. Our work broadened the use of inkjet printing technology for membrane fabrication and validated the technology as a promising method for producing multilayer OSN membranes, which may open a new avenue for OSN membrane preparations.
Wang, C, Park, MJ, Yu, H, Matsuyama, H, Drioli, E & Shon, HK 2022, 'Recent advances of nanocomposite membranes using layer-by-layer assembly', Journal of Membrane Science, vol. 661, pp. 120926-120926.
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Layer-by-layer (LBL) assembly is a versatile technology with the ability to produce charged thin film active layers by absorbing oppositely charged polyelectrolytes or nanomaterials through various interactions, which has been proven to be a promising method for preparing separation membranes with desired properties. Recently, nanocomposite membranes fabricated by incorporating various kinds of nanomaterials through the LBL technique have gained increasing interest due to their excellent membrane performances in terms of improved permeability, selectivity, anti-fouling, chlorine resistance, and long-term stability. This review aims to provide a comprehensive investigation of the state-of-the-art achievements of the nanocomposite membranes prepared by LBL assembly. Different LBL assembly methods such as dip coating, spray coating, spin coating, inkjet printing, electric field, and high gravity technologies are introduced. The detailed membrane fabrication processes and their applications in different separation areas including nanofiltration, reverse osmosis, ultrafiltration, microfiltration, pressure retarded osmosis, forward osmosis, pervaporation and organic solvent nanofiltration are summarised and discussed. The advantages and challenges of the LBL nanocomposite membranes are also addressed. Overall, this review provides some fundamental clues for the exploration of LBL assembly techniques for the preparation of separation membranes with preferable performances and applications.
Wang, C, Wei, W, Chen, Z, Wang, Y, Chen, X & Ni, B-J 2022, 'Polystyrene microplastics and nanoplastics distinctively affect anaerobic sludge treatment for hydrogen and methane production', Science of The Total Environment, vol. 850, pp. 158085-158085.
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Microplastics and nanoplastics generally accumulated in waste activated sludge (WAS) after biological wastewater treatment. Currently, researches mainly focused on how plastics affected a particular sludge treatment method, without the comparison of different sludge systems. Herein, distinct responses of hydrogen-producing and methane-producing sludge systems were comprehensively evaluated with polystyrene microplastics (PS-MPs) and nanoplastics (PS-NPs) existence. Experimental results showed that PS particles would stimulate inhibition on anaerobic gas production except that PS-MPs were conducive to hydrogen accumulation, which was caused by the enhanced solubilization. Mechanistic investigation demonstrated that severe inhibition of PS-NPs to hydrogen production was derived from the excessively inhibitory hydrolysis despite of improving solubilization. Varying degrees of inhibition to acidification and methanation collectively contributed to reduced methane accumulation with exposure to PS-MPs and PS-NPs. Excessive oxidative stress would be generated in the presence of PS-MPs or PS-NPs, deteriorating microbial activities and richness of species responsible for hydrogen or methane production.
Wang, C, Wei, W, Dai, X & Ni, B-J 2022, 'Calcium peroxide significantly enhances volatile solids destruction in aerobic sludge digestion through improving sludge biodegradability', Bioresource Technology, vol. 346, pp. 126655-126655.
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This work put up a novel strategy of applying calcium peroxide (CaO2) in aerobic sludge digestion and provided insights into such system. The degradation percentage of sludge and total inorganic nitrogen production in the digesters with CaO2 at 0.02 g/g-VS-WAS increased by 25.8% and 18.8% of control. CaO2 addition allowed various key microbes related to organics degradation to accumulate in the system. Moreover, the modelling and chemical (i.e., excitation emission matrix (EEM) fluorescence and fourier transformation spectroscopy (FTIR)) analyses revealed that CaO2 addition enhanced sludge biodegradability with more release of biodegradable organics and increased degradation of recalcitrant organics, which can be transformed into biodegradable organics with the action of CaO2. Subsequent transformation test indicated that CaO2 enabled to promote hydrolysis and catabolism of biodegradable substrates in sludge. Further investigations on function mechanism suggested that CaO2 carried on positive action for sludge aerobic digestion mainly through the enhancement by ·OH.
Wang, C, Wei, W, Dai, X & Ni, B-J 2022, 'Zero valent iron greatly improves sludge destruction and nitrogen removal in aerobic sludge digestion', Chemical Engineering Journal, vol. 433, pp. 134459-134459.
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Zero-valent iron (ZVI), a low-cost metallic material, has been previously applied in effectively enhancing sewage sludge anaerobic digestion. However, the potential role of ZVI on aerobic digestion of sludge, a completely different sludge treatment method from anaerobic digestion, is still unknown. Herein, the effects of ZVI on the performance of aerobic sludge digestion were systematically studied, focusing on the sludge degradation, nitrogen removal and sludge dewaterability. Results showed ZVI greatly increased the volatile solids (VS) destruction from 27.0 ± 1.3% to 50.0 ± 1.0% and significantly enhanced the TCOD removal from 26.0 ± 1.2% to 47.9 ± 0.9% in aerobic digesters with different ZVI levels (0–20 g/L). The metabolic intermediate transformation steps of solubilization, hydrolysis and catabolism processes in aerobic digestion were all revealed to be enhanced by ZVI. More importantly, the aerobic digesters with higher ZVI levels achieved higher inorganic nitrogen removal, even with higher sludge degradation for ammonium release, due to the occurrence of both chemical and biological denitrification induced by ZVI. Correspondingly, the microbial compositions in the digesters with ZVI shifted towards the direction that was conducive to sludge degradation and nitrogen removal (e.g., aerobic denitrification) compared to control. Further, the dewaterability of the aerobically digested sludge was also improved with ZVI addition, supported by the reducing capillary suction time (CST) and negative surface potential.
Wang, C, Wei, W, Mannina, G, Dai, X & Ni, B-J 2022, 'Unveiling the distinctive role of titanium dioxide nanoparticles in aerobic sludge digestion', Science of The Total Environment, vol. 813, pp. 151872-151872.
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Aerobic digestion is considered to be a common process for the stabilization of waste activated sludge (WAS) in the small-sized wastewater treatment systems, while the broad application of titanium dioxide nanoparticles (TiO2 NPs) results in their unavoidable existence in WAS aerobic digestion, with its role in aerobic sludge digestion being never documented. This study set up a series of aerobic sludge digesters to evaluate the previously unknown role of TiO2 NPs on the performance of the digesters. The volatile solids (VS) degradation percentage increased from 21.9 ± 0.6% to 26.9 ± 0.1% - 30.0 ± 0.3% with the different contents of TiO2 NPs (0, 1, 20 and 50 mg/L). Similarly, the total inorganic nitrogen production increased from 23.1 ± 0.3 to 31.0 ± 0.1 mg N/g VS with the rising TiO2 NPs concentrations from 0 to 50 mg/L. The microbial analysis suggested that TiO2 NPs contributed to the accumulation of specific microbes correlated with the degradation of organic substances and the conversion of nitrogen compounds. Model-based analysis showed the higher biodegradability and hydrolysis rate of sludge with TiO2 NPs. Further mechanistic studies indicated that the enhancement of WAS solubilization and the degradation of recalcitrant substances (e.g., humic acid and cellulose) contributed to the better performance of experimental aerobic digesters, which was confirmed by the fourier transformation infrared spectroscopy (FTIR) indicating the converting of these materials into biodegradable substrates for digestion with TiO2 NPs. It could be inferred from this investigation that aerobic digestion rather than anaerobic digestion would be a more suitable treatment method for sludge containing TiO2 NPs.
Wang, C, Wei, W, Zhang, Y-T & Ni, B-J 2022, 'Evaluating the role of biochar in mitigating the inhibition of polyethylene nanoplastics on anaerobic granular sludge', Water Research, vol. 221, pp. 118855-118855.
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The extensive application of anaerobic granular sludge (AGS) to wastewater treatment for methane recovery has drawn considerable attention to the system performances affected by the presence of emerging contaminants in wastewater such as nanoplastics. However, effective strategies on how to mitigate the inhibition caused by nanoplastics remained unavailable. In this study, a novel strategy using biochar to mitigate the inhibition on the AGS performances caused by polyethylene nanoplastics (PE-NPs) was proposed and the corresponding mitigating mechanisms involved were explored. The PE-NPs solely decreased the level of methane recovery of AGS to 71.3 ± 2.7% of control, which was subsequently increased to 85.6 ± 0.8% of control with the presences of both biochar and PE-NPs, although biochar solely showed no obvious effect on methane production. The addition of biochar also elevated the granule size of AGS, along with AGS integrity based on the morphological observation. Moreover, the distributions of live cells and functional microbes related to acidification and methanation increased with biochar addition compared to sole PE-NPs exposure. More extracellular polymeric substance (EPS) was secreted when biochar was involved in AGS systems, with more protein being detected to maintain the granule structure of AGS. Evaluation of adsorption tests indicated that biochar possessed stronger affinity for PE-NPs than AGS, thus capturing the PE-NPs that would originally contact AGS and posing less toxicity to microorganisms.
Wang, C, Wei, W, Zhang, Y-T, Dai, X & Ni, B-J 2022, 'Different sizes of polystyrene microplastics induced distinct microbial responses of anaerobic granular sludge', Water Research, vol. 220, pp. 118607-118607.
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Recent investigations confirmed the inhibitory effect of microplastics with single sizes on the anaerobic granular sludge (AGS) wastewater treatment system. However, the differences of toxicity from different sizes of microplastics toward AGS and their underlying mechanism are still unclear. In this work, the responds of AGS exposed to different particle sizes of polystyrene microplastics (PS-MPs) were reported. The results showed that the increasing particle sizes (from 0.5 μm to 150 μm) of PS-MPs induced a gradually increasing and distinct inhibitory (from 6.7% to 16.2%) effect on the cumulative methane production by AGS, accompanied by the similar decreasing organic carbon degradation trends. Correspondingly, the integrity and the cell viability of the AGS granules were damaged and the populations of the key acidogens and methanogens were reduced when exposed to PS-MPs, which was particularly evident in the reactors affected by the larger micron-sized PS-MPs. The zeta potential and contact angle indicated that the larger-sized PS-MPs had the stronger dispersive properties and affinity for AGS, causing the higher oxidative stress and leachates toxicity. Further investigation revealed that the tolerance of AGS to PS-MPs toxicity also exhibited size-dependent trend. Larger particles (e.g., 150 μm) of PS-MPs inhibited extracellular polymeric substance (EPS) secretion, while smaller particles (e.g., 0.5 μm) promoted EPS generation with the release of more humic acid, alleviating their toxicity.
Wang, CT, Lan, TH, Chong, WT, Ong, HC & Chen, SX 2022, 'An Optimal Inlet Flow Angle Design of Vascular-type Micromixer', Journal of the Chinese Society of Mechanical Engineers Transactions of the Chinese Institute of Engineers Series C Chung Kuo Chi Hsueh Kung Ch Eng Hsuebo Pao, vol. 43, no. 1, pp. 57-62.
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Micromixers are important modules in medical applications. Novel and excellent biometric micromixers have been designed with an effective mechanism of flow splitting and recombining (SAR). The aim of this study is to modify the prototype of a biometric micromixer by designing the inlet flow channel and generating a better flow geometry in the blood vessel-micromixer. The blood vessel-micromixer was also investigated with different inlet channel angles and various Reynolds number ratios (ReI2 and Rer) for the estimation of their influence on the mixing performance of the micro-mixer. The ReI2 is the inlet 2 and the Rer is the combination of side flow and middle flow effects during the different flow conditions. Results showed that the blood vessel-micromixer with an inlet channel angle of 30° (Ø =30°) can be optimized for future research works. In addition, optimal performance with a Amixing index of 0.88 would be achieved at a condition of ReI2= 1 and Rer = 0.7. These findings will be definitely useful for the improvement of micromixer applications in the future.
Wang, D, Wang, X, Yang, Q, Zhang, Y & Wu, C 2022, 'Dynamic response analysis of a large commercial aircraft hitting the AP1000 containment vessel', Zhendong Yu Chongji Journal of Vibration and Shock, vol. 41, no. 10, pp. 260-291.
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Since the event of '9•11', 2001, the protection of nuclear power plants against the impact of large commercial aircraft has been a hot issue in the field of nuclear safety. Using ANSYS/LS-DYNA software, the refinement finite element models of a Boeing 737 MAX 8 and a AP1000 containment vessel were established. The accuracy and validity of the finite element modelling of the plane hitting were validated by using the Riera method. Five different initial impact velocities (100 m/s, 150 m/s, 200 m/s, 250 m/s and 300 m/s) and five different impact heights (39 m, 30 m, 47 m, 54 m and 65 m) in the plane hitting process were taken into account in the numerical simulation. The time history of the impact force and kinetic energy of the aircraft, the dynamic response of the steel containment, the equivalent stress distribution and the local damage of the aircraft were studied and analyzed. The research results show that the engine's contribution to the aircraft impact force is about 3-4 times of that of the front of the fuselage; the peak impact force on the steel tube body in the equivalent beam segment is larger, than that on other segments, the largest one is up to 171% of the latter (at the rate of 300 m/s); the junction part of the containment cylinder body to the dome is the most dangerous position, where the penetrated sizes are greater than those at other locations, the largest penetrated size in ring direction is 29.68 m, and that in vertical direction is 17.86 m. The dome in all conditions are not damaged. The equivalent steel beam segment can withstand the aircraft impact very well. When the impact velocity of the aircraft is greater than 150 m/s, the influence range of the equivalent steel plate stress in the impact area of the containment vessel decreases with the increase of initial impact velocity, and the distribution range of the equivalent steel plate stress in the impact area of the equivalent beam segment is larger than that of the non-equivale...
Wang, D, Zhang, J, Li, J, Wang, W, Shon, HK, Huang, H, Zhao, Y & Wang, Z 2022, 'Inorganic scaling in the treatment of shale gas wastewater by fertilizer drawn forward osmosis process', Desalination, vol. 521, pp. 115396-115396.
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In this study, fertilizer drawn forward osmosis (FDFO) process was applied for the treatment of shale gas wastewater. The forward osmosis (FO) experiments with simulated shale gas wastewater and real shale gas wastewater were carried out, respectively. The effects of reverse salt diffusion on the inorganic fouling to the membrane surface was systematically investigated. Two commercial FO membranes were selected and the optimized operating conditions were evaluated. It was found that calcium sulfate scaling can be alleviated by optimizing the operating parameters, including increasing flow rate and decreasing temperature. Furthermore, the Aquaporin FO membrane, which has lower reverse salt flux and less surface charge potential, exhibited lower fouling tendency. Under the optimal operating conditions, the effects of reverse salt diffusion on the barium sulfate scaling were also analyzed. The presence of calcium ions can alleviate barium sulfate scaling, while sodium chloride will aggravate the barium sulfate scaling. In addition, the scaling behavior of real shale gas wastewater was further explored. Inorganic scaling phenomenon seriously affected the FO membrane performance and lower pH had beneficial effect on recycling the real shale gas wastewater. The present study provided both theoretical fundamentals and industry applicable practices for implementing FO technology in the treatment and resource recovery of shale gas wastewater.
Wang, D, Zhang, X, Wan, Y, Yu, D, Xu, G & Deng, S 2022, 'Modeling Sequential Listening Behaviors With Attentive Temporal Point Process for Next and Next New Music Recommendation', IEEE Transactions on Multimedia, vol. 24, pp. 4170-4182.
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Wang, E, Yao, R, Luo, Q, Li, Q, Lv, G & Sun, G 2022, 'High-temperature and dynamic mechanical characterization of closed-cell aluminum foams', International Journal of Mechanical Sciences, vol. 230, pp. 107548-107548.
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Wang, E, Yuan, X, Wang, Y, Chen, W, Zhou, X, Hu, S & Yuan, S 2022, 'Blood conservation outcomes and safety of tranexamic acid in coronary artery bypass graft surgery', International Journal of Cardiology, vol. 348, pp. 50-56.
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Wang, F, Long, G, Bai, M, Wang, J, Yang, Z, Zhou, X & Zhou, JL 2022, 'Cleaner and safer disposal of electrolytic manganese residues in cement-based materials using direct electric curing', Journal of Cleaner Production, vol. 356, pp. 131842-131842.
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The direct stockpiling of electrolytic manganese residues (EMR) poses a major environmental issue, and more eco-friendly disposal is urgently needed. The combination of cement solidified waste (CSW) and direct electric curing (DEC) provides a potential solution for hazard-free value-added utilization of EMR. The effects of DEC voltages and EMR dosages on mechanical properties, hydrated products, pore structure of mixture were investigated. The influencing mechanism of DEC on the properties of cement hydration was explored in-depth using TG and XRD results. The environmental and economic evolution of DEC was analyzed, and the leaching test was conducted to evaluate the immobilization of heavy metals. Results indicate that cement-EMR pastes cured in higher DEC voltage and reduced EMR dosage increase mechanical strength and improve pore structure and capillary water absorption with respect to indoor curing (IC). The increased cement dosage improves the effectiveness of CSW, while the increased DEC voltage enhances the ionic driving force. The boosted ettringite formation occurs in system after introducing DEC and amplifying the DEC voltage. The improvement of ion concentration in DEC accelerates the formation of hydration products. The CO2-e per MPa (EIF) and cost per MPa (CIF) values of paste DMP-7 cured in 12V-DEC exhibit the lowest values with respect to those cured in other voltages and IC. The decrease in the leaching amount of Mn2+ and NH4+-N as the DEC voltage increases, and the 28-d leaching concentration of Mn2+ and NH4+-N in pastes are in accordance with the national standards. The application of DEC and cement-solidified disposal for EMR could provide a potential solution for high-value and large-capacity disposal of hazardous solid waste.
Wang, F, Long, G, Bai, M, Wang, J, Zhou, JL & Zhou, X 2022, 'Application of electrolytic manganese residues in cement products through pozzolanic activity motivation and calcination', Journal of Cleaner Production, vol. 338, pp. 130629-130629.
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Degradation in grade of manganese ore aggravates the complexity of electrolytic manganese residue (EMR). Calcination is one of the most practical pretreatment methods to improve EMR activity and dispose the hazardous elements. In this paper, the evolution of mineral phase, pozzolanic activity, pore structure and harmful components induced by calcining EMR was investigated. The results show that EMR calcined at 800 °C has the strength activity index (SAI) of 84.79 at 28 d, which is attributed to the decomposition of dihydrate gypsum and the formation of activated calcium, silicon and aluminum oxide. The formation of β-type hemihydrate gypsum increases the pozzolan activity, while the latter is limited by the formation of stable Mn-spinel (Mn3O4) and Mn-hercynite (MnFe2O4). In the EMR-doped mortar matrix, the production of a large amount of ettringite due to the existence of gypsum, as well as common C-S-H, portlandite and AFm, which strongly verify the pozzolanic activity of EMR. Leaching results show that Mn2+ and NH4+-N could not be eliminated completely at low temperature (<600 °C), but could be completely stabilized in the alkaline environment provided by the cement. The Mn2+ and NH4+-N levels in mortar are fully below the regulatory standards when calcinated above 800 °C. All heavy metals are fixed in the cement and calcination process, ensuring the cleaner utilization of EMR in building materials.
Wang, F, Long, G, He, J, Xie, Y, Tang, Z, Zhou, X, Bai, M & Zhou, JL 2022, 'Fabrication of Energy-Efficient Carbonate-Based Cementitious Material Using Sodium Meta-Aluminate Activated Limestone Powder', ACS Sustainable Chemistry & Engineering, vol. 10, no. 20, pp. 6559-6572.
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Limestone powder (LP) and sodium meta-aluminate (SMA) were used to fabricate calcium carbonate-based cementitious material, as a solution to address the solid waste problem. The effects of SMA doses and curing conditions on the hydration properties and mechanical performance of paste were investigated. The results show that the 28-day unconfined compressive strength and flexural strength of the paste with an LP/SMA ratio of 2/1 were 49.7 and 15.9 MPa, respectively. The characterization by scanning electron microscopy, X-ray diffraction, and thermal gravimetry shows that the calcium aluminum carbonate hydroxide hydrate (CACHH) was the predominant hydrated product and had a dense layered double hydroxide structure (LDHs). The microbridge effect developed by LDHs significantly increases the flexural strength of the paste. Meanwhile, the developed paste exhibited an extremely low carbon emission and energy consumption. This study also reveals the mechanism of LP incorporated with SMA to form CACHH. Overall, this work provides an approach of high value-added utilization for LP as a binder without tedious operation, which could address carbon emission reduction and circular economy of LP.
Wang, G, Choi, K-S, Teoh, JY-C & Lu, J 2022, 'Deep Cross-Output Knowledge Transfer Using Stacked-Structure Least-Squares Support Vector Machines', IEEE Transactions on Cybernetics, vol. 52, no. 5, pp. 3207-3220.
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This article presents a new deep cross-output knowledge transfer approach based on least-squares support vector machines, called DCOT-LS-SVMs. Its aim is to improve the generalizability of least-squares support vector machines (LS-SVMs) while avoiding the complicated parameter tuning process that occurs in many kernel machines. The proposed approach has two significant characteristics: 1) DCOT-LS-SVMs is inspired by a stacked hierarchical architecture that combines several layer-by-layer LS-SVMs modules. The module that forms the higher layer has additional input features that consider the predictions from all previous modules and 2) cross-output knowledge transfer is used to leverage knowledge from the predictions of the previous module to improve the learning process in the current module. With this approach, the model's parameters, such as a tradeoff parameter C and a kernel width δ, can be randomly assigned to each module in order to greatly simplify the learning process. Moreover, DCOT-LS-SVMs is able to autonomously and quickly decide the extent of the cross-output knowledge transfer between adjacent modules through a fast leave-one-out cross-validation strategy. In addition, we present an imbalanced version of DCOT-LS-SVMs, called IDCOT-LS-SVMs, given that imbalanced datasets are common in real-world scenarios. The effectiveness of the proposed approaches is demonstrated through a comparison with five comparative methods on UCI datasets and with a case study on the diagnosis of prostate cancer.
Wang, G, Cong, G, Zhang, Y, Hai, Z & Ye, J 2022, 'A Synopsis Based Approach for Itemset Frequency Estimation over Massive Multi-Transaction Stream', ACM Transactions on Knowledge Discovery from Data, vol. 16, no. 2, pp. 1-30.
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The streams where multiple transactions are associated with the same key are prevalent in practice, e.g., a customer has multiple shopping records arriving at different time. Itemset frequency estimation on such streams is very challenging since sampling based methods, such as the popularly used reservoir sampling, cannot be used. In this article, we propose a novel k -Minimum Value (KMV) synopsis based method to estimate the frequency of itemsets over multi-transaction streams. First, we extract the KMV synopses for each item from the stream. Then, we propose a novel estimator to estimate the frequency of an itemset over the KMV synopses. Comparing to the existing estimator, our method is not only more accurate and efficient to calculate but also follows the downward-closure property. These properties enable the incorporation of our new estimator with existing frequent itemset mining (FIM) algorithm (e.g., FP-Growth) to mine frequent itemsets over multi-transaction streams. To demonstrate this, we implement a KMV synopsis based FIM algorithm by integrating our estimator into existing FIM algorithms, and we prove it is capable of guaranteeing the accuracy of FIM with a bounded size of KMV synopsis. Experimental results on massive streams show our estimator can significantly improve on the accuracy for both estimating itemset frequency and FIM compared to the existing estimators.
Wang, G, Weng, L, Huang, Y, Ling, Y, Zhen, Z, Lin, Z, Hu, H, Li, C, Guo, J, Zhou, JL, Chen, S, Jia, Y & Ren, L 2022, 'Microbiome-metabolome analysis directed isolation of rhizobacteria capable of enhancing salt tolerance of Sea Rice 86', Science of The Total Environment, vol. 843, pp. 156817-156817.
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Soil salinization has been recognized as one of the main factors causing the decrease of cultivated land area and global plant productivity. Application of salt tolerant plants and improvement of plant salt tolerance are recognized as the major routes for saline soil restoration and utilization. Sea rice 86 (SR86) is known as a rice cultivar capable of growing in saline soil. Genome sequencing and transcriptome analysis of SR86 have been conducted to explore its salt tolerance mechanisms while the contribution of rhizobacteria is underexplored. In the present study, we examined the rhizosphere bacterial diversity and soil metabolome of SR86 seedlings under different salinity to understand their contribution to plant salt tolerance. We found that salt stress could significantly change rhizobacterial diversity and rhizosphere metabolites. Keystone taxa were identified via co-occurrence analysis and the correlation analysis between keystone taxa and rhizosphere metabolites indicated lipids and their derivatives might play an important role in plant salt tolerance. Further, four plant growth promoting rhizobacteria (PGPR), capable of promoting the salt tolerance of SR86, were isolated and characterized. These findings might provide novel insights into the mechanisms of plant salt tolerance mediated by plant-microbe interaction, and promote the isolation and application of PGPR in the restoration and utilization of saline soil.
Wang, G, Xing, D, Liu, W, Zhu, Y, Liu, H, Yan, L, Fan, K, Liu, P, Yu, B, Li, JJ & Wang, B 2022, 'Preclinical studies and clinical trials on mesenchymal stem cell therapy for knee osteoarthritis: A systematic review on models and cell doses', International Journal of Rheumatic Diseases, vol. 25, no. 5, pp. 532-562.
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AbstractAimTo provide a systematic analysis of the study design in knee osteoarthritis (OA) preclinical studies, focusing on the characteristics of animal models and cell doses, and to compare these to the characteristics of clinical trials using mesenchymal stem cells (MSCs) for the treatment of knee OA.MethodA systematic and comprehensive search was conducted using the PubMed, Web of Science, Ovid, and Embase electronic databases for research papers published in 2009‐2020 on testing MSC treatment in OA animal models. The PubMed database and ClinicalTrials.gov website were used to search for published studies reporting clinical trials of MSC therapy for knee OA.ResultsIn total, 9234 articles and two additional records were retrieved, of which 120 studies comprising preclinical and clinical studies were included for analysis. Among the preclinical studies, rats were the most commonly used species for modeling knee OA, and anterior cruciate ligament transection was the most commonly used method for inducing OA. There was a correlation between the cell dose and body weight of the animal. In clinical trials, there was large variation in the dose of MSCs used to treat knee OA, ranging from 1 × 106 to 200 × 106 cells with an average of 37.91 × 106 cells.ConclusionMesenchymal stem cells have shown great potential in improving pain relief and tissue protection in both preclinical and clinical studies of knee OA. Further high‐quality preclinical and clinical studies are needed to explore the dose effectiveness relationship of MSC therapy and to translate the findings from preclinical studies to humans.
Wang, G, Zhou, T, Choi, K-S & Lu, J 2022, 'A Deep-Ensemble-Level-Based Interpretable Takagi–Sugeno–Kang Fuzzy Classifier for Imbalanced Data', IEEE Transactions on Cybernetics, vol. 52, no. 5, pp. 3805-3818.
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Existing research reveals that the misclassification rate for imbalanced data depends heavily on the problematic areas due to the existence of small disjoints, class overlap, borderline, and rare data samples. In this study, by stacking zero-order Takagi-Sugeno-Kang (TSK) fuzzy subclassifiers on the minority class and its problematic areas in the deep ensemble, a novel deep-ensemble-level-based TSK fuzzy classifier (IDE-TSK-FC) for imbalanced data classification tasks is presented to achieve both promising classification performance and high interpretability of zero-order TSK fuzzy classifiers. Simultaneously, according to the stacked generalization principle, the proposed classifier lifts up oversampling from the data level to the deep ensemble level with a guarantee of enhanced generalization capability for class imbalance learning. In the structure of IDE-TSK-FC, the first interpretable zero-order TSK fuzzy subclassifier is built on the original training dataset. After that, several successive zero-order TSK fuzzy subclassifiers are stacked layer by layer on the newly identified problematic areas from the original training dataset plus the corresponding interpretable predictions obtained by the averaging strategy on all previous layers. IDE-TSK-FC simply takes the classical K-nearest neighboring algorithm at each layer to identify its problematic area that consists of the minority samples and its surrounding K majority neighbors. After randomly neglecting certain input features and randomly selecting the five Gaussian membership functions for all the chosen input features and the augmented feature in the premise of each fuzzy rule, each subclassifier can be quickly obtained by using the least learning machine to determine the consequent part of each fuzzy rule. The experimental results on both the public datasets and a real-world healthcare dataset demonstrate IDE-TSK-FC's superiority in class imbalanced learning.
Wang, H, Ding, S, Yang, S, Liu, C, Yu, S & Zheng, X 2022, 'Guided Activity Prediction for Minimally Invasive Surgery Safety Improvement in the Internet of Medical Things', IEEE Internet of Things Journal, vol. 9, no. 6, pp. 4758-4768.
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With the application of the Internet of Medical Things (IoMT) in minimally invasive surgery (MIS), surgeons now have a better chance at hard-to-treat cases by carrying out more complicated MIS workflows. However, a scheduled surgical workflow is often required to be updated based on the patient's internal tissue states. Perioperative complications could occur if in-time adjustments are lacking in the operating rooms when needed. To help manage the uncertainty of live surgical workflows in the IoMT environment, we propose a MIS safety improvement framework. It helps surgeons in predicting surgical workflows with limited MIS video frames by embedding our proposed model GuidedNet. To predict future surgical activities, we first build three isomorphic neural networks to capture the spatiotemporal information. Then, we establish a guidance fusion module to handle the contextual information. It guides the GuidedNet to recognize the surgical stage. Moreover, we build a novel joint loss function to train the GuidedNet to predict the future surgical stage. We evaluate the approach on a large data set that contains 80 cholecystectomy videos (Cholec-80) and compare it with the state of the art. Experiments show that the GuidedNet can assist surgeons in carrying out MIS as well as guide the next stage of surgery for improving surgical safety. Comparing to the state of the art, our approach can obtain better predict accuracy (up to 79%) with less computing resource consumption. The result also shows that our approach has a high application prospect in video classification in other Internet of Things scenarios.
Wang, H, Lian, D, Liu, W, Wen, D, Chen, C & Wang, X 2022, 'Powerful graph of graphs neural network for structured entity analysis', World Wide Web, vol. 25, no. 2, pp. 609-629.
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Wang, H, Obeidy, P, Wang, Z, Zhao, Y, Wang, Y, Su, QP, Cox, CD & Ju, LA 2022, 'Fluorescence-coupled micropipette aspiration assay to examine calcium mobilization caused by red blood cell mechanosensing', European Biophysics Journal, vol. 51, no. 2, pp. 135-146.
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AbstractMechanical stimuli such as tension, compression, and shear stress play critical roles in the physiological functions of red blood cells (RBCs) and their homeostasis, ATP release, and rheological properties. Intracellular calcium (Ca2+) mobilization reflects RBC mechanosensing as they transverse the complex vasculature. Emerging studies have demonstrated the presence of mechanosensitive Ca2+ permeable ion channels and their function has been implicated in the regulation of RBC volume and deformability. However, how these mechanoreceptors trigger Ca2+ influx and subsequent cellular responses are still unclear. Here, we introduce a fluorescence-coupled micropipette aspiration assay to examine RBC mechanosensing at the single-cell level. To achieve a wide range of cell aspirations, we implemented and compared two negative pressure adjusting apparatuses: a homemade water manometer (− 2.94 to 0 mmH2O) and a pneumatic high-speed pressure clamp (− 25 to 0 mmHg). To visualize Ca2+ influx, RBCs were pre-loaded with an intensiometric probe Cal-520 AM, then imaged under a confocal microscope with concurrent bright-field and fluorescent imaging at acquisition rates of 10 frames per second. Remarkably, we observed the related changes in intracellular Ca2+ levels immediately after aspirating individual RBCs in a pressure-dependent manner. The RBC aspirated by the water manometer only displayed 1.1-fold increase in fluorescence intensity, whereas the RBC aspirated by the pneumatic clamp showed up to threefold increase. These results demonstrated the water manometer as a gentle tool for cell manipulation with minimal pre-activation, while the high-speed pneumatic clamp as a much stronger pressure actuator to examine cell mechanosensing directly. Together, this multimodal platform enables us to precisely co...
Wang, J, An, Y, Li, Z & Lu, H 2022, 'A novel combined forecasting model based on neural networks, deep learning approaches, and multi-objective optimization for short-term wind speed forecasting', Energy, vol. 251, pp. 123960-123960.
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Wang, J, Li, Q, Ma, X & Lu, H 2022, 'Distribution parameter-determining method comparison for airborne wind energy potential assessment in the eastern coastal area of China', Sustainable Energy Technologies and Assessments, vol. 52, pp. 102161-102161.
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Wang, J, Liang, J, Yao, J, Song, HX, Yang, XT, Wu, FC, Ye, Y, Li, JH & Wu, T 2022, 'Meta-analysis of clinical trials focusing on hypertonic dextrose prolotherapy (HDP) for knee osteoarthritis', Aging Clinical and Experimental Research, vol. 34, no. 4, pp. 715-724.
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Wang, J, Liu, Y, Morsch, M, Lu, Y, Shangguan, P, Han, L, Wang, Z, Chen, X, Song, C, Liu, S, Shi, B & Tang, BZ 2022, 'Brain‐Targeted Aggregation‐Induced‐Emission Nanoparticles with Near‐Infrared Imaging at 1550 nm Boosts Orthotopic Glioblastoma Theranostics', Advanced Materials, vol. 34, no. 5.
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AbstractA remaining challenge in the treatment of glioblastoma multiforme (GBM) is surmounting the blood–brain barrier (BBB). Such a challenge prevents the development of efficient theranostic approaches that combine reliable diagnosis with targeted therapy. In this study, brain‐targeted near‐infrared IIb (NIR‐IIb) aggregation‐induced‐emission (AIE) nanoparticles are developed via rational design, which involves twisting the planar molecular backbone with steric hindrance. The resulting nanoparticles can balance competing responsiveness demands for radiation‐mediated NIR fluorescence imaging at 1550 nm and non‐radiation NIR photothermal therapy (NIR‐PTT). The brain‐targeting peptide apolipoprotein E peptide (ApoE) is grafted onto these nanoparticles (termed as ApoE‐Ph NPs) to target glioma and promote efficient BBB traversal. A long imaging wavelength 1550 nm band‐pass filter is utilized to monitor the in vivo biodistribution and accumulation of the nanoparticles in a model of orthotopic glioma, which overcomes previous limitations in wavelength range and equipment. The results demonstrate that the ApoE‐Ph NPs have a higher PTT efficiency and significantly enhanced survival of mice bearing orthotopic GBM with moderate irradiation (0.5 W cm−2). Collectively, the work highlights the smart design of a brain‐targeted NIR‐II AIE theranostic approach that opens new diagnosis and treatment options in the photonic therapy of GBM.
Wang, J, Sun, Y, Mahfoud, RJ, Alhelou, HH & Siano, P 2022, 'Integrated modeling of regional and park-level multi-heterogeneous energy systems', Energy Reports, vol. 8, pp. 3141-3155.
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Wang, J, Wang, S, Zeng, B & Lu, H 2022, 'A novel ensemble probabilistic forecasting system for uncertainty in wind speed', Applied Energy, vol. 313, pp. 118796-118796.
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The quantification of wind speed uncertainty is of great significance for real-time control of wind turbines and power grid dispatching. However, the intermittence and fluctuation of wind energy present great challenges in modeling its uncertainty; research in this field is limited. A quantile regression bi-directional long short-term memory network (QrBiLStm) and a novel ensemble probabilistic forecasting strategy are proposed in this study to explore ensemble probabilistic forecasting. To verify the reliability of the proposed ensemble probabilistic forecasting system, the uncertainties of wind speed at wind farms in China were modeled as a case study. The results of comparative experiments including 15 other models demonstrate the superiority of this ensemble probabilistic forecasting system in terms of sharpness while maintaining high interval coverage. More specifically, it was observed that the prediction interval coverage probability obtained by the proposed system is above 97%, and the sharpness is improved by at least 24.21% as compared with the commonly used single models. The proposed ensemble probabilistic forecasting system can accurately quantify the uncertainty of wind speed, and also reduce the operation cost of power systems by improving the efficiency of wind energy utilization.
Wang, K, Bao, G, Fan, Q, Zhu, L, Yang, L, Liu, T, Zhang, Z, Li, G, Chen, X, Xu, X, Xu, X, He, B & Zheng, Y 2022, 'Feasibility evaluation of a Cu-38 Zn alloy for intrauterine devices: In vitro and in vivo studies', Acta Biomaterialia, vol. 138, pp. 561-575.
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The existing adverse effects of copper in copper-containing intrauterine devices (Cu-IUDs) have raised concerns regarding their use. These adverse effects include burst release of cupric ions (Cu2+) at the initial stage and an increasingly rough surface of the Cu-IUDs. In this study, we investigated the use of two copper alloys, Cu-38 Zn and H62 as the new upgrading or alternative material for IUDs. Their corrosive properties were studied in simulated uterine fluid (SUF) by using electrochemical methods, with pure Cu as a control. We studied the in vitro long-term corrosion behaviors in SUF, cytotoxicity to uterine cells (human endometrial epithelial cells and human endometrial stromal cells), in vivo biocompatibility and contraceptive efficacy of pure Cu, H62, and Cu-38 Zn. In the first month, the burst release rate of Cu2+ in the Cu-38 Zn group was significantly lower than those in the pure Cu and H62 groups. The in vitro cytocompatibility Cu-38 Zn was better than that of pure Cu and H62. Moreover, Cu-38 Zn showed improved tissue biocompatibility in vivo experiments. Therefore, the contraceptive efficacy of the Cu-38 Zn is still maintained as high as the pure Cu while the adverse effects are significantly eased, suggesting that Cu-38 Zn can be a suitable potential candidate material for IUDs. STATEMENT OF SIGNIFICANCE: The existing adverse effects associated with the intrinsic properties of copper materials for copper-containing intrauterine devices (Cu-IUD) are of concern in their employment. Such as, burst release of cupric ions (Cu2+) at the initial stage and an increasingly rough surface of the Cu-IUD. In this work, Cu alloyed with a high amount of bioactive Zn was used for a Cu-IUD. The Cu-38 Zn alloy exhibited reduced burst release of Cu2+ within the first month compared with the pure Cu and H62. Furthermore, the Cu-38 Zn alloy displayed significantly improved biocompatibility and a much smoother surface. Therefore, high antifertility efficacy o...
Wang, K, Dou, Y, Sun, T, Qiao, P & Wen, D 2022, 'An automatic learning rate decay strategy for stochastic gradient descent optimization methods in neural networks', International Journal of Intelligent Systems, vol. 37, no. 10, pp. 7334-7355.
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Wang, K, Lin, X, Qin, L, Zhang, W & Zhang, Y 2022, 'Towards efficient solutions of bitruss decomposition for large-scale bipartite graphs', The VLDB Journal, vol. 31, no. 2, pp. 203-226.
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In recent years, cohesive subgraph mining in bipartite graphs becomes a popular research topic. An important cohesive subgraph model k-bitruss is the maximal cohesive subgraph where each edge is contained in at least k butterflies (i.e., (2, 2)-bicliques). In this paper, we study the bitruss decomposition problem which aims to find all the k-bitrusses for k≥ 0. The existing algorithms follow a bottom-up strategy which peels the edges with the lowest butterfly support iteratively. In this peeling process, these algorithms are time-consuming to enumerate all the supporting butterflies for each edge. To solve this issue, we propose a novel online index, the BE-Index which compresses butterflies into k-blooms (i.e., (2, k)-bicliques). Based on the BE-Index, the new bitruss decomposition algorithm BiT-BU is proposed, along with two batch-based optimizations, to accomplish the butterfly enumeration of the peeling process efficiently. Furthermore, the BiT-PC algorithm is designed which is more efficient against handling the edges with high butterfly supports. Besides, we explore shared-memory parallel solutions to handle large graphs in a more efficient way. In the parallel algorithms, we propose effective techniques to reduce conflicts among threads. We theoretically show that our new algorithms significantly reduce the time complexities of the existing algorithms. In addition, extensive empirical evaluations are conducted on real-world datasets. The experimental results further validate the effectiveness of the bitruss model and demonstrate that our proposed solutions significantly outperform the state-of-the-art techniques by several orders of magnitude.
Wang, K, Ling, Y, Zhang, Y, Yu, Z, Wang, H, Bai, G, Ooi, BC & Dong, JS 2022, 'Characterizing Cryptocurrency-themed Malicious Browser Extensions', Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 6, no. 3, pp. 1-31.
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Due to the surging popularity of various cryptocurrencies in recent years, a large number of browser extensions have been developed as portals to access relevant services, such as cryptocurrency exchanges and wallets. This has stimulated a wild growth of cryptocurrency themed malicious extensions that cause heavy financial losses to the users and legitimate service providers. They have shown their capability of evading the stringent vetting processes of the extension stores, highlighting a lack of understanding of this emerging type of malware in our community. In this work, we conduct the first systematic study to identify and characterize cryptocurrency-themed malicious extensions. We monitor seven official and third-party extension distribution venues for 18 months (December 2020 to June 2022) and have collected around 3600 unique cryptocurrency-themed extensions. Leveraging a hybrid analysis, we have identified 186 malicious extensions that belong to five categories. We then characterize those extensions from various perspectives including their distribution channels, life cycles, developers, illicit behaviors, and illegal gains. Our work unveils the status quo of the cryptocurrency-themed malicious extensions and reveals their disguises and programmatic features on which detection techniques can be based. Our work serves as a warning to extension users, and an appeal to extension store operators to enact dedicated countermeasures. To facilitate future research in this area, we release our dataset of the identified malicious extensions and open-source our analyzer.
Wang, K, Lu, J, Liu, A, Song, Y, Xiong, L & Zhang, G 2022, 'Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation', Neurocomputing, vol. 491, pp. 288-304.
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As an excellent ensemble algorithm, Gradient Boosting Decision Tree (GBDT) has been tested extensively with static data. However, real-world applications often involve dynamic data streams, which suffer from concept drift problems where the data distribution changes overtime. The performance of GBDT model is degraded when applied to predict data streams with concept drift. Although incremental learning can help to alleviate such degrading, finding a perfect learning rate (i.e., the iteration in GBDT) that suits all time periods with all their different drift severity levels can be difficult. In this paper, we convert the issue of determining an optimal learning rate into the issue of choosing the best adaptive iterations when tuning GBDT. We theoretically prove that drift severity is closely related to the convergence rate of model. Accordingly, we propose a novel drift adaptation method, called adaptive iterations (AdIter), that automatically chooses the number of iterations for different drift severities to improve the prediction accuracy for data streams under concept drift. In a series of comprehensive tests with seven state-of-the-art drift adaptation methods on both synthetic and real-world data, AdIter yielded superior accuracy levels.
Wang, K, Wang, J, Zeng, B & Lu, H 2022, 'An integrated power load point-interval forecasting system based on information entropy and multi-objective optimization', Applied Energy, vol. 314, pp. 118938-118938.
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During an era of rapid growth in electricity demand throughout society, accurate forecasting of electricity loads has become increasingly important to guarantee a stable power supply. Nevertheless, historical models do not address the structure of the data itself, and a single model cannot accurately determine the nonlinear characteristics of the data. This would not allow for accurate and stable predictions. With the aim of filling this gap, this paper proposes an innovative intelligent power load point-interval forecasting system. The system discretizes the time series, then performs efficient dimensionality reduction by fuzzification, and multi-level optimization of five benchmark deep learning models by the proposed multi-objective optimization algorithm, and finally analyzes the uncertainty of the prediction results. Experiments comparing the developed prediction system with other models were conducted on three datasets, and the prediction results were discussed for validation from multiple perspectives. The simulation results show that the proposed model has superior prediction accuracy, robustness and uncertainty analysis capability, and can provide accurate deterministic prediction information and fluctuation interval analysis to ensure the long-term safety and stability and operation of the grid.
Wang, K, Wang, S, Cao, X & Qin, L 2022, 'Efficient Radius-Bounded Community Search in Geo-Social Networks', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 9, pp. 4186-4200.
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Driven by real-life applications in geo-social networks, we study the problem of computing radius-bounded k-cores (RB-k-cores) that aims to find communities satisfying both social and spatial constraints. In particular, the model k-core (i.e., the subgraph where each vertex has at least k neighbors) is used to ensure the social cohesiveness, and a radius-bounded circle is used to restrict the locations of users in an RB-k-core. We explore several algorithmic paradigms to compute RB-k-cores, including a triple-vertex-based paradigm, a binary-vertex-based paradigm, and a paradigm utilizing the concept of rotating circles. The rotating-circle-based paradigm is further enhanced by several pruning techniques to achieve better efficiency. In addition, to find representative RB-k-cores, we study the diversified radius-bounded k-core search problem, which finds t RB-k-cores to cover the most number of vertices. We first propose a baseline algorithm that identifies the distinctive RB-k-cores after finding all the RB-k-cores. Beyond this, we design algorithms that can efficiently maintain the top-t candidate RB-k-cores and also achieve a guaranteed approximation ratio. Experimental studies on both real and synthetic datasets demonstrate that our proposed techniques can efficiently compute (diversified) RB-k-cores. Moreover, our techniques can be used to compute the minimum-circle-bounded k-core and significantly outperform the existing techniques.
Wang, K, Wang, S, Cao, X & Qin, L 2022, 'Efficient Radius-Bounded Community Search in Geo-Social Networks.', IEEE Trans. Knowl. Data Eng., vol. 34, pp. 4186-4200.
Wang, L, Huang, W, Zhang, M, Pan, S, Chang, X & Su, SW 2022, 'Pruning graph neural networks by evaluating edge properties', Knowledge-Based Systems, vol. 256, pp. 109847-109847.
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The emergence of larger and deeper graph neural networks (GNNs) makes their training and inference increasingly expensive. Existing GNN pruning methods simultaneously prune the graph adjacency matrix and the model weights on a pretrained neural network by directly leveraging the lottery-ticket hypothesis, but the benefits of such methods are mainly via weight pruning, and methods based on saliency metrics struggle to outperform random pruning when pruning only the graph adjacency matrix. This motivates us to use different scoring standards for graph edges and network weights during GNN pruning. Thus, rather than measuring the importance of graph edges based on saliency metrics, we formulate the performance of GNNs mathematically with respect to the properties of their edges, elucidating how the performance drop can be avoided by pruning negative edges and nonbridges. This leads to our simple but effective two-step method for GNN pruning, leveraging the saliency metrics for the network pruning while sparsifying the graph with preservation of the loss performance. Experimental results show the effectiveness and efficiency of the proposed method on both small-scale graph datasets (Cora, Citeseer, and PubMed) and a large-scale dataset (Ogbn-ArXiv), where our method saves up to 98% of floating-point operations per second (FLOPs) on the small graphs and 94% of FLOPs on the large one, with no significant drop in accuracy.
Wang, L, Tang, S, Chen, TE, Li, W & Gunasekara, C 2022, 'Sustainable High-Performance Hydraulic Concrete', Sustainability, vol. 14, no. 2, pp. 695-695.
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Concrete has always been indispensable as a material for the engineering and construction of hydraulic structures (e [...]
Wang, L, Wu, C, Fan, L & Wang, M 2022, 'Effective velocity of reflected wave in rock mass with different wave impedances of normal incidence of stress wave', International Journal for Numerical and Analytical Methods in Geomechanics, vol. 46, no. 9, pp. 1607-1619.
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AbstractThe effective velocity of the reflected wave in rock mass is of significance to the detection of crustal structure and the geophysical seismic exploration. In this paper, the modified characteristic method was introduced to solve P‐wave reflection in rock mass with different wave impedances on two sides of the joint. Effective velocity was defined to characterize the propagation velocity of the reflected wave in jointed rock mass. The effects of incident frequency, joint stiffness and wave impedance ratio on the effective velocity were discussed. The results show that when the stress wave propagation in 'hard‐to‐soft' rock mass, the effective velocity increases firstly and then decreases as the incident frequency and the joint stiffness increase, while the effective velocity always decreases as the wave impedance ratio increases; when the stress wave propagation in 'soft‐to‐hard' rock mass, the effective velocity decreases as the incident frequency increases, increases as the joint stiffness increases and decreases as the wave impedance ratio increases. The wave impedance ratio has an important influence on the effective velocity. The effective velocity without considering wave impedance ratio is smaller than that of stress wave propagation in 'soft‐to‐hard' rock mass, but larger than that of stress wave propagation in 'hard‐to‐soft' rock mass.
Wang, L, Yang, Y, Gao, F, Teng, S, Tan, Z-G, Zhang, X, Lou, J & Deng, L 2022, 'Terahertz reconfigurable dielectric metasurface hybridized with vanadium dioxide for two-dimensional multichannel multiplexing', Frontiers in Physics, vol. 10.
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The metasurface hybridized with vanadium dioxide (VO2) can be dynamically tuned, which has attracted enormous attention in recent years and orbital angular momentum (OAM) multiplexing based on metasurfaces has shown promising prospects in terahertz communications. However, existing research on VO2 metasurface focuses on the metallic metasurface. The dielectric VO2 metasurface used for OAM multiplexing is rarely reported to the present. This paper proposed a terahertz reconfigurable dielectric metasurface hybridized with VO2 for two-dimensional multichannel multiplexing combing with spatial and frequency domains. The metasurface works in both reflection and transmission modes and simultaneously the polarization control and operating frequency band regulation can be realized by switching the VO2 from the metallic state to the insulator state. For the reflective or transmissive metasurface, when 4×M-channel (M is a positive integer) off-axis plane waves are incident on the metasurface, the co-polarization reflected or cross-polarization transmitted waves are transformed into 4×M-channel orthogonal on-axis beams with topological or frequency orthogonality. A metasurface composed of 14 × 14 unit cells is designed for verification. The simulated result shows that two-dimensional 12-channel multiplexing combing with OAM and frequency by the designed metasurface can be realized on the reflection and transmission modes in two different frequency bands. The proposed metasurface has great potential in terahertz communications.
Wang, N, Liu, ZX, Ding, C, Zhang, J-N, Sui, G-R, Jia, H-Z & Gao, X-M 2022, 'High Efficiency Thermoelectric Temperature Control System With Improved Proportional Integral Differential Algorithm Using Energy Feedback Technique', IEEE Transactions on Industrial Electronics, vol. 69, no. 5, pp. 5225-5234.
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This paper proposes an efficient thermoelectric temperature control system based on an improved proportional integral differential algorithm in which energy feedback technology is used to enhance thermoelectric cooling. In the proposed power management system, two groups of batteries are efficiently and alternatingly charged and discharged such that the information of the circuit can be monitored in real time. The PID algorithm is improved by using the idea of a state machine to control the thermoelectric coolers through an H-bridge circuit with pulse-width modulation. Finally, the energy feedback circuit combined with improved synchronous switching technology is designed to recycle the energy to drive the sensor. By inputting current of 3.1A, a wide range of temperature control from 1.437 to 60.187 was implemented. While targeting a temperature of 10 at an ambient temperature of 22, the proposed temperature control system had a control time of 30.5s, compared with 287s when using the conventional method, with an accuracy of 0.1, and an error of only 0.35. The results confirm that electric energy at a peak voltage of 1.2V and current of 24A can be recovered. The proposed energy feedback system can thus improve the efficiency of energy utilization of TEC from peripheral circuits.
Wang, N, Yang, J, Ding, C, Jia, H-Z & Zhai, J-H 2022, 'Symmetrical Multilayer Dielectric Model of Thermal Stress and Strain of Silicon-Core Coaxial Through-Silicon Vias in 3-D Integrated Circuit', IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 12, no. 7, pp. 1122-1129.
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In this work, an analytical model of strain and stress of symmetrical multilayer medium is proposed to solve the thermal problem that occurs in silicon-core coaxial through-silicon vias (S-CTSV). Based on the 3-D Kane-Mindlin theory, the proposed analytical model of strain considers both elastic strain and thermal strain. In addition, the stress is discussed in segments using planar stress and Hooke's law in the model of S-CTSVs to improve the accuracy. The results indicate that the average relative errors in terms of strain and stress between the results of the proposed analytical model and the finite-element method (FEM) were 4.06% and 0.17%, respectively. Compared with the back propagation (BP) neural network-based prediction algorithm, the average relative errors in strain and stress between the proposed model and the FEM were decreased by 3.97% and 3.23%, respectively. Moreover, the stress of three different CTSVs was also compared. The stress of the proposed S-CTSVs model was lower than those of the two traditional CTSVs, which ensure higher reliability. The results in this article would provide some design guides for S-CTSVs in 3-D integration.
Wang, N, Zhang, J-N, Ni, H, Jia, H-Z & Ding, C 2022, 'Improved MPPT System Based on FTSMC for Thermoelectric Generator Array Under Dynamic Temperature and Impedance', IEEE Transactions on Industrial Electronics, vol. 69, no. 10, pp. 10715-10723.
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The thermoelectric generator (TEG) is typically used as a clean power supply to harvest waste heat energy in applications involving a large thermal gradient, such as industrial heat removal and power electronic equipment systems. However, it is often difficult to achieve the optimal output power in the loop of the array system of the TEG owing to different output loads. This study proposes an improved fast terminal sliding-mode variable-structure control algorithm (FTSMC) to maximize power point tracking. The variable-structure sliding-mode control function used in the nonlinear sliding-mode surface of the algorithm allows us to obtain the characteristics of global stability that can enable it to converge to the sliding-mode surface at any position to reduce chatter. Digital modeling and simulation as well as experimental developmental Field Programmable Gate Array (FPGA) platforms were built to verify the effectiveness of the proposed FTSMC. It can attain the nonlinear sliding mode more quickly than the traditional sliding-mode algorithm. The results of experiments show that it can reach a tracking response speed of 0.08 s and a maximum conversion efficiency of 99.91%. The work here provides a new way for the efficient use of the TEG array for waste heat recovery.
Wang, P, Li, L, Xie, Q, Wang, R & Xu, G 2022, 'Social dual-effect driven group modeling for neural group recommendation', Neurocomputing, vol. 481, pp. 258-269.
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Wang, Q, Guan, J, Liu, J, Zhang, Z & Ying, M 2022, 'New Quantum Algorithms for Computing Quantum Entropies and Distances.', CoRR, vol. abs/2203.13522.
Wang, Q, Li, R & Ying, M 2022, 'Equivalence Checking of Sequential Quantum Circuits.', IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., vol. 41, no. 9, pp. 3143-3156.
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We define a formal framework for equivalence checking of sequential quantum circuits. The model we adopt is a quantum state machine, which is a natural quantum generalisation of Mealy machines. A major difficulty in checking quantum circuits (but not present in checking classical circuits) is that the state spaces of quantum circuits are continuums. This difficulty is resolved by our main theorem showing that equivalence checking of two quantum Mealy machines can be done with input sequences that are taken from some chosen basis (which are finite) and have a length quadratic in the dimensions of the state Hilbert spaces of the machines. Based on this theoretical result, we develop an (and to the best of our knowledge, the first) algorithm for checking equivalence of sequential quantum circuits with running time O(23m+5l(23m+23l)), where m and l denote the numbers of input and internal qubits, respectively. The complexity of our algorithm is comparable with that of the known algorithms for checking classical sequential circuits in the sense that both are exponential in the number of (qu)bits. Several case studies and experiments are presented.
Wang, Q, Liu, D, Carmichael, MG, Aldini, S & Lin, C-T 2022, 'Computational Model of Robot Trust in Human Co-Worker for Physical Human-Robot Collaboration', IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 3146-3153.
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Trust is key to achieving successful Human-Robot Interaction (HRI). Besides trust of the human co-worker in the robot, trust of the robot in its human co-worker should also be considered. A computational model of a robot's trust in its human co-worker for physical human-robot collaboration (pHRC) is proposed. The trust model is a function of the human co-worker's performance which can be characterized by factors including safety, robot singularity, smoothness, physical performance and cognitive performance. Experiments with a collaborative robot are conducted to verify the developed trust model.
Wang, Q, Xiao, Y, Dampage, U, Alkuhayli, A, Alhelou, HH, Annuk, A & Mohamed, MA 2022, 'An effective fault section location method based three-line defense scheme considering distribution systems resilience', Energy Reports, vol. 8, pp. 10937-10949.
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Wang, S, Cao, L, Wang, Y, Sheng, QZ, Orgun, MA & Lian, D 2022, 'A Survey on Session-based Recommender Systems', ACM Computing Surveys, vol. 54, no. 7, pp. 1-38.
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Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized economy. In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs. Different from other RSs such as content-based RSs and collaborative filtering-based RSs that usually model long-term yet static user preferences, SBRSs aim to capture short-term but dynamic user preferences to provide more timely and accurate recommendations sensitive to the evolution of their session contexts. Although SBRSs have been intensively studied, neither unified problem statements for SBRSs nor in-depth elaboration of SBRS characteristics and challenges are available. It is also unclear to what extent SBRS challenges have been addressed and what the overall research landscape of SBRSs is. This comprehensive review of SBRSs addresses the above aspects by exploring in depth the SBRS entities (e.g., sessions), behaviours (e.g., users’ clicks on items), and their properties (e.g., session length). We propose a general problem statement of SBRSs, summarize the diversified data characteristics and challenges of SBRSs, and define a taxonomy to categorize the representative SBRS research. Finally, we discuss new research opportunities in this exciting and vibrant area.
Wang, S, Cao, Y, Chen, X, Yao, L, Wang, X & Sheng, QZ 2022, 'Adversarial Robustness of Deep Reinforcement Learning Based Dynamic Recommender Systems', Frontiers in Big Data, vol. 5, p. 822783.
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Adversarial attacks, e.g., adversarial perturbations of the input and adversarial samples, pose significant challenges to machine learning and deep learning techniques, including interactive recommendation systems. The latent embedding space of those techniques makes adversarial attacks challenging to detect at an early stage. Recent advance in causality shows that counterfactual can also be considered one of the ways to generate the adversarial samples drawn from different distribution as the training samples. We propose to explore adversarial examples and attack agnostic detection on reinforcement learning (RL)-based interactive recommendation systems. We first craft different types of adversarial examples by adding perturbations to the input and intervening on the casual factors. Then, we augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data. Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods. Our extensive experiments show that most adversarial attacks are effective, and both attack strength and attack frequency impact the attack performance. The strategically-timed attack achieves comparative attack performance with only 1/3 to 1/2 attack frequency. Besides, our white-box detector trained with one crafting method has the generalization ability over several other crafting methods.
Wang, S, Lu, J, Li, B, Liu, C, Wang, Y, Lei, G, Guo, Y & Zhu, J 2022, 'Design and analysis of mechanical flux-weakening device of axial flux permanent magnet machines', Journal of Power Electronics, vol. 22, no. 4, pp. 653-663.
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Due to the low inductance of an axial flux permanent magnet machine (AFPMM), the constant power speed regulation range is small. A new mechanical flux-weakening method for single-rotor single-stator AFPMMs is proposed in this paper. By installing a mechanical flux-weakening device on one side of the stator and rotating it certain angle, the speed regulation of the flux-weakening can be realized. The device is simple in structure, easy to operate, and can be operated in the process of machine operation. The validity of the device is verified by applying it to a machine. Finite-element software is used to calculate and analyze the performances of two machines with the device.
Wang, S, Tao, J, Qiu, X & Burnett, IS 2022, 'A natural ventilation window for transformer noise control based on coiled-up silencers consisting of coupled tubes', Applied Acoustics, vol. 192, pp. 108744-108744.
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For transformers located inside rooms, openings in the walls are often required for ventilation and heat dissipation with the result that transformer noise radiates to the outside. A soundproof window for indoor transformers is proposed in this paper, which provides both air circulation and noise reduction simultaneously. A silencer consisting of two coupled tubes with different cross sections is designed and coiled up in space to minimize the thickness of the structure. With carefully chosen parameters, just one such silencer can achieve sound attenuation at up to 4 frequencies. With a combination of a staggered window and specially designed silencers, effective noise reduction is obtained at 100 Hz, 200 Hz, 300 Hz, 400 Hz and 500 Hz, where harmonic components contribute the most to the transformer noise. The experimental results with a 1:4 scale down model show the feasibility of the proposed design.
Wang, S, Tao, J, Qiu, X & Burnett, IS 2022, 'Improving the performance of an active staggered window with multiple resonant absorbers', The Journal of the Acoustical Society of America, vol. 151, no. 3, pp. 1661-1671.
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The active noise control (ANC) technique has been applied in staggered windows to improve the noise reduction at low frequencies. The control performance of such a system deteriorates significantly at some frequencies where the secondary source cannot radiate effectively due to the reflection at the boundaries of the staggered window. A resonant absorber consisting of a perforated panel and coiled up tubes is proposed to solve the problem. By designing a combination of different absorbers, a proper sound absorption coefficient is achieved around the ineffective frequency. Numerical simulations show that the active sound power reduction increases by 13.5 dB at the frequency with the absorbers attached on one end of the staggered window, and the overall sound power reduction between 100 and 500 Hz increases from 25.9 to 31.2 dB. Attaching the sound absorbers elsewhere in the upstream of the secondary source, for example, on the side walls of the duct also works. The active sound power reduction at 435 Hz increases by 6.3 dB after attaching the absorbers in the experiments, and the noise reduction increment at the evaluation point is 13.6 dB, which agrees with simulation results and demonstrates the feasibility of the proposed sound absorbers.
Wang, S, Yuen, C, Ni, W, Guan, YL & Lv, T 2022, 'Multiagent Deep Reinforcement Learning for Cost- and Delay-Sensitive Virtual Network Function Placement and Routing', IEEE Transactions on Communications, vol. 70, no. 8, pp. 5208-5224.
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Wang, S-N, Fang, F, Li, K-Y, Yue, Y-R, Xu, R-Z, Luo, J-Y, Ni, B-J & Cao, J-S 2022, 'Sludge reduction and microbial community evolution of activated sludge induced by metabolic uncoupler o-chlorophenol in long-term anaerobic-oxic process', Journal of Environmental Management, vol. 316, pp. 115230-115230.
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Excess sludge management is a restrictive factor for the development of municipal wastewater treatment plants. The addition of metabolic uncouplers has been proven to be effective in sludge reduction. However, the long-term effect of metabolic uncoupler o-chlorophenol (oCP) on the biological wastewater treatment system operated in anaerobic-oxic mode is still unclear. To this end, two parallel reactors operated in anaerobic-oxic mode with and without 10 mg/L of oCP addition were investigated for 91 days. The results showed that 56.1 ± 2.3% of sludge reduction was achieved in the oCP-added system, and the nitrogen and phosphorus removal ability were negatively affected. Dosing oCP stimulated the formation of microbial products and increased the DNA concentration, but resulted in a decrease in the electronic transport activity of activated sludge. Microbial community analysis further demonstrated that a significant reduction of bacterial richness and diversity occurred after oCP dosing. However, after stopping oCP addition, the pollutant removal ability of activated sludge was gradually increased, but the sludge yield, as well as species richness and diversity, did not recover to the previous level. This study will provide insightful guidance on the long-term application of metabolic uncouplers in the activated sludge system.
Wang, W, Zhang, Y, Sui, Y, Wan, Y, Zhao, Z, Wu, J, Yu, PS & Xu, G 2022, 'Reinforcement-Learning-Guided Source Code Summarization Using Hierarchical Attention', IEEE Transactions on Software Engineering, vol. 48, no. 1, pp. 102-119.
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Wang, W, Zhao, L, Ni, B-J, Yin, T-M, Zhang, R-C, Yu, M, Shao, B, Xu, X-J, Xing, D-F, Lee, D-J, Ren, N-Q & Chen, C 2022, 'A novel sulfide-driven denitrification methane oxidation (SDMO) system: Operational performance and metabolic mechanisms', Water Research, vol. 222, pp. 118909-118909.
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Microbial denitrification is a crucial biological process for the treatment of nitrogen-polluted water. Traditional denitrification process consumes external organic carbon leading to an increase in treatment costs. We developed a novel sulfide-driven denitrification methane oxidation (SDMO) system that integrates autotrophic denitrification (AD) and denitrification anaerobic methane oxidation (DAMO) for cost-effective denitrification and biogas utilization in situ. Two SDMO systems were operated for 735 days, with nitrate and nitrite serving as electron acceptors, to explore the performance of sewage denitrification and characterize metabolic mechanisms. Results showed SDMO system could reach as high as 100% efficiency of nitrogen removal and biogas desulfurization without an external carbon source when HRT was 10 days and inflow nitrogen concentrations were 50-100 mgN·L-1. Besides, nitrate was a preferable electron acceptor for SDMO system. Biogas not only enhanced nitrogen removal but also intensified the DAMO, nitrogen removed through DAMO contribution doubled as original period from 2.9 mgN·(L·d)-1 to 6.2 mgN·(L·d)-1, and the ratio of nitrate removal through AD to DAMO was 1.2:1 with nitrate as electron acceptor. While nitrogen removed almost all through AD contribution and DAMO was weaken as before, the ratio of nitrate removal through AD to DAMO was 21.2:1 with nitrite as electron acceptor. Biogas introduced into SDMO system with nitrate inspired the growth of DAMO bacteria Candidatus Methylomirabilis from 0.3% to 19.6% and motivated its potentiality to remove nitrate without ANME archaea participation accompanying with gene mfnE upregulating ∼100 times. According to the reconstructed genome from binning analysis, the dramatically upregulated gene mfnE was derived from Candidatus Methylomirabilis, which may represent a novel metabolism pathway for DAMO bacteria to replace the role of archaea for nitrate reduction.
Wang, X, Chen, H-T & Lin, C-T 2022, 'Error-related potential-based shared autonomy via deep recurrent reinforcement learning', Journal of Neural Engineering, vol. 19, no. 6, pp. 066023-066023.
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Abstract Objective. Error-related potential (ErrP)-based brain–computer interfaces (BCIs) have received a considerable amount of attention in the human–robot interaction community. In contrast to traditional BCI, which requires continuous and explicit commands from an operator, ErrP-based BCI leverages the ErrP, which is evoked when an operator observes unexpected behaviours from the robot counterpart. This paper proposes a novel shared autonomy model for ErrP-based human–robot interaction. Approach. We incorporate ErrP information provided by a BCI as useful observations for an agent and formulate the shared autonomy problem as a partially observable Markov decision process. A recurrent neural network-based actor-critic model is used to address the uncertainty in the ErrP signal. We evaluate the proposed framework in a simulated human-in-the-loop robot navigation task with both simulated users and real users. Main results. The results show that the proposed ErrP-based shared autonomy model enables an autonomous robot to complete navigation tasks more efficiently. In a simulation with 70% ErrP accuracy, agents completed the task 14.1% faster than in the no ErrP condition, while with real users, agents completed the navigation task 14.9% faster. Significance. The evaluation results confirmed that the shared autonomy via deep recurrent reinforcement learning is an effective way to deal with uncertain human feedback in a complex human–robot interaction task.
Wang, X, Fei, Z, Zhang, JA & Huang, J 2022, 'Sensing-Assisted Secure Uplink Communications With Full-Duplex Base Station', IEEE Communications Letters, vol. 26, no. 2, pp. 249-253.
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This letter proposes a sensing-assisted uplink communications framework between a single-antenna user and a full-duplex (FD) base station (BS) against an aerial eavesdropper (AE). To protect the information from being overheard, the BS transmits radar signals to localize and jam AE while receiving uplink signals. The radar signal transmission is divided into detection phase and tracking phase. In detection phase, the BS synthesizes a wide beam to localize the AE under the secrecy rate constraint; while in tracking phase, the BS maximizes the signal-to-interference-plus-noise ratio (SINR) of its received signals under the AE’s SINR constraint while guaranteeing a predefined radar echo signal signal-to-noise ratio (SNR) level. To deal with the self interference, we jointly optimize the radar waveform and receive beamforming vector. An alternating optimization algorithm and a successive convex approximation (SCA) based algorithm are proposed to solve the two formulated problems, respectively. Simulation results verify the effectiveness of the proposed algorithms. They also show that the secrecy rate can be significantly improved with the assistance of BS sensing.
Wang, X, Fei, Z, Zhang, JA & Xu, J 2022, 'Partially-Connected Hybrid Beamforming Design for Integrated Sensing and Communication Systems', IEEE Transactions on Communications, vol. 70, no. 10, pp. 6648-6660.
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Beamforming design is an important technique for enhancing the performance of integrated sensing and communication (ISAC) systems. However, related research based on the hybrid analog-digital (HAD) architecture is still limited. In this paper, we investigate the partially-connected hybrid beamforming design for multi-user ISAC systems. Instead of the commonly used beampattern related metric, the Cramér-Rao bound (CRB) is employed as the sensing performance metric for direction of arrival (DOA) estimation. We aim to minimize the CRB while satisfying the signal-to-interference-plus-noise ratio (SINR) constraints for individual communication users by jointly optimizing the digital and analog beamformers. Subsequently, we propose an alternating optimization based framework, which is significantly different from the conventional methods based on the approximation of the optimal fully-digital beamformer with a hybrid one. We also consider an alternative formulation of optimizing the SINR of radar echo signals. Based on optimal receive beamformer design, we transform the SINR based joint transmitter and receiver optimization problem to a series of problems sharing a similar form with the CRB based transmitter optimization problem, which can be efficiently solved via the proposed algorithm. Simulation results show that the proposed designs provide significant performance gains in DOA estimation over the existing beampattern approximation based design.
Wang, X, Gao, J, Chen, Z, Chen, H, Zhao, Y, Huang, Y & Chen, Z 2022, 'Evaluation of hydrous ethanol as a fuel for internal combustion engines: A review', Renewable Energy, vol. 194, pp. 504-525.
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Ethanol has been extensively used worldwide as a renewable biofuel to partly substitute fossil fuels, aiming to reduce pollutant and greenhouse gas emissions. However, due to the azeotropic points of water and ethanol, the production of anhydrous ethanol is energy intensive as significant energy is consumed in the distillation and dehydration processes. Therefore, the direct use of hydrous ethanol in engines can dramatically conserve energy and reduce costs. Under this background, this review focuses on the direct use of hydrous ethanol in internal combustion engines. This paper begins with a brief description of the fuel physicochemical properties relevant to engine applications. Furthermore, fundamental combustion characteristics, including the laminar burning velocity, ignition delay time and flame instability, are introduced. Then, the applications of hydrous ethanol or its blends with gasoline in spark ignition engines are summarized. Next, compression ignition engines running on hydrous ethanol in blended and dual-fuel modes are described. Subsequently, the use of hydrous ethanol in advanced combustion concepts, such as homogeneous charge compression ignition and thermally stratified compression ignition, is reviewed. Finally, the conclusions are presented and recommendations for future research are proposed.
Wang, X, Li, W, Luo, Z, Wang, K & Shah, SP 2022, 'A critical review on phase change materials (PCM) for sustainable and energy efficient building: Design, characteristic, performance and application', Energy and Buildings, vol. 260, pp. 111923-111923.
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Building construction deserves many attentions due to its huge energy consumption, while Phase Change Materials (PCMs) provide positive solutions for improving energy efficiency and enhancing the thermal properties of construction materials. However, PCMs also present some negative impacts, such as weakening mechanical properties and increasing costs, chemical instability and so on. In this paper, the main characteristics of PCMs, design and incorporating methods, effects on energy consumption and construction reliability are comprehensively reviewed and discussed. Although many materials have the capacity of phase change, some organic PCMs are more suitable due to the higher latent heat and favourable phase change point in buildings, when eutectic PCMs present greater potential to become the optimal one but much effort is required for investigations. Current design methods and application in construction materials can meet the essential requirements, but the effectiveness is inadequate, including low efficiency of phase changing, leading to low energy storage. Subsequently, some promising research direction and critical areas for optimization are also proposed accordingly in this paper. Future development of PCMs, including novel PCM and efficient incorporation, real applications and functions in buildings are proposed. Additionally, multifunctional construction materials combining PCM deserve much attention and possess promising prospect for energy saving in sustainable and energy efficient building construction.
Wang, X, Liu, S-F, Wang, Z-Y, Hao, T-B, Balamurugan, S, Li, D-W, He, Y, Li, H-Y & Lin, CSK 2022, 'A waste upcycling loop: Two-factor adaptive evolution of microalgae to increase polyunsaturated fatty acid production using food waste', Journal of Cleaner Production, vol. 331, pp. 130018-130018.
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Wang, X, Qin, P-Y, Tuyen Le, A, Zhang, H, Jin, R & Guo, YJ 2022, 'Beam Scanning Transmitarray Employing Reconfigurable Dual-Layer Huygens Element', IEEE Transactions on Antennas and Propagation, vol. 70, no. 9, pp. 7491-7500.
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A Ku-band electronic 2-dimensional (2-D) beam-scanning transmitarray employing a new reconfigurable dual-layer Huygens element is developed in this article. The Huygens element consists of two metallic crosses printed on two layers of a dielectric substrate, which enables a near nonreflection Huygens resonance. A 1 bit phase compensation with low transmission loss is realized by controlling two p-i-n diodes on the element. Compared with many other reconfigurable transmitarray elements using multilayer structures with metallic vias, the proposed reconfigurable Huygens element has a much simpler configuration with a simpler biasing network, and it is not affected by multilayer alignment errors. This particularly facilitates large aperture array development at higher frequencies. To validate the design concept, an electronically reconfigurable transmitarray with the proposed element is fabricated at 13 GHz. Good agreement between the measured and simulated results is found, showing 2-D scanning beams within ±50° in the E-plane and ±40° in the H-plane with a maximum realized gain of 18.4 dBi.
Wang, X, Wang, Y, Cui, Q, Chen, K-C & Ni, W 2022, 'Machine Learning Enables Radio Resource Allocation in the Downlink of Ultra-Low Latency Vehicular Networks', IEEE Access, vol. 10, pp. 44710-44723.
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Wang, X, Xie, G-J, Tian, N, Dang, C-C, Cai, C, Ding, J, Liu, B-F, Xing, D-F, Ren, N-Q & Wang, Q 2022, 'Anaerobic microbial manganese oxidation and reduction: A critical review', Science of The Total Environment, vol. 822, pp. 153513-153513.
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Manganese is a vital heavy metal abundant in terrestrial and aquatic environments. Anaerobic manganese redox reactions mediated by microorganisms have been recognized for a long time, which promote elements mobility and bioavailability in the environment. Biological anaerobic redox of manganese serves two reactions, including Mn(II) oxidation and Mn(IV) reduction. This review provides a comprehensive analysis of manganese redox cycles in the environment, closely related to greenhouse gas mitigation, the fate of nutrients, microbial bioremediation, and global biogeochemical cycle, including nitrogen, sulfur, and carbon. The oxidation and reduction of manganese occur cyclically and simultaneously in the environment. Anaerobic reduction of Mn(IV) receives electrons from methane, ammonium and sulfide, while Mn(II) can function as an electron source for manganese-oxidizing microorganisms for autotrophic denitrification and photosynthesis. The anaerobic redox transition between Mn(II) and Mn(IV) promotes a dynamic biogeochemical cycle coupled to microorganisms in water, soil and sediment environments. The discussion of reaction mechanisms, microorganism diversity, environmental influence bioremediation and application identify the research gaps for future investigation, which provides promising opportunities for further development of biotechnological applications to remediate contaminated environments.
Wang, X, Yang, S, Guo, Z, Wen, S & Huang, T 2022, 'A Distributed Network System for Nonsmooth Coupled-Constrained Optimization', IEEE Transactions on Network Science and Engineering, vol. 9, no. 5, pp. 3691-3700.
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This paper addresses a class of distributed nonsmooth optimization problems whose objective function is a sum of convex local objective functions subjected to local set constraints and heterogeneous coupled constraints, including inequality and equality ones. To settle the problem, based on the consensus protocol for the Lagrangian multipliers of coupled constraints, we propose a distributed multi-agent network system with projected output feedback, which is different from the common projected primal-dual subgradient flow. It is proved that the output vector of the system is convergent to the optimal solution of the optimization problem from any initial state over connected communication networks. Finally, the effectiveness of the system is illustrated via two numerical examples.
Wang, X, Yu, G, Liu, RP, Zhang, J, Wu, Q, Su, SW, He, Y, Zhang, Z, Yu, L, Liu, T, Zhang, W, Loneragan, P, Dutkiewicz, E, Poole, E & Paton, N 2022, 'Blockchain-Enabled Fish Provenance and Quality Tracking System', IEEE Internet of Things Journal, vol. 9, no. 11, pp. 8130-8142.
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Wang, Y, Jin, D, He, D, Musial, K & Dang, J 2022, 'Community Detection in Social Networks Considering Social Behaviors', IEEE Access, vol. 10, pp. 109969-109982.
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The study of community detection in networks has drawn great attention in recent years. To find communities and to understand community semantics, both network topology and network content are utilized. Unfortunately, none of them can explain the driving factors of generating community structure with semantics, which is significant for understanding the mechanisms of community generation. Our observations on a large number of networks show that specific user social behaviors are underlying factors for the generation of community structure. We exploit four types of social behaviors that widely exist in networks, i.e., reciprocity of interactions, posting preference, multitopic preference, and temporal variation of topics. We investigate their impacts on the formation process of links and content in networks, during which communities with topics form. Our analysis shows that they are highly related to community structure. Consequently, a generative community detection model SBCD (social behavior-based community detection) is proposed by combining network topology and content, in which the above social behaviors play a core role. The model is evaluated on two real datasets. The experimental results show that SBCD outperforms state-of-the-art baselines. Finally, a case study illustrates several significant observations with respect to the proposed social behaviors.
Wang, Y, Li, S, Ni, W, Abbott, D, Johnson, M, Pei, G & Hedley, M 2022, 'Cooperative Localization and Association of Commercial-Off-the-Shelf Sensors in Three-Dimensional Aircraft Cabin', IEEE Transactions on Automation Science and Engineering, vol. 19, no. 4, pp. 3508-3519.
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Wang, Y, Luo, Q, Xie, H, Li, Q & Sun, G 2022, 'Digital image correlation (DIC) based damage detection for CFRP laminates by using machine learning based image semantic segmentation', International Journal of Mechanical Sciences, vol. 230, pp. 107529-107529.
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Vision-based damage detection in carbon fiber-reinforced plastic (CFRP) composites can be interfered by such factors as surface texture, stains and lighting. A digital image correlation (DIC) based surface strain monitoring technique, on the other hand, enables to track the change of strain distribution. It is promising to develop a new approach for online structural health monitoring (SHM), in which the DIC strain contours can be scrutinized automatically and the results are no longer substantially subjected to human interference. In this study, a convolutional neural network (CNN) based image semantic segmentation technique is proposed for pixel-level classification of DIC strain field images. A DeepLabv3+ encoder-decoder architecture combined with different feature extraction networks is investigated. The training dataset and validation of the model are obtained through finite element (FE) simulation. The images of quasi-static axial tensile strain field obtained from 2D-DIC are used to test the accuracy and efficiency of the trained CNN model. It is found that use of a pre-trained ResNet-50 CNN model as the backbone network of DeepLabv3+ architecture through a transfer learning algorithm can make the semantic segmentation results reach a mean intersection over union of 0.9236. The prediction accuracy of the semantic segmentation model trained from the FE data is comparable with that of the model trained from the experimental data, which demonstrates that the proposed machine learning approach for DIC measurement is cost-effective.
Wang, Y, Mukherjee, A & Castel, A 2022, 'Non-destructive monitoring of incipient corrosion in reinforced concrete with top-bar defect using a combination of electrochemical and ultrasonic techniques', Construction and Building Materials, vol. 360, pp. 129346-129346.
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Wang, Y, Sun, Y, Geng, N, Zheng, M, Zou, Y & Shi, B 2022, 'A Biomimetic Nanomedicine Targets Orthotopic Glioblastoma by Combinatorial Co‐Delivery of Temozolomide and a Methylguanine‐DNA Methyltransferase Inhibitor', Advanced Therapeutics, vol. 5, no. 12.
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AbstractGlioblastoma (GBM) is the most aggressive intracranial tumor that occurs in the central nervous system (CNS) and has no effective treatment due to the fact of drug resistance. To combat drug resistance, TMZ is generally administrated in combination with other chemotherapeutic drugs. Unfortunately, drug combinations used to date only show modest improvements in anti‐tumor effects and have increased toxicity. Herein, an ApoE peptide decorated GBM cancer cell membrane camouflaged nanomedicine (AMNPs@TMZ+LM) to specifically co‐deliver TMZ and the MGMT inhibitor lomeguatrib (LM) for combinatorial GBM treatment is developed. Incorporation of LM not only effectively suppresses the repair of damaged DNA, but also inhibits tumor cell proliferation by inducing cell cycle arrest. Importantly, this biomimetic nanomedicine achieves high BBB penetration and enhances sensitivity to both TMZ‐resistant (U251‐TR) and GBM stem cells (GSCs). Notably, AMNPs@TMZ+LM results in significant inhibition of the growth of orthotopically implanted U251‐TR tumor in mice. In comparison, treatment with single drug loaded nanomedicines results in compromised anti‐GBM effects. Histological analyses and blood parameter studies show good biocompatibility of the nanomedicine. Hence, this biomimetic nanomedicine provides a potential new approach to overcome the limitations of current GBM chemotherapy regimens by co‐delivery of TMZ and the MGMT inhibitor LM.
Wang, Y, Wang, X, Xu, S, He, C, Zhang, Y, Ren, J & Yu, S 2022, 'FlexMon: A flexible and fine-grained traffic monitor for programmable networks', Journal of Network and Computer Applications, vol. 201, pp. 103344-103344.
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Accurate and fine-grained traffic measurements are crucial for various network management tasks. Recent researches introduce counter-based and sketch-based approaches to traffic measurement. However, implementing accurate and fine-grained traffic measurements is very challenging due to the rigid constraints of measurement resources. The counter-based approaches are limited by the memory space constraints that prevent covering each flow in the network, and the sketch-based approaches produce inefficient throughput and lower measurement accuracy. Emerging programmable networking techniques provide programmable, flexible, and fine-grained traffic control capabilities, paving the way for realizing fine-grained and accurate traffic measurements. In this paper, we aim to design efficient traffic measurement schemes for programmable networks. We first propose a single-node traffic measurement scheme called FlexMon to accurately measure fine-grained flows in a single network node. The FlexMon separates large flows from small ones and uses dedicated flow rules and sketches to measure large and small flows, respectively. Then, to further improve the measurement performance by efficiently leveraging the network-wide measurement resource, we propose a network-wide traffic measurement scheme and extend FlexMon to support network-wide measurement. We implement the FlexMon on FPGA and CPU to process five typical measurement tasks. Experimental results show that both the single-node and network-wide measurement schemes can achieve much faster speed and higher accuracy compared to the state-of-the-art.
Wang, Y, Wei, W, Dai, X & Ni, B-J 2022, 'Corncob ash boosts fermentative hydrogen production from waste activated sludge', Science of The Total Environment, vol. 807, no. Pt 3, pp. 151064-151064.
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With the increasing demand for sustainable development, the recycling and utilization of wastes has received widespread attention. This study proposed a green method of using one waste, corncob ash, to boost microbial the production of hydrogen from another waste, waste activated sludge, during anaerobic fermentation. The corncob ash dosage and the fermentative hydrogen production was positively correlated, and the maximum production of hydrogen reached up to 46.8 ± 1.0 mL/g VS, which was about 3.5 times that of the control group without corncob ash dosage (17.0 ± 0.9 mL/g VS). Mechanistic studies found that corncob ash was beneficial to the solubilization, hydrolysis and acetogenesis processes involved in fermentative hydrogen production process. The microbial community analysis indicated that corncob ash enriched more hydrolytic microorganisms (e.g., Bacteroides sp. and Leptolinea sp.), and has less impact on acidifying microorganisms, compared to the control group. The strategy of using corncob ash to boost the production of hydrogen during anaerobic waste activated sludge fermentation proposed in this study might provide a new waste-control-waste paradigm, making sludge disposal and wastewater treatment more sustainable.
Wang, Y, Zhang, A, Zhang, P, Qu, Y & Yu, S 2022, 'Security-Aware and Privacy-Preserving Personal Health Record Sharing Using Consortium Blockchain', IEEE Internet of Things Journal, vol. 9, no. 14, pp. 12014-12028.
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With the fast boom of Internet of Medical Things (IoMT) devices and an increasing focus on personal health, personal health data are extensively collected by IoMT and stored as personal health records (PHRs). PHRs are frequently shared for accurate diagnosis, prognosis prediction, health advice consulting, etc. Since PHRs are highly private, the data sharing process leads to wide-ranging concerns on privacy leakage and security compromise. Existing research has shown that the centralized systems, as the mainstream mode, are under the great risks. Motivated by this, we propose a consortium blockchain based PHR management and sharing scheme, which is both security-aware and privacy-preserving. We adopt the interplanetary file system (IPFS) to store PHR ciphertext of IoMT. Then, Zero-knowledge proof can provide evidence for verifying keyword index authentication on blockchain. Moreover, the scheme jointly leverages modified attribute-based cryptographic primitives and tailor-made smart contracts to achieve secure search, privacy preservation, and personalized access control in IoMT scenarios. Security analysis is conducted to show the designed protocols attain the expected design goals. This is followed by extensive evaluation results derived from real-world datasets, which demonstrate the superiority of the proposed scheme over current leading ones.
Wang, Y, Zhao, M, Li, S, Yuan, X & Ni, W 2022, 'Dispersed Pixel Perturbation-Based Imperceptible Backdoor Trigger for Image Classifier Models', IEEE Transactions on Information Forensics and Security, vol. 17, pp. 3091-3106.
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Typical deep neural network (DNN) backdoor attacks are based on triggers embedded in inputs. Existing imperceptible triggers are computationally expensive or low in attack success. In this paper, we propose a new backdoor trigger, which is easy to generate, imperceptible, and highly effective. The new trigger is a uniformly randomly generated three-dimensional (3D) binary pattern that can be horizontally and/or vertically repeated and mirrored and superposed onto three-channel images for training a backdoored DNN model. Dispersed throughout an image, the new trigger produces weak perturbation to individual pixels, but collectively holds a strong recognizable pattern to train and activate the backdoor of the DNN. We also analytically reveal that the trigger is increasingly effective with the improving resolution of the images. Experiments are conducted using the ResNet-18 and MLP models on the MNIST, CIFAR-10, and BTSR datasets. In terms of imperceptibility, the new trigger outperforms existing triggers, such as BadNets, Trojaned NN, and Hidden Backdoor, by over an order of magnitude. The new trigger achieves an almost 100% attack success rate, only reduces the classification accuracy by less than 0.7%-2.4%, and invalidates the state-of-the-art defense techniques.
Wang, Y, Zhu, S, Shao, H, Feng, Y, Wang, L & Wen, S 2022, 'Comprehensive analysis of fixed-time stability and energy cost for delay neural networks', Neural Networks, vol. 155, pp. 413-421.
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This paper focuses on comprehensive analysis of fixed-time stability and energy consumed by controller in nonlinear neural networks with time-varying delays. A sufficient condition is provided to assure fixed-time stability by developing a global composite switched controller and employing inequality techniques. Then the specific expression of the upper of energy required for achieving control is deduced. Moreover, the comprehensive analysis of the energy cost and fixed-time stability is investigated utilizing a dual-objective optimization function. It illustrates that adjusting the control parameters can make the system converge to the equilibrium point under better control state. Finally, one numerical example is presented to verify the effectiveness of the provided control scheme.
Wang, Y, Zhu, S, Shao, H, Wang, L & Wen, S 2022, 'Trade off analysis between fixed-time stabilization and energy consumption of nonlinear neural networks', Neural Networks, vol. 148, pp. 66-73.
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This paper concentrates on trade off analysis between fixed-time stabilization and energy consumption for a type of nonlinear neural networks (NNs). By constructing a compound switching controller and utilizing inequality techniques, a sufficient condition is proposed to ensure the fixed-time stabilization. Then, an estimate of the upper bound of the energy consumed by the controller in the control process is given. Furthermore, the quantitative analysis of the trade-off between the control time and energy consumption is studied. This article reveals that appropriate control parameters can balance the above two indicators to achieve an optimal control state. Finally, the presented theoretical results are verified by two numerical examples.
Wang, Y, Zhuang, J-L, Lu, Q-Q, Cui, C-Z, Liu, Y-D, Ni, B-J & Li, W 2022, 'Halophilic Martelella sp. AD-3 enhanced phenanthrene degradation in a bioaugmented activated sludge system through syntrophic interaction', Water Research, vol. 218, pp. 118432-118432.
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Polycyclic aromatic hydrocarbons (PAHs) are a group of common recalcitrant pollutant in industrial saline wastewater that raised significant concerns, whereas traditional activated sludge (AS) has limited tolerance to high salinity and PAHs toxicity, restricting its capacity to degrade PAHs. It is therefore urgent to develop a bioaugmented sludge (BS) system to aid in the effective degradation of these types of compounds under saline condition. In this study, a novel bioaugmentation strategy was developed by using halophilic Martelella sp. AD-3 for effectively augmented phenanthrene (PHE) degradation under 3% salinity. It was found that a 0.5∼1.5% (w/w) ratio of strain AD-3 to activated sludge was optimal for achieving high PHE degradation activity of the BS system with degradation rates reaching 2.2 mg⋅gVSS-1⋅h-1, nearly 25 times that of the AS system. Although 1-hydroxy-2-naphthoic acid (1H2N) was accumulated obviously, the mineralization of PHE was more complete in the BS system. Reads-based metagenomic coupled metatranscriptomic analysis revealed that the expression values of ndoB, encoding a dioxygenase associated with PHE ring-cleavage, was 5600-fold higher in the BS system than in the AS system. Metagenome assembly showed the members of the Corynebacterium and Alcaligenes genera were abundant in the strain AD-3 bioaugmented BS system with expression of 10.3±1.8% and 1.9±0.26%, respectively. Moreover, phdI and nahG accused for metabolism of 1H2N have been annotated in both above two genera. Degradation assays of intermediates of PHE confirmed that the activated sludge actually possessed considerable degradation capacity for downstream intermediates of PHE including 1H2N. The degradation capacity ratio of 1H2N to PHE was 87% in BS system, while it was 26% in strain AD-3. These results indicated that strain AD-3 contributed mainly in transforming PHE to 1H2N in BS system, while species in activated sludge utilized 1H2N as substrate to grow, thus est...
Wang, Z, Chen, C & Dong, D 2022, 'Lifelong Incremental Reinforcement Learning With Online Bayesian Inference', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 8, pp. 4003-4016.
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Wang, Z, Jiang, J, Han, B, Feng, L, An, B, Niu, G & Long, G 2022, 'SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning', Transactions on Machine Learning Research, vol. 2022-July.
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Deep learning with noisy labels is a challenging task, which has received much attention from the machine learning and computer vision communities. Recent prominent methods that build on a specific sample selection (SS) strategy and a specific semi-supervised learning (SSL) model achieved state-of-the-art performance. Intuitively, better performance could be achieved if stronger SS strategies and SSL models are employed. Following this intuition, one might easily derive various effective noisy-label learning methods using different combinations of SS strategies and SSL models, which is, however, simply reinventing the wheel in essence. To prevent this problem, we propose SemiNLL, a versatile framework that investi-gates how to naturally combine different SS and SSL components based on their effects and efficiencies. We conduct a systematic and detailed analysis of the combinations of possible components based on our framework. Our framework can absorb various SS strategies and SSL backbones, utilizing their power to achieve promising performance. The instantiations of our framework demonstrate substantial improvements over state-of-the-art methods on benchmark-simulated and real-world datasets with noisy labels.
Wang, Z, Jiang, Y, Han, F, Yu, S, Li, W, Ji, Y & Cai, W 2022, 'A thermodynamic configuration method of combined supercritical CO2 power system for marine engine waste heat recovery based on recuperative effects', Applied Thermal Engineering, vol. 200, pp. 117645-117645.
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Wang, Z, Jin, X, Kaw, HY, Fatima, Z, Quinto, M, Zhou, JL, Jin, D, He, M & Li, D 2022, 'Tracing historical changes, degradation, and original sources of airborne polycyclic aromatic hydrocarbons (PAHs) in Jilin Province, China, by Abies holophylla and Pinus tabuliformis needle leaves', Environmental Science and Pollution Research, vol. 29, no. 5, pp. 7079-7088.
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Due to their wide distribution and availability, plant leaves can be considered interesting candidates as biomonitoring substrates for the evaluation of atmospheric pollution. In addition, some species can also retain historical information, for example, related to environmental pollution, due to their leaf class age. In this study, the content of polycyclic aromatic hydrocarbons (PAHs) in Abies holophylla and Pinus tabuliformis needle samples in the function of their class age has been investigated to obtain information regarding the degradation constant for each PAH under investigation (α values ranging from 0.173 to 1.870) and to evaluate the possibility to correlate the presence of PAHs in needles with some important pollution environmental factors. Considering air pollutant variables registered in Jilin Province, interesting correlations (at 95% confidence level) have been found between coal consumption per year and anthracene contents in needles, while fluorene, phenanthrene, and anthracene results correlated with coal consumption. Furthermore, it has been demonstrated that the total PAH concentration in needles, for both species, increased with their age (from 804 to 3604 ng g-1 dry weight), showing a general tendency to accumulate these substances through years. PAH degradation rates increased instead with molecular complexity. This study could be considered a first trial to obtain historical environmental information by pine needles biomonitoring.
Wang, Z, Luo, J, Gong, Z, Luo, Q, Li, Q & Sun, G 2022, 'On correlation of stamping process with fiber angle variation and structural performance of thermoplastic composites', Composites Part B: Engineering, vol. 247, pp. 110270-110270.
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Wang, Z, Lv, T, Zeng, J & Ni, W 2022, 'Placement and Resource Allocation of Wireless-Powered Multiantenna UAV for Energy-Efficient Multiuser NOMA', IEEE Transactions on Wireless Communications, vol. 21, no. 10, pp. 8757-8771.
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Wang, Z, Mou, J, Qin, Z, He, Y, Sun, Z, Wang, X & Lin, CSK 2022, 'An auxin-like supermolecule to simultaneously enhance growth and cumulative eicosapentaenoic acid production in Phaeodactylum tricornutum', Bioresource Technology, vol. 345, pp. 126564-126564.
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Wang, Z, Yuan, B, Cao, J, Huang, Y, Cheng, X, Wang, Y, Zhang, X & Liu, H 2022, 'A new shift mechanism for micro-explosion of water-diesel emulsion droplets at different ambient temperatures', Applied Energy, vol. 323, pp. 119448-119448.
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Wang, Z, Yuan, B, Huang, Y, Cao, J, Wang, Y & Cheng, X 2022, 'Progress in experimental investigations on evaporation characteristics of a fuel droplet', Fuel Processing Technology, vol. 231, pp. 107243-107243.
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Investigating the evaporation characteristics of a fuel droplet is critical for understanding spray and combustion processes, which provides valuable information and guidance for optimizing engine performance. This paper systematically reviews the droplet evaporation characteristics of various fuels. Firstly, experimental methods for fuel droplet evaporation are introduced, including flying droplet, suspension and levitation, of which the latter two are the most widely applied due to their simple setups and convenient measurements. Secondly, droplet evaporation mechanisms of different fuels are comprehensively discussed. The evaporation process of single-component fuel droplets includes transient heating and equilibrium evaporation phases. Miscible and immiscible multi-component fuel droplets could experience puffing and micro-explosion phenomena, which increase droplet surface area and evaporation rate. Droplet evaporation may be the best when light component concentration is around 50% due to the strongest puffing and micro-explosion. The water droplets in emulsified fuel are slightly superheated by 0–30 °C when micro-explosion occurs. Nanoparticles could enhance droplet evaporation at low concentrations (0–1.25%) but inhibit droplet evaporation at higher concentrations. Finally, future research directions of fuel droplets are elaborated. More advanced experimental and numerical methods should be developed. Meanwhile, investigations on droplet evaporation should be combined with spray and combustion.
Wang, Z, Zhang, JA, Xiao, F & Xu, M 2022, 'Accurate AoA Estimation for RFID Tag Array With Mutual Coupling', IEEE Internet of Things Journal, vol. 9, no. 15, pp. 12954-12972.
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Angle-of-Arrival (AoA) estimation is an important problem in passive radio-frequency identification (RFID) systems. Affixing an RFID tag array to an object enables to acquire its orientation information. However, the electromagnetic interaction between the tags can induce mutual coupling interference, distorting the RFID fingerprint measurements used for AoA estimation. Moreover, RFID reader modes with radio-frequency (RF) noise-tolerant Miller encoding can induce π-radians phase jump. In this article, we propose a scheme called RF-Mirror that can resolve the mutual coupling and phase jump problems and achieve accurate AoA estimation for an array with two or more tags. First, we characterize the impact of mutual coupling on a tag's signal fingerprint and develop novel RSSI/phase-distance models. We then develop new experimental methods and signal processing techniques to verify the effectiveness of the proposed models. Based on the validated models, we develop new AoA estimation algorithms for tag arrays that deal with the mutual coupling effect explicitly. We provide extensive experimental results, which demonstrate that RF-Mirror can achieve significantly improved performance compared to baseline schemes, with median AoA estimation errors of 11.65° and 6.29° for two- and four-tag arrays, respectively.
Weber, NH, Delva, CS, Stockenhuber, SP, Grimison, CC, Lucas, JA, Mackie, JC, Stockenhuber, M & Kennedy, EM 2022, 'Modeling and Experimental Study on the Thermal Decomposition of Perfluorooctanesulfonic Acid (PFOS) in an α-Alumina Reactor', Industrial & Engineering Chemistry Research, vol. 61, no. 16, pp. 5453-5463.
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Weber, NH, Delva, CS, Stockenhuber, SP, Grimison, CC, Lucas, JA, Mackie, JC, Stockenhuber, M & Kennedy, EM 2022, 'Thermal Decomposition of Perfluorooctanesulfonic Acid (PFOS) in the Presence of Water Vapor', Industrial & Engineering Chemistry Research, vol. 61, no. 41, pp. 15146-15155.
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Wee, CK, Zhou, X, Gururajan, R, Tao, X, Chen, J, Gururajan, R, Wee, N & Barua, PD 2022, 'Notice of Removal: Automated Triaging Medical Referral for Otorhinolaryngology Using Data Mining and Machine Learning Techniques', IEEE Access, vol. 10, pp. 44531-44548.
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Wei, J, Yang, Q, Yu, Y, Wang, Q, Zhou, L & Chen, F 2022, 'Study of Bond–Slip Behavior and Constitutive Model of a New M-Section Steel-Skeleton Concrete', Materials, vol. 15, no. 19, pp. 6776-6776.
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In this study, the bond–slip behavior between a new type of M-section steel skeleton (i.e., M-section steel) applied in assembled shear wall structures and concrete was investigated. First, push-out tests were conducted on 21 M-section steel–concrete (MSSC) specimens, wherein the effects of the concrete cover, concrete strength, and anchorage length on the bond strength between the M-section steel and concrete were considered. Further, the crack patterns, strain distribution of M-section steel, and bond–slip curves of the MSSC specimens were investigated using conventional strain measurement and a non-contact optical three-dimensional deformation measurement system, Digital Image Correlation-3D (DIC-3D). The experimental results demonstrated that the bond–slip curves of the MSSC specimens were divided into four stages: the linear ascending, non-linear ascending, non-linear descending, and residual stages. The initial average bond strength τ¯s was mainly affected by the concrete strength and anchorage length, whereas the concrete cover and anchorage length influenced the residual average bond strength τ¯r, and the ultimate average bond strength τ¯u was affected by the concrete strength, concrete cover, and anchorage length. Consequently, a bond–slip constitutive model of M-section steel and concrete was proposed based on the experimental results, and consistency was observed in comparison with the test results, which verified the applicability of the proposed model. Furthermore, to verify the rationality of the bond–slip constitutive model, a numerical simulation was performed, wherein the bond–slip curves, stress clouds, and interfacial bond damage process of the MSSC specimens were investigated. The numerical simulation results indicated that the bond–slip constitutive model could accurately predict the entire failure process of the MSSC specimens.
Wei, Q, Ma, H, Chen, C & Dong, D 2022, 'Deep Reinforcement Learning With Quantum-Inspired Experience Replay', IEEE Transactions on Cybernetics, vol. 52, no. 9, pp. 9326-9338.
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Wei, W, Shi, X, Wu, L, Liu, X & Ni, B-J 2022, 'Calcium peroxide pre-treatment improved the anaerobic digestion of primary sludge and its co-digestion with waste activated sludge', Science of The Total Environment, vol. 828, pp. 154404-154404.
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Primary sludge (PS) and Waste activated sludge (WAS) as two main sludge streams in wastewater treatment plants are commonly anaerobically co-digested, which though may be differently affected by pretreatment. Previous work has found that calcium peroxide (CaO2) pretreatment effectively enhanced anaerobic digestion of WAS. However, the feasibilities of this strategy on PS anaerobic digestion and co-digestion of WAS and PS are still unclear. Herein, this work provided new insights into these systems. Biomethane potential test demonstrated that CaO2 pretreatment at 0.02-0.26 g/g-volatile suspended solids (VSS) promoted anaerobic digestion of PS. Then the feasibility of CaO2 pretreatment for improving anaerobic co-digestion of PS and WAS mixture was confirmed, with the highest improvement in methane production, VSS destruction and sludge reduction being approximately 37.4%, 38.9% and 19.9%, achieved at 0.14 g/g-VSS of CaO2. Process modelling analysis revealed that CaO2 pretreatment increased both degradable faction and actually degraded fraction in sludge mixture. The changes of sludge characteristics via pretreatment and key enzyme activity in sludge anaerobic co-digestion system demonstrated that increased CaO2 concentration resulted in increased soluble organics release from sludge mixture in the pretreatment stage and inhibited activity of coenzyme F420 responsible for methanogenesis. Further mechanism investigation disclosed that OH-, O2- and OH were main contribution factors, and the order of their contributions were OH- >O2- >OH. This work laid the theoretical foundation and provided guidance for the practical application of CaO2 pre-treatment technology.
Wei, W, Wang, C, Shi, X, Zhang, Y-T, Chen, Z, Wu, L & Ni, B-J 2022, 'Multiple microplastics induced stress on anaerobic granular sludge and an effectively overcoming strategy using hydrochar', Water Research, vol. 222, pp. 118895-118895.
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Previous studies mostly focused on the responses of anaerobic granular sludge (AGS) to one kind of microplastics during wastewater treatment. However, a wide variety of microplastics has been detected in wastewater. The multiple microplastics induced stress on AGS and the effectively mitigating strategy still remain unavailable. Herein, this work comprehensively excavated the influences of multiple microplastics (i.e., polyethylene terephthalate (PET), polystyrene (PS), polyethylene (PE) and polypropylene (PP)) coexisting in the wastewater on AGS system from macroscopic to microcosmic aspects. Experimental results illustrated that microplastics decreased AGS granule size, increased cell inactivation and caused deteriorative methane recovery from wastewater. As such, this study then put great emphasis on proposing a mitigating strategy using hydrochar and disclosing the role of hydrochar in overcoming the stress induced by coexisting microplastics to AGS system. Physiological characterization and microbial community analysis demonstrated that hydrochar effectively mitigated the reductions in methane production by 50.6% and cell viability by 68.8% of microplastics-bearing AGS and reduced the toxicity of microplastics to microbial community in the AGS. Mechanisms investigation by fluorescence tagging and excitation emission matrix fluorescence spectroscopy with fluorescence regional integration (EEM-FRI) analysis revealed that hydrochar adsorbed/accumulated microplastics and enhanced microplastics-bearing AGS to secrete extracellular polymeric substance (EPS) with more humic acid generation, thus reducing the direct contact between microplastics and AGS. In addition, hydrochar weakened the AGS intracellular oxidative stress induced by microplastics, thereby completely eliminating the inhibition of microplastics on acidification efficiency of AGS, and partially mitigating the suppression on methanation.
Wei, W, Zhang, Y-T, Wang, C, Guo, W, Ngo, HH, Chen, X & Ni, B-J 2022, 'Responses of anaerobic hydrogen-producing granules to acute microplastics exposure during biological hydrogen production from wastewater', Water Research, vol. 220, pp. 118680-118680.
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Anaerobic hydrogen-producing granule (AHPG) has been successfully applied in hydrogen production from wastewater. While various types of microplastics in large amounts are readily detected in both municipal and industrial wastewaters, however, to date the response of AHPG to multiple coexisting microplastics in wastewater is unknown yet. Herein, this study provided a first insight into the acute exposure-response relationship between multiple coexisting microplastics and the AHPG during biological hydrogen production from wastewater. Fluorescence tagging found that many microplastics accumulated and covered on the surface of the whole granule. Morphology and particle size of microplastics-bearing AHPG were characterized by microscopic observation, showing that the shock load of microplastics in the wastewater at the studied concentrations (40 and 80 mg/L) made the granule loose and even break down with the decreased particle size. The visualization of extracellular polymeric substances (EPS) structure revealed that microplastics decreased EPS production by 8.8-16.7%. Microbial community analysis demonstrated that the acute exposure of microplastics did not drive the change in the microbial community diversity and composition. However, toxic leachates and upgraded oxidative stress induced by microplastics increased cell death up to 14.7% and decreased hydrogen production by 18.7%, when the AHPG exposed to 80 mg/L of microplastics. This work gained a new insight into the response of anaerobic microorganisms to coexisting microplastics in the real environment.
Wei, Y, Jiang, W, Liu, Y, Bai, X, Hao, D & Ni, B-J 2022, 'Recent advances in photocatalytic nitrogen fixation and beyond', Nanoscale, vol. 14, no. 8, pp. 2990-2997.
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The traditional synthesis of ammonia is an industrial process with high energy consumption that is not environmentally friendly; thus, it is urgent to develop cost-effective approaches to synthesize ammonia under ambient conditions.
Weibel, J-B, Patten, T & Vincze, M 2022, 'Robust Sim2Real 3D Object Classification Using Graph Representations and a Deep Center Voting Scheme', IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 8028-8035.
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Weidner, E, Karbassiyazdi, E, Altaee, A, Jesionowski, T & Ciesielczyk, F 2022, 'Hybrid Metal Oxide/Biochar Materials for Wastewater Treatment Technology: A Review', ACS Omega, vol. 7, no. 31, pp. 27062-27078.
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This paper discusses the properties of metal oxide/biochar systems for use in wastewater treatment. Titanium, zinc, and iron compounds are most often combined with biochar; therefore, combinations of their oxides with biochar are the focus of this review. The first part of this paper presents the most important information about biochar, including its advantages, disadvantages, and possible modification, emphasizing the incorporation of inorganic oxides into its structure. In the next four sections, systems of biochar combined with TiO2, ZnO, Fe3O4, and other metal oxides are discussed in detail. In the next to last section probable degradation mechanisms are discussed. Literature studies revealed that the dispersion of a metal oxide in a carbonaceous matrix causes the creation or enhancement of surface properties and catalytic or, in some cases, magnetic activity. Addition of metallic species into biochars increases their weight, facilitating their separation by enabling the sedimentation process and thus facilitating the recovery of the materials from the water medium after the purification process. Therefore, materials based on the combination of inorganic oxide and biochar reveal a wide range of possibilities for environmental applications in aquatic media purification.
Wen, D, Yang, B, Qin, L, Zhang, Y, Chang, L & Li, R-H 2022, 'Computing K-Cores in Large Uncertain Graphs: An Index-Based Optimal Approach.', IEEE Trans. Knowl. Data Eng., vol. 34, pp. 3126-3138.
Wen, D, Yang, B, Zhang, Y, Qin, L, Cheng, D & Zhang, W 2022, 'Span-reachability querying in large temporal graphs', The VLDB Journal, vol. 31, no. 4, pp. 629-647.
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Reachability is a fundamental problem in graph analysis. In applications such as social networks and collaboration networks, edges are always associated with timestamps. Most existing works on reachability queries in temporal graphs assume that two vertices are related if they are connected by a path with non-decreasing timestamps (time-respecting) of edges. This assumption fails to capture the relationship between entities involved in the same group or activity with no time-respecting path connecting them. In this paper, we define a new reachability model, called span-reachability, designed to relax the time order dependency and identify the relationship between entities in a given time period. We adopt the idea of two-hop cover and propose an index-based method to answer span-reachability queries. Several optimizations are also given to improve the efficiency of index construction and query processing. We conduct extensive experiments on eighteen real-world datasets to show the efficiency of our proposed solution.
Wen, J, Gabrys, B & Musial, K 2022, 'Toward Digital Twin Oriented Modeling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey', IEEE Access, vol. 10, no. 99, pp. 66886-66923.
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This paper aims to provide a comprehensive critical overview on how entities and their interactions in Complex Networked Systems (CNS) are modelled across disciplines as they approach their ultimate goal of creating a Digital Twin (DT) that perfectly matches the reality. We propose four complexity dimensions for the network representation and five generations of models for the dynamics modelling to describe the increasing complexity level of the CNS that will be developed towards achieving DT (e.g. CNS dynamics modelled offline in the 1st generation v.s. CNS dynamics modelled simultaneously with a two-way real time feedback between reality and the CNS in the 5th generation). Based on that, we propose a new framework to conceptually compare diverse existing modelling paradigms from different perspectives and create unified assessment criteria to evaluate their respective capabilities of reaching such an ultimate goal. Using the proposed criteria, we also appraise how far the reviewed current state-of-the-art approaches are from the idealised DTs. Finally, we identify and propose potential directions and ways of building a DT-orientated CNS based on the convergence and integration of CNS and DT utilising a variety of cross-disciplinary techniques.
Wen, L, Zhou, J, Huang, W & Chen, F 2022, 'A Survey of Facial Capture for Virtual Reality', IEEE Access, vol. 10, pp. 6042-6052.
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Wen, S, Feng, ZK & Xiong, NN 2022, 'Guest Editorial: Special Issue on “Selected papers from ISAIC 2021”', Journal of Internet Technology, vol. 23, no. 7, pp. 1585-1586.
Wen, S, Feng, Z-K & Xiong, NN 2022, 'Guest Editorial: Special Issue on 'Selected papers from ISAIC 2021'', JOURNAL OF INTERNET TECHNOLOGY, vol. 23, no. 7, pp. 1585-1586.
Wen, S, Feng, Z-K, Huang, T & Zhang, N 2022, 'Theoretical analysis of advanced intelligent computing in environmental research', Environmental Research Letters, vol. 17, no. 4, pp. 040401-040401.
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Wen, S, Huang, T, Schuller, BW & Taher Azar, A 2022, 'Guest Editorial: Introduction to the Special Section on Efficient Network Design for Convergence of Deep Learning and Edge Computing', IEEE Transactions on Network Science and Engineering, vol. 9, no. 1, pp. 109-110.
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Wen, S, Ni, X, Wang, H, Zhu, S, Shi, K & Huang, T 2022, 'Observer-Based Adaptive Synchronization of Multiagent Systems With Unknown Parameters Under Attacks', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 7, pp. 3109-3119.
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This article studies the observer-based adaptive synchronization of multiagent systems (MASs) with unknown parameters under attacks. First, to estimate the state of agents, the observer for MAS is introduced. When disturbance, nonlinear function, and system model uncertainty are not considered, the nominal controller is proposed to achieve synchronization and state estimation. Then, in order to eliminate the effect of unknown parameters in the disturbance, nonlinear function, and system model uncertainty, the adaptive controller with switching term is introduced. However, the attack will lead to the destruction of the network topology so as the destruction of the nominal controller. By constructing an appropriate Lyapunov function, we analyze the effect caused by attacks, and the security control law is given to make sure the synchronization of the MASs under attacks. Finally, a numerical simulation is given to verify the validness of the obtained theorem.
Wen, Y, Liu, B, Cao, J, Xie, R, Song, L & Li, Z 2022, 'IdentityMask: Deep Motion Flow Guided Reversible Face Video De-Identification', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 12, pp. 8353-8367.
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Wen, Y, Liu, B, Ding, M, Xie, R & Song, L 2022, 'IdentityDP: Differential private identification protection for face images', Neurocomputing, vol. 501, pp. 197-211.
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Because of the explosive growth of face photos as well as their widespread dissemination and easy accessibility in social media, the security and privacy of personal identity information become an unprecedented challenge. Meanwhile, the convenience brought by advanced identity-agnostic computer vision technologies is attractive. Therefore, it is important to use face images while taking careful consideration in protecting people's identities. Given a face image, face de-identification, also known as face anonymization, refers to generating another image with similar appearance and the same background, while the real identity is hidden. Although extensive efforts have been made, existing face de-identification techniques are either insufficient in photo-reality or incapable of well-balancing privacy and utility. In this paper, we focus on tackling these challenges to improve face de-identification. We propose IdentityDP, a face anonymization framework that combines a data-driven deep neural network with a differential privacy (DP) mechanism. This framework encompasses three stages: facial representations disentanglement, ∊-IdentityDP perturbation and image reconstruction. Our model can effectively obfuscate the identity-related information of faces, preserve significant visual similarity, and generate high-quality images that can be used for identity-agnostic computer vision tasks, such as detection, tracking, etc. Different from the previous methods, we can adjust the balance of privacy and utility through the privacy budget according to practical demands and provide a diversity of results without pre-annotations. Extensive experiments demonstrate the effectiveness and generalization ability of our proposed anonymization framework.
Wen, Y, Qin, P-Y, Wei, G-M & Ziolkowski, RW 2022, 'Circular Array of Endfire Yagi-Uda Monopoles With a Full 360° Azimuthal Beam Scanning', IEEE Transactions on Antennas and Propagation, vol. 70, no. 7, pp. 6042-6047.
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Why, ESK, Ong, HC, Lee, HV, Chen, W-H, Asikin-Mijan, N, Varman, M & Loh, WJ 2022, 'Single-step catalytic deoxygenation of palm feedstocks for the production of sustainable bio-jet fuel', Energy, vol. 239, pp. 122017-122017.
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The production of jet fuel from renewable source (i.e., biomass) has been improving since the past few years. In Malaysia, palm-based biomass is being widely studied for the production of transportation fuels due to its abundant supply. Hence, this study focused on the production of bio-jet fuel from different types of palm oil (e.g., palm-based waste cooking oil, palm olein, palm kernel oil) through deoxygenation process. Several types of deoxygenation catalysts (e.g., CaO, Zeolite, V2O5, Pd/C, TiO2) were selected to investigate the efficiency of jet fuel-based hydrocarbon production under condition of 400 °C for 2 h with different catalyst loading (e.g., 0 wt%, 5 wt%, 6 wt%, 7 wt%, 8 wt%, 9 wt% and 10 wt%). The physico-chemical properties of yielded liquid fuel were tested by using GC-MS analyses, as well as density, kinematic viscosity, cloud point, pour point, smoke point, flash point and final boiling point. The deoxygenation of PKO over Pd/C at 8 wt% yielded the highest molar concentration of 96% liquid product (e.g., n-paraffins, isoparaffins, olefins, naphthenes, aromatic) and 73% of jet paraffins selectivity (C8–C16) under 400 °C for 2 h. In addition, the physicochemical properties of palm-based liquid fuel are complied with standard Jet A-1 fuel, in accordance to ASTM standards. The low temperature fluidity, combustion characteristics, and fuel volatility of this liquid product were comparable to Jet A-1 fuel.
Whyte, T, Lind, E, Richards, A, Eager, D, Bilston, LE & Brown, J 2022, 'Neck Loads During Head-First Entries into Trampoline Dismount Foam Pits: Considerations for Trampoline Park Safety', Annals of Biomedical Engineering, vol. 50, no. 6, pp. 691-702.
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AbstractSerious cervical spine injuries have been documented from falls into foam pits at trampoline parks. To address the lack of evidence on how foam pits should be designed for mitigating neck injury risk, this study aimed to quantify neck loads during head-first entry into varying foam pit designs. An instrumented Hybrid III anthropomorphic test device was dropped head-first from a height of up to 1.5 m into three differently constructed foam pits, each using a different mechanism to prevent direct contact between the falling person and the floor (foam slab, trampoline or net bed). Measured neck loads were compared to published injury reference values. In the simplest, foam-only pit design, increasing foam depth tended to reduce peak compressive force. At least one injury assessment reference metric was exceeded in all pit conditions tested for 1.5 m falls, most commonly the time-dependent neck compression criterion. The results highlight the importance of adequate foam depth in combination with appropriate pit design in minimizing injury risk. The risk of cervical spine injury may not be reduced sufficiently with current foam pit designs.
Wickramanayake, B, He, Z, Ouyang, C, Moreira, C, Xu, Y & Sindhgatta, R 2022, 'Building interpretable models for business process prediction using shared and specialised attention mechanisms', Knowledge-Based Systems, vol. 248, pp. 108773-108773.
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Wickramanayake, S, Thiyagarajan, K & Kodagoda, S 2022, 'Deep Learning for Estimating Low-Range Concrete Sub-Surface Boundary Depths Using Ground Penetrating Radar Signals', IEEE Sensors Letters, vol. 6, no. 3, pp. 1-4.
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Wickramanayake, S, Thiyagarajan, K, Kodagoda, S & Piyathilaka, L 2022, 'Ultrasonic thickness measuring in-pipe robot for real-time non-destructive evaluation of polymeric spray linings in drinking water pipe infrastructure', Mechatronics, vol. 88, pp. 102913-102913.
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Williams, P, Kirby, R & Karimi, M 2022, 'Sound power radiated from acoustically thick, fluid loaded, axisymmetric pipes excited by a central monopole', Journal of Sound and Vibration, vol. 527, pp. 116843-116843.
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The aim of this study is to determine how the breakout noise of infinite length cylindrical shells excited by a central internal point source differ between acoustically thin and thick walls. This will further our understanding of acoustic radiation from axisymmetric pipes with thick walls, whose breakout noise excited by an internal monopole has not been studied previously. To accomplish this, pipes filled with air and immersed in an external fluid are investigated. This is performed numerically using the semi analytical finite element method to generate predictions above the critical frequency of the duct. It is observed that the maxima in the breakout noise occur when a certain class of eigenmodes with sound energy lying predominantly in the structure veer away from those where the sound energy lies predominantly in the fluid. For lightly fluid loaded pipes with thin walls this veering and associated increase to breakout noise is observed at the ring frequency and above the critical frequency. However for thick walled pipes, no corresponding increase in breakout noise is observed at the critical frequency for a pipe immersed in air. Instead the increase in breakout noise is observed only at the ring frequency and above. However, if the pipe is immersed in water then the increase in breakout noise is observed to occur below the ring frequency.
Wisanmongkol, J, Taparugssanagorn, A, Tran, LC, Le, AT, Huang, X, Ritz, C, Dutkiewicz, E & Phung, SL 2022, 'An ensemble approach to deep‐learning‐based wireless indoor localization', IET Wireless Sensor Systems, vol. 12, no. 2, pp. 33-55.
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AbstractThe authors investigate the use of deep learning in wireless indoor localization to address the shortcomings of the existing range‐based (e.g. trilateration and triangulation) and range‐free (e.g. fingerprinting) localization. Instead of relying on geometric models and hand‐picked features, deep learning can automatically extract the relationship between the observed data and the target's location. Nevertheless, a deep neural network (DNN) model providing a satisfactory accuracy might perform differently when it is retrained in the deployment. To mitigate this issue, the authors propose an ensemble method where DNN models obtained from multiple training sessions are combined to locate the target. In the authors' evaluation, several DNN models are trained on the data, which consists of the received signal strength (RSS), angle of arrival (AOA), and channel state information (CSI), used in the existing hybrid RSS/AOA and RSS/CSI fingerprinting, and their root‐mean‐square error (RMSE) values are compared accordingly. The results show that the proposed method achieves the lower RMSE than the existing methods, and the RMSE can be lowered by up to 1.47 m compared with the ones obtained from a single model. Moreover, for some DNN models, the RMSE values are even lower than the minimum RMSE obtained by their single‐model counterparts.
Wong, CM, Yau, YH, Ong, HC & Chin, WM 2022, 'Study of climate change impacts on the lifespan of a bin weather data set in Senai, Malaysia', Urban Climate, vol. 44, pp. 101219-101219.
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Wong, SYK, Chan, JSK, Azizi, L & Xu, RYD 2022, 'Time‐varying neural network for stock return prediction', Intelligent Systems in Accounting, Finance and Management, vol. 29, no. 1, pp. 3-18.
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AbstractWe consider the problem of neural network training in a time‐varying context. Machine learning algorithms have excelled in problems that do not change over time. However, problems encountered in financial markets are often time varying. We propose the online early stopping algorithm and show that a neural network trained using this algorithm can track a function changing with unknown dynamics. We compare the proposed algorithm to current approaches on predicting monthly US stock returns and show its superiority. We also show that prominent factors (such as the size and momentum effects) and industry indicators exhibit time‐varying predictive power on stock returns. We find that during market distress, industry indicators experience an increase in importance at the expense of firm level features. This indicates that industries play a role in explaining stock returns during periods of heightened risk.
Wooster, EIF, Fleck, R, Torpy, F, Ramp, D & Irga, PJ 2022, 'Urban green roofs promote metropolitan biodiversity: A comparative case study', Building and Environment, vol. 207, pp. 108458-108458.
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Wright, E, Zhou, J, Lindsay, D, Przhedetsky, L, Chen, F & Davison, A 2022, 'SMEs and Explainable AI: Australian Case Studies', Computers & Law: Journal for the Australian and New Zealand Societies of Computers and the Law, vol. 94.
Wu, B, Zeng, J, Shao, S, Ni, W & Tang, Y 2022, 'New Game-Theoretic Approach to Decentralized Path Selection and Sleep Scheduling for Mobile Edge Computing', IEEE Transactions on Wireless Communications, vol. 21, no. 8, pp. 6125-6140.
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Wu, C, Xia, Y & Bi, K 2022, 'Guest editorial', Advances in Structural Engineering, vol. 25, no. 7, pp. 1371-1372.
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Wu, EQ, Lin, C-T, Zhu, L-M, Tang, ZR, Jie, Y-W & Zhou, G-R 2022, 'Fatigue Detection of Pilots’ Brain Through Brains Cognitive Map and Multilayer Latent Incremental Learning Model', IEEE Transactions on Cybernetics, vol. 52, no. 11, pp. 12302-12314.
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This work proposes a nonparametric prior induced deep sum-logarithmic-multinomial mixture (DSLMM) model to detect pilots' cognitive states through the developed brain power map. DSLMM uses multinormal distribution to infer the latent variable of each neuron in the first layer of the network. These latent variables obeyed a sum-logarithmic distribution that is backpropagated to its observation vector and the number of neurons in the next layer. Multinormal distribution is used to segment the extended observation vector to form a matrix associated with the width of the next layer. This work also proposes an adaptive topic-layer stochastic gradient Riemann (ATL-SGR) Markov chain Monte Carlo (MCMC) inference method to learn its global parameters without heuristic assumptions. The experimental results indicate that DSLMM can extract more probability distribution contained in the brain power map layer by layer, and achieve higher pilot cognition detection accuracy.
Wu, F, Li, F, Zhao, X, Bolan, NS, Fu, P, Lam, SS, Mašek, O, Ong, HC, Pan, B, Qiu, X, Rinklebe, J, Tsang, DCW, Van Zwieten, L, Vithanage, M, Wang, S, Xing, B, Zhang, G & Wang, H 2022, 'Meet the challenges in the “Carbon Age”', Carbon Research, vol. 1, no. 1.
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Wu, H, Lin, X, Zhou, A & Zhang, YX 2022, 'A temperature-dependent material model for numerical simulation of steel fibre reinforced concrete', Construction and Building Materials, vol. 320, pp. 126329-126329.
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Wu, H, Wei, D, Hee, AC, Huang, S, Xing, Z, Jiao, S, Huang, H & Jiang, Z 2022, 'The influence of water-based nanolubrication on mill load and friction during hot rolling of 304 stainless steel', The International Journal of Advanced Manufacturing Technology, vol. 121, no. 11-12, pp. 7779-7792.
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AbstractUsing pure water in comparison to water-based lubricant containing 4% TiO2 nanoparticles (NPs), the hot rolling tests of 304 stainless steel were carried out at a rolling temperature of 1050 °C under varying rolling reductions and speeds. The effects of lubrication on rolling force, torque, power and contact friction were systematically investigated. The coefficient of friction (COF) during steady-state hot steel rolling was inversely calculated using a developed flow stress model. The COF models including the effects of rolling reduction and speed were proposed via multiple linear regression. The results indicated that the use of the nanolubricant enabled a reduction of rolling force up to 6.1% and decreases in rolling torque and power up to 21.6%, compared to that of water condition. The results obtained from the linear regression agreed well with those from the inverse calculation, suggesting the developed COF models had high accuracy. The lubrication mechanisms were derived from a boundary lubrication regime, owing to ball bearing and mending effects of TiO2 NPs, and formation of thin lubricant film under high rolling pressure.
Wu, H-L, Chong, Y-H, Ong, H-C & Shu, C-M 2022, 'Thermal stability of modified lithium-ion battery electrolyte by flame retardant, tris (2,2,2-trifluoroethyl) phosphite', Journal of Thermal Analysis and Calorimetry, vol. 147, no. 6, pp. 4245-4252.
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With the increasing awareness of green energy, electric vehicles have become the future trend, with lithium-ion batteries (LIBs) regarded as the most suitable energy storage carrier. Therefore, more and more research topics are focused on LIBs, and all parties are working hard to improve the performance of LIBs. Yet, the safety concerns caused by the failure of LIBs cannot be ignored. LIBs themselves are energetic materials, and the causes of accidents often go through multistage irreversible reactions. Several studies have also pointed out that the electrolyte has a significant correlation with the response characteristics because, in the process of LIBs thermal runaway, the electrolyte participating in the oxidation of the entire battery leads to a considerable amount of heat and even runaway reaction as well. Accordingly, it is necessary to obtain a safer electrolyte by modification. In this study, a significant flame retardant (FR) additive, tris (2,2,2-trifluoroethyl) phosphite (TTFP), is used to suppress lithium-ion battery fires or even explosions and maintain typical battery performance. The performance of the electrolyte was tested by differential scanning calorimetry and thermogravimetric analyzer, and the electrolysis was examined on liquid flash point (FP), self-extinguishing time (SET), and conductivity. During the heating process, adding TTFP to the electrolyte effectively delayed the exothermic peak, reduced the amount of heat, improved the FP, and curtailed the SET. The hazard degree of the electrolyte under high-temperature environment was much lower than before adding the additives, and the additives were finally obtained. It can conclusively prove the safety of lithium batteries without lessening the practical performance of the batteries.
Wu, J, Jiang, Z, Chen, Q, Wen, S, Men, A & Wang, H 2022, 'Toward a perceptive pretraining framework for Audio-Visual Video Parsing', Information Sciences, vol. 609, pp. 897-912.
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Audio-Visual Video Parsing (AVVP) is a new multi-modal weakly supervised task which aims to detect and localize events leveraging the partial alignment of audio and visual streams and weak labels. We identified two significant challenges in the AVVP: Cross-mode semantic misalignment and Contextual audio-visual dataset bias. For challenge 1, the existing methods tend to leverage the temporal similarity of the features. However, it is inappropriate for our AVVP task because multi-modal features with the same label do not always have the same semantics. Thus, we propose an instance-adaptive multi-modal time series max-margin loss (MTSM) which uses the temporal information to align features adaptively. Furthermore, to restrict the inescapable noise introduced during the feature fusion, we reuse the expression of MTSM in the single-mode. For the second challenge, we argue that bias mitigation should seek help from model generalization. Thus, we propose collocating pre-trained models: either” traverse” or based on domain-adaptation. First, we prove a hypothesis and then propose a method based on the Alternating Direction Method of Multipliers(ADMM) to decouple the optimal pre-trained model collocation solution, which reduces the time consumption. Experiments show that our method outperforms the contrastive methods.
Wu, K, Beydoun, G, Sohaib, O & Gill, A 2022, 'The Co-construct/ Co-evolving Process between Organization's Absorptive Capacity and Enterprise System Practice under Changing Context: The Case of ERP Practice.', Inf. Syst. Frontiers, vol. 24, no. 6, pp. 2123-2138.
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AbstractLong term sustainability in a competitive and changing environment requires an organization to continuously learn and adapt. The ability to access and use new knowledge is contingent on the organisational absorptive capacity (AC). In this paper, we focus on how an organization’s absorptive capacity and its enterprise system practices develop and co-evolve over time. Analysing a fifteen years’ ERP practice in an organisational context, this study synthesizes a new AC analysis framework that takes into account the dynamic nature of AC. This high-level analysis coupled with a longitudinal view resolves inconsistent results between current AC studies and suggest further directions for organisational AC research.
Wu, K, Zhang, JA & Guo, YJ 2022, 'Fast and Accurate Linear Fitting for an Incompletely Sampled Gaussian Function With a Long Tail [Tips & Tricks]', IEEE Signal Processing Magazine, vol. 39, no. 6, pp. 76-84.
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Fitting experiment data onto a curve is a common signal processing technique to extract data features and establish the relationship between variables. Often, we expect the curve to comply with some analytical function and then turn data fitting into estimating the unknown parameters of a function. Among analytical functions for data fitting, the Gaussian function is the most widely used one due to its extensive applications in numerous science and engineering fields. To name just a few, the Gaussian function is highly popular in statistical signal processing and analysis, thanks to the central limit theorem [1], and the Gaussian function frequently appears in the quantum harmonic oscillator, quantum field theory, optics, lasers, and many other theories and models in physics [2]; moreover, the Gaussian function is widely applied in chemistry for depicting molecular orbitals, in computer science for imaging processing, and in artificial intelligence for defining neural networks.
Wu, K, Zhang, JA, Huang, X & Guo, YJ 2022, 'Frequency-Hopping MIMO Radar-Based Communications: An Overview', IEEE Aerospace and Electronic Systems Magazine, vol. 37, no. 4, pp. 42-54.
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Abstract—Enabled by the advancement in radio frequency technologies, the convergence of radar and communication systems becomes increasingly promising and is envisioned as a key feature of future sixth-generation networks. Recently, the frequency-hopping (FH) MIMO radar is introduced to underlay dual-function radar-communication (DFRC) systems. Superior to many previous radar-centric DFRC designs, the symbol rate of FH-MIMO radar-based DFRC (FH-MIMO DFRC) can exceed the radar pulse repetition frequency. However, many practical issues, particularly those crucial to achieving effective data communications, are unexplored or unsolved. To promote the awareness and general understanding of the novel DFRC, this article is devoted to providing a timely introduction of FH-MIMO DFRC. We comprehensively review many essential aspects of the novel DFRC: channel/signal models, signaling strategies, modulation/demodulation processing and channel estimation methods, to name a few. We also highlight major remaining issues in FHMIMO DFRC and suggest potential solutions to shed light on future research directions.
Wu, K, Zhang, JA, Huang, X & Guo, YJ 2022, 'Integrating Low-Complexity and Flexible Sensing Into Communication Systems', IEEE Journal on Selected Areas in Communications, vol. 40, no. 6, pp. 1873-1889.
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Integrating sensing into standardized communication systems can potentially benefit many consumer applications that require both radio frequency functions. However, without an effective sensing method, such integration may not achieve the expected gains of cost and energy efficiency. Existing sensing methods, which use communication payload signals, either have limited sensing performance or suffer from high complexity. In this paper, we develop a novel and flexible sensing framework which has a complexity only dominated by a Fourier transform and also provides the flexibility in adapting to different sensing needs. We propose to segment a whole block of echo signal evenly into sub-blocks; adjacent ones are allowed to overlap. We design a virtual cyclic prefix (VCP) for each sub-block that allows us to employ two common ways of removing communication data symbols and generate two types of range-Doppler maps (RDMs) for sensing. We perform a comprehensive analysis of the signal components in the RDMs, proving that their interference-plus-noise (IN) terms are approximately Gaussian distributed. The statistical properties of the distributions are derived, which leads to the analytical comparisons between the two RDMs as well as between the prior and our sensing methods. Moreover, the impact of the lengths of sub-block, VCP and overlapping signal on sensing performance is analyzed. Criteria for designing these lengths for better sensing performance are also provided. Extensive simulations validate the superiority of the proposed sensing framework over prior methods in terms of signal-to-IN ratios in RDMs, detecting performance and flexibility.
Wu, K, Zhang, JA, Huang, X & Guo, YJ 2022, 'Integrating Secure Communications Into Frequency Hopping MIMO Radar With Improved Data Rate', IEEE Transactions on Wireless Communications, vol. 21, no. 7, pp. 5392-5405.
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Wu, K, Zhang, JA, Huang, X & Guo, YJ 2022, 'Joint Communications and Sensing Employing Multi- or Single-Carrier OFDM Communication Signals: A Tutorial on Sensing Methods, Recent Progress and a Novel Design', Sensors, vol. 22, no. 4, pp. 1613-1613.
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Joint communications and sensing (JCAS) has recently attracted extensive attention due to its potential in substantially improving the cost, energy and spectral efficiency of Internet of Things (IoT) systems that need both radio frequency functions. Given the wide applicability of orthogonal frequency division multiplexing (OFDM) in modern communications, OFDM sensing has become one of the major research topics of JCAS. To raise the awareness of some critical yet long-overlooked issues that restrict the OFDM sensing capability, a comprehensive overview of OFDM sensing is provided first in this paper, and then a tutorial on the issues is presented. Moreover, some recent research efforts for addressing the issues are reviewed, with interesting designs and results highlighted. In addition, the redundancy in OFDM sensing signals is unveiled, on which, a novel method is based and developed in order to remove the redundancy by introducing efficient signal decimation. Corroborated by analysis and simulation results, the new method further reduces the sensing complexity over one of the most efficient methods to date, with a minimal impact on the sensing performance.
Wu, K, Zhang, JA, Huang, X & Guo, YJ 2022, 'Removing False Targets for Cyclic Prefixed OFDM Sensing with Extended Ranging', Sensors, vol. 22, no. 22, pp. 9015-9015.
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Employing a cyclic prefixed OFDM (CP-OFDM) communication waveform for sensing has attracted extensive attention in vehicular integrated sensing and communications (ISAC). A unified sensing framework was developed recently, enabling CP-OFDM sensing to surpass the conventional limits imposed by underlying communications. However, a false target issue still remains unsolved. In this paper, we investigate and solve this issue. Specifically, we unveil that false targets are caused by periodic cyclic prefixes (CPs) in CP-OFDM waveforms. We also derive the relation between the locations of false and true targets, and other features, e.g., strength, of false targets. Moreover, we develop an effective solution to remove false targets. Simulations are provided to confirm the validity of our analysis and the effectiveness of the proposed solution. In particular, our design can reduce the false alarm rate caused by false targets by over 50% compared with the prior art.
Wu, K, Zhang, JA, Huang, X, Guo, YJ, Nguyen, DN, Kekirigoda, A & Hui, K-P 2022, 'Analog-Domain Suppression of Strong Interference Using Hybrid Antenna Array', Sensors, vol. 22, no. 6, pp. 2417-2417.
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The proliferation of wireless applications, the ever-increasing spectrum crowdedness, as well as cell densification makes the issue of interference increasingly severe in many emerging wireless applications. Most interference management/mitigation methods in the literature are problem-specific and require some cooperation/coordination between different radio frequency systems. Aiming to seek a more versatile solution to counteracting strong interference, we resort to the hybrid array of analog subarrays and suppress interference in the analog domain so as to greatly reduce the required quantization bits of the analog-to-digital converters and their power consumption. To this end, we design a real-time algorithm to steer nulls towards the interference directions and maintain flat in non-interference directions, solely using constant-modulus phase shifters. To ensure sufficient null depth for interference suppression, we also develop a two-stage method for accurately estimating interference directions. The proposed solution can be applicable to most (if not all) wireless systems as neither training/reference signal nor cooperation/coordination is required. Extensive simulations show that more than 65 dB of suppression can be achieved for 3 spatially resolvable interference signals yet with random directions.
Wu, L, Wang, L, Wei, W, Song, L & Ni, B 2022, 'Sulfur‐driven autotrophic denitrification of nitric oxide for efficient nitrous oxide recovery', Biotechnology and Bioengineering, vol. 119, no. 1, pp. 257-267.
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AbstractNitrous oxide (N2O) was previously deemed as a potent greenhouse gas but is actually an untapped energy source, which can accumulate during the microbial denitrification of nitric oxide (NO). Compared with the organic electron donor required in heterotrophic denitrification, elemental sulfur (S0) is a promising electron donor alternative due to its cheap cost and low biomass yield in sulfur‐driven autotrophic denitrification. However, no effort has been made to test N2O recovery from sulfur‐driven denitrification of NO so far. Therefore, in this study, batch and continuous experiments were carried out to investigate the NO removal performance and N2O recovery potential via sulfur‐driven NO‐based denitrification under various Fe(II)EDTA‐NO concentrations. Efficient energy recovery was achieved, as up to 35.5%–40.9% of NO was converted to N2O under various NO concentrations. N2O recovery from Fe(II)EDTA‐NO could be enhanced by the low bioavailability of sulfur and the acid environment caused by sulfur oxidation. The NO reductase (NOR) and N2O reductase (N2OR) were inhibited distinctively at relatively low NO levels, leading to efficient N2O accumulation, but were suppressed irreversibly at NO level beyond 15 mM in continuous experiments. Such results indicated that the regulation of NO at a relatively low level would benefit the system stability and NO removal capacity during long‐term system operation. The continuous operation of the sulfur‐driven Fe(II)EDTA‐NO‐based denitrification reduced the overall microbial diversity but enriched several key microbial community. Thauera, Thermomonas, and Arenimonas that are able to carry out sulfur‐driven autot...
Wu, L, Wang, L-K, Wei, W & Ni, B-J 2022, 'Autotrophic denitrification of NO for effectively recovering N2O through using thiosulfate as sole electron donor', Bioresource Technology, vol. 347, pp. 126681-126681.
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To reclaim nitrous oxide (N2O) as an energy resource economically, this study developed an autotrophic denitrification-based system with thiosulfate (S2O32-) and nitric oxide (NO) as electron donor and acceptor, respectively. NO from flue gases is absorbed on Fe(II)EDTA to overcome its low solubility in liquid phase by forming Fe(II)EDTA-NO. Short-term batch tests and long-term continuous experiments were conducted to investigate the N2O production profile and NO conversion efficiency from thiosulfate-based denitrification under varied Fe (II)EDTA-NO conditions (5-20 mM). Up to 39% of NO was converted to gaseous N2O at 20 mM Fe(II)EDTA-NO amid batch test due to the inhibition of key enzymatic activities by NO and the acidic conditions following thiosulfate oxidation. Higher Fe(II)EDTA-NO levels induced lower enzymatic activities with N2OR being suppressed harder than NOR. Microbial diversity was reduced in the continuous thiosulfate-driven Fe(II)EDTA-NO-based denitrification system. NO-resistant bacteria and sulfide-tolerant denitrifiers were enriched, facilitating NO conversion to N2O thereafter.
Wu, L, Wei, W, Chen, Z & Ni, B-J 2022, 'Medium-chain carboxylate productions through open-culture fermentation of organic wastes', Journal of Cleaner Production, vol. 373, pp. 133911-133911.
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The growing global demand for petroleum-derived products and alarms concerning to depletion of crude oil have encouraged the conversion of organic wastes into medium-chain fatty acids (MCFAs), the precursor molecules biofuels. To this end, anaerobic fermentation-based technology has received a great deal of interests, as such eco-friendly technique can produce these value-added chemicals efficiently and sustainably. Open-culture fermentation is preferred to generate the said carboxylates given to its lower capital and operating costs than axenic systems. However, the underlying microbial pathways and the microbial interactions are not well understood. Therefore, a comprehensive understanding of the MCFAs productions from open-culture fermentation would benefit the valorisation of wastes by forming products with higher commercial value. To this end, this review article firstly covered a systematic introduction regarding the MCFAs formations through open-culture fermentation from the aspects of metabolic platforms and competitive bio-reactions. Suitable operational conditions and challenges are then scrutinized to discuss the feasibility of up-to-date strategies towards higher productivities. The potential opportunities for improving MCFAs productions biologically are finally proposed based on the content of the review.
Wu, L, Wei, W, Liu, X, Wang, D & Ni, B-J 2022, 'Potentiality of recovering bioresource from food waste through multi-stage Co-digestion with enzymatic pretreatment', Journal of Environmental Management, vol. 319, pp. 115777-115777.
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Food waste (FW) is not only a major social, nutritional and environmental issue, but also an underutilized resource with significant energy, which has not been fully explored currently. Considering co-digestion can adjust carbon to nitrogen ratio (C/N) of the feedstock and improve the synergetic interactions among microorganisms, anaerobic co-digestion (AnCoD) is then becoming an emerging approach to achieve higher energy recovery from FW while ensuring the stability of the system. To obtain higher economic gain from such biodegradable wastes, increasing attention has been paid on optimizing the system configuration or applying enzymatic hydrolysis before digesting FW. A better understanding on the potentiality of correlating enzymatic pretreatment and AnCoD operated in various system configuration would enhance the bioresource recovery from FW and increase revenue through treating this organic waste. Specifically, the biobased chemicals outputs from FW-related co-digestion system with different configuration were firstly compared in this review. A deep discussion concerning the challenges for achieving bioresources recovery from FW co-digestion systems with enzymatic pretreatment was then given. Recommendations for future studies regarding FW co-digestion were then proposed at last.
Wu, P, Wu, C, Liu, Z, Xu, S, Li, J & Li, J 2022, 'Triaxial strength and failure criterion of ultra-high performance concrete', Advances in Structural Engineering, vol. 25, no. 9, pp. 1893-1906.
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Over the past few decades, ultra-high performance concrete (UHPC) has been widely studied and applied because of its outstanding mechanical properties. A large number of studies have been conducted on the uniaxial static and dynamic performance of UHPC materials, however, limited investigations exist on the triaxial compression properties of UHPC. In this study, 98 cylindrical samples of UHPC with different steel fiber volumetric ratios (0.0%–1.5%) were tested to investigate the triaxial behavior of UHPC under different confining pressures (0 MPa–40 MPa). The confining pressure and steel fiber contents have clear impact on the triaxial strength, failure mode, crack width, and the angle between the oblique crack and the axial direction. The triaxial compressive strength and compressive toughness of UHPC subjected to various confining pressures are obtained from the tests and discussed in the study. Based on the testing data, the triaxial compression failure criterion of UHPC is established according to the unified strength theory. Finally, the simplified empirical equations for the full stress-strain curves of UHPC specimens subjected to uniaxial and multiaxial loads are derived, and good agreement with the experimental results is achieved.
Wu, RMX 2022, 'Which Objective Weight Method Is Better: PCA or Entropy?', Scientific Journal of Research & Reviews, vol. 3, no. 2.
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Multi-criteria decision-making (MCDM) methods have significantly been used for evaluating and ranking critical factors with conflicting characteristics in different fields and disciplines. Up-to-date literature indicated no study reported which method was most suitable for assessing hazard, risk, and emergency assessment. Practicians were still seeking a single responsive approach to keep the computing system’s lower load. The recent study indicated the PCA as the predominant, and the Entropy method as the second most widely adopted method. However, there was no answer for a better approach between the PCA and Entropy method. The practical implication suggested that comparative analysis should always be conducted to each case and determine the appropriate weighting method in the relevant circumstances or business system applications.
Wu, S-L, Wei, W, Wang, Y, Song, L & Ni, B-J 2022, 'Transforming waste activated sludge into medium chain fatty acids in continuous two-stage anaerobic fermentation: Demonstration at different pH levels', Chemosphere, vol. 288, no. Pt 1, pp. 132474-132474.
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Bioenergy recovery in the form of medium-chain fatty acids (MCFAs) from waste activated sludge (WAS) is increasingly attractive, which are valuable building blocks for fuel production. This study experimentally demonstrated the long-term MCFAs (C6-C8) production from WAS in two-stage anaerobic sludge fermentation at different pH conditions, using continuously operated bench-scale anaerobic reactors. The WAS was continuously converted to short chain fatty acids (SCFAs, 3500-3800 mg chemical oxygen demand (COD)/L) at the first stage via alkaline anaerobic fermentation, which was directly fed into the second stage as both substrates and inoculum for MCFAs production through chain elongation (CE). The productions of MCFAs at the second stage were continuously studied under three different pH conditions (i.e., 10, 7 and 5.5). The results demonstrated that there was no significant MCFAs production at pH 10 during the steady state, whereas the MCFAs productions were clearly observed at both pH 7 and pH 5.5, with much higher MCFAs production from WAS at pH 7 (i.e., 10.32 g COD/L MCFAs) than that at pH 5.5 (i.e., 8.73 g COD/L MCFAs) during the steady state. A higher MCFAs selectivity of 62.3% was also achieved at pH 7. The relatively lower MCFAs production and selectivity at pH 5.5 was likely due to the higher undissociated MCFAs generated at pH 5.5, which would pose toxicity impact on CE microbes and thus inhibit the CE process. Microbial community analysis confirmed that the relative abundances of CE related microbes (e.g., Clostridium sensu stricto 12 sp. and Clostridium sensu stricto 1) increased at pH 7 compared to those at pH 5.5, which enabled more efficient MCFAs production from WAS.
Wu, T, Ma, H, Wang, C, Qiao, S, Zhang, L & Yu, S 2022, 'Heterogeneous representation learning and matching for few-shot relation prediction', Pattern Recognition, vol. 131, pp. 108830-108830.
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The recent explosive development of knowledge graphs (KGs) in artificial intelligence tasks coupled with incomplete or partial information has triggered considerable research interest in relation prediction. However, many challenges still remain unsolved: (i) the previous relation prediction methods require a significant amount of training instances (i.e., head-tail entity pairs) for every relation, which is infeasible in practical scenarios; and (ii) the representation learning of entities and relations always assumes that all local neighbors and their features contribute equally to the embedding, not sufficiently considering the heterogeneity of the information; and (iii) the state-of-the-art methods usually require a lot of training time, resulting in a high cost in real-world applications. To overcome these challenges, we propose a heterogeneous representation learning and matching approach, Multi-metric Feature Extraction Network (MFEN for short), for few-shot relation prediction in KGs. Our method focuses on knowledge graphs to sufficiently explore the topological structure and node content in graphs. Rather than taking the average of the embeddings of all relational neighbors, a heterogeneity-aware representation learning method is proposed to generate high-expressive embeddings, which capture the heterogenous roles of the relational neighbors of given entity and all of their features via a convolutional encoder. To learn the expressive representations efficiently, a single-layer CNN architecture with multi-scale filters is devised. In addition, multiple heuristic metrics are combined to efficiently improve the accuracy of similarity calculation. The proposed MFEN model is evaluated on two representative benchmark datasets NELL and Wiki. Extensive experiments have demonstrated that our method gets more than 5% accuracy improvement and three times speedup to state-of-the-art models. Code is available on https://github.com/summer-funny/MFEN.
Wu, Y, Wang, Y, Huang, X, Yang, F, Ling, SH & Su, SW 2022, 'Multimodal Learning for Non-small Cell Lung Cancer Prognosis.', CoRR, vol. abs/2211.03280.
Wu, Z, Khalilpour, K & Hämäläinen, RP 2022, 'A decision support tool for multi-attribute evaluation of demand-side commercial battery storage products', Sustainable Energy Technologies and Assessments, vol. 50, pp. 101723-101723.
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With the diversification of commercial energy storage technologies, choosing a suitable technology is becoming a complex decision-making process. The complexity is rooted in the many decision criteria such as technology, brand reputation, energy capacity, volume, weight, aging, and warranty among many others. As such, for non-expert users, particularly small households or enterprises, the act of energy storage adoption is becoming growingly cumbersome. To address this problem, this paper introduces a decision support tool for the evaluation of commercial (small-scale) energy storage products. It then identifies the most suitable option(s) based on the users' preferences. For the reasons elaborated in the paper, nine multi-criteria decision-making (MCDM) methodologies have been employed. Altogether, 19 attributes are identified for the evaluation of (battery) energy storage technologies. The decision support tool is developed in the Matlab environment and includes a graphical user interface for easier interaction of non-expert users. For the demonstration, three scenario cases have been studied for users with different preferences. The ranking results clearly show the marked impact of users preferences on the recommended energy storage technologies. This implies that a tool like this can help small users in the selection of their right technology and avoid resource loss due to inappropriate technology selection, which can be neither economical nor sustainable.
Wu, Z-Y, Xu, J, Wu, L & Ni, B-J 2022, 'Three-dimensional biofilm electrode reactors (3D-BERs) for wastewater treatment', Bioresource Technology, vol. 344, no. Pt B, pp. 126274-126274.
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Three-dimensional biofilm electrode reactors (3D-BERs) are highly efficient in refractory wastewater treatment. In comparison to conventional bio-electrochemical systems, the filled particle electrodes act as both electrodes and microbial carriers in 3D-BERs. This article reviews the conception and basic mechanisms of 3D-BERs, as well as their current development. The advantages of 3D-BERs are illustrated with an emphasis on the synergy of electricity and microorganisms. Electrode materials utilized in 3D-BERs are systematically summarized, especially the critical particle electrodes. The configurations of 3D-BERs and their integration with wastewater treatment reactors are introduced. Operational parameters and the adaptation of 3D-BERs to varieties of wastewater are discussed. The prospects and challenges of 3D-BERs for wastewater treatment are then presented, and the future research directions are proposed. We believe that this timely review will help to attract more attentions on 3D-BERs investigation, thus promoting the potential application of 3D-BERs in wastewater treatment.
Xi, Y, Jia, W, Miao, Q, Liu, X, Fan, X & Li, H 2022, 'FiFoNet: Fine-Grained Target Focusing Network for Object Detection in UAV Images', Remote Sensing, vol. 14, no. 16, pp. 3919-3919.
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Detecting objects from images captured by Unmanned Aerial Vehicles (UAVs) is a highly demanding task. It is also considered a very challenging task due to the typically cluttered background and diverse dimensions of the foreground targets, especially small object areas that contain only very limited information. Multi-scale representation learning presents a remarkable approach to recognizing small objects. However, this strategy ignores the combination of the sub-parts in an object and also suffers from the background interference in the feature fusion process. To this end, we propose a Fine-grained Target Focusing Network (FiFoNet) which can effectively select a combination of multi-scale features for an object and block background interference, which further revitalizes the differentiability of the multi-scale feature representation. Furthermore, we propose a Global–Local Context Collector (GLCC) to extract global and local contextual information and enhance low-quality representations of small objects. We evaluate the performance of the proposed FiFoNet on the challenging task of object detection in UAV images. A comparison of the experiment results on three datasets, namely VisDrone2019, UAVDT, and our VisDrone_Foggy, demonstrates the effectiveness of FiFoNet, which outperforms the ten baseline and state-of-the-art models with remarkable performance improvements. When deployed on an edge device NVIDIA JETSON XAVIER NX, our FiFoNet only takes about 80 milliseconds to process an drone-captured image.
Xi, Y, Jia, W, Miao, Q, Liu, X, Fan, X & Lou, J 2022, 'DyCC-Net: Dynamic Context Collection Network for Input-Aware Drone-View Object Detection', Remote Sensing, vol. 14, no. 24, pp. 6313-6313.
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Benefiting from the advancement of deep neural networks (DNNs), detecting objects from drone-view images has achieved great success in recent years. It is a very challenging task to deploy such DNN-based detectors on drones in real-life applications due to their excessive computational costs and limited onboard computational resources. Large redundant computation exists because existing drone-view detectors infer all inputs with nearly identical computation. Detectors with less complexity can be sufficient for a large portion of inputs, which contain a small number of sparse distributed large-size objects. Therefore, a drone-view detector supporting input-aware inference, i.e., capable of dynamically adapting its architecture to different inputs, is highly desirable. In this work, we present a Dynamic Context Collection Network (DyCC-Net), which can perform input-aware inference by dynamically adapting its structure to inputs of different levels of complexities. DyCC-Net can significantly improve inference efficiency by skipping or executing a context collector conditioned on the complexity of the input images. Furthermore, since the weakly supervised learning strategy for computational resource allocation lacks of supervision, models may execute the computationally-expensive context collector even for easy images to minimize the detection loss. We present a Pseudo-label-based semi-supervised Learning strategy (Pseudo Learning), which uses automatically generated pseudo labels as supervision signals, to determine whether to perform context collector according to the input. Extensive experiment results on VisDrone2021 and UAVDT, show that our DyCC-Net can detect objects in drone-captured images efficiently. The proposed DyCC-Net reduces the inference time of state-of-the-art (SOTA) drone-view detectors by over 30 percent, and DyCC-Net outperforms them by 1.94% in AP75.
Xia, J, Zhang, H, Wen, S, Yang, S & Xu, M 2022, 'An efficient multitask neural network for face alignment, head pose estimation and face tracking', Expert Systems with Applications, vol. 205, pp. 117368-117368.
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Xiang, C, Petersen, IR & Dong, D 2022, 'Guaranteed cost coherent control for quantum systems with non-quadratic perturbations in the system Hamiltonian', Automatica, vol. 139, pp. 110201-110201.
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Xiang, J & Zhang, Y 2022, 'Relationship Analysis between Psychological State of College Students and Epidemic Situation Based on Big Data Mining', Mobile Information Systems, vol. 2022, pp. 1-8.
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COVID-19 is a sudden and highly contagious infectious disease, which has a very bad impact on the psychology of college students in early adulthood. In order to grasp the psychological state of college students in real-time, this work studies the psychological state of college students during COVID-19. First, this study introduces the relevant theories of data mining, and the research object and method are determined. Then, the features of the model are analyzed and constructed from two aspects which are static features and dynamic features, and the characteristics related to the psychological state are excavated. Finally, the GA is selected to build the model and the model is evaluated; the results show that the model can accurately predict the psychological state of students during COVID-19.
Xiang, S, Wen, D, Cheng, D, Zhang, Y, Qin, L, Qian, Z & Lin, X 2022, 'General graph generators: experiments, analyses, and improvements', The VLDB Journal, vol. 31, no. 5, pp. 897-925.
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Graph simulation is one of the most fundamental problems in graph processing and analytics. It can help users to generate new graphs on different scales to mimic observed real-life graphs in many applications such as social networks, biology networks, and information technology. In this paper, we focus on one of the most important types of graph generators: general graph generators, which aim to reproduce the properties of the observed graphs regardless of the domains. Though a variety of graph generators have been proposed in the literature, there are still several important research gaps in this area. In this paper, we first give an overview of the existing general graph generators, including recently emerged deep learning-based approaches. We classify them into four categories: simple model-based generators, complex model-based generators, autoencoder-based generators, and GAN-based generators. Then we conduct a comprehensive experimental evaluation of 20 representative graph generators based on 17 evaluation metrics and 12 real-life graphs. We provide a general roadmap of recommendations for how to select general graph generators under different settings. Furthermore, we propose a new method that can achieve a good trade-off between simulation quality and efficiency. To help researchers and practitioners apply general graph generators in their applications or make a comprehensive evaluation of their proposed general graph generators, we also implement an end-to-end platform that is publicly available.
Xiao, D, Chen, S, Ni, W, Zhang, J, Zhang, A & Liu, R 2022, 'A sub-action aided deep reinforcement learning framework for latency-sensitive network slicing', Computer Networks, vol. 217, pp. 109279-109279.
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Network slicing is a core technique of fifth-generation (5G) systems and beyond. To maximize the number of accepted network slices with limited hardware resources, service providers must avoid over-provisioning of quality-of-service (QoS), which could prevent them from lowering capital expenditures (CAPEX)/operating expenses (OPEX) for 5G infrastructure. In this paper, we propose a sub-action aided double deep Q-network (SADDQN)-based network slicing algorithm for latency-aware services. Specifically, we model network slicing as a Markov decision process (MDP), where we consider virtual network function (VNF) placements to be the actions of the MDP, and define a reward function based on cost and service priority. Furthermore, we adopt the Dijkstra algorithm to determine the forwarding graph (FG) embedding for a given VNF placement and design a resource allocation algorithm – binary search assisted gradient descent (BSAGD) – to allocate resources to VNFs given the VNF-FG placement. For every service request, we first use the DDQN to choose an MDP action to determine the VNF placement (main action). Next, we employ the Dijkstra algorithm (first-phase sub-action) to find the shortest path for each pair of adjacent VNFs in the given VNF chain. Finally, we implement the BSAGD (second-phase sub-action) to realize this service with the minimum cost. The joint action results in an MDP reward that can be utilized to train the DDQN. Numerical evaluations show that, compared to state-of-the-art algorithms, the proposed algorithm can improve the cost-efficiency while giving priority to higher-priority services and maximizing the acceptance ratio.
Xiao, F, Guan, J, Lan, H, Zhu, Q & Wang, W 2022, 'Local Information Assisted Attention-Free Decoder for Audio Captioning', IEEE Signal Processing Letters, vol. 29, pp. 1604-1608.
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Automated audio captioning aims to describe audio data with captions using natural language. Existing methods often employ an encoder-decoder structure, where the attention-based decoder (e.g., Transformer decoder) is widely used and achieves state-of-the-art performance. Although this method effectively captures global information within audio data via the self-attention mechanism, it may ignore the event with short time duration, due to its limitation in capturing local information in an audio signal, leading to inaccurate prediction of captions. To address this issue, we propose a method using the pretrained audio neural networks (PANNs) as the encoder and local information assisted attention-free Transformer (LocalAFT) as the decoder. The novelty of our method is in the proposal of the LocalAFT decoder, which allows local information within an audio signal to be captured while retaining the global information. This enables the events of different duration, including short duration, to be captured for more precise caption generation. Experiments show that our method outperforms the state-of-the-art methods in Task 6 of the DCASE 2021 Challenge with the standard attention-based decoder for caption generation.
Xiao, J, Guo, X, Li, Y, Wen, S, Shi, K & Tang, Y 2022, 'Extended analysis on the global Mittag-Leffler synchronization problem for fractional-order octonion-valued BAM neural networks', Neural Networks, vol. 154, pp. 491-507.
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In this paper, a new case of neural networks called fractional-order octonion-valued bidirectional associative memory neural networks (FOOVBAMNNs) is established. First, the higher dimensional models are formulated for FOOVBAMNNs with general activation functions and the special linear threshold ones, respectively. On one hand, employing Cayley-Dichson construction in octonion multiplication which is essentially neither commutative nor associative, the system of FOOVBAMNNs is divided into four fractional-order complex-valued ones. On the other hand, Caputo fractional derivative's character and BAM's interactive feature are also properly dealt with. Second, the general criteria are obtained by the new design of LKFs, the application of the related inequalities and the construction of the linear feedback controllers for the global Mittag-Leffler synchronization problem of FOOVBAMNNs. Finally, we present two numerical examples to show the realizability and progress of the derived results.
Xiao, J, Zhong, S & Wen, S 2022, 'Unified Analysis on the Global Dissipativity and Stability of Fractional-Order Multidimension-Valued Memristive Neural Networks With Time Delay', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 10, pp. 5656-5665.
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The unified criteria are analyzed on the global dissipativity and stability for the delayed fractional-order systems of multidimension-valued memristive neural networks (FSMVMNNs) in this article. First, based on the comprehensive knowledge about multidimensional algebra, fractional derivatives, and nonsmooth analysis, we establish the unified model for the studied FSMVMNNs in order to propose a more uniform method to analyze the dynamic behaviors of multidimensional neural networks. Then, by mainly applying the Lyapunov method, employing several new lemmas, and solving some mathematical difficulties, without any separation, we acquire the unified and concise criteria. The derived criteria have many advantages in a smaller calculation, lower conservatism, more diversity, and higher flexibility. Finally, we provide two numerical examples to express the availability and improvements of the theoretical results.
Xiao, M, Li, H, Huang, Q, Yu, S & Susilo, W 2022, 'Attribute-Based Hierarchical Access Control With Extendable Policy', IEEE Transactions on Information Forensics and Security, vol. 17, pp. 1868-1883.
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Attribute-based encryption scheme is a promising mechanism to realize one-to-many fine-grained access control which strengthens the security in cloud computing. However, massive amounts of data and various data sharing requirements bring great challenges to the complex but isolated and fixed access structures in most of the existing attribute-based encryption schemes. In this paper, we propose an attribute-based hierarchical encryption scheme with extendable policy, called Extendable Hierarchical Ciphertext-Policy Attribute-Based Encryption (EH-CP-ABE), to improve the data sharing efficiency and security simultaneously. The scheme realizes the function of hierarchical encryption, in which, data with hierarchical access control relationships could be encrypted together flexibly to improve the efficiency. The scheme also achieves external and internal extension of the access structure to further encrypt newly added hierarchical data without updating the original ciphertexts or with only a minor update depending on the data sharing requirements, which simplifies the encryption process and greatly reduces the computation overhead. We formally prove the security of the scheme is IND-CCA secure in the random oracle model based on bilinear Diffie-Hellman assumption, and we also implement our scheme to demonstrate its efficiency and practicality.
Xiao, S, Wang, Y, Dong, D & Zhang, J 2022, 'Optimal and two-step adaptive quantum detector tomography', Automatica, vol. 141, pp. 110296-110296.
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Xie, C, Nguyen, H, Choi, Y & Jahed Armaghani, D 2022, 'Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays', Geoscience Frontiers, vol. 13, no. 2, pp. 101313-101313.
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Xie, H, Mengersen, K, Di, C, Zhang, Y, Lipman, J & Van Huffel, S 2022, 'A Variational Bayesian Gaussian Mixture-Nonnegative Matrix Factorization Model to Extract Movement Primitives for Robust Control', IEEE Transactions on Human-Machine Systems, vol. 52, no. 6, pp. 1258-1270.
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Nonnegative matrix factorization (NMF) is a powerful tool for parameter estimation applied in numerous robotics applications, such as path planning, motion trajectory prediction, and motion intention detection. In particular, NMF has been successfully used to extract simplified and organized movement primitives from myoelectric signal (MES) for robust control of multi-degree of freedom humanoid robots. However, MES is typically contaminated by complex noise sources. The system performance often degrades due to the simplified Gaussian assumption of the noise distribution in existing NMF methods. Furthermore, most existing NMF models are unable to automatically determine the rank of the latent matrices. To address these issues, this article presents a hybrid variational Bayesian Gaussian mixture and NMF (GMNMF) model with a finite Gaussian mixture model adopted to fit the mixed noise density function of MES. In addition, the automatic relevant determination criterion is applied to automatically infer the number of movement primitives. The coordinate descent update rules for the proposed model are formulated by mean-field variational Bayesian inference. We assess the model performance on five synthetic noise distribution functions and an experimental MES dataset to perform six wrist movements. The results demonstrate that GMNMF yields low error and high robustness in extracting the movement primitives over four competitive methods for robust cybernetic control.
Xie, H, Zheng, J, Sun, Z, Wang, H & Chai, R 2022, 'Finite-time tracking control for nonholonomic wheeled mobile robot using adaptive fast nonsingular terminal sliding mode', Nonlinear Dynamics, vol. 110, no. 2, pp. 1437-1453.
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AbstractSystem uncertainties and external disturbances are the major causes of the trajectory tracking performance degradation in nonholonomic wheeled mobile robots (NWMRs). In this article, an adaptive fast nonsingular terminal sliding mode dynamic control (AFNTSMDC) method is proposed to provide enhanced robust and finite-time tracking performance for the NWMR. The proposed AFNTSMDC is a systematic design method based upon both the kinematic and dynamic model of the NWMR. The proposed controller has a simple form without singularity issue in the control input, which makes it practically implementable. The finite-time stability of the proposed tracking-error function is also proved using the Lyapunov function. Finally, circular trajectory tracking experiments are conducted to validate the robustness and convergence rate of the proposed AFNTSMDC scheme in comparison with the existing methods including classic kinematic control, robust sliding mode kinematic control, and conventional sliding mode dynamic control in the presence of uncertainties and external disturbances.
Xie, J, Liu, C-H, Huang, Y & Mok, W-C 2022, 'Effect of sampling duration on the estimate of pollutant concentration behind a heavy-duty vehicle: A large-eddy simulation', Environmental Pollution, vol. 305, pp. 119132-119132.
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Plume chasing is cost-effective, measuring individual, on-road vehicular emissions. Whereas, wake-flow-generated turbulence results in intermittent, rapid pollutant dilution and substantial fluctuating concentrations right behind the vehicle being chased. The sampling duration is therefore one of the important factors for acquiring representative (average) concentrations, which, however, has been seldom addressed. This paper, which is based on the detailed spatio-temporal dispersion data after a heavy-duty truck calculated by large-eddy simulation (LES), examines how sampling duration affects the uncertainty of the measured concentrations in plume chasing. The tailpipe dispersion is largely driven by the jet-like flows through the vehicle underbody with approximate Gaussian concentration distribution for x ≤ 0.6h, where x is the distance after the vehicle and h the characteristic vehicle size. Thereafter for x ≥ 0.6h, the major recirculation plays an important role in near-wake pollutant transport whose concentrations are highly fluctuating and positively shewed. Plume chasing for a longer sampling duration is more favourable but is logistically impractical in busy traffic. Sampling duration, also known as averaging time in the statistical analysis, thus has a crucial role in sampling accuracy. With a longer sampling (averaging) duration, the sample mean concentration converges to the population mean, improving the sample reliability. However, this effect is less pronounced in long sampling duration. The sampling accuracy is also influenced by the locations of sampling points. For the region x > 0.6h, the sampling accuracy is degraded to a large extent. As a result, acceptable sample mean is hardly achievable. Finally, frequency analysis unveils the mechanism leading to the variance in concentration measurements which is attributed to sampling duration. Those data with frequency higher than the sampling frequency are filtered out by moving average in th...
Xie, X, Wen, S, Feng, Y & Onasanya, BO 2022, 'Three-Stage-Impulse Control of Memristor-Based Chen Hyper-Chaotic System', Mathematics, vol. 10, no. 23, pp. 4560-4560.
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In this paper, on the basis of the three-dimensional Chen system, a smooth continuous nonlinear flux-controlled memristor model is used as the positive feedback term of this system, a hyper-chaotic circuit system is successfully constructed, and a simulated equivalent circuit is built for simulation using Multisim software, which agrees with the numerical simulation results by comparison. Meanwhile, a new impulsive control mode called the three-stage-impulse is put forward. It is a cyclic system with three components: continuous inputs are exerted in the first and third parts of the cycle while giving no input in the second part of the cycle, an impulse is exerted at the end of each continuous subsystem, the controller is simple in structure and effective in stabilizing most existing nonlinear systems. The Chen hyper-chaotic system will be controlled based on the three-stage-impulse control method combined with the Lyapunov stability principle. At the end of this paper, we have employed and simulated a numerical example; the experimental results show that the controller is effective for controlling and stabilizing the newly designed hyper-chaotic system.
Xing, B & Tsang, IW 2022, 'Out of Context: A New Clue for Context Modeling of Aspect-based Sentiment Analysis', Journal of Artificial Intelligence Research, vol. 74, pp. 627-659.
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Aspect-based sentiment analysis (ABSA) aims to predict the sentiment expressed in a review with respect to a given aspect. The core of ABSA is to model the interaction between the context and given aspect to extract aspect-related information. In prior work, attention mechanisms and dependency graph networks are commonly adopted to capture the relations between the context and given aspect. And the weighted sum of context hidden states is used as the final representation fed to the classifier. However, the information related to the given aspect may be already discarded and adverse information may be retained in the context modeling processes of existing models. Such a problem cannot be solved by subsequent modules due to two reasons. First, their operations are conducted on the encoder-generated context hidden states, whose value cannot be changed after the encoder. Second, existing encoders only consider the context while not the given aspect. To address this problem, we argue the given aspect should be considered as a new clue out of context in the context modeling process. As for solutions, we design three streams of aspect-aware context encoders: an aspect-aware LSTM, an aspect-aware GCN, and three aspect-aware BERTs. They are dedicated to generating aspect-aware hidden states which are tailored for the ABSA task. In these aspect-aware context encoders, the semantics of the given aspect is used to regulate the information flow. Consequently, the aspect-related information can be retained and aspect-irrelevant information can be excluded in the generated hidden states. We conduct extensive experiments on several benchmark datasets with empirical analysis, demonstrating the efficacies and advantages of our proposed aspect-aware context encoders.
Xing, B & Tsang, IW 2022, 'Understand Me, if You Refer to Aspect Knowledge: Knowledge-Aware Gated Recurrent Memory Network', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 5, pp. 1092-1102.
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Aspect-level sentiment classification (ASC) aims to predict the fine-grained sentiment polarity towards a given aspect mentioned in a review. Despite recent advances in ASC, enabling machines to preciously infer aspect sentiments is still challenging. This paper tackles two challenges in ASC: (1) due to lack of aspect knowledge, aspect representation derived in prior works is inadequate to represent aspect's exact meaning and property information; (2) prior works only capture either local syntactic information or global relational information, thus missing either one of them leads to insufficient syntactic information. To tackle these challenges, we propose a novel ASC model which not only end-to-end embeds and leverages aspect knowledge but also marries the two kinds of syntactic information and lets them compensate for each other. Our model includes four key components: (1) a knowledge-aware gated recurrent memory network recurrently integrates dynamically summarized aspect knowledge; (2) a dual syntax graph network combines both kinds of syntactic information to comprehensively capture sufficient syntactic information; (3) a knowledge integrating gate re-enhances the final representation with further needed aspect knowledge; (4) an aspect-to-context attention mechanism aggregates the aspect-related semantics from all hidden states into the final representation. Experimental results on several benchmark datasets demonstrate the effectiveness of our model, which overpass previous state-of-the-art models by large margins in terms of both Accuracy and Macro-F1. To facilitate further research in the community, we have released our source code at https://github.com/XingBowen714/KaGRMN-DSG-ABSA.
Xing, D, Li, R, Li, JJ, Tao, K, Lin, J, Yan, T & Zhou, D 2022, 'Catastrophic Periprosthetic Osteolysis in Total Hip Arthroplasty at 20 Years: A Case Report and Literature Review', Orthopaedic Surgery, vol. 14, no. 8, pp. 1918-1926.
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BackgroundPeriprosthetic osteolysis is a serious complication following total hip arthroplasty (THA). However, most orthopedic surgeons only focus on bone loss and hip reconstruction. Thus, it was required to understand the treatment algorithm for periprosthetic osteolysis integrally.Case PresentationA 52‐year‐old Asian male presented with chronic hip pain. A mass appeared on the medial side of the proximal left thigh at more than 20 years after bilateral THA. Radiographs revealed catastrophic periprosthetic osteolysis, especially on the acetabular side. Large amounts of necrotic tissue and bloody fluids were thoroughly debrided during revision THA. A modular hemipelvic prosthesis was used for revision of the left hip. Four years later, the patient presented with right hip pain, where a mass appeared on the medial side of the proximal right thigh. A primary acetabular implant with augment was used for revision of the right hip. Laboratory evaluation of bloody fluid retrieved from surgery revealed elevated levels of inflammatory markers.ConclusionInflammatory responses to polyethylene wear debris can lead to severe bone resorption and aseptic loosening in the long‐term following THA. Therefore, in spite of revision THA, interrupting the cascade inflammatory might be the treatment principle for periprosthetic osteolysis.
Xing, L, Yang, J, Ni, B-J, Yang, C, Yuan, C & Li, A 2022, 'Insight into the generation and consumption mechanism of tightly bound and loosely bound extracellular polymeric substances by mathematical modeling', Science of The Total Environment, vol. 811, pp. 152359-152359.
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The quantity of tightly bound extracellular polymeric substances (TB-EPS) and loosely bound extracellular polymeric substances (LB-EPS) are recognized to be crucial for activated sludge flocculability and settleability. However, the generation and consumption mechanisms of TB-EPS and LB-EPS are vague, and there is no effective model to quantitatively predict LB-EPS and TB-EPS. In this work, a decrease in LB-EPS and TB-EPS was verified to increase the absolute value of the zeta potential and decrease the sludge settling volume, which affects the flocculation and settling performance of sludge. Hence, we comparatively developed, calibrated and validated two different mathematical model structure (named expanded unified model-TL1 and expanded unified model-TL2), aiming to systematically reveal the generation and consumption mechanism of TB-EPS and LB-EPS and quantitatively predict changes of TB-EPS and LB-EPS. On the basis of microbial physiology and the existing literature, two different mechanisms of the generation and consumption of TB-EPS and LB-EPS are described. According to the validation performed, expanded unified model-TL2 fit better with experimental TB-EPS and LB-EPS, which described with the hypotheses: (i) TB-EPS and LB-EPS are simultaneously generated while activate biomass growth on external substrate, (ii) LB-EPS can also be hydrolyzed by TB-EPS, and (iii) Biomass-associated products (BAP) are hydrolyzed by LB-EPS, and it was further proven to be more realistic from the perspective of microbial physiology. This study systematically revealed the generation and consumption mechanism of TB-EPS and LB-EPS by mathematical modeling, and provides a basis for regulating the concentrations of them to improve sludge settling capacity and system stability.
Xing, L, Yang, J, Zhang, Y, Ni, B-J, Yang, C, Yuan, C & Li, A 2022, 'Model-based evaluation of the impacts of aeration on tightly bound and loosely bound extracellular polymeric substance production under non-steady-state conditions', Science of The Total Environment, vol. 852, pp. 158566-158566.
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Tightly bound extracellular polymeric substances (TB-EPS) and loosely bound extracellular polymeric substances (LB-EPS) affect the flocculability and settleability of sludge and the transfer of oxygen, which are highly related to aeration. In this study, we systemically evaluated the expanded unified model-TL2.1 for its long-term simulation of TB-EPS and LB-EPS. Two different aeration conditions and three different influent carbon sources were used to evaluate the model, and the simulation results fit well with the experimental data. TB-EPS and LB-EPS production increased with aeration intensity. The influence of aeration parameters on TB-EPS and LB-EPS production in a short-term batch system and long-term sequencing batch reactor (SBR) system was compared. The aeration parameters included the total transfer coefficient (kLa) and the concentration of dissolved oxygen at the interface (CS). To ensure a high removal rate of substrates and ammonia nitrogen and achieve a stable active biomass concentration, the following aeration parameters can be adopted to reduce energy wastage during aeration: when CS is 2 mg/L, kLa can be set above 30 h-1 and below 50 h-1; when kLa is 50 h-1, CS can be set above 1 mg/L and below 1.5 mg/L. This study systematically revealed the influence of aeration on TB-EPS and LB-EPS formation in an SBR system through a mathematical model, and it provides a theoretical basis for better understanding aeration.
Xing, Q, Wang, J, Lu, H & Wang, S 2022, 'Research of a novel short-term wind forecasting system based on multi-objective Aquila optimizer for point and interval forecast', Energy Conversion and Management, vol. 263, pp. 115583-115583.
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Facing the increasing depletion of traditional energy resources and the worsening environmental issues, wind energy sources have been widely considered. As an essential renewable energy resource, wind energy features abundant deposits, extensive distribution, non-pollution, etc. In recent years, wind power generation occupies a non-negligible position in the electric power industry. Stable and reliable power system operation demands accurate wind speed prediction (WSP), but the inherent randomness of wind speed sequences complicates their fluctuations and causes them to be uncontrollable. In this paper, an innovative WSP system is proposed, which combines data pre-processing technique, benchmark model selection, an advanced optimizer for point forecast and interval forecast. Furthermore, this paper theoretically demonstrates that the weights allocated by this optimizer are Pareto optimal solutions. Six interval data from two sites in China are utilized to validate the forecasting performance of our developed model. The experimental results indicate that the developed model can achieve superior accuracy compared to the tested models in all cases for point forecast, and also obtains the forecasting interval with high coverage and low width error, which is an extremely crucial instruction to guarantee the security and stability of the power system.
Xiong, H, Huang, X, Yang, M, Wang, L & Yu, S 2022, 'Unbounded and Efficient Revocable Attribute-Based Encryption With Adaptive Security for Cloud-Assisted Internet of Things', IEEE Internet of Things Journal, vol. 9, no. 4, pp. 3097-3111.
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Xiong, H, Yao, T, Wang, H, Feng, J & Yu, S 2022, 'A Survey of Public-Key Encryption With Search Functionality for Cloud-Assisted IoT', IEEE Internet of Things Journal, vol. 9, no. 1, pp. 401-418.
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Nowadays, Internet of Things (IoT) is an attractive system to provide broad connectivity of a wide range of applications, and clouds are natural promoters. Cloud-assisted IoT combines the advantages of cloud computing and IoT, which is able to collect data from the real world and maximizes the value of the collected data by the means of data sharing and data analysis. Meanwhile, secure and convenient data retrieval in cloud servers becomes an important requirement for both enterprises and individual users. Public-key encryption with search functionality (shorten as PKE-SF) is a widely used cryptographic technique that allows users to retrieve encrypted data without decryption. PKE-SF mainly contains the primitives of public-key encryption with keyword search (PKE-KS), public-key encryption with equality test (PKE-ET), and plaintext-checkable encryption (PCE). In light of the overwhelming variety and multitude of PKE-SF schemes, this survey presents these schemes from different perspectives to provide better comprehension for beginners and advanced researchers. More concretely, this survey concentrates on the state of the art of PKE-SF by analyzing the design rationale, examining the framework and security model, and assessing the existing schemes in accordance with theoretic efficiency, security properties, and experimental performance. Furthermore, we discuss the extensions of traditional PKE-SF schemes which feature with the access control delegation, conjunctive keyword search, certificate-free, and offline keyword guessing attack resilience. Finally, we point out some promising directions for readers.
Xu, B, Qiu, W, Du, J, Wan, Z, Zhou, JL, Chen, H, Liu, R, Magnuson, JT & Zheng, C 2022, 'Translocation, bioaccumulation, and distribution of perfluoroalkyl and polyfluoroalkyl substances (PFASs) in plants', iScience, vol. 25, no. 4, pp. 104061-104061.
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Perfluoroalkyl and polyfluoroalkyl substances (PFASs) are persistent in the environment and have been detected in a variety of plants such as vegetables, cereals, and fruits. Increasing evidence shows that plants are at a risk of being adversely affected by PFASs. This review concludes that PFASs are predominantly absorbed by roots from sources in the soil; besides, the review also discusses several factors such as soil properties and the species of PFASs and plants. In addition, following uptake by root, long-chain PFASs (C ≥ 7 for PFCA and C ≥ 6 for PFSA) were preferentially retained within the root, whereas the short-chain PFASs were distributed across tissues above the ground - according to the studies. The bioaccumulation potential of PFASs within various plant structures are further expressed by calculating bioaccumulation factor (BAF) across various plant species. The results show that PFASs have a wide range of BAF values within root tissue, followed by straw, and then grain. Furthermore, owing to its high water solubility than other PFASs, PFOA is the predominant compound accumulated in both the soil itself and within the plant tissues. Among different plant groups, the potential BAF values rank from highest to lowest as follows: leaf vegetables > root vegetables > flower vegetables > shoot vegetables. Several PFAS groups such as PFOA, PFBA, and PFOS, may have an increased public health risk based on the daily intake rate (ID). Finally, future research is suggested on the possible PFASs degradation occurring in plant tissues and the explanations at genetic-level for the metabolite changes that occur under PFASs stress.
Xu, B-H, Indraratna, B, Rujikiatkamjorn, C & Trung Nguyen, T 2022, 'A large-strain radial consolidation model incorporating soil destructuration and isotache concept', Computers and Geotechnics, vol. 147, pp. 104761-104761.
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Xu, H, Bu, Y, Liu, M, Zhang, C, Sun, M, Zhang, Y, Meyer, E, Salas, E & Ding, Y 2022, 'Team power dynamics and team impact: New perspectives on scientific collaboration using career age as a proxy for team power', Journal of the Association for Information Science and Technology, vol. 73, no. 10, pp. 1489-1505.
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AbstractPower dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision‐making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all coauthors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of coauthors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size.
Xu, H, Li, C, Zhang, Y, Duan, L, Tsang, IW & Shao, J 2022, 'MetaCAR: Cross-Domain Meta-Augmentation for Content-Aware Recommendation', IEEE Transactions on Knowledge and Data Engineering, vol. PP, no. 99, pp. 1-14.
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Cold-start has become critical for recommendations, especially for sparse user-item interactions. Recent approaches based on meta-learning succeed in alleviating the issue, owing to the fact that these methods have strong generalization, so they can fast adapt to new tasks under cold-start settings. However, these meta-learning-based recommendation models learned with single and spase ratings are easily falling into the meta-overfitting, since the one and only rating $r_{ui}$ to a specific item $i$ cannot reflect a user's diverse interests under various circumstances(e.g., time, mood, age, etc), i.e. if $r_{ui}$ equals to 1 in the historical dataset, but $r_{ui}$ could be 0 in some circumstance. In meta-learning, tasks with these single ratings are called Non-Mutually-Exclusive(Non-ME) tasks, and tasks with diverse ratings are called Mutually-Exclusive(ME) tasks. Fortunately, a meta-augmentation technique is proposed to relief the meta-overfitting for meta-learning methods by transferring Non-ME tasks into ME tasks by adding noises to labels without changing inputs. Motivated by the meta-augmentation method, in this paper, we propose a cross-domain meta-augmentation technique for content-aware recommendation systems (MetaCAR) to construct ME tasks in the recommendation scenario. Our proposed method consists of two stages: meta-augmentation and meta-learning. In the meta-augmentation stage, we first conduct domain adaptation by a dual conditional variational autoencoder (CVAE) with a multi-view information bottleneck constraint, and then apply the learned CVAE to generate ratings for users in the target domain. In the meta-learning stage, we introduce both the true and genera...
Xu, J, Sun, Q, Mo, H & Dong, D 2022, 'Online routing for smart electricity network under hybrid uncertainty', Automatica, vol. 145, pp. 110538-110538.
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Xu, J, Zhang, B, Wang, Z, Wang, Y, Chen, F, Gao, J & Feng, DD 2022, 'Affective Audio Annotation of Public Speeches with Convolutional Clustering Neural Network', IEEE Transactions on Affective Computing, vol. 13, no. 1, pp. 238-249.
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Xu, M, Hoang, DT, Kang, J, Niyato, D, Yan, Q & Kim, DI 2022, 'Secure and Reliable Transfer Learning Framework for 6G-Enabled Internet of Vehicles', IEEE Wireless Communications, vol. 29, no. 4, pp. 132-139.
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In the coming 6G era, Internet of Vehicles (IoV) has been evolving towards 6G-enabled IoV with super-high data rate, seamless networking coverage, and ubiquitous intelligence by Artificial Intelligence (AI). Transfer Learning (TL) has great potential to empower promising 6G-enabled IoV, such as smart driving assistance, with its outstanding features including enhancing the quality and quantity of training data, speeding up learning processes, and reducing computing demands. Although TL had been widely adopted in wireless applications (e.g., spectrum management and caching), its reliability and security in 6G-enabled IoV were still not well investigated. For instance, malicious vehicles in source domains may transfer and share untrustworthy models (i.e., knowledge) about connection availability to target domains, thus adversely affecting the performance of learning processes. Therefore, it is important to select and also incentivize trustworthy vehicles to participate in TL. In this article, we first introduce the integration of TL and 6G-enabled IoV and provide TL applications for 6G-enabled IoV. We then design a secure and reliable transfer learning framework by using reputation to evaluate the reliability of pre-trained models and utilizing the consortium blockchain to achieve secure and efficient decentralized reputation management. Moreover, a deep learning-based auction scheme for the TL model market is designed to motivate high-reputation vehicles to participate in model sharing. Finally, the simulation results demonstrate that the proposed framework is secure and reliable with well-designed incentives for TL in 6G-enabled IoV.
Xu, Q, Su, Z, Lu, R & Yu, S 2022, 'Ubiquitous Transmission Service: Hierarchical Wireless Data Rate Provisioning in Space-Air-Ocean Integrated Networks', IEEE Transactions on Wireless Communications, vol. 21, no. 9, pp. 7821-7836.
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Space-air-ocean integrated networks (SAOINs), composed of low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and unmanned surface vehicles (USVs), have been advocated to provide seamless, high-rate, and reliable wireless transmission services for USVs. However, due to the restrictions of limited resources (e.g., spectrum bandwidth, transmission power, etc.), diverse demands of USVs, and selfishness of both UAVs and LEOs, there comes a significant challenge to provision high-quality wireless data rate for USVs to achieve their satisfied quality of experience (QoE). To this end, in this paper, we propose a hierarchical on-demand wireless data rate provisioning scheme to provide ubiquitous transmission services for USVs. Specifically, we first devise a hierarchical wireless data rate provisioning framework. The LEO satellite with an extensive wireless coverage is utilized to provide LEO satellite-to-UAV (L2U) data rate for UAVs with a certain L2U data rate price. Each UAV is employed to provide UAV-to-USV (U2U) data rate for covered multiple USVs with a certain U2U data rate price. We then propose a modified three-stage Stackelberg game to model the wireless data rate assignments among LEO satellites, UAVs, and USVs, where the time-varying data rate demands of USVs are considered to formulate the utility maximization problem. Afterwards, the backward induction approach is leveraged to attain the Stackelberg equilibrium as the solution of the formulated problem, where the closed-form expressions on the optimal strategies of both USVs and UAVs under different data rate budgets are obtained by the nonlinear programming method. Besides, an accelerated conjugate gradient descent (ACGD) based iteration algorithm is also designed to obtain the optimal strategies of the LEO satellites on the L2U data rate prices. At last, extensive simulations are carried out to demonstrate that the proposed scheme can significantly increase the utilities of USVs, ...
Xu, Q, Su, Z, Yu, S & Wang, Y 2022, 'Trust Based Incentive Scheme to Allocate Big Data Tasks with Mobile Social Cloud', IEEE Transactions on Big Data, vol. 8, no. 1, pp. 113-124.
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Xu, R-Z, Cao, J-S, Feng, G, Luo, J-Y, Feng, Q, Ni, B-J & Fang, F 2022, 'Fast identification of fluorescent components in three-dimensional excitation-emission matrix fluorescence spectra via deep learning', Chemical Engineering Journal, vol. 430, pp. 132893-132893.
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Three-dimensional excitation-emission matrix (3D-EEM) fluorescence spectroscopy has been widely applied to detect the fluorescent components in samples from natural water bodies to wastewater treatment processes. Data interpretation methods such as parallel factor analysis (PARAFAC) are required to decompose the overlapped fluorescent signals in the 3D-EEM spectra. However, strict requirements of data and complicated procedures of the PARAFAC limit the online monitoring and analysis of samples. Here we develop a fast fluorescent identification network (FFI-Net) model based on the deep learning approach to fast predict the numbers and maps of fluorescent components by simply inputting a single 3D-EEM spectrum. Two types of convolutional neural networks (CNN) are trained to classify the numbers of fluorescent components with an accuracy of 0.956 and predict the maps of fluorescent components with the min mean absolute error of 8.9 × 10-4. We demonstrate that the accuracy of the FFI-Net model will be further improved when more 3D-EEM data are available as a training dataset. Meanwhile, a user-friendly interface is designed to facilitate practical applications. Our approach gives a robust way to overcome the shortage of the PARAFAC and provides a new platform for online analysis of the fluorescent components in water samples.
Xu, R-Z, Cao, J-S, Luo, J-Y, Feng, Q, Ni, B-J & Fang, F 2022, 'Integrating Mechanistic and Deep Learning Models for Accurately Predicting the Enrichment of Polyhydroxyalkanoates Accumulating Bacteria in Mixed Microbial Cultures', Bioresour Technol, vol. 344, no. Pt B, pp. 126276-126276.
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The enrichment of polyhydroxyalkanoates (PHA) accumulating bacteria (PAB) in mixed microbial cultures (MMC) is extremely difficult to be predicted and optimized. Here we demonstrate that mechanistic and deep learning models can be integrated innovatively to accurately predict the dynamic enrichment of PAB. Well-calibrated activated sludge models (ASM) of the PAB enrichment process provide time-dependent data under different operating conditions. Recurrent neural network (RNN) models are trained and tested based on the time-dependent dataset generated by ASM. The accurate prediction performance is achieved (R2 > 0.991) for three different PAB enrichment datasets by the optimized RNN model. The optimized RNN model can also predict the equilibrium concentration of PAB (R2 = 0.944) and corresponding time, which represents the end of the PAB enrichment process. This study demonstrates the strength of integrating mechanistic and deep learning models to predict long-term variations of specific microbes, helping to optimize their selection process for PHA production.
Xu, R-Z, Cao, J-S, Ye, T, Wang, S-N, Luo, J-Y, Ni, B-J & Fang, F 2022, 'Automated machine learning-based prediction of microplastics induced impacts on methane production in anaerobic digestion', Water Research, vol. 223, pp. 118975-118975.
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Microplastics as emerging pollutants have been heavily accumulated in the waste activated sludge (WAS) during biological wastewater treatment, which showed significantly diverse impacts on the subsequent anaerobic sludge digestion for methane production. However, a robust modeling approach for predicting and unveiling the complex effects of accumulated microplastics within WAS on methane production is still missing. In this study, four automated machine learning (AutoML) approach was applied to model the effects of microplastics on anaerobic digestion processes, and integrated explainable analysis was explored to reveal the relationships between key variables (e.g., concentration, type, and size of microplastics) and methane production. The results showed that the gradient boosting machine had better prediction performance (mean squared error (MSE) = 17.0) than common neural networks models (MSE = 58.0), demonstrating that the AutoML algorithms succeeded in predicting the methane production and could select the best machine learning model without human intervention. Explainable analysis results indicated that the variable of microplastic types was more important than the variable of microplastic diameter and concentration. The existence of polystyrene was associated with higher methane production, whereas increasing microplastic diameter and concentration both inhibited methane production. This work also provided a novel modeling approach for comprehensively understanding the complex effects of microplastics on methane production, which revealed the dependence relationships between methane production and key variables and may be served as a reference for optimizing operational adjustments in anaerobic digestion processes.
Xu, S, Faust, O, Seoni, S, Chakraborty, S, Barua, PD, Loh, HW, Elphick, H, Molinari, F & Acharya, UR 2022, 'A review of automated sleep disorder detection', Computers in Biology and Medicine, vol. 150, pp. 106100-106100.
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Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of...
Xu, S, He, R, Zhao, S, Shon, HK & He, T 2022, 'Is conductivity measurement or inductively coupled plasma-atomic emission spectrometry reliable to define rejection of different ions?', Desalination, vol. 543, pp. 116097-116097.
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Rejection of single salts or ions is a basic and crucial characteristic of nanofiltration (NF) membranes. The simple and most pursued method to quantify the salt concentration has been via conductivity measurement. Pitfalls exist when ions hydrolysis or feed water contains monovalent ions. This could be explained in two possible scenarios: (1) easily hydrolyzed single salts form low charged ions and reduce feed pH, resulting in increased permeate conductivity and low nominal rejection; (2) for membranes with high multivalent ion rejections (>99%) or the concentration of target ions in feed is low, conductivity measurement results in low rejection due to the passage of monovalent ions if deionized water is used for the feed solution. A correction formula by subtracting the concentration of monovalent ions in water to obtain an accurate rejection value is proposed. This work provides an accurate, simple and robust evaluation of rejection for NF membranes, which promotes fair comparison of performance in literature, reliable analysis of separation mechanisms as well as a precise determination of product purity.
Xu, S, Yang, Y, Wu, C & Liu, K 2022, 'Electromagnetic wave absorption performance of UHPC incorporated with carbon black and carbon fiber', Archives of Civil and Mechanical Engineering, vol. 22, no. 2.
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This study focuses on the electromagnetic wave absorption performance (EWAP) of ultra-high-performance concrete (UHPC) incorporated with carbon black (CB) and carbon fiber (CF) in 2–18 GHz frequency range (required for the radar wave absorbing materials). The reflectivity of the traditional UHPC was investigated and compared to the cement-based composites reported in the literatures, so as to illustrate the advantages of novel UHPCs with respect to EWAP. Afterwards, the effect of CB and CF on the compressive strength, complex permittivity and reflectivity of the novel UHPCs was investigated. The microstructure of the novel UHPCs was also explored via scanning electron microscopy to illustrate the mechanism of performance enhancement on incorporating CB and CF. The results indicated that EWAP of the traditional UHPC was similar or inferior (at specific frequencies) to the literature reported cement-based composites. However, EWAP of the novel UHPCs was significantly improved after reinforcing with CB or CF. A positive effect of CB and CF was also observed on the compressive strength of the developed UHPCs. This study provides avenues for the use of UHPCs in protecting structures for absorbing the electromagnetic waves and safeguarding these structures against extreme loads, including blast and penetration.
Xu, T, Yang, G, Li, Y & Lai, T 2022, 'Influence of inerter‐based damper installations on control efficiency of building structures', Structural Control and Health Monitoring, vol. 29, no. 5.
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Xu, X, Li, X, Wen, D, Zhao, C, Fan, L, Wu, C, Liu, Z, Han, K, Zhao, M, Zhang, S & Yao, Y 2022, 'Sublethal and transgenerational effects of a potential plant‐derived insecticide, β‐asarone, on population fitness of brown planthopper, Nilaparvata lugens', Entomologia Experimentalis et Applicata, vol. 170, no. 7, pp. 555-564.
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AbstractMany essential oils have excellent insecticidal activity and have the potential to be developed as alternatives to chemical insecticides for pest control. Previously, we showed that β‐asarone, a major constituent of the essential oil derived from Acorus calamus L. (Acoraceae), has strong pesticidal activity against brown planthopper, Nilaparvata lugens (Stål) (Hemiptera: Delphacidae), the most notorious rice pest in Asia. Here, we report the first evaluation of the sublethal and transgenerational effects of β‐asarone on N. lugens. β‐Asarone significantly decreased female longevity, male longevity, fecundity, and hatchability of F0 generation individuals exposed to the LD30 and LD45 of β‐asarone relative to the acetone control. Moreover, compared with acetone, exposure to LD30 and LD45 of β‐asarone significantly shortened the duration of the egg stage, developmental duration of first instars, and female longevity of F1 generation individuals. Furthermore, the intrinsic rate of increase (r), finite rate of increase (λ), and net reproductive rates (R0) of insects treated with LD30 and LD45 of β‐asarone were significantly lower than those of insects treated with acetone. Compared with acetone, the fecundity and hatchability of F1 generation individuals were significantly decreased after exposure to β‐asarone at LD30 and LD45. These findings indicate the negative effects of sublethal doses of β‐asarone on N. lugens and provide novel information on the potential use of β‐asarone as a substitute chemical pesticide.
Xu, X, Xu, G, Chen, J, Liu, Z, Chen, X, Zhang, Y, Fang, J & Gao, Y 2022, 'Multi-objective design optimization using hybrid search algorithms with interval uncertainty for thin-walled structures', Thin-Walled Structures, vol. 175, pp. 109218-109218.
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Xu, X, Zhang, Y, Fang, J, Chen, X, Liu, Z, Xu, Y & Gao, Y 2022, 'Axial mechanical properties and robust optimization of foam-filled hierarchical structures', Composite Structures, vol. 289, pp. 115501-115501.
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Xu, X, Zhang, Y, Wang, X, Fang, J, Chen, J & Li, J 2022, 'Searching superior crashworthiness performance by constructing variable thickness honeycombs with biomimetic cells', International Journal of Mechanical Sciences, vol. 235, pp. 107718-107718.
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Xu, Y, Fang, M, Chen, L, Xu, G, Du, Y & Zhang, C 2022, 'Reinforcement Learning With Multiple Relational Attention for Solving Vehicle Routing Problems', IEEE Transactions on Cybernetics, vol. 52, no. 10, pp. 11107-11120.
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In this article, we study the reinforcement learning (RL) for vehicle routing problems (VRPs). Recent works have shown that attention-based RL models outperform recurrent neural network-based methods on these problems in terms of both effectiveness and efficiency. However, existing RL models simply aggregate node embeddings to generate the context embedding without taking into account the dynamic network structures, making them incapable of modeling the state transition and action selection dynamics. In this work, we develop a new attention-based RL model that provides enhanced node embeddings via batch normalization reordering and gate aggregation, as well as dynamic-aware context embedding through an attentive aggregation module on multiple relational structures. We conduct experiments on five types of VRPs: 1) travelling salesman problem (TSP); 2) capacitated VRP (CVRP); 3) split delivery VRP (SDVRP); 4) orienteering problem (OP); and 5) prize collecting TSP (PCTSP). The results show that our model not only outperforms the learning-based baselines but also solves the problems much faster than the traditional baselines. In addition, our model shows improved generalizability when being evaluated in large-scale problems, as well as problems with different data distributions.
Xu, Y, Gao, Y, Wu, C, Fang, J, Sun, G, Steven, GP & Li, Q 2022, 'Concurrent optimization of topological configuration and continuous fiber path for composite structures — A unified level set approach', Computer Methods in Applied Mechanics and Engineering, vol. 399, pp. 115350-115350.
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This study proposes a novel topology optimization approach for design of continuous steering fiber path for composite structures using a level set method. The radial basis function (RBF) is employed to construct the level set function (LSF). Fiber orientations are parameterized by LSF and fiber paths can be determined instinctively for the inherent advantages of the level set approach. Besides, the fast-marching method is employed to extrapolate the primary fiber paths to the secondary fiber paths, which can avoid the manufacturing drawbacks such as gaps and overlaps to a large extent. A detection and filtering technique is proposed here to alleviate the orientation disorder at the intersection of the diffusion surfaces. Two design schemes are developed to optimize both structural topology and fiber path. In a sequential procedure, topology optimization is conducted first with isotropic materials; and then fiber paths are optimized on the basis of fixed topological boundary. In a simultaneous optimization procedure, structural boundaries and fiber paths are optimized alternately through two inner loops. In this study, three numerical examples are presented to demonstrate the effectiveness of the proposed methods, and the results show that optimization of fiber path is beneficial to improvement of structural performance. In general, the simultaneous optimization scheme exhibits better optimal outcome in comparison with the sequential optimization scheme.
Xu, Y, Yu, X, Zhang, J, Zhu, L & Wang, D 2022, 'Weakly Supervised RGB-D Salient Object Detection With Prediction Consistency Training and Active Scribble Boosting', IEEE Transactions on Image Processing, vol. 31, pp. 2148-2161.
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RGB-D salient object detection (SOD) has attracted increasingly more attention as it shows more robust results in complex scenes compared with RGB SOD. However, state-of-the-art RGB-D SOD approaches heavily rely on a large amount of pixel-wise annotated data for training. Such densely labeled annotations are often labor-intensive and costly. To reduce the annotation burden, we investigate RGB-D SOD from a weakly supervised perspective. More specifically, we use annotator-friendly scribble annotations as supervision signals for model training. Since scribble annotations are much sparser compared to ground-truth masks, some critical object structure information might be neglected. To preserve such structure information, we explicitly exploit the complementary edge information from two modalities (i.e., RGB and depth). Specifically, we leverage the dual-modal edge guidance and introduce a new network architecture with a dual-edge detection module and a modality-aware feature fusion module. In order to use the useful information of unlabeled pixels, we introduce a prediction consistency training scheme by comparing the predictions of two networks optimized by different strategies. Moreover, we develop an active scribble boosting strategy to provide extra supervision signals with negligible annotation cost, leading to significant SOD performance improvement. Extensive experiments on seven benchmarks validate the superiority of our proposed method. Remarkably, the proposed method with scribble annotations achieves competitive performance in comparison to fully supervised state-of-the-art methods.
Xu, Z, Khabbaz, H, Fatahi, B & Wu, D 2022, 'Real-time determination of sandy soil stiffness during vibratory compaction incorporating machine learning method for intelligent compaction', Journal of Rock Mechanics and Geotechnical Engineering, vol. 14, no. 5, pp. 1609-1625.
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An emerging real-time ground compaction and quality control, known as intelligent compaction (IC), has been applied for efficiently optimising the full-area compaction. Although IC technology can provide real-time assessment of uniformity of the compacted area, accurate determination of the soil stiffness required for quality control and design remains challenging. In this paper, a novel and advanced numerical model simulating the interaction of vibratory drum and soil beneath is developed. The model is capable of evaluating the nonlinear behaviour of underlying soil subjected to dynamic loading by capturing the variations of damping with the cyclic shear strains and degradation of soil modulus. The interaction of the drum and the soil is simulated via the finite element method to develop a comprehensive dataset capturing the dynamic responses of the drum and the soil. Indeed, more than a thousand three-dimensional (3D) numerical models covering various soil characteristics, roller weights, vibration amplitudes and frequencies were adopted. The developed dataset is then used to train the inverse solver using an innovative machine learning approach, i.e. the extended support vector regression, to simulate the stiffness of the compacted soil by adopting drum acceleration records. Furthermore, the impacts of the amplitude and frequency of the vibration on the level of underlying soil compaction are discussed. The proposed machine learning approach is promising for real-time extraction of actual soil stiffness during compaction. Results of the study can be employed by practising engineers to interpret roller drum acceleration data to estimate the level of compaction and ground stiffness during compaction.
Xu, Z, Li, J, Qian, H & Wu, C 2022, 'Blast resistance of hybrid steel and polypropylene fibre reinforced ultra-high performance concrete after exposure to elevated temperatures', Composite Structures, vol. 294, pp. 115771-115771.
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In this study, the blast resistance of fibre reinforced ultra-high performance concrete (UHPC) components after exposure to elevated temperatures was investigated. With a hybrid steel and polypropylene (PP) fibre reinforcement, this fire resistant UHPC maintained approximately 60% of its original compressive strength after exposed to 800 °C temperature. Uniaxial and triaxial material behaviour after exposure to high temperatures was studied experimentally and then incorporated into a plasticity concrete model, i.e. Karagozian & Case Concrete (KCC model) model for the blast induced structural response analysis. Material strength and failure surfaces, volumetric change with pressure, strain rate effect and material damage parameters were updated with consideration of fire hazards. The simulated UHPC uniaxial stress–strain curves after exposure to 200, 400, 600 and 800 °C elevated temperatures, together with the simulated post-fire blast tests results on UHPC members were compared with available experimental results. The reasonable agreement between the tests and simulation results validated the proposed model in both material and structural scopes. The numerical model was further applied to predict the blast response of reinforced UHPC components after exposed to thermal hazards.
Xu, Z, Ma, Y, Li, Y, Li, G, Nghiem, L & Luo, W 2022, 'Comparison between Cold Plasma, Ultrasonication, and Alkaline Hydrogen Peroxide Pretreatments of Garden Waste to Enhance Humification in Subsequent Composting with Kitchen Waste: Performance and Mechanisms', Bioresour Technol, vol. 354, p. 127228.
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This study compared the performance and mechanisms of cold plasma, ultrasonication, and alkali-assisted hydrogen peroxide for garden waste pretreatment to advance humification in composting with kitchen waste. High-throughput sequencing integrated with Functional Annotation of Prokaryotic Taxa was used to relate bacterial dynamics to humification. Results show that all pretreatment techniques accelerated humification by 37.5% - 45.7% during composting in comparison to the control treatment. Ultrasonication and alkalization preferred to decompose lignocellulose to produce humus precursors in garden waste, thereby facilitating humus formation at the beginning of composting. By contrast, cold plasma was much faster and simpler than other pretreatment techniques to effectively disrupt the surface structure and reduce the crystallinity of garden waste to enrich functional bacteria for aerobic chemoheterotrophy, xylanolysis, cellulolysis, and ligninolysis during composting. As such, a more robust bacterial community was developed after cold plasma pretreatment to advance humification at the mature stage of composting.
Xue, C 2022, 'Cracking and autogenous self-healing on the performance of fiber-reinforced MgO-cement composites in seawater and NaCl solutions', Construction and Building Materials, vol. 326, pp. 126870-126870.
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Xue, C 2022, 'Performance and mechanisms of stimulated self-healing in cement-based composites exposed to saline environments', Cement and Concrete Composites, vol. 129, pp. 104470-104470.
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Yadav, N, Patel, V, McCourt, L, Ruppert, M, Miller, M, Inerbaev, T, Mahasivam, S, Bansal, V, Vinu, A, Singh, S & Karakoti, A 2022, 'Tuning the enzyme-like activities of cerium oxide nanoparticles using a triethyl phosphite ligand', Biomaterials Science, vol. 10, no. 12, pp. 3245-3258.
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Tuning the enzyme mimetic activity of cerium oxide nanoparticles using triethylphosphine modifies its enzyme mimetic activities and improves the antimicrobial activity.
Yadav, S, Ibrar, I, Al-Juboori, RA, Singh, L, Ganbat, N, Kazwini, T, Karbassiyazdi, E, Samal, AK, Subbiah, S & Altaee, A 2022, 'Updated review on emerging technologies for PFAS contaminated water treatment', Chemical Engineering Research and Design, vol. 182, pp. 667-700.
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Yadav, S, Ibrar, I, Altaee, A, Samal, AK & Zhou, J 2022, 'Surface modification of nanofiltration membrane with kappa-carrageenan/graphene oxide for leachate wastewater treatment', Journal of Membrane Science, vol. 659, pp. 120776-120776.
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Yadav, S, Ibrar, I, Altaee, A, Samal, AK, Karbassiyazdi, E, Zhou, J & Bartocci, P 2022, 'High-Performance mild annealed CNT/GO-PVA composite membrane for brackish water treatment', Separation and Purification Technology, vol. 285, pp. 120361-120361.
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Yadav, S, Ibrar, I, Samal, AK, Altaee, A, Déon, S, Zhou, J & Ghaffour, N 2022, 'Preparation of fouling resistant and highly perm-selective novel PSf/GO-vanillin nanofiltration membrane for efficient water purification', Journal of Hazardous Materials, vol. 421, pp. 126744-126744.
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To meet the rising global demand for water, it is necessary to develop membranes capable of efficiently purifying contaminated water sources. Herein, we report a series of novel polysulfone (PSf)/GO-vanillin nanofiltration membranes highly permeable, selective, and fouling resistant. The membranes are composed of two-dimensional (2D) graphite oxide (GO) layers embedded with vanillin as porogen and PSf as the base polymer. There is a growing interest in addressing the synergistic effect of GO and vanillin on improving the permeability and antifouling characteristics of membranes. Various spectroscopic and microscopic techniques were used to perform detailed physicochemical and morphological analyses. The optimized PSf16/GO0.15-vanillin0.8 membrane demonstrated 92.5% and 25.4% rejection rate for 2000 ppm magnesium sulphate (MgSO4) and sodium chloride (NaCl) solutions respectively. Antifouling results showed over 99% rejection for BSA and 93.57% flux recovery ratio (FRR). Experimental work evaluated the antifouling characteristics of prepared membranes to treat landfill leachate wastewater. The results showed 84-90% rejection for magnesium (Mg+2) and calcium (Ca+2) with 90.32 FRR. The study experimentally demonstrated that adding GO and vanillin to the polymeric matrix significantly improves fouling resistance and membrane performance. Future research will focus on molecular sieving for industrial separations and other niche applications using mixed matrix membranes.
Yan, B, Zhao, Q, Li, M, Zhang, J, Zhang, JA & Yao, X 2022, 'Fitness landscape analysis and niching genetic approach for hybrid beamforming in RIS-aided communications', Applied Soft Computing, vol. 131, pp. 109725-109725.
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Reconfigurable intelligent surface (RIS) is a revolutionizing technology to achieve cost-effective communications. The active beamforming at the base station (BS) and the discrete phase shifts at RIS should be jointly designed to customize the propagation environment. However, current phase-shift setting methods ignore the non-separable property of phase shifts, degrading the performance, especially in cases with a large-sized RIS. To understand the problem characteristics related to the phase shifts and further tailor an eligible method with such characteristics, this paper, for the first time, analyzes the fitness landscape of the sum-rate maximization problem (maximizing the sum rate of users in a downlink multi-user multiple-input single-output system assisted by a RIS). Results show that the problem has a severe unstructured and rugged landscape, especially in cases with a large-sized RIS. This observation answers why current methods are ineligible and provides insightful guidance for designing a more intelligent method. With the landscape findings in mind, this paper introduces a niching genetic algorithm to solve the problem. In particular, the niching idea is employed to locate multiple local optima. These local optima act as stepping stones to facilitate approaching the global optima. Simulation results demonstrate that the proposed niching genetic algorithm obtains significant capacity gains over current methods in cases with large-sized RIS.
Yan, C, Chang, X, Li, Z, Guan, W, Ge, Z, Zhu, L & Zheng, Q 2022, 'ZeroNAS: Differentiable Generative Adversarial Networks Search for Zero-Shot Learning', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 12, pp. 9733-9740.
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In recent years, remarkable progress in zero-shot learning (ZSL has been achieved by generative adversarial networks (GAN . To compensate for the lack of training samples in ZSL, a surge of GAN architectures have been developed by human experts through trial-and-error testing. Despite their efficacy, however, there is still no guarantee that these hand-crafted models can consistently achieve good performance across diversified datasets or scenarios. Accordingly, in this paper, we turn to neural architecture search (NAS and make the first attempt to bring NAS techniques into the ZSL realm. Specifically, we propose a differentiable GAN architecture search method over a specifically designed search space for zero-shot learning, referred to as ZeroNAS. Considering the relevance and balance of the generator and discriminator, ZeroNAS jointly searches their architectures in a min-max player game via adversarial training. Extensive experiments conducted on four widely used benchmark datasets demonstrate that ZeroNAS is capable of discovering desirable architectures that perform favorably against state-of-the-art ZSL and generalized zero-shot learning (GZSL approaches.
Yan, J, Hanif, S, Zhang, D, Ismail, M, Wang, X, Li, Q, Shi, B, Muhammad, P & Wu, H 2022, 'Arsenic Prodrug-Mediated Tumor Microenvironment Modulation Platform for Synergetic Glioblastoma Therapy', ACS Applied Materials & Interfaces, vol. 14, no. 32, pp. 36487-36502.
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Yan, J, Sun, Y, Ji, T, Zhang, C, Liu, L & Liu, Y 2022, 'Room-temperature synthesis of defect-engineered Zirconium-MOF membrane enabling superior CO2/N2 selectivity with zirconium-oxo cluster source', Journal of Membrane Science, vol. 653, pp. 120496-120496.
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Yan, P & Yu, N 2022, 'The QQUIC Transport Protocol: Quantum-Assisted UDP Internet Connections', Entropy, vol. 24, no. 10, pp. 1488-1488.
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Quantum key distribution, initialized in 1984, is a commercialized secure communication method that enables two parties to produce a shared random secret key using quantum mechanics. We propose a QQUIC (Quantum-assisted Quick UDP Internet Connections) transport protocol, which modifies the well-known QUIC transport protocol by employing quantum key distribution instead of the original classical algorithms in the key exchange stage. Due to the provable security of quantum key distribution, the security of the QQUIC key does not depend on computational assumptions. It is possible that, surprisingly, QQUIC can reduce network latency in some circumstances even compared with QUIC. To achieve this, the attached quantum connections are used as the dedicated lines for key generation.
Yan, P, Jiang, H & Yu, N 2022, 'On incorrectness logic for Quantum programs', Proceedings of the ACM on Programming Languages, vol. 6, no. OOPSLA1, pp. 1-28.
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Bug-catching is important for developing quantum programs. Motivated by the incorrectness logic for classical programs, we propose an incorrectness logic towards a logical foundation for static bug-catching in quantum programming. The validity of formulas in this logic is dual to that of quantum Hoare logics. We justify the formulation of validity by an intuitive explanation from a reachability point of view and a comparison against several alternative formulations. Compared with existing works focusing on dynamic analysis, our logic provides sound and complete arguments. We further demonstrate the usefulness of the logic by reasoning several examples, including Grover's search, quantum teleportation, and a repeat-until-success program. We also automate the reasoning procedure by a prototyped static analyzer built on top of the logic rules.
Yan, Z, Yang, LT, Li, T, Miche, Y, Yu, S & Yau, SS 2022, 'Guest Editorial: Trust, Security and Privacy of 6G', IEEE Network, vol. 36, no. 4, pp. 100-102.
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Yang, F, Cai, Z, Chen, Y, Dong, S, Deng, C, Niu, S, Zeng, W & Wen, S 2022, 'A Robotic Polishing Trajectory Planning Method Combining Reverse Engineering and Finite Element Mesh Technology for Aero-Engine Turbine Blade TBCs', Journal of Thermal Spray Technology, vol. 31, no. 7, pp. 2050-2067.
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The roughness of thermal barrier coatings (TBCs) prepared on the surface of aero-engine turbine blades affects the lifetime of the coating and the life cycle and aerodynamic performance of the blades. To reduce the TBC surface roughness, this study proposes a robot polishing trajectory planning method that combines reverse engineering and finite element mesh technology. First, a 3D model of the blade was reconstructed in reverse engineering by using the fast surface modeling method. Then, a dense mesh with controlled spacing was obtained by mapping the finite element mesh arbitrary quadrilateral elements on the surface of the blade model. Finally, a robot polishing path of the blade was generated by sorting the index of mesh nodes. Using this approach, polishing experiments of aero-engine turbine blades were systematically carried out, and the coordinate system conversion method from the robot off-line programming simulation environment to the actual work station was used to map the robot trajectory. Meanwhile, the point cloud registration method was introduced to improve the system calibration accuracy. The experiments showed that the technical solution proposed in this paper could reduce the overall surface roughness of the thermal barrier coating from above Ra 8 μm to about Ra 0.5 μm, which contributes to the performance improvement for the TBCs of aero-engine blades.
Yang, F, Zhang, X, Zhao, Z, Guo, W & Ngo, HH 2022, 'Fate of typical organic halogen compounds in the coexistence of endogenic chlorine atoms and exogenic X-', Chemosphere, vol. 309, pp. 136761-136761.
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Yang, G, Guan, R, Zhen, H, Ou, K, Fang, J, Li, D-S, Fu, Q & Sun, Y 2022, 'Tunable Size of Hierarchically Porous Alumina Ceramics Based on DIW 3D Printing Supramolecular Gel', ACS Applied Materials & Interfaces, vol. 14, no. 8, pp. 10998-11005.
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A new three-dimensional (3D) printing gel is developed to construct hierarchically porous ceramics with adjustable millimeter-, micrometer-, and nanometer-scale size for application in thermal management. Not only does the gel based on supramolecular micelles exhibit excellent DIW 3D printability but also the supramolecular micelles act as templates that can precisely control the structure of micrometer-scale pores. The effect of millimeter- and μmicrometer-scale size on properties of porous ceramics is investigated in detail. The 3D-printed ceramic foam with millimeter-scale pores and smaller micrometer-scale pores shows better thermal insulation and lower compressive strength. For the thermal insulation, the local temperature of a chip exposed to contact heat is only 34.2 °C in the presence of a printed foam cap with a pore size of 41.5 μm, while the local temperature is 54.8 °C in the absence of the printed foam cap. The study provides a new method to construct hierarchically porous alumina ceramics with precisely tunable size, avoiding the issues of subtractive manufacturing and opening up new applications in portable devices or consumer electronics.
Yang, G, Lei, J, Xie, W, Fang, Z, Li, Y, Wang, J & Zhang, X 2022, 'Algorithm/Hardware Codesign for Real-Time On-Satellite CNN-Based Ship Detection in SAR Imagery', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-18.
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Yang, G, Qin, L, Li, M, Ou, K, Fang, J, Fu, Q & Sun, Y 2022, 'Shear-induced alignment in 3D-printed nitrile rubber-reinforced glass fiber composites', Composites Part B: Engineering, vol. 229, pp. 109479-109479.
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Nitrile rubber composite with aligned glass fibers (GFs/NBR composites) were prepared by direct-ink-writing (DIW) technology for application in flexible thermal management of electronic equipment.The alignment and orientation (0°, 45° and 90°) of glass fibers was precisely tuned by shear force field and 3D printing direction. Furthermore, the effect of print direction on the mechanical properties, thermal conductivity and heat dissipation performance were investigated. The tensile strength (1.78 MPa) and thermal conductivity (1.2 W m−1 K−1) of GFs/NBR composites with a 90° orientation was improved to be 149.6% and 300% compared to the composites with disordered orientation, respectively. The temperature of LED device and computer’ CPU covered with GFs/NBR composites with a 90° orientation was reduced by ca. 8.1 °C and 4.1 °C, respectively. The study confirmed the formation of GFs/NBR composites with controlled alignment and orientation for various applications.
Yang, H, Chen, L, Pan, S, Wang, H & Zhang, P 2022, 'Discrete embedding for attributed graphs', Pattern Recognition, vol. 123, pp. 108368-108368.
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Yang, J, Chen, G & Wen, S 2022, 'Finite-time dissipative control for bidirectional associative memory neural networks with state-dependent switching and time-varying delays', Knowledge-Based Systems, vol. 252, pp. 109338-109338.
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This paper focuses on finite-time exponential dissipative analysis and control problems for bidirectional associative memory neural networks (BAMNNs) with state-dependent switching and time-varying delays. Firstly, the state-dependent switching parameters of BAMNNs are interpreted as interval parameters by the interval matrix method instead of differential inclusion theory and set-value map. Under the framework of Filippov's solution and differential inclusions, some sufficient conditions of finite-time bounded (FTB) and finite-time exponential (Q,S,R)−γ dissipative (FTED) for BAMNNs are obtained based on Lyapunov–Krasovskii functional (LKF) and some inequality techniques. Then, the finite-time dissipative PI controllers are designed by solving linear matrix inequality (LMI). Finally, a numerical example is given to illustrate the correctness of the proposed results and the effectiveness of the designed controllers.
Yang, K, Lu, J, Wan, W, Zhang, G & Hou, L 2022, 'Transfer learning based on sparse Gaussian process for regression', Information Sciences, vol. 605, pp. 286-300.
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Transfer learning is to use the knowledge obtained from the source domain to improve the learning efficiency when the target domain has insufficient labeled data. For regression problems, when the conditional distribution function and the marginal distribution function of the source domain and the target domain are different, how to effectively extract similar knowledge for transfer learning is still a problem. In this paper, we propose a transfer learning method for regression problem based on the sparse Gaussian process (GP). GP models are very popular in regression modeling, as they have the capability to produce uncertainty estimation, however, they cannot be used directly for transfer learning. We propose an adaptive neural kernel network (ANKN) to ensure that the GP model can effectively transfer knowledge. Additionally, although many sparse GP methods are proposed to solve the time consumption problem of the GP models in large datasets, they cannot maintain the transfer performance. We propose a transfer inducing point (TIP) algorithm for data selection in large datasets to maintain the transfer performance. The experiments with transfer regression problems on both real-world small datasets and large datasets indicate that the our method significantly increases prediction accuracy and effectiveness.
Yang, L, Li, C, Cheng, Y, Yu, S & Ma, J 2022, 'Achieving privacy-preserving sensitive attributes for large universe based on private set intersection', Information Sciences, vol. 582, pp. 529-546.
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Nowadays, an increasing amount of data has been sent to the cloud for analysis and storage, and data security in the cloud has been widely concerned. Among them, CP-ABE is regarded as one of the most promising technologies to protect outsourced data. However, in most CP-ABE schemes, attackers may obtain user privacy information from policy plaintext. More likely, partial policy hiding programme neither satisfies full policy hiding nor applies to the needs of the large universe. In this paper,we hide both attribute values and names in policy by using private set intersection (PSI). The CP-ABE programme not only supports a complete hiding policy, but also can calculate the authorization relationship and mapping relationship between user passwords and keys. We use a polynomial-based PSI and a recursive algorithm to calculate the authorization relationship. And with the help of the algorithm and the label vector, the mapping is determined under communication restrictions. Through the outsourcing index, we achieve an efficient hiding strategy and effectively reduce the user's computing overhead. Finally, theoretical analysis and experiments show that our model has better performance while effectively protecting sensitive attributes.
Yang, M, Sharma, D & Shi, X 2022, 'Policy entry points for facilitating a transition towards a low-carbon electricity future', Frontiers of Engineering Management, vol. 9, no. 3, pp. 462-472.
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AbstractThis study extends the ambit of the debate on electricity transition by specifically identifying possible policy entry points through which transformative and enduring changes can be made in the electricity and socio—economic systems to facilitate the transition process. Guided by the “essence” of the multi-level perspective — a prominent framework for the study of energy transition, four such entry points have been identified: 1) destabilising the dominant, fossil fuel-based electricity regime to create room for renewable technologies to break through; 2) reconfiguring the electricity regime, which encompasses technology, short-term operational practices and long-term planning processes, to improve flexibility for accommodating large outputs from variable renewable sources whilst maintaining supply security; 3) addressing the impact of coal power phase-out on coal mining regions in terms of economic development and jobs; and 4) facilitating a shift in transition governance towards a learning-based, reflexive process. Specific areas for policy interventions within each of these entry points have also been discussed in the paper.
Yang, M, Sharma, D, Shi, X, Mamaril, K, Jiang, H & Candlin, A 2022, 'Power connectivity in the Greater Mekong Subregion (GMS) – The need for a wider discourse', Energy Policy, vol. 165, pp. 112994-112994.
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Despite nearly over 30 years of efforts, power connectivity in the GMS remains rather low, limited to a few uncoordinated bilateral exchanges of electricity. Much of the existing thinking attributes this slow progress to factors that are proximate to the electricity industry, namely, insufficient infrastructure, lack of technical competence, and uncoordinated regulation. Through an analysis of the historical evolution of power connectivity in the GMS, this Policy Perspective demonstrates that this thinking (on industry-centric factors) is inadequate for providing a fuller appreciation for the reasons for the slow progress towards power connectivity and hence potential remedies to expedite the pace of connectivity. Such appreciation can instead be gained, this paper contends, by developing a wider discourse on the geopolitical and socio-economic issues, especially those issues that are central to creating a backdrop which is essential for converting GMS's growing physical electricity connectivity into a region-wide coordinated electricity market. Such issues include: reconciliation between geopolitical and regional interests; convergence of national and regional interests across the GMS countries; and national v regional identity.
Yang, M, Zhang, X, Yang, Y, Liu, Q, Nghiem, LD, Guo, W & Ngo, HH 2022, 'Effective destruction of perfluorooctanoic acid by zero-valent iron laden biochar obtained from carbothermal reduction: Experimental and simulation study', Science of The Total Environment, vol. 805, pp. 150326-150326.
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This study investigated the degradation of perfluorooctanoic acid (PFOA) on zerovalent iron-laden biochar (BC-ZVI) prepared by carbothermal reduction. Results show that over 99% PFOA can be removed by BC-ZVI in hydrothermal conditions under 240 °C within 6 h. The maximum defluorination rate of 63.2% was achieved after 192 h, and this outcome was significantly better than biochar (BC) and zero-valent iron (ZVI) alone. The short-chain perfluorinated compounds (PFCs) and perfluoroheptanal were detected in the liquid phase after degradation, suggesting that the degradation of PFOAs by BC-ZVI followed the Kobel decarboxylation process. XRD and SEM-EDS analyses strongly suggested that carbothermal reduction could avoid the agglomeration of ZVI loaded onto biochar, which helped make the PFOA degradation more efficient. The frontier molecular orbital theory calculated by density functional theory revealed there were two possibilities for ZVI loading on BC (edged or internal loading), while the edge loaded ZVI had a greater tendency to provide electrons for the defluorination of PFOA than internally loaded ZVI.
Yang, R, Zhang, Y, Qian, J & Lee, JE-Y 2022, 'Effect of Phononic Crystal Orientation on AlN-on-Silicon Lamb Wave Micromechanical Resonators', IEEE Sensors Journal, vol. 22, no. 17, pp. 16811-16819.
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Phononic crystals (PnCs) have been used to boost the quality factor (Q) of AlN-on-Silicon Lamb Wave Resonators (LWRs). But most reports on applying PnCs to resonators have focused on the common <110> orientation within (100) silicon. Little is known on the applicability of other crystal orientations. In this work, we explore the effect of orientation on the acoustic band gap (ABG) of two PnC designs and their effect on boosting Q: a disk PnC and a ring PnC. From Finite Element simulation, we show that the disk PnC’s ABG is insensitive to orientation while adding a hole into the disk to form a ring changes its ABG to be much more sensitive to orientation. Leveraging the PnCs as anchoring boundary of LWRs, the disk PnC exhibits comparable effectiveness to boost Q > 11,000 in the <110> and <100> directions while the ring PnC is effective only in the <110> direction. We further corroborate these trends by incorporating the disk PnC into delay lines in either crystal axis.
Yang, S, Wu, S, Liu, T & Xu, M 2022, 'Bridging the Gap Between Few-Shot and Many-Shot Learning via Distribution Calibration', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 12, pp. 9830-9843.
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A major gap between few-shot and many-shot learning is the data distribution empirically observed by the model during training. In few-shot learning, the learned model can easily become over-fitted based on the biased distribution formed by only a few training examples, while the ground-truth data distribution is more accurately uncovered in many-shot learning to learn a well-generalized model. In this paper, we propose to calibrate the distribution of these few-sample classes to be more unbiased to alleviate such an over-fitting problem. The distribution calibration is achieved by transferring statistics from the classes with sufficient examples to those few-sample classes. After calibration, an adequate number of examples can be sampled from the calibrated distribution to expand the inputs to the classifier. Extensive experiments on three datasets, miniImageNet, tieredImageNet, and CUB, show that a simple linear classifier trained using the features sampled from our calibrated distribution can outperform the state-of-the-art accuracy by a large margin. We also establish a generalization error bound for the proposed distribution-calibration-based few-shot learning, which consists of the distribution assumption error, the distribution approximation error, and the estimation error. This generalization error bound theoretically justifies the effectiveness of the proposed method.
Yang, S-R, Jung, T-P, Lin, C-T, Huang, K-C, Wei, C-S, Chiueh, H, Hsin, Y-L, Liou, G-T & Wang, L-C 2022, 'Recognizing Tonal and Nontonal Mandarin Sentences for EEG-Based Brain–Computer Interface', IEEE Transactions on Cognitive and Developmental Systems, vol. 14, no. 4, pp. 1666-1677.
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Most current research has focused on non-tonal languages such as English. However, more than 60world’s population speaks tonal languages. Mandarin is the most spoken tonal languages in the world. Interestingly, the use of tone in tonal languages may represent different meanings of words and reflect feelings, which is very different from non-tonal languages. The objective of this study is to determine whether a spoken Mandarin sentence with or without tone can be distinguished by analyzing electroencephalographic signals (EEG). We first constructed a new Brain Research Center Speech (BRCSpeech) database to recognize Mandarin. The EEG data of 14 participants were recorded, while they articulated pre-selected sentences. To our knowledge, this is the first study to apply the method of asymmetric feature extraction method for speech recognition using EEG signals. This study shows that the feature extraction method of Rational Asymmetry (RASM) can achieve the best accuracy in the classification of cross-subjects. In addition, our proposed Binomial Variable Algorithm methodology can achieve 98.82% accuracy in cross-subject classification. Furthermore, we demonstrate that the use of eight channels ((F7, F8), (C5, C6), (P5, P6), and (O1, O2)) can achieve an accurate of 94.44%. This study explores the neuro-physiological correlation of Mandarin pronunciation, which can help develop a tonal language synthesis system based on BCI in the future.
Yang, T, Miro, JV, Wang, Y & Xiong, R 2022, 'Optimal Task-Space Tracking With Minimum Manipulator Reconfiguration', IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 5079-5086.
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An optimal solution to the task-space tracking problem using a non-redundant manipulator is proposed. This is a recurring occurrence in automated manufacturing settings, e.g. welding, deburring, painting, or quality control inspections. Given a pre-defined path for the end-effector to follow, there may not exist a joint-space continuous solution for task-space tracking when the non-linear manipulator kinematics and collision avoidance with obstacles in the workcell are considered. This introduces undesirable manipulator reconfigurations where the end-effector is required to deviate temporarily from the pre-defined path. The unwanted motion results in pausing task-space tracking, often incurring not only ineffective time and energy demands but potentially compromising the quality of the task at hand due to the additional discontinuities. An algorithm is proposed that provides a globally optimal perspective to the choice of suitable joint-space connected segments so that the minimum number of manipulator reconfigurations during task-space tracking is guaranteed. By carefully selecting the inverse kinematic solutions, all sequences ensuring minimum reconfigurability are proven collected by Dynamic Programming. Moreover, a faster greedy strategy is suggested to increase the computational efficiency of the tracker whilst still preserving global optimality and completeness. The effectiveness of the proposed algorithm is validated against traditional sampling-based solvers in simulation and illustrated on challenging real-world tracking experimentation with a Universal Robotics manipulator and a curved-surface object, depicted also in an accompanying video. An open-source implementation has also been provided for the benefit of the robotics community.
Yang, T, Xu, S, Liu, Z, Li, J, Wu, P, Yang, Y & Wu, C 2022, 'Experimental and numerical investigation of bond behavior between geopolymer based ultra-high-performance concrete and steel bars', Construction and Building Materials, vol. 345, pp. 128220-128220.
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In this study, a total of twenty groups of specimens were tested to investigate bond behavior between the geopolymer based ultra-high-performance concrete (G-UHPC) and steel bars. The failure modes and bond stress-slip relationships were analyzed and discussed in detail. Subsequently, a detailed 3D numerical model was developed and validated against the experimental findings. The validated numerical model was then employed to perform parametric studies to evaluate the effects of steel strength, bond length, and protective layer thickness on the bond behavior between G-UHPC and the steel bar. It was revealed that the bond strength between the steel bar and G-UHPC was enhanced upon increasing the steel bar strength and protective layer thickness, along with reducing the steel bar diameter. The bond slip decreased with an increase in the steel fiber length and volume fraction. Further, the protective layer thickness exhibited an insignificant effect on the linear ascending stage of the bond stress-slip relationships, but positively impacted the maximum bond strength of the specimen. Finally, a bond stress-slip constitutive model was proposed to precisely predict the bond behavior between G-UHPC and the steel bars.
Yang, W, Wang, S, Yin, X, Wang, X & Hu, J 2022, 'A Review on Security Issues and Solutions of the Internet of Drones', IEEE Open Journal of the Computer Society, vol. 3, pp. 96-110.
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The Internet of Drones (IoD) has attracted increasing attention in recent years because of its portability and automation, and is being deployed in a wide range of fields (e.g., military, rescue and entertainment). Nevertheless, as a result of the inherently open nature of radio transmission paths in the IoD, data collected, generated or handled by drones is plagued by many security concerns. Since security and privacy are among the foremost challenges for the IoD, in this paper we conduct a comprehensive review on security issues and solutions for IoD security, discussing IoD-related security requirements and identifying the latest advancement in IoD security research. This review analyzes a host of important security technologies with emphases on authentication techniques and blockchain-powered schemes. Based on a detailed analysis, we present the challenges faced by current methodologies and recommend future IoD security research directions. This review shows that appropriate security measures are needed to address IoD security issues, and that newly designed security solutions should particularly consider the balance between the level of security and cost efficiency.
Yang, X, Hsia, T, Merenda, A, AL-Attabi, R, Dumee, LF, Thang, SH & Kong, L 2022, 'Constructing novel nanofibrous polyacrylonitrile (PAN)-based anion exchange membrane adsorber for protein separation', Separation and Purification Technology, vol. 285, pp. 120364-120364.
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Purification of biopharmaceutical streams is essential for producing high quality therapeutic bioproducts. This work developed a novel polyacrylonitrile (PAN)-based nanofibrous membrane with strong anion exchange functionality via electrospinning. The key material functionality was obtained via RAFT copolymerization of acrylonitrile and dimethylaminoethyl acrylate (pAD), followed by quaternization to form quaternary amine (QA) ligands, namely pAQ, a series of nanofibrous PAN-pAQ membranes were electrospun by blending the pAQ copolymer with PAN homopolymer at varying ratios. The chemistry of the respective pAQ copolymer and resulting membranes was confirmed by NMR and FTIR, evidencing successful functionalization. As compared to the reference pure membrane PAN4 that was negatively charged, the resulting composite membranes showed a positive surface charge. The investigation on surface morphology revealed that the nanofiber diameter increased from 300 nm to 1 μm with an increasing blend ratio from 1:4 to 1:7 for the PAN-pAQ membranes. Such trend in surface micro/nano morphology changes strongly influenced other surface properties such as increased pore size, reduced specific surface area and increased hydrophobicity. The static binding of model protein BSA of PAN-pAQ membranes firstly increased with blend ratio from 1:4 to 1:5, and then decreased at 1:7, which was attributed to the complex trade-off relationship between surface micro/nano-structure and hence distribution/density of quaternary functional groups. The PAN-pAQ membranes showed about a 10-fold increase in static binding capacity compared to PAN4, up to 310–320 mg·g−1 at a blend ratio of 1:5. Thus through this study, we were able to demonstrate a facile route to incorporate pre-functionalized copolymers into conventional polymers to form chromatographic membranes, with many possibilities to tailor membrane functionality for a wide range of applications.
Yang, X, Liu, W & Liu, W 2022, 'Tensor Canonical Correlation Analysis Networks for Multi-View Remote Sensing Scene Recognition', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 6, pp. 2948-2961.
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Yang, X, Merenda, A, AL-Attabi, R, Dumée, LF, Zhang, X, Thang, SH, Pham, H & Kong, L 2022, 'Towards next generation high throughput ion exchange membranes for downstream bioprocessing: A review', Journal of Membrane Science, vol. 647, pp. 120325-120325.
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Membrane chromatography is recognised as a potential solution to streamline downstream processing for protein purification, where ion exchange membrane chromatography (IEMC) as a polishing step to remove impurities has been successfully demonstrated in small scales. Despite limited commercial adoption in large-scale production, the concept of IEMC attracts many interests and tremendous progress is made. To fill the review gap for advancements in the last decade, this article provides a timely analysis on key performance-determining aspects in IEMC systems. Modern laboratory-made membranes with polymeric chains of tuneable surface area and charge allow for high binding capacity (up to 10-fold higher than that of traditional resins) while simultaneously mitigating the loss of permeance due to the introduction of grafted layers up to 40%. Nevertheless, robust evaluation are yet to be conducted. Despite making equal contribution to binding, the review on process-related work was supported by only <1/3 of the cited articles, where a transition of empirical to mechanistic models was identified, enabling rationale system design and upscaling. The use of molecular simulation into binding studies reveals the roles of membrane properties but limited work was found. While highlighting disconnection between academic and commercial efforts, research gaps for future work were identified.
Yang, X, Wang, S, Xing, Y, Li, L, Xu, RYD, Friston, KJ & Guo, Y 2022, 'Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19', PLOS Computational Biology, vol. 18, no. 2, pp. e1009807-e1009807.
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Estimating the changes of epidemiological parameters, such as instantaneous reproduction number, Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to estimate the instantaneous reproduction number Rt during emerging epidemics, resulting in the state-of-the-art ‘DARt’ system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt; the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in describing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for making accurate and timely estimation for transmission dynamics based on reported data.
Yang, X, Zhang, X, Ngo, HH, Guo, W, Huo, J, Du, Q, Zhang, Y, Li, C & Yang, F 2022, 'Sorptive removal of ibuprofen from water by natural porous biochar derived from recyclable plane tree leaf waste', Journal of Water Process Engineering, vol. 46, pp. 102627-102627.
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To remove ibuprofen (IBP) in water efficiently and economically, plane tree leaf-derived biochar (P-BC) as a new adsorbent was prepared via pyrolysis at 600 °C. Textural characterizations of P-BC exhibited a porous structure and abundant hydroxyl groups. The results of FTIR and XPS indicated that -OH functional groups played a key role in the adsorption process. Batch adsorption studies were carried out at pH values of 2 to 8, adsorbent dosage of 0.1 to 2.0 g/L and initial concentrations of 500 to 5000 μg/L. Adsorption results showed that P-BC (1.0 g/L) could remove as much as 96.34% of ibuprofen (2000 μg/L) in a strong acidic environment (i.e. pH 2). The adsorption of ibuprofen by P-BC was found to be more consistent with the pseudo-second order kinetic model and Langmuir isothermal model with higher correlation coefficients of 0.999 and 0.996, respectively. Its maximum adsorption capacity was up to 10,410 μg/g. A mechanism analysis demonstrated that the -OH functional groups on the surface of P-BC could form hydrogen bonds with IBP as donors and acceptors, respectively. It played a predominant role in removing IBP. In particular the fabricated P-BC is an effective and recyclable sorbent and its efficiency in removing ibuprofen can still reach more than 70% after five regenerations. The total production cost of P-BC is 4.05 USD / kg, which is equivalent to the treatment cost of only 0.004 USD/L wastewater. The results revealed that P-BC is an environment-friendly, low-cost and efficient adsorbent for removing IBP from water.
Yang, Y, Liu, H, Zhang, J, Zhang, Z & Tang, Y 2022, 'Synthesis of efficient CaO based on biotemplate for the application of no-glycerol biodiesel preparation', Inorganic and Nano-Metal Chemistry, vol. 52, no. 7, pp. 1030-1040.
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Yang, Y, Liu, S, Dong, Z, Huang, Z, Lu, C, Wu, Y, Gao, M, Liu, Y & Pan, H 2022, 'Hierarchical conformal coating enables highly stable microparticle Si anodes for advanced Li-ion batteries', Applied Materials Today, vol. 26, pp. 101403-101403.
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Microsized silicon powders have great potential for high capacity anode materials in next-generation lithium ion batteries, because of the high gravimetric and volumetric energy densities, ease of mass production and low costs. However, large volume change and consequently rapid capacity fading upon lithiation and delithiation prevent its practical applications. Herein, we demonstrate an effective hierarchical conformal coating strategy for high-performance microsized Si anodes. The Si-based composites consist of an amorphous Li-Si-O inner coating layer and a graphene outer encapsulation layer, which are prepared by coupling reactive milling with electrostatic self-assembly. This unique hierarchical conformal coating structure not only strengthens the mechanical property (31.8 GPa for the elastic modulus) and promotes the ionic diffusion (2.03 × 10−10 cm2 s−1) of Si anode, but also effectively stabilizes the electrode/electrolyte interfaces and increases the electronic conductivity. As a result, a high reversible capacity (1450 mA⋅h g−1 at 0.1 A g−1), good cycling stability (97.7% of capacity retention from the 2nd to the 310th cycle at 0.5 A g−1), and high rate capability (703 mA⋅h g−1 at 5 A g−1) have been successfully achieved. These findings provide new insights into the improvement of electrochemical properties of microsized Si composite anodes for high-performance Li-ion batteries.
Yang, Y, Phuong Nguyen, TM, Van, HT, Nguyen, QT, Nguyen, TH, Lien Nguyen, TB, Hoang, LP, Van Thanh, D, Nguyen, TV, Nguyen, VQ, Thang, PQ, Yılmaz, M & Le, VG 2022, 'ZnO nanoparticles loaded rice husk biochar as an effective adsorbent for removing reactive red 24 from aqueous solution', Materials Science in Semiconductor Processing, vol. 150, pp. 106960-106960.
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Yang, Y, Wang, L, Su, S, Watsford, M, Wood, LM & Duffield, R 2022, 'Inertial Sensor Estimation of Initial and Terminal Contact during In-Field Running', Sensors, vol. 22, no. 13, pp. 4812-4812.
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Given the popularity of running-based sports and the rapid development of Micro-electromechanical systems (MEMS), portable wireless sensors can provide in-field monitoring and analysis of running gait parameters during exercise. This paper proposed an intelligent analysis system from wireless micro–Inertial Measurement Unit (IMU) data to estimate contact time (CT) and flight time (FT) during running based on gyroscope and accelerometer sensors in a single location (ankle). Furthermore, a pre-processing system that detected the running period was introduced to analyse and enhance CT and FT detection accuracy and reduce noise. Results showed pre-processing successfully detected the designated running periods to remove noise of non-running periods. Furthermore, accelerometer and gyroscope algorithms showed good consistency within 95% confidence interval, and average absolute error of 31.53 ms and 24.77 ms, respectively. In turn, the combined system obtained a consistency of 84–100% agreement within tolerance values of 50 ms and 30 ms, respectively. Interestingly, both accuracy and consistency showed a decreasing trend as speed increased (36% at high-speed fore-foot strike). Successful CT and FT detection and output validation with consistency checking algorithms make in-field measurement of running gait possible using ankle-worn IMU sensors. Accordingly, accurate IMU-based gait analysis from gyroscope and accelerometer information can inform future research on in-field gait analysis.
Yang, Y, Wu, C, Liu, Z & Zhang, H 2022, '3D-printing ultra-high performance fiber-reinforced concrete under triaxial confining loads', Additive Manufacturing, vol. 50, pp. 102568-102568.
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3D-printing concrete structural members may experience complex stress states, while external reinforcement (wrapping steel tube or fiber-reinforced polymer) may be one of the effective ways to improve performance. Therefore, triaxial mechanical properties of 3D-printing concrete should be explored. This study presents an experimental investigation of the triaxial behavior of 3D-printing ultra-high performance fiber-reinforced concrete (3DP-UHPFRC) loaded in the Z-direction. Mold-casting ultra-high performance fiber reinforced concrete (MC-UHPFRC) was used as the reference specimen. Based on the test data, the failure mode and mechanical properties of the 3D-printing specimens were analyzed, and the failure criteria were explored. The experimental results showed that 3DP-UHPFRC possessed triaxial failure modes, mechanical properties, and failure criteria as MC-UHPFRC. All 3DP-UHPFRC specimens exhibited oblique shear cracks under triaxial compression. The fitting effect of Mohr-Coulomb failure criterion on 3D-printing specimens without steel fiber is poor (R2 is less than 0.9), which is due to the linear relationship of Mohr-Coulomb failure criterion and the obvious nonlinear increase in strength of 3D-printing specimens without steel fiber with the confining pressure, whereas the Power-law and Willam-Warnke failure criteria were good for all 3D-printing specimens. A modified model was established for predicting the stress-strain curves of 3DP-UHPFRC under triaxial confining pressure.
Yang, Y, Wu, C, Liu, Z, Li, J, Yang, T & Jiang, X 2022, 'Characteristics of 3D-printing ultra-high performance fibre-reinforced concrete under impact loading', International Journal of Impact Engineering, vol. 164, pp. 104205-104205.
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3D-printing concrete exhibits anisotropy under static loads, owing to its unique additive manufacturing process, while its dynamic performance study is still insufficient. In particular, the dynamic properties of 3D-printing ultra-high performance fibre reinforced concrete (3DP-UHPFRC) have not been studied yet. Therefore, this study explores the characteristics of 3DP-UHPFRC under impact loads using the SHPB tests. Three impact velocities of 3.886, 6.026, and 8.538 m/s were studied in the tests. The impact process was recorded by a high-speed camera. The dynamic mechanical characteristics of 3D-printing ultra-high performance concrete (3DP-UHPC) without fibre, 3DP-UHPFRC and reference specimens were investigated in terms of fibre type, fibre content, preparation method, loading direction, and impact velocity. The characteristics of strain rate, dynamic compressive stress, dynamic increase factor (DIF), energy absorption capacity and failure process were evaluated. The findings of this study indicated that the degree of failure of 3DP-UHPC was similar in all directions, while the degree of failure of 3DP-UHPFRC in all directions was different. The degree of failure in the X-direction was the worst, followed in decreasing order by the degrees of failure in the Y- and Z-directions. At the same impact velocity, the elastic modulus and strain rate effect of the 3D-printing specimens exhibited anisotropic characteristics, owing to the different elastic modulus of the 3D-printing specimens in each direction. Furthermore, the specimens were more susceptible to deformation in the X-direction than that in the Y- and Z-directions. As the impact velocity was increased, the dynamic peak stresses for 3DP-UHPFRC were isotropic at the same impact velocity, owing to the strain rate effect. Finally, the DIF of the 3D-printing specimens was observed to be anisotropic, and in the X-direction the specimens exhibited the most significant strain rate sensitivity.
Yang, Y, Zhang, X, Ngo, HH, Guo, W, Li, Z, Wang, X, Zhang, J & Long, T 2022, 'A new spent coffee grounds based biochar - Persulfate catalytic system for enhancement of urea removal in reclaimed water for ultrapure water production', Chemosphere, vol. 288, pp. 132459-132459.
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Yang, Y, Zhao, L & Liu, X 2022, 'Iterative Zero-Shot Localization via Semantic-Assisted Location Network', IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 5974-5981.
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This paper considers zero-shot localization problem where the images used for localization are taken from new locations that are not included in the training dataset. We propose the Semantic-Assisted Location Network (SLN), which considers a new location essentially as a new combination of certain semantic classes. Moreover, we propose an iterative zero-shot learning method based on Expectation-Maximization (EM) algorithm to deal with the problem that the inter-class relationships of class representations in image embedding space and class embedding space are inconsistent. Experiments show that the proposed iterative zero-shot learning method outperforms start-of-the-art zero-shot localization methods by a large margin.
Yang, Z, Liu, X, Cribbin, EM, Kim, AM, Li, JJ & Yong, K-T 2022, 'Liver-on-a-chip: Considerations, advances, and beyond', Biomicrofluidics, vol. 16, no. 6, pp. 061502-061502.
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The liver is the largest internal organ in the human body with largest mass of glandular tissue. Modeling the liver has been challenging due to its variety of major functions, including processing nutrients and vitamins, detoxification, and regulating body metabolism. The intrinsic shortfalls of conventional two-dimensional (2D) cell culture methods for studying pharmacokinetics in parenchymal cells (hepatocytes) have contributed to suboptimal outcomes in clinical trials and drug development. This prompts the development of highly automated, biomimetic liver-on-a-chip (LOC) devices to simulate native liver structure and function, with the aid of recent progress in microfluidics. LOC offers a cost-effective and accurate model for pharmacokinetics, pharmacodynamics, and toxicity studies. This review provides a critical update on recent developments in designing LOCs and fabrication strategies. We highlight biomimetic design approaches for LOCs, including mimicking liver structure and function, and their diverse applications in areas such as drug screening, toxicity assessment, and real-time biosensing. We capture the newest ideas in the field to advance the field of LOCs and address current challenges.
Yang, Z, Pan, J, Chen, J, Zi, Y, Oberst, S, Schwingshackl, CW & Hoffmann, N 2022, 'A novel unknown-input and single-output approach to extract vibration patterns via a roving continuous random excitation', ISA Transactions, vol. 129, pp. 675-686.
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Yang, Z, Tran, LC, Safaei, F, Le, AT & Taparugssanagorn, A 2022, 'Real-Time Step Length Estimation in Indoor and Outdoor Scenarios', Sensors, vol. 22, no. 21, pp. 8472-8472.
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In this paper, human step length is estimated based on the wireless channel properties and the received signal strength indicator (RSSI) method. The path loss between two ankles, called the on-ankle path loss, is converted from the RSSI, which is measured by our developed wearable hardware in indoor and outdoor ambulation scenarios. The human walking step length is estimated by a reliable range of RSSI values. The upper threshold and the lower threshold of this range are determined experimentally. This paper advances our previous step length measurement technique by proposing a novel exponential weighted moving average (EWMA) algorithm to update the upper and lower thresholds, and thus the step length estimation, recursively. The EWMA algorithm allows our measurement technique to process each shorter subset of the dataset, called a time window, and estimate the step length, rather than having to process the whole dataset at a time. The step length is periodically updated on the fly when the time window is “sliding” forwards. Thus, the EWMA algorithm facilitates the step length estimation in real-time. The impact of the EWMA parameter is analysed, and the optimal parameter is discovered for different experimental scenarios. Our experiments show that the EWMA algorithm could achieve comparable accuracy as our previously proposed technique with errors as small as 3.02% and 0.30% for the indoor and outdoor scenarios, respectively, while the processing time required to output an estimation of the step length could be significantly shortened by 53.96% and 60% for the indoor walking and outdoor walking, respectively.
Yao, H, Liu, C, Zhang, P, Wu, S, Jiang, C & Yu, S 2022, 'Identification of Encrypted Traffic Through Attention Mechanism Based Long Short Term Memory', IEEE Transactions on Big Data, vol. 8, no. 1, pp. 241-252.
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Network traffic classification has become an important part of network management, which is beneficial for achieving intelligent network operation and maintenance, enhancing the network quality of service (QoS), and for network security. Given the rapid development of various applications and protocols, more and more encrypted traffic has emerged in networks. Traditional traffic classification methods exhibited the unsatisfied performance since the encrypted traffic is no longer in plain text. In this work, we modeled the time-series network traffic by the recurrent neural network (RNN). Moreover, the attention mechanism was introduced for assisting network traffic classification in the form of the following two models, the attention aided long short term memory (LSTM) as well as the hierarchical attention network (HAN). Finally, relying on the ISCX VPN-NonVPN dataset, extensive experiments were conducted, showing that the proposed methods achieved 91.2 percent in accuracy while the highest accuracy of other methods was 89.8 percent relying on the same dataset.
Yao, L, Kusakunniran, W, Wu, Q, Xu, J & Zhang, J 2022, 'Collaborative Feature Learning for Gait Recognition Under Cloth Changes', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 6, pp. 3615-3629.
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Since gait can be utilized to identify individuals from a far distance without their interaction and coordination, recently many gait recognition methods have been proposed. However, due to a real-world scenario of clothing changes, a degradation occurs for most of these methods. Thus in this paper, a more efficient gait recognition method is proposed to address the problem of clothing variances. First, part-based gait features are formulated from two different perspectives, i.e., the separated body parts that are more robust to clothing changes and the estimated human skeleton key-point regions. It is reasonable to formulate such features for cloth-changing gait recognition, because these two perspectives are both less vulnerable to clothing changes. Given that each feature has its own advantages and disadvantages, a more efficient gait feature is generated in this paper by assembling these two features together. Moreover, since local features are more discriminative than global features, in this paper more attention is focused on the local short-range features. Also, unlike most methods, in our method we treat the estimated key-point features as a set of word embeddings, and a transformer encoder is specifically used to learn the dependence of each correlative key-points. The robustness and effectiveness of our proposed method are certified by experiments on CASIA Gait Dataset B, and it has achieved the state-of-the-art performance on this dataset.
Yao, L, Kusakunniran, W, Wu, Q, Xu, J & Zhang, J 2022, 'Recognizing Gaits Across Walking and Running Speeds', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 18, no. 3, pp. 1-22.
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For decades, very few methods were proposed for cross-mode (i.e., walking vs. running) gait recognition. Thus, it remains largely unexplored regarding how to recognize persons by the way they walk and run. Existing cross-mode methods handle the walking-versus-running problem in two ways, either by exploring the generic mapping relation between walking and running modes or by extracting gait features which are non-/less vulnerable to the changes across these two modes. However, for the first approach, a mapping relation fit for one person may not be applicable to another person. There is no generic mapping relation given that walking and running are two highly self-related motions. The second approach does not give more attention to the disparity between walking and running modes, since mode labels are not involved in their feature learning processes. Distinct from these existing cross-mode methods, in our method, mode labels are used in the feature learning process, and a mode-invariant gait descriptor is hybridized for cross-mode gait recognition to handle this walking-versus-running problem. Further research is organized in this article to investigate the disparity between walking and running. Running is different from walking not only in the speed variances but also, more significantly, in prominent gesture/motion changes. According to these rationales, in our proposed method, we give more attention to the differences between walking and running modes, and a robust gait descriptor is developed to hybridize the mode-invariant spatial and temporal features. Two multi-task learning-based networks are proposed in this method to explore these mode-invariant features. Spatial features describe the body parts non-/less affected by mode changes, and temporal features depict the instinct motion relation of each person. Mode labels are also adopted in the training phase to guide the network to give more attention to the disparity across walking and run...
Yao, Y, Yuan, X, He, L, Yu, Y, Du, Y, Liu, G, Tian, L, Ma, Z, Zhang, Y & Ma, J 2022, 'Patient Blood Management: Single Center Evidence and Practice at Fuwai Hospital', Chinese Medical Sciences Journal, vol. 37, no. 3, pp. 246-260.
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Yazdani, D, Cheng, R, He, C & Branke, J 2022, 'Adaptive Control of Subpopulations in Evolutionary Dynamic Optimization', IEEE Transactions on Cybernetics, vol. 52, no. 7, pp. 6476-6489.
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Multipopulation methods are highly effective in solving dynamic optimization problems. Three factors affect this significantly: 1) the exclusion mechanisms to avoid the convergence to the same peak by multiple subpopulations; 2) the resource allocation mechanism that assigns the computational resources to the subpopulations; and 3) the control mechanisms to adaptively adjust the number of subpopulations by considering the number of optima and available computational resources. In the existing exclusion mechanisms, when the distance (i.e., the distance between their best found positions) between two subpopulations becomes less than a predefined threshold, the inferior one will be removed/reinitialized. However, this leads to incapability of algorithms in covering peaks/optima that are closer than the threshold. Moreover, despite the importance of resource allocation due to the limited available computational resources between environmental changes, it has not been well studied in the literature. Finally, the number of subpopulations should be adapted to the number of optima. However, in most existing adaptive multipopulation methods, there is no predefined upper bound for generating subpopulations. Consequently, in problems with large numbers of peaks, they can generate too many subpopulations sharing limited computational resources. In this article, a multipopulation framework is proposed to address the aforementioned issues by using three adaptive approaches: 1) subpopulation generation; 2) double-layer exclusion; and 3) computational resource allocation. The experimental results demonstrate the superiority of the proposed framework over several peer approaches in solving various benchmark problems.
Yazdani, D, Omidvar, MN, Cheng, R, Branke, J, Nguyen, TT & Yao, X 2022, 'Benchmarking Continuous Dynamic Optimization: Survey and Generalized Test Suite', IEEE Transactions on Cybernetics, vol. 52, no. 5, pp. 3380-3393.
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Dynamic changes are an important and inescapable aspect of many real-world optimization problems. Designing algorithms to find and track desirable solutions while facing challenges of dynamic optimization problems is an active research topic in the field of swarm and evolutionary computation. To evaluate and compare the performance of algorithms, it is imperative to use a suitable benchmark that generates problem instances with different controllable characteristics. In this article, we give a comprehensive review of existing benchmarks and investigate their shortcomings in capturing different problem features. We then propose a highly configurable benchmark suite, the generalized moving peaks benchmark, capable of generating problem instances whose components have a variety of properties, such as different levels of ill-conditioning, variable interactions, shape, and complexity. Moreover, components generated by the proposed benchmark can be highly dynamic with respect to the gradients, heights, optimum locations, condition numbers, shapes, complexities, and variable interactions. Finally, several well-known optimizers and dynamic optimization algorithms are chosen to solve generated problems by the proposed benchmark. The experimental results show the poor performance of the existing methods in facing new challenges posed by the addition of new properties.
Ye, D, Shen, S, Zhu, T, Liu, B & Zhou, W 2022, 'One Parameter Defense—Defending Against Data Inference Attacks via Differential Privacy', IEEE Transactions on Information Forensics and Security, vol. 17, pp. 1466-1480.
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Machine learning models are vulnerable to data inference attacks, such as membership inference and model inversion attacks. In these types of breaches, an adversary attempts to infer a data record's membership in a dataset or even reconstruct this data record using a confidence score vector predicted by the target model. However, most existing defense methods only protect against membership inference attacks. Methods that can combat both types of attacks require a new model to be trained, which may not be time-efficient. In this paper, we propose a differentially private defense method that handles both types of attacks in a time-efficient manner by tuning only one parameter, the privacy budget. The central idea is to modify and normalize the confidence score vectors with a differential privacy mechanism which preserves privacy and obscures membership and reconstructed data. Moreover, this method can guarantee the order of scores in the vector to avoid any loss in classification accuracy. The experimental results show the method to be an effective and timely defense against both membership inference and model inversion attacks with no reduction in accuracy.
Ye, D, Zhu, T, Cheng, Z, Zhou, W & Yu, PS 2022, 'Differential Advising in Multiagent Reinforcement Learning', IEEE Transactions on Cybernetics, vol. 52, no. 6, pp. 5508-5521.
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Agent advising is one of the main approaches to improve agent learning performance by enabling agents to share advice. Existing advising methods have a common limitation that an adviser agent can offer advice to an advisee agent only if the advice is created in the same state as the advisee's concerned state. However, in complex environments, it is a very strong requirement that two states are the same, because a state may consist of multiple dimensions and two states being the same means that all these dimensions in the two states are correspondingly identical. Therefore, this requirement may limit the applicability of existing advising methods to complex environments. In this article, inspired by the differential privacy scheme, we propose a differential advising method that relaxes this requirement by enabling agents to use advice in a state even if the advice is created in a slightly different state. Compared with the existing methods, agents using the proposed method have more opportunity to take advice from others. This article is the first to adopt the concept of differential privacy on advising to improve agent learning performance instead of addressing security issues. The experimental results demonstrate that the proposed method is more efficient in complex environments than the existing methods.
Ye, J, Dalle, J, Nezami, R, Hasanipanah, M & Armaghani, DJ 2022, 'Stochastic fractal search-tuned ANFIS model to predict blast-induced air overpressure', Engineering with Computers, vol. 38, no. 1, pp. 497-511.
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Ye, K & Ji, JC 2022, 'An origami inspired quasi-zero stiffness vibration isolator using a novel truss-spring based stack Miura-ori structure', Mechanical Systems and Signal Processing, vol. 165, pp. 108383-108383.
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In this paper, an origami-inspired vibration isolator is proposed and numerically investigated to achieve a quasi-zero-stiffness (QZS) property. A truss-spring based stack Miura-ori (TS-SMO) structure is introduced in the vibration isolation system to provide a desired stiffness for high-static-low-dynamic requirement. The proposed TS-SMO structure is different from the traditional origami structures which include rigid facets and deformable creases. It uses coil spring sets to replace all the creases, to improve the physical realization in engineering applications. Nonlinear force response and the QZS feature can be achieved through the geometric nonlinearity and its unique Poisson's ratio profile. The static force and stiffness characteristics of the developed TS-SMO structure are numerically discussed to meet specific feature requirements. Then a QZS vibration isolator is presented under specific parameter design. The force–displacement response and stiffness diagram are obtained to verify the static performance as an isolation system. Furthermore, the displacement transmissibility is derived through dynamic analysis by employing both harmonic balance method (HBM) and numerical simulations. The isolation performance under variable viscous damping is also discussed to examine the effects of the system damping.
Ye, Y, Hao Ngo, H, Guo, W, Woong Chang, S, Duc Nguyen, D, Fu, Q, Wei, W, Ni, B, Cheng, D & Liu, Y 2022, 'A critical review on utilization of sewage sludge as environmental functional materials', Bioresource Technology, vol. 363, pp. 127984-127984.
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Sewage sludge (SS) is increasingly used as an environment functional material to reduce or control pollution and improve plant growth because of the large amounts of carbon and essential plant nutrients in it. To achieve the best application results, it is essential to comprehensively review recent progress in SS utilization. This review aims to fill the gaps in knowledge by describing the properties of SS, and its usage as adsorbents, catalysts and fertilizers, and certain application mechanisms. Although SS generates several benefits for the environment and humans, many challenges still exist to limit the application, including the risks posed by potentially toxic substances (e.g., heavy metals) in SS. Therefore, future research directions are discussed and how to make SS applications more feasible in terms of technology and economy.
Ye, Y, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Varjani, S, Liu, Q, Bui, XT & Hoang, NB 2022, 'Bio-membrane integrated systems for nitrogen recovery from wastewater in circular bioeconomy', Chemosphere, vol. 289, pp. 133175-133175.
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Ye, Z, Silva, DM, Traini, D, Young, P, Cheng, S & Ong, HX 2022, 'An adaptable microreactor to investigate the influence of interfaces on Pseudomonas aeruginosa biofilm growth', Applied Microbiology and Biotechnology, vol. 106, no. 3, pp. 1067-1077.
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AbstractBiofilms are ubiquitous and notoriously difficult to eradicate and control, complicating human infections and industrial and agricultural biofouling. However, most of the study had used the biofilm model that attached to solid surface and developed in liquid submerged environments which generally have neglected the impact of interfaces. In our study, a reusable dual-chamber microreactor with interchangeable porous membranes was developed to establish multiple growth interfaces for biofilm culture and test. Protocol for culturing Pseudomonas aeruginosa (PAO1) on the air–liquid interface (ALI) and liquid–liquid interface (LLI) under static environmental conditions for 48 h was optimized using this novel device. This study shows that LLI model biofilms are more susceptible to physical disruption compared to ALI model biofilm. SEM images revealed a unique “dome-shaped” microcolonies morphological feature, which is more distinct on ALI biofilms than LLI. Furthermore, the study showed that ALI and LLI biofilms produced a similar amount of extracellular polymeric substances (EPS). As differences in biofilm structure and properties may lead to different outcomes when using the same eradication approaches, the antimicrobial effect of an antibiotic, ciprofloxacin (CIP), was chosen to test the susceptibility of a 48-h-oldP. aeruginosabiofilms grown on ALI and LLI. Our results show that the minimum biofilm eradication concentration (MBEC) of 6-h CIP exposure for ALI and LLI biofilms is significantly different, which are 400 μg/mL and 200 μg/mL, respectively. These results highlight the importance of growth interface when developing more targeted biofilm management strategies, and our novel device provides a promising tool that enables manipulation of realistic biofilm growth.Key points• ...
Yi, JL, Chen, JH, Qu, C, Zhang, YX & Cui, YH 2022, 'A Hybrid Method for Predicting Fatigue Life of Lead-Free Solder Joints Under Coupled Electrical–Thermal–Mechanical Fields', International Journal of Applied Mechanics, vol. 14, no. 06.
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A hybrid method was developed in this study for fatigue life prediction (FLP) of lead-free solder (LFS) joints in ball grid array package under the coupled electrical–thermal–mechanical (ETM) fields. A modified constitutive model for lead-free solder joints under coupled ETM fields was proposed to describe the mechanical and fatigue behavior of solder joints under coupled ETM fields. A modified damage model under coupled ETM fields was then developed to obtain the fatigue life of LFS joints under coupled ETM fields. Uniaxial tension tests of an Sn–3.0Ag–0.5Cu (SAC305) solders and coupled multifields fatigue experiments were performed to verify the accuracy, applicability and validity by the modified constitutive model and fatigue damage model under coupled ETM fields. Based on the modified constitutive model and fatigue damage model, a hybrid method, including current, temperature and mechanics parameters, was proposed for the FLP of the LFS joints, which was demonstrated to be able to predict the results well. A hybrid method provides an idea for investigating the fatigue life of lead-free solder joints in coupled fields.
Yin, L, Wang, D, Li, X, He, Y, Liu, X, Xu, Y & Chen, H 2022, 'One-pot synthesis of oxygen-vacancy-rich Cu-doped UiO-66 for collaborative adsorption and photocatalytic degradation of ciprofloxacin', Science of The Total Environment, vol. 815, pp. 151962-151962.
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UiO-66, as one of the most stable metal-organic frameworks (MOFs), has attracted a lot of attention in the field of adsorption and photocatalysis. However, this application of UiO-66 is still limited due to either the low accessibility of micropores or the poor electron-hole charge separation capability. This study aims to promote UiO-66 accessibility of micropores and charge separation through the construction of oxygen vacancies (OVs) and mesopore defects as well as copper incorporation. Herein, mesopore Cu doped UiO-66 with rich OVs was synthesized by a one-pot method and demonstrated high efficiency for the removal of ciprofloxacin (CIP) from the aquatic system. First of all, denatured mesopore defects were produced in Cu doped UiO-66 which possessed a 58% increase in specific surface area compared to UiO-66, facilitating the adsorption of molecular oxygen. Secondly, e- was preferentially trapped by OVs under light irradiation. Electron (e-) reacted rapidly with the surface adsorbed oxygen to generate superoxide radical (O2-). Meanwhile, copper incorporation increased the photocurrent and reduced the interfacial charge transfer resistance, thereby improving the charge separation efficiency. As a result, the adsorption efficiency and photocatalytic performance of mesopore Cu doped UiO-66 with OVs were 8.1 and 3.7 times higher than those of UiO-66, respectively. This study paved a way for the one-step synthesis of MOFs containing OVs and broadened the possibilities of practical applications for photo-induced removal of antibiotics from effluent.
Ying, M 2022, 'Birkhoff-von Neumann Quantum Logic as an Assertion Language for Quantum Programs.', CoRR, vol. abs/2205.01959.
Ying, M, Zhou, L, Li, Y & Feng, Y 2022, 'A proof system for disjoint parallel quantum programs.', Theor. Comput. Sci., vol. 897, pp. 164-184.
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In this paper, we define the operational and denotational semantics of a special class of parallel quantum programs, namely disjoint parallel quantum programs. Based on them, a proof system for reasoning about disjoint parallel quantum programs is developed, which is (relatively) complete even when entanglement between different processes appears in the preconditions and postconditions.
You, F, Ni, W, Li, J & Jamalipour, A 2022, 'New Three-Tier Game-Theoretic Approach for Computation Offloading in Multi-Access Edge Computing', IEEE Transactions on Vehicular Technology, vol. 71, no. 9, pp. 9817-9829.
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Yousefi, AM, Samali, B, Hajirasouliha, I, Yu, Y & Clifton, GC 2022, 'Unified design equations for web crippling failure of cold-formed ferritic stainless steel unlipped channel-sections with web holes', Journal of Building Engineering, vol. 45, pp. 103685-103685.
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This paper addresses the design of cold-formed ferritic stainless steel unlipped channel-sections with offset web holes and fastened flanges subject to web crippling under one-flange load scenarios. The results of a total of 18 new experimental tests, not reported previously, are presented; these were conducted on ferritic 1.4016 grade unlipped channel-sections under both interior- and end-one-flange loadings. The results are used to validate a non-linear quasi-static finite element model to simulate the behaviour of such sections. A comprehensive parametric study including a total of 576 FE models is then undertaken to determine the web crippling strengths for unlipped channel-sections with different web heights, web thicknesses and location of web holes under both interior- and end-one-flange loadings. In addition, the experimental and FE results are compared against strengths predicted in accordance with the American Iron and Steel Institute (AISI S100-16) for cold-formed carbon steel plain lipped channel-sections as well as the equations proposed in previous studies for stainless steel plain lipped channel-sections. It is found that the current design equations are unreliable and unconservative to use for cold-formed ferritic stainless steel unlipped channel-sections by as much as 22%. To address this issue, based on the results of this study, two new reliable web crippling strength reduction factor equations are proposed for such unlipped channel-sections. The proposed equations should prove useful for practical design of cold-formed ferritic stainless steel unlipped channel-sections with offset web holes and fastened flanges. For practical design application, the equations are then unified by combining the proposed equations with existing equations from the literature to allow a unified design equation to be developed for ferritic stainless steel unlipped channel-sections with centred and offset web holes and with both flanges fastened and unfaste...
Yousefi, M, Tabatabaei, SH, Rikhtehgaran, R, Pour, AB & Pradhan, B 2022, 'Detection of alteration zones using the Dirichlet process Stick-Breaking model-based clustering algorithm to hyperion data: the case study of Kuh-Panj porphyry copper deposits, Southern Iran', Geocarto International, vol. 37, no. 25, pp. 9788-9816.
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Youssef, AM, Pradhan, B, Dikshit, A & Mahdi, AM 2022, 'Comparative study of convolutional neural network (CNN) and support vector machine (SVM) for flood susceptibility mapping: a case study at Ras Gharib, Red Sea, Egypt', Geocarto International, vol. 37, no. 26, pp. 11088-11115.
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Geohazard risk is high in Arab countries due to ineffective disaster preparedness measures, mismanagement, lack of public awareness, inadequate funding and lack of stakeholder support. One such country is Egypt, which is hit by floods every year that cost lives and bring the economy to a standstill. Moreover, not much has been done to map flood-prone areas. In this paper, flood susceptibility modelling was evaluated in the Ras Gharib region of Egypt using two effective techniques machine learning technique-MLT (Support Vector Machine (SVM)) and deep learning method-DL (Convolutional Neural Networks (CNN)). Thirteen flood related factors and flood inventory layer were prepared to construct these models. Validation was performed with 30% of the flood locations where receiver operating characteristic (ROC) curves showed that the deep learning technique (CNN) gave a prediction accuracy of 86.5% (high performance), while the MLTs (SVM) gave 71.6% (medium performance). The results show that CNN provides 17% better than SVM which indicates a powerful and accurate model in flood susceptibility mapping. Results were confirmed using the Astro Digital images shortly after the 2016 flood, in which the CNN model provides a good agreement.
Youssef, AM, Pradhan, B, Dikshit, A, Al-Katheri, MM, Matar, SS & Mahdi, AM 2022, 'Landslide susceptibility mapping using CNN-1D and 2D deep learning algorithms: comparison of their performance at Asir Region, KSA', Bulletin of Engineering Geology and the Environment, vol. 81, no. 4.
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Yu, E, Ma, J, Sun, J, Chang, X, Zhang, H & Hauptmann, AG 2022, 'Deep Discrete Cross-Modal Hashing with Multiple Supervision', Neurocomputing, vol. 486, pp. 215-224.
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Deep hashing has been widely used for large-scale cross-modal retrieval benefited from the low storage cost and fast search speed. However, most existing deep supervised methods only preserve the instance-pairwise relationship supervised by the semantic similarity matrix, which always inufficient heterogeneous correlation. Thus, we propose the Deep Discrete Cross-Modal Hashing with Multiple Supervision (DDCHms) to further enhance the semantic consistency of heterogeneous modalities. It improves the performance of semantic information retrieval with the joint supervision of instance-pairwise, instance-labeled and class-wise similarities. Specifically, we firstly utilize the instance-pairwise similarity matrix to supervise the learning process of heterogeneous networks and it keeps the pairwise correlation from the perspective of instance-instance. Specially, we design a semantic network to fully exploit the semantic information implicated in labels, which is also used to supervise multi-modal networks on instance-label level. Furthermore, we propose the class-wise hash codes to cooperate with the intrinsic label matrix as the prototypes, and it guides the hash learning and further ensures the precision and compactness of the learned hash codes. In addition, we design different discrete optimization strategies to optimize the class-wise hash codes and unified hash codes, respectively. That avoids the optimization errors and ensures the high-quality of learned hash codes. Experiments on three popular datasets indicate that our method outperforms other state-of-the-art methods in terms of cross-modal retrieval.
Yu, E, Song, Y, Zhang, G & Lu, J 2022, 'Learn-to-adapt: Concept drift adaptation for hybrid multiple streams', Neurocomputing, vol. 496, pp. 121-130.
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Existing concept drift adaptation (CDA) methods aim to continually update outdated classifiers in a single-labeled stream scenario. However, real-world data streams are massive, with hybrids of labeled and unlabeled streams. In this paper, we discuss CDA in multiple data streams that may contain unlabeled drifting streams. To address this realistic and complex problem, we rethink the concept drift problem by adopting a meta-learning approach and introduce a Learn-to-Adapt framework (L2A). The L2A framework simultaneously 1) makes adaptations for drifting labeled streams, and 2) leverages knowledge from labeled drifting streams to make adaptations for unlabeled stream prediction. In L2A, a meta-representor with an adapter in the meta-training stage is designed to learn the invariant representations for drifting streams, enabling the model to quickly produce a good generalization of new concepts with limited training samples. In the online stage, the meta-representor will be adapted continually under the control of the adapter and will contribute to adapting the classifiers for unlabeled drifting stream prediction. Compared to existing CDA methods which mostly only adapt the classifiers, L2A adapts the feature extractor and classifier in a feedback process, which is advanced in dealing with more complex and high-dimensional data streams.
Yu, H, Guo, Y, Ye, L & Su, SW 2022, 'Statistical Analysis of In-Field Magnetometer Calibration for Two Representative Methods', IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-8.
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Yu, H, Li, W, Chen, C, Liang, J, Gui, W, Wang, M & Chen, H 2022, 'Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis', Engineering with Computers, vol. 38, no. S1, pp. 743-771.
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The Fruit Fly Optimization Algorithm (FOA) is a recent algorithm inspired by the foraging behavior of fruit fly populations. However, the original FOA easily falls into the local optimum in the process of solving practical problems, and has a high probability of escaping from the optimal solution. In order to improve the global search capability and the quality of solutions, a dynamic step length mechanism, abandonment mechanism and Gaussian bare-bones mechanism are introduced into FOA, termed as BareFOA. Firstly, the random and ambiguous behavior of fruit flies during the olfactory phase is described using the abandonment mechanism. The search range of fruit fly populations is automatically adjusted using an update strategy with dynamic step length. As a result, the convergence speed and convergence accuracy of FOA have been greatly improved. Secondly, the Gaussian bare-bones mechanism that overcomes local optimal constraints is introduced, which greatly improves the global search capability of the FOA. Finally, 30 benchmark functions for CEC2017 and seven engineering optimization problems are experimented with and compared to the best-known solutions reported in the literature. The computational results show that the BareFOA not only significantly achieved the superior results on the benchmark problems than other competitive counterparts, but also can offer better results on the engineering optimization design problems.
Yu, H, Lu, J & Zhang, G 2022, 'Continuous Support Vector Regression for Nonstationary Streaming Data', IEEE Transactions on Cybernetics, vol. 52, no. 5, pp. 3592-3605.
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Quadratic programming is the process of solving a special type of mathematical optimization problem. Recent advances in online solutions for quadratic programming problems (QPPs) have created opportunities to widen the scope of applications for support vector regression (SVR). In this vein, efforts to make SVR compatible with streaming data have been met with substantial success. However, streaming data with concept drift remain problematic because the trained prediction function in SVR tends to drift as the data distribution drifts. Aiming to contribute a solution to this aspect of SVR's advancement, we have developed continuous SVR (C-SVR) to solve regression problems with nonstationary streaming data, that is, data where the optimal input-output prediction function can drift over time. The basic idea of C-SVR is to continuously learn a series of input-output functions over a series of time windows to make predictions about different periods. However, strikingly, the learning process in different time windows is not independent. An additional similarity term in the QPP, which is solved incrementally, threads the various input-output functions together by conveying some learned knowledge through consecutive time windows. How much learned knowledge is transferred is determined by the extent of the concept drift. Experimental evaluations with both synthetic and real-world datasets indicate that C-SVR has better performance than most existing methods for nonstationary streaming data regression.
Yu, H, Lu, J & Zhang, G 2022, 'MORStreaming: A Multioutput Regression System for Streaming Data', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 8, pp. 4862-4874.
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With the continuous generation of huge volumes of streaming data, streaming data regression has become more complicated. A regressor that predicts two or more outputs, i.e., multioutput regression, is commonly used in many applications. However, current multioutput regressors use a batch method to handle data, which presents compatibility issues for streaming data as they need to be analyzed online. To address this issue, we present a multioutput regression system, called MORStreaming, for streaming data. MORStreaming uses an instance-based model to make predictions because this model can quickly adapt to change by only storing new instances or by throwing away old instances. However, learning instances in our regression system are constrained by online demand and need to consider the relationship between outputs. Therefore, MORStreaming consists of two algorithms: 1) an online algorithm based on topology networks which is designed to learn the instances and 2) an online algorithm based on adaptive rules which is designed to learn the correlation between outputs automatically. Experiments involving both artificial and real-world datasets indicate MORStreaming can achieve superior performance compared with other multioutput methods.
Yu, H, Lu, J & Zhang, G 2022, 'Topology Learning-Based Fuzzy Random Neural Networks for Streaming Data Regression', IEEE Transactions on Fuzzy Systems, vol. 30, no. 2, pp. 412-425.
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IEEE As a type of evolving-fuzzy system, the evolving-fuzzy-neuro (EFN) system uses the structure inspired by neural networks to determine its parameters (fuzzy sets and fuzzy rules), so EFN system can inherit the advantages of neural networks. However, for streaming data regression, EFN systems still have several drawbacks: 1) determining fuzzy sets is not robust to data sequence; 2) determining fuzzy rules is complex due to subspaces that can approximate to a Takagi-Sugeno-Kang (TSK) rule need to be obtained, and many parameters need to be optimized; 3) it is difficult to detect and adapt to changes in the data distribution, i.e., concept drift, if the output is a continuous variable. Hence, in this paper, a novel evolving-fuzzy-neuro system, called the topology learning-based fuzzy random neural network (TLFRNN), is proposed. In TLFRNN, an online topology learning algorithm is designed to self-organize each layer of TLFRNN. Different from current EFN systems, TLFRNN learns multiple fuzzy sets to reduce the impact of noises on each fuzzy set, and a randomness layer is designed, which assigning the probability of each fuzzy set. Also, TLFRNN does not utilize TSK rules instead uses a simple inference which considering fuzzy and random information of data simultaneously. More importantly, in TLFRNN, concept drift can be detected and adapted easily and rapidly. The experiments demonstrate that TLFRNN achieves superior performance compared to other EFSs.
Yu, H, Lu, J, Liu, A, Wang, B, Li, R & Zhang, G 2022, 'Real-Time Prediction System of Train Carriage Load Based on Multi-Stream Fuzzy Learning', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 9, pp. 15155-15165.
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When a train leaves a platform, knowing the carriage load (the number of passengers in each carriage) of this train will support train managers to guide passengers at the next platform to choose carriages to avoid congestion. This capacity has become critical since the onset of the pandemic. However, with the dynamicity of passengers and the speed of trains improved (about 3 minutes travel between stations) as well as the station stop period reduced (60–90 second per station), the real-time prediction is more challenging. This paper presents an intelligent system, which is developed in collaboration with Sydney Trains, for real-time predicting carriage load across a city passenger train network. The system comprises three innovations. First, a fuzzy time-matching method significantly improves prediction accuracy in the uncertain situations and allows noisy historical data to be used for training. Second, the LightGBM model is extended with an incremental learning scheme to make forecasting in real-time possible. Third, a new multi-stream learning strategy that merges data streams with similar concept drift patterns is pioneered to increase the amount of suitable training data while reducing generalization errors. A comprehensive suite of practical tests on real-world datasets demonstrates the merit of these solutions.
Yu, H, Naidu, G, Zhang, C, Wang, C, Razmjou, A, Han, DS, He, T & Shon, H 2022, 'Metal-based adsorbents for lithium recovery from aqueous resources', Desalination, vol. 539, pp. 115951-115951.
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The continuous increase of demand for lithium (Li) chemicals in industrial applications calls for exploring affordable Li production and sustainable options beyond land mining. Thus, aqueous resources, such as geothermal brine, salt lake brine, and seawater, play an essential role in continuous Li supply due to abundant storage and low cost. Adsorption technology is promising in Li recovery with the advantages of attaining high selectivity for Li over other major ions present in aqueous resources at low cost and low energy demand with facile synthesis processes that enable practical large-scale production. Metal-based adsorbents are conspicuous among various adsorbents for presenting the visible prospect closest to industrial applications. This review presents a comprehensive summary and critical analysis of the synthesis methods for metal-based adsorbents, the mechanisms for Li selective recovery, and the performance of Li adsorption. The advantages and challenges are discussed for different adsorbents and preparation methods. A specific focused case study on an industrial application of Al-based adsorbent production and Li recovery processes and operations on an engineering and economic scale is discussed in detail to provide a comprehensive overview of the practical industrial application of metal-based adsorbent.
Yu, H, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2022, 'Maximizing the Geometric Mean of User-Rates to Improve Rate-Fairness: Proper vs. Improper Gaussian Signaling', IEEE Transactions on Wireless Communications, vol. 21, no. 1, pp. 295-309.
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This paper considers a reconfigurable intelligent surface (RIS)-aided network, which relies on a multiple antenna array aided base station (BS) and an RIS for serving multiple single antenna downlink users. To provide reliable links to all users over the same bandwidth and same time-slot, the paper proposes the joint design of linear transmit beamformers and the programmable reflecting coefficients of an RIS to maximize the geometric mean (GM) of the users' rates. A new computationally efficient alternating descent algorithm is developed, which is based on closed-forms only for generating improved feasible points of this nonconvex problem. We also consider the joint design of widely linear transmit beamformers and the programmable reflecting coefficients to further improve the GM of the users' rates. Hence another alternating descent algorithm is developed for its solution, which is also based on closed forms only for generating improved feasible points. Numerical examples are provided to demonstrate the efficiency of the proposed approach.
Yu, H, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2022, 'RIS-Aided Zero-Forcing and Regularized Zero-Forcing Beamforming in Integrated Information and Energy Delivery', IEEE Transactions on Wireless Communications, vol. 21, no. 7, pp. 5500-5513.
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This paper considers a network of a multi-antenna array base station (BS) and a reconfigurable intelligent surface (RIS) to deliver both information to information users (IUs) and power to energy users (EUs). The RIS links the connection between the IUs and the BS as there is no direct path between the former and the latter. The EUs are located nearby the BS in order to effectively harvest energy from the high-power signal from the BS, while the much weaker signal reflected from the RIS hardly contributes to the EUs' harvested energy. To provide reliable links for all users over the same time-slot, we adopt the transmit time-switching (transmit-TS) approach, under which information and energy are delivered over different time-slot fractions. This allows us to rely on conjugate beamforming for energy links and zero-forcing/regularized zero-forcing beamforming (ZFB/RZFB) and on the programmable reflecting coefficients (PRCs) of the RIS for information links. We show that ZFB/RZFB and PRCs can be still separately optimized in their joint design, where PRC optimization is based on iterative closed-form expressions. We then develop a path-following algorithm for solving the max-min IU throughput optimization problem subject to a realistic constraint on the quality-of-energy-service in terms of the EUs' harvested energy thresholds. We also propose a new RZFB for substantially improving the IUs' throughput.
Yu, H, Tuan, HD, Nasir, AA, Debbah, M & Fang, Y 2022, 'New generalized zero forcing beamforming for serving more users in energy-harvesting enabled networks', Physical Communication, vol. 50, pp. 101500-101500.
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Yu, H, Zhang, Q, Liu, T, Lu, J, Wen, Y & Zhang, G 2022, 'Meta-ADD: A meta-learning based pre-trained model for concept drift active detection', Information Sciences, vol. 608, pp. 996-1009.
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Concept drift is a phenomenon that commonly happened in data streams and need to be detected, because it means the statistical properties of a target variable, which the model is trying to predict, change over time in an unseen way. Most current detection methods are based on a hypothesis test framework. As a result, in these detection methods, a hypothesis test is need to be set, and more importantly, cannot obtain the type of drift. The setting of a hypothesis test requires an understanding of data streams, and cannot obtain the type of concept drift results in the loss of drift information. Hence, in this paper, to get rid of the setting of hypothesis test, and obtain the type of concept drift, we propose Active Drift Detection based on Meta learning (Meta-ADD), a novel framework that learns to classify concept drift by offline pre-training a model on data stream with known drifts, then online fine-tuning model to improve detection accuracy. Specifically, in the pre-trained phase, we extract meta-features based on the error rates of various concept drift, after which a pre-trained model called meta-detector is developed via a prototypical neural network by representing various concept drift classes as corresponding prototypes. In the detection phase, the meta-detector is fine-tuned to adapt to the real data stream via a simple stream-based active learning. Hence, Meta-ADD does not need a hypothesis test to detect concept drifts and identify their types automatically, which can directly support drift understand. The experiment results verify the effectiveness of Meta-ADD.
Yu, L, Li, Z, Xu, M, Gao, Y, Luo, J & Zhang, J 2022, 'Distribution-Aware Margin Calibration for Semantic Segmentation in Images', International Journal of Computer Vision, vol. 130, no. 1, pp. 95-110.
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Yu, L, Zhang, J & Wu, Q 2022, 'Dual Attention on Pyramid Feature Maps for Image Captioning', IEEE Transactions on Multimedia, vol. 24, no. 99, pp. 1775-1786.
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Yu, N & Zhou, L 2022, 'Comments on and Corrections to “When Is the Chernoff Exponent for Quantum Operations Finite?”', IEEE Transactions on Information Theory, vol. 68, no. 6, pp. 3989-3990.
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Yu, P, Ni, W, Liu, RP, Zhang, Z, Zhang, H & Wen, Q 2022, 'Efficient Encrypted Range Query on Cloud Platforms', ACM Transactions on Cyber-Physical Systems, vol. 6, no. 3, pp. 1-23.
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In the Internet of Things (IoT) era, various IoT devices are equipped with sensing capabilities and employed to support clinical applications. The massive electronic health records (EHRs) are expected to be stored in the cloud, where the data are usually encrypted, and the encrypted data can be used for disease diagnosis. There exist some numeric health indicators, such as blood pressure and heart rate. These numeric indicators can be classified into multiple ranges, and each range may represent an indication of normality or abnormity. Once receiving encrypted IoT data, the CS maps it to one of the ranges, achieving timely monitoring and diagnosis of health indicators. This article presents a new approach to identify the range that an encrypted numeric value corresponds to without exposing the explicit value. We establish the sufficient and necessary condition to convert a range query to matchings of encrypted binary sequences with the minimum number of matching operations. We further apply the minimization of range queries to design and implement a secure range query system, where numeric health indicators encrypted independently by multiple IoT devices can be cohesively stored and efficiently queried by using Lagrange polynomial interpolation. Comprehensive performance studies show that the proposed approach can protect both the health records and range query against untrusted cloud platforms and requires less computational and communication cost than existing techniques.
Yu, P, Ni, W, Zhang, H, Ping Liu, R, Wen, Q, Li, W & Gao, F 2022, 'Secure and Differentiated Fog-Assisted Data Access for Internet of Things', The Computer Journal, vol. 65, no. 8, pp. 1948-1963.
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Abstract The ability of Fog computing to admit and process huge volumes of heterogeneous data is the catalyst for the fast expansion of Internet of things (IoT). The critical challenge is secure and differentiated access to the data, given limited computation capability and trustworthiness in typical IoT devices and Fog servers, respectively. This paper designs and develops a new approach for secure, efficient and differentiated data access. Secret sharing is decoupled to allow the Fog servers to assist the IoT devices with attribute-based encryption of data while preventing the Fog servers from tampering with the data and the access structure. The proposed encryption supports direct revocation and can be decoupled among multiple Fog servers for acceleration. Based on the decisional $q$-parallel bilinear Diffie–Hellman exponent assumption, we propose a new extended $q$-parallel bilinear Diffie–Hellman exponent (E$q$-PBDHE) assumption and prove that the proposed approach provides ‘indistinguishably chosen-plaintext attacks secure’ data access for legitimate data subscribers. As numerically and experimentally verified, the proposed approach is able to reduce the encryption time by 20% at the IoT devices and by 50% at the Fog network using parallel computing as compared to the state of the art .
Yu, Q, Dong, D & Petersen, IR 2022, 'Hybrid Filtering for a Class of Nonlinear Quantum Systems Subject to Classical Stochastic Disturbances', IEEE Transactions on Cybernetics, vol. 52, no. 2, pp. 1073-1085.
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Yu, Q, Wang, Y, Dong, D, Petersen, IR & Xiang, G-Y 2022, 'Generation of Accessible Sets in the Dynamical Modeling of Quantum Network Systems', IEEE Transactions on Control of Network Systems, vol. 9, no. 2, pp. 682-694.
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Yu, Q, Yokoyama, S, Dong, D, McManus, D & Yonezawa, H 2022, 'Simultaneous Estimation of Parameters and the State of an Optical Parametric Oscillator System', IEEE Transactions on Quantum Engineering, vol. 3, pp. 1-9.
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Yu, X, Li, H, Zhang, JA, Huang, X & Cheng, Z 2022, 'Enhanced Angle-of-Arrival and Polarization Parameter Estimation Using Localized Hybrid Dual-Polarized Arrays', Sensors, vol. 22, no. 14, pp. 5207-5207.
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The millimeter wave (mmWave) channel is dominated by line-of-sight propagation. Therefore, the acquisition of angle-of-arrival (AoA) and polarization state of the wave is of great significance to the receiver. In this paper, we investigate AoA and polarization estimation in a mmWave system employing dual-polarized antenna arrays. We propose an enhanced AoA estimation method using a localized hybrid dual-polarized array for a polarized mmWave signal. The use of dual-polarized arrays greatly improves the calibration of differential signals and the signal-to-noise ratio (SNR) of the phase offset estimation between adjacent subarrays. Given the estimated phase offset, an initial AoA estimate can be obtained, and is then used to update the phase offset estimation. This leads to a recursive estimation with improved accuracy. We further propose an enhanced polarization estimation method, which uses the power of total received signals at dual-polarized antennas to compute the cross-correlation-to-power ratio instead of using only one axis dipole. Thus the accuracy of polarization parameter estimation is improved. We also derive a closed-form expression for mean square error lower bounds of AoA estimation and present an average SNR analysis for polarization estimation performance. Simulation results demonstrate the superiority of the enhanced AoA and polarization parameter estimation methods compared to the state of the art.
Yu, Y, Rashidi, M, Samali, B, Mohammadi, M, Nguyen, TN & Zhou, X 2022, 'Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm', Structural Health Monitoring, vol. 21, no. 5, pp. 2244-2263.
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With the rapid increase of ageing infrastructures worldwide, effective and robust inspection techniques are highly demanding to evaluate structural conditions and residual lifetime. The damages on structural surfaces, for example, spalling, crack, rebar buckling and exposure, are important indicators to assess the structural condition. In fact, several state-of-the-art automated inspection techniques using these indicators have been developed to reduce human-conducted onsite inspection activities. However, the efficiency of these techniques is still required to be improved in terms of accuracy and computational cost. In this study, a vision-based crack diagnosis method is developed using deep convolutional neural network (DCNN) and enhanced chicken swarm algorithm (ECSA). A DCNN model is designed with a deep architecture, consisting of six convolutional layers, two pooling layers and three fully connected layers. To enhance the generalisation capacity of trained model, ECSA is introduced to optimize meta-parameters of the DCNN model. The model is trained and tested using image patches cropped from raw images obtained from damaged concrete samples. Finally, a comparative study on different crack detection techniques is conducted to evaluate performance of the proposed method via a group of statistical evaluation indicators.
Yu, Y, Zhang, YX, Liu, A & Fu, J 2022, 'Performance decay analysis of cementitious composite cladding structure under stochastic aging', Engineering Structures, vol. 273, pp. 115064-115064.
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Yu, Z, Shi, X, Zhou, J, Gou, Y, Huo, X, Zhang, J & Armaghani, DJ 2022, 'A new multikernel relevance vector machine based on the HPSOGWO algorithm for predicting and controlling blast-induced ground vibration', Engineering with Computers, vol. 38, no. 2, pp. 1905-1920.
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Yuan, D, Chang, X, Li, Z & He, Z 2022, 'Learning Adaptive Spatial-Temporal Context-Aware Correlation Filters for UAV Tracking', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 18, no. 3, pp. 1-18.
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Tracking in the unmanned aerial vehicle (UAV) scenarios is one of the main components of target-tracking tasks. Different from the target-tracking task in the general scenarios, the target-tracking task in the UAV scenarios is very challenging because of factors such as small scale and aerial view. Although the discriminative correlation filter (DCF)-based tracker has achieved good results in tracking tasks in general scenarios, the boundary effect caused by the dense sampling method will reduce the tracking accuracy, especially in UAV-tracking scenarios. In this work, we propose learning an adaptive spatial-temporal context-aware (ASTCA) model in the DCF-based tracking framework to improve the tracking accuracy and reduce the influence of boundary effect, thereby enabling our tracker to more appropriately handle UAV-tracking tasks. Specifically, our ASTCA model can learn a spatial-temporal context weight, which can precisely distinguish the target and background in the UAV-tracking scenarios. Besides, considering the small target scale and the aerial view in UAV-tracking scenarios, our ASTCA model incorporates spatial context information within the DCF-based tracker, which could effectively alleviate background interference. Extensive experiments demonstrate that our ASTCA method performs favorably against state-of-the-art tracking methods on some standard UAV datasets.
Yuan, D, Shu, X, Fan, N, Chang, X, Liu, Q & He, Z 2022, 'Accurate bounding-box regression with distance-IoU loss for visual tracking', Journal of Visual Communication and Image Representation, vol. 83, pp. 103428-103428.
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Most existing trackers are based on using a classifier and multi-scale estimation to estimate the target state. Consequently, and as expected, trackers have become more stable while tracking accuracy has stagnated. While trackers adopt a maximum overlap method based on an intersection-over-union (IoU) loss to mitigate this problem, there are defects in the IoU loss itself, that make it impossible to continue to optimize the objective function when a given bounding box is completely contained within/without another bounding box; this makes it very challenging to accurately estimate the target state. Accordingly, in this paper, we address the above-mentioned problem by proposing a novel tracking method based on a distance-IoU (DIoU) loss, such that the proposed tracker consists of target estimation and target classification. The target estimation part is trained to predict the DIoU score between the target ground-truth bounding-box and the estimated bounding-box. The DIoU loss can maintain the advantage provided by the IoU loss while minimizing the distance between the center points of two bounding boxes, thereby making the target estimation more accurate. Moreover, we introduce a classification part that is trained online and optimized with a Conjugate-Gradient-based strategy to guarantee real-time tracking speed. Comprehensive experimental results demonstrate that the proposed method achieves competitive tracking accuracy when compared to state-of-the-art trackers while with a real-time tracking speed.
Yuan, L & Rizoiu, M-A 2022, 'Generalizing Hate Speech Detection Using Multi-Task Learning: A Case Study of Political Public Figures', Language, vol. 89, p. 101690.
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Automatic identification of hateful and abusive content is vital in combatingthe spread of harmful online content and its damaging effects. Most existingworks evaluate models by examining the generalization error on train-testsplits on hate speech datasets. These datasets often differ in theirdefinitions and labeling criteria, leading to poor generalization performancewhen predicting across new domains and datasets. This work proposes a newMulti-task Learning (MTL) pipeline that trains simultaneously across multiplehate speech datasets to construct a more encompassing classification model.Using a dataset-level leave-one-out evaluation (designating a dataset fortesting and jointly training on all others), we trial the MTL detection on new,previously unseen datasets. Our results consistently outperform a large sampleof existing work. We show strong results when examining the generalizationerror in train-test splits and substantial improvements when predicting onpreviously unseen datasets. Furthermore, we assemble a novel dataset, dubbedPubFigs, focusing on the problematic speech of American Public PoliticalFigures. We crowdsource-label using Amazon MTurk more than $20,000$ tweets andmachine-label problematic speech in all the $305,235$ tweets in PubFigs. Wefind that the abusive and hate tweeting mainly originates from right-leaningfigures and relates to six topics, including Islam, women, ethnicity, andimmigrants. We show that MTL builds embeddings that can simultaneously separateabusive from hate speech, and identify its topics.
Yuan, M, Cao, B, Liu, H, Meng, C, Wu, J, Zhang, S, Li, A, Chen, X & Song, H 2022, 'Sodium Storage Mechanism of Nongraphitic Carbons: A General Model and the Function of Accessible Closed Pores', Chemistry of Materials, vol. 34, no. 7, pp. 3489-3500.
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Yuan, P, Xu, S, Liu, J, Su, Y, Li, J, Qu, K, Liu, C & Wu, C 2022, 'Correction to: Experimental investigation of G-HPC-based sandwich walls incorporated with metallic tube core under contact explosion', Archives of Civil and Mechanical Engineering, vol. 22, no. 4.
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Yuan, P, Xu, S, Liu, J, Su, Y, Li, J, Qu, K, Liu, C & Wu, C 2022, 'Experimental investigation of G-HPC-based sandwich walls incorporated with metallic tube core under contact explosion', Archives of Civil and Mechanical Engineering, vol. 22, no. 4.
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A novel geopolymer-based high-performance concrete (G-HPC) sandwich wall consisting of two G-HPC layers separated by a metallic tube core possessing high strength and lightweight structure was developed in this study. The contact blast tests with 1 kg TNT were subsequently conducted to explore the blast resistance of the developed sandwich walls. For this purpose, three sandwich walls and a C40 reinforced concrete (RC) slab were employed. The superior blast resistance of the sandwich walls was verified based on the experimental results as compared to the RC slab. The sandwich wall with a circular steel tube core exhibited a superior blast resistance than the wall with a circular aluminum alloy tube core, whereas the sandwich wall with a rectangular steel tube core revealed the best performance. The blast resistance and damage mechanism of the sandwich walls were subsequently analyzed. The accuracy of the available empirical formulas was also examined for predicting the damage in the sandwich walls under contact explosion conditions.
Yuan, X, Seneviratne, JA, Du, S, Xu, Y, Chen, Y, Jin, Q, Jin, X, Balachandran, A, Huang, S, Xu, Y, Zhai, Y, Lu, L, Tang, M, Dong, Y, Cheung, BB, Marshall, GM, Shi, W, Carter, DR & Zhang, C 2022, 'Single-cell profiling of peripheral neuroblastic tumors identifies an aggressive transitional state that bridges an adrenergic-mesenchymal trajectory', Cell Reports, vol. 41, no. 1, pp. 111455-111455.
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Peripheral neuroblastic tumors (PNTs) represent a spectrum of neural-crest-derived tumors, including neuroblastoma, ganglioneuroblastoma, and ganglioneuroma. Malignant cells in PNTs are theorized to interconvert between adrenergic/noradrenergic and mesenchymal/neural crest cell states. Here, single-cell RNA-sequencing analysis of 10 PNTs demonstrates extensive transcriptomic heterogeneity. Trajectory modeling suggests that malignant neuroblasts move between adrenergic and mesenchymal cell states via an intermediate state that we term 'transitional.' Transitional cells express programs linked to a sympathoadrenal development and aggressive tumor phenotypes such as rapid proliferation and tumor dissemination. Among primary bulk tumor patient cohorts, high expression of the transitional gene signature is predictive of poor prognosis compared with adrenergic and mesenchymal expression patterns. High transitional gene expression in neuroblastoma cell lines identifies a similar transitional H3K27-acetylation super-enhancer landscape. Collectively, our study supports the concept that PNTs have phenotypic plasticity and uncovers potential biomarkers and therapeutic targets.
Yusoff, MNAM, Zulkifli, NWM, Sukiman, NL, Kalam, MA, Masjuki, HH, Syahir, AZ, Awang, MSN, Mujtaba, MA, Milano, J & Shamsuddin, AH 2022, 'Microwave irradiation-assisted transesterification of ternary oil mixture of waste cooking oil – Jatropha curcas – Palm oil: Optimization and characterization', Alexandria Engineering Journal, vol. 61, no. 12, pp. 9569-9582.
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Zahmatkesh, S, Ni, B-J, Klemeš, JJ, Bokhari, A & Hajiaghaei-Keshteli, M 2022, 'Carbon quantum dots-Ag nanoparticle membrane for preventing emerging contaminants in oil produced water', Journal of Water Process Engineering, vol. 50, pp. 103309-103309.
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A polycarbonate copolymer was used to create a novel low-cost microfiltration membrane. Due to their antibacterial properties, carbon quantum dots and silver nanoparticles (CD-Ag NPs) have been synthesized and incorporated into a poly(acrylonitrile-styrene) membrane by the Creighton method. This work examined the membranes using Infrared Vibrational Spectroscopy, Ultraviolet-Visible Spectroscopy, and a Scanning Electron Microscope (SEM). An investigation was conducted to determine the effects of the CDs-Ag NPs amount on the matrix's morphology, pore size, porosity, permeability, and mechanical strength. Also, photobleaching was used to reduce and stabilize CDs-Ag NPs, as reflected by a red shift in the spectra for CDs compared to CDs-Ag NPs from 290 nm to 570 nm in the UV/Vis spectrum, indicating the nanoparticle was generated. Pore size and mechanical strength are reduced when CDs-Ag NPs are added to neat membranes. However, it is followed by a decrease in porosity and mechanical strength. The optimized membrane exhibited a 0.66 pore size. In addition to removing oils and metals from wastewater, they also remove dyes, antibiotics, and other organic and inorganic compounds. The colony forming unit (CFU) test also showed that the percentage of CFU decreased as AgNO3 concentration increased so that at a concentration of 4, the percentage of CFU for E. coli and S. aureus was 5 % and 1.9 % for S. aureus. The Creighton methodology was therefore proven suitable for functionalizing the membrane, and discs-Ag NPs membranes have been demonstrated to be promising wastewater treatment membranes.
Zainal, BS, Gunasegaran, K, Tan, GYA, Danaee, M, Mohd, NS, Ibrahim, S, Chyuan, OH, Nghiem, LD & Mahlia, TMI 2022, 'Effect of temperature and hydraulic retention time on hydrogen production from palm oil mill effluent (POME) in an integrated up-flow anaerobic sludge fixed-film (UASFF) bioreactor', Environmental Technology & Innovation, vol. 28, pp. 102903-102903.
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Zamani, H, Nadimi-Shahraki, MH & Gandomi, AH 2022, 'Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization', Computer Methods in Applied Mechanics and Engineering, vol. 392, pp. 114616-114616.
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This paper presents a novel bio-inspired algorithm inspired by starlings’ behaviors during their stunning murmuration named starling murmuration optimizer (SMO) to solve complex and engineering optimization problems as the most appropriate application of metaheuristic algorithms. The SMO introduces a dynamic multi-flock construction and three new search strategies, separating, diving, and whirling. The separating search strategy aims to enhance the population diversity and local optima avoidance using a new separating operator based on the quantum harmonic oscillator. The diving search strategy aims to explore the search space sufficiently by a new quantum random dive operator, whereas the whirling search strategy exploits the vicinity of promising regions using a new operator called cohesion force. The SMO strikes a balance between exploration and exploitation by selecting either a diving strategy or a whirling strategy based on the flocks’ quality. The SMO was tested using various benchmark functions with dimensions 30, 50, 100. The experimental results prove that the SMO is more competitive than other state-of-the-art algorithms regarding solution quality and convergence rate. Then, the SMO is applied to solve several mechanical engineering problems in which results demonstrate that it can provide more accurate solutions. A statistical analysis shows that SMO is superior to the other contenders.
Zamee, MA, Han, D & Won, D 2022, 'Online Hour-Ahead Load Forecasting Using Appropriate Time-Delay Neural Network Based on Multiple Correlation–Multicollinearity Analysis in IoT Energy Network', IEEE Internet of Things Journal, vol. 9, no. 14, pp. 12041-12055.
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Zamri, MFMA, Milano, J, Shamsuddin, AH, Roslan, MEM, Salleh, SF, Rahman, AA, Bahru, R, Fattah, IMR & Mahlia, TMI 2022, 'An overview of palm oil biomass for power generation sector decarbonization in Malaysia: Progress, challenges, and prospects', WIREs Energy and Environment, vol. 11, no. 4.
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AbstractWith the ever‐increasing danger of climate change, power plants are shifting from polluting fossil fuels to sustainable bioenergy fuels. As Malaysia continues to pledge to decrease glasshouse gas (GHG) emissions, quick and dramatic action should resolve the reliance on fossil fuel power plants. Furthermore, the coal‐fired power station is Malaysia's biggest supplier of energy and the final power plant to be decommissioned. In Malaysia, a significant portion of palm oil biomass has the potential to replace coal in the generation of renewable energy power. However, the deployment of palm oil biomass as a renewable energy source has not been fully achieved. Furthermore, the surplus of unutilized biomass from the palm oil milling process has emerged as the key talking point leading to environmental concerns. As estimated, this palm oil biomass can generate approximately 5000 MW of electricity under 40% of operation efficiency. This significant power potential has the ability to replace Malaysia's yearly reliance on coal. Nonetheless, the limitations of technological stability, budgetary constraints, and other government policy concerns have prevented the potentials from being fulfilled. This necessitates an integrated framework that synergizes the decarbonization drive in order to realize the primary advantages of energy renewability and carbon neutrality. Among the suggested actions to decarbonize the power generating sector is an integrated scheme of palm oil production, biogas plant for electricity and steam generation, and biofuel pellet manufacture. This review provides an in‐depth overview of palm oil biomass for Malaysian power production decarbonization.This article is categorized under:Sustainable Energy > BioenergyClimate and Environment > Net Zero Planning and Decarbonization
Zandavi, SM, Koch, FC, Vijayan, A, Zanini, F, Mora, FV, Ortega, DG & Vafaee, F 2022, 'Disentangling single-cell omics representation with a power spectral density-based feature extraction', Nucleic Acids Research, vol. 50, no. 10, pp. 5482-5492.
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Abstract Emerging single-cell technologies provide high-resolution measurements of distinct cellular modalities opening new avenues for generating detailed cellular atlases of many and diverse tissues. The high dimensionality, sparsity, and inaccuracy of single cell sequencing measurements, however, can obscure discriminatory information, mask cellular subtype variations and complicate downstream analyses which can limit our understanding of cell function and tissue heterogeneity. Here, we present a novel pre-processing method (scPSD) inspired by power spectral density analysis that enhances the accuracy for cell subtype separation from large-scale single-cell omics data. We comprehensively benchmarked our method on a wide range of single-cell RNA-sequencing datasets and showed that scPSD pre-processing, while being fast and scalable, significantly reduces data complexity, enhances cell-type separation, and enables rare cell identification. Additionally, we applied scPSD to transcriptomics and chromatin accessibility cell atlases and demonstrated its capacity to discriminate over 100 cell types across the whole organism and across different modalities of single-cell omics data.
Zangoei, A, Monjezi, M, Armaghani, DJ, Mehrdanesh, A & Ahmadian, S 2022, 'Prediction and optimization of flyrock and oversize boulder induced by mine blasting using artificial intelligence techniques', Environmental Earth Sciences, vol. 81, no. 13.
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Zare, M, Nikoo, MR, Nematollahi, B, Gandomi, AH, Al-Wardy, M & Al-Rawas, GA 2022, 'Progressive improvement of DRASTICA and SI models for groundwater vulnerability assessment based on evolutionary algorithms', Environmental Science and Pollution Research, vol. 29, no. 37, pp. 55845-55865.
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Groundwater management is essential in water and environmental engineering from both quantity and quality aspects due to the growing urban population. Groundwater vulnerability evaluation models play a prominent role in groundwater resource management, such as the DRASTIC model that has been used successfully in numerous areas. Several studies have focused on improving this model by changing the initial parameters or the rates and weights. The presented study investigated results produced by the DRASTIC model by simultaneously exerting both modifications. For this purpose, two land use-based DRASTIC-derived models, DRASTICA and susceptibility index (SI), were implemented in the Shiraz plain, Iran, a semi-arid region and the primary resource of groundwater currently struggling with groundwater pollution. To develop the novel proposed framework for the progressive improvement of the mentioned rating-based techniques, three main calculation steps for rates and weights are presented: (1) original rates and weights; (2) modified rates by Wilcoxon tests and original weights; and (3) adjusted rates and optimized weights using the genetic algorithm (GA) and particle swarm optimization (PSO) algorithms. To validate the results of this framework applied to the case study, the concentrations of three contamination pollutants, NO3, SO4, and toxic metals, were considered. The results indicated that the DRASTICA model yielded more accurate contamination concentrations for vulnerability evaluations than the SI model. Moreover, both models initially displayed well-matched results for the SO4 concentrations, specifically 0.7 for DRASTICA and 0.58 for SI, respectively. Comparatively, the DRASTICA model showed a higher correlation with NO3 concentrations (0.8) than the SI model (0.6) through improved steps. Furthermore, although both original models demonstrated less correlation with toxic metal concentrations (0.05) compared to SO4 and NO3 concentrations, the DRASTICA an...
Zdarta, J, Jesionowski, T, Pinelo, M, Meyer, AS, Iqbal, HMN, Bilal, M, Nguyen, LN & Nghiem, LD 2022, 'Free and immobilized biocatalysts for removing micropollutants from water and wastewater: Recent progress and challenges', Bioresource Technology, vol. 344, no. Pt B, pp. 126201-126201.
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Enzymatic conversion of micropollutants into less-toxic derivatives is an important bioremediation strategy. This paper aims to critically review the progress in water and wastewater treatment by both free and immobilized enzymes presenting this approach as highly efficient and performed under environmentally benign and friendly conditions. The review also summarises the effects of inorganic and organic wastewater matrix constituents on enzymatic activity and degradation efficiency of micropollutants. Finally, application of enzymatic reactors facilitate continuous treatment of wastewater and obtaining of pure final effluents. Of a particular note, enzymatic treatment of micropollutants from wastewater has been mostly reported by laboratory scale studies. Thus, this review also highlights key research gaps of the existing techniques and provides future perspectives to facilitate the transfer of the lab-scale solutions to a larger scale and to improve operationability of biodegradation processes.
Zdarta, J, Nguyen, LN, Jankowska, K, Jesionowski, T & Nghiem, LD 2022, 'A contemporary review of enzymatic applications in the remediation of emerging estrogenic compounds', Critical Reviews in Environmental Science and Technology, vol. 52, no. 15, pp. 2661-2690.
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Zeng, J, Mohammed, AS, Mirzaei, F, Moosavi, SMH, Armaghani, DJ & Samui, P 2022, 'A parametric study of ground vibration induced by quarry blasting: an application of group method of data handling', Environmental Earth Sciences, vol. 81, no. 4.
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Zeng, J, Roy, B, Kumar, D, Mohammed, AS, Armaghani, DJ, Zhou, J & Mohamad, ET 2022, 'Proposing several hybrid PSO-extreme learning machine techniques to predict TBM performance', Engineering with Computers, vol. 38, no. S5, pp. 3811-3827.
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Zeng, J, Xu, Q, Fan, X, Ye, N, Ni, W & Guo, YJ 2022, 'Achieving URLLC by MU-MIMO With Imperfect CSI: Under κ–μ Shadowed Fading', IEEE Wireless Communications Letters, vol. 11, no. 12, pp. 2560-2564.
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Zeng, S, Zhou, J, Zhang, C & Merigó, JM 2022, 'Intuitionistic fuzzy social network hybrid MCDM model for an assessment of digital reforms of manufacturing industry in China', Technological Forecasting and Social Change, vol. 176, pp. 121435-121435.
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Digital reform requires enterprises to use digital technology to create a deep integration between their production, management, and operational processes, and generate a data chain for the entire process, thereby meeting the personalized requirements and expectations of customers. The achievements of digital reform in manufacturing enterprises need to be evaluated scientifically, which can help the enterprises adjust their development strategies for a digital reform in a timely manner. We therefore propose a multi-criteria model based on a social network for assessing a digital reform under an intuitionistic fuzzy environment, wherein an intuitionistic fuzzy hybrid average and geometric operator is proposed to aggregate evaluation information more effectively than with existing methods. In addition, because the trust relationships between experts can affect their decisions, a social network is introduced to determine the weights assigned to these experts. Finally, a case study of four manufacturing enterprises is presented to verify the effectiveness of the proposed method.
Zhai, C, Yu, Y, Zhu, Y, Zhang, J, Zhong, Y, Yeo, J & Wang, M 2022, 'The Impact of Foaming Effect on the Physical and Mechanical Properties of Foam Glasses with Molecular-Level Insights', Molecules, vol. 27, no. 3, pp. 876-876.
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Foaming effect strongly impacts the physical and mechanical properties of foam glass materials, but an understanding of its mechanism especially at the molecular level is still limited. In this study, the foaming effects of dextrin, a mixture of dextrin and carbon, and different carbon allotropes are investigated with respect to surface morphology as well as physical and mechanical properties, in which 1 wt.% carbon black is identified as an optimal choice for a well-balanced material property. More importantly, the different foaming effects are elucidated by all-atomistic molecular dynamics simulations with molecular-level insights into the structure–property relationships. The results show that smaller pores and more uniform pore structure benefit the mechanical properties of the foam glass samples. The foam glass samples show excellent chemical and thermal stability with 1 wt.% carbon as the foaming agent. Furthermore, the foaming effects of CaSO4 and Na2HPO4 are investigated, which both create more uniform pore structures. This work may inspire more systematic approaches to control foaming effect for customized engineering needs by establishing molecular-level structure–property–process relationships, thereby, leading to efficient production of foam glass materials with desired foaming effects.
Zhai, C, Zhong, Y, Zhang, J, Wang, M, Yu, Y & Zhu, Y 2022, 'Enhancing the foaming effects and mechanical strength of foam glasses sintered at low temperatures', Journal of Physics and Chemistry of Solids, vol. 165, pp. 110698-110698.
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Effectively promoting the foaming effects and precisely controlling the pore structures remain challenging owing to insufficient understanding of the molecular mechanism. Herein, we first investigate the effects of two foaming promoters, MnO2 and CaCO3, to the pore structures and physical and mechanical properties of the foam glasses, where an optimal amount of 3 wt% MnO2 and 1 wt% CaCO3 are determined, which yield a balanced combination of light weight and high mechanical strength. More importantly, the molecular mechanism of the effects of CaCO3 is elucidated by molecular simulations. In addition, the effects of foaming intensifier ZnO are explored, where the mechanical strength is generally enhanced by adding ZnO. Aiming at a precise control of the pore structures and desirable material behaviors with certain additions, this work should inspire systematic approaches to controlling the foaming effects by bottom-up material design for diversified engineering needs without the conventional trial-and-error approaches.
Zhang, B, Yao, R, Fang, J, Ma, R, Pang, T & Zhou, D 2022, 'Energy absorption behaviors and optimization design of thin-walled double-hat beam under bending', Thin-Walled Structures, vol. 179, pp. 109577-109577.
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Zhang, C, Cui, L, Yu, S & Yu, JJQ 2022, 'A Communication-Efficient Federated Learning Scheme for IoT-Based Traffic Forecasting', IEEE Internet of Things Journal, vol. 9, no. 14, pp. 11918-11931.
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Federated Learning (FL) is widely adopted in traffic forecasting tasks involving large-scale IoT-enabled sensor data since its decentralization nature enables data providers’ privacy to be preserved. When employing state-of-the-art deep learning-based traffic predictors in FL systems, the existing FL frameworks confront overlarge communication overhead when transmitting these models’ parameter updates since the modelling depth and breadth renders them incorporating enormous number of parameters. In this paper, we propose a practical FL scheme, namely, Clustering-based hierarchical and Two-step-optimized FL (CTFed), to tackle this issue. The proposed scheme follows a divide et impera strategy that clusters the clients into multiple groups based on the similarity between their local models’ parameters. We integrate the particle swarm optimization algorithm and devises a two-step approach for local model optimization. This scheme enables only one but representative local model update from each cluster to be uploaded to the central server, thus reduces the communication overhead of the model updates transmission in FL. CTFed is orthogonal to the gradient compression-or sparsification-based approaches so that they can orchestrate to optimize the communication overhead. Extensive case studies on three real-world datasets and three state-of-the-art models demonstrate the outstanding training efficiency, accurate prediction performance and robustness to unstable network environments of the proposed scheme.
Zhang, C, Meng, G, Xu, RYD, Xiang, S & Pan, C 2022, 'Learning adversarial point-wise domain alignment for stereo matching', Neurocomputing, vol. 491, pp. 564-574.
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Zhang, C, Wang, Z & Wang, X 2022, 'Machine learning-based data-driven robust optimization approach under uncertainty', Journal of Process Control, vol. 115, pp. 1-11.
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Zhang, C, Yan, J, Ji, T, Du, D, Sun, Y, Liu, L, Zhang, X & Liu, Y 2022, 'Fabrication of highly (110)-Oriented ZIF-8 membrane at low temperature using nanosheet seed layer', Journal of Membrane Science, vol. 641, pp. 119915-119915.
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Zhang, C, Zhu, Y, Markos, C, Yu, S & Yu, JJQ 2022, 'Toward Crowdsourced Transportation Mode Identification: A Semisupervised Federated Learning Approach', IEEE Internet of Things Journal, vol. 9, no. 14, pp. 11868-11882.
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Privacy-preserving Transportation Mode Identification (TMI) is among the key challenges towards future intelligent transportation systems. With recent developments in federated learning (FL), crowdsourcing has emerged as a promising cost-effective data source for training powerful TMI classifiers without compromising users’ data privacy. However, existing TMI approaches have relied heavily on the availability of transportation mode labels, which is often limited in real-world applications. While recent semi-supervised studies have partially addressed this issue by assigning pseudo-labels to unlabeled data, such practice often degrades classification performance as more unlabeled data are incorporated. In response to this issue, we present a semi-supervised FL scheme for TMI termed Mean Teacher Semi-Supervised Federated Learning (MTSSFL). MTSSFL trains a deep neural network ensemble under a novel semi-supervised FL framework, achieving highly accurate and privacy-protected crowdsourced TMI without depending on the availability of massive labeled data. MTSSFL introduces consistency-updating to insert the global model in the gradient updates of the local models that only have unlabeled data to improve their training. We also devise mean-teacher-averaging, a secure parameter aggregation mechanism that further boosts the global model’s TMI performance without requiring additional training. Our extensive case studies on a real-world dataset demonstrate that MTSSFL’s classification accuracy is merely 1.1% lower than the state-of-the-art semi-supervised TMI approach while being the only one to satisfy FL’s privacy-preserving constraints. In addition, MTSSFL can achieve high accuracy with less training overhead due to the proposed semi-supervised learning design.
Zhang, D, Sun, Y, Wang, S, Zou, Y, Zheng, M & Shi, B 2022, 'Brain‐Targeting Metastatic Tumor Cell Membrane Cloaked Biomimetic Nanomedicines Mediate Potent Chemodynamic and RNAi Combinational Therapy of Glioblastoma', Advanced Functional Materials, vol. 32, no. 51.
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AbstractThe complex genetic heterogeneity of glioblastoma (GBM) together with the existence of blood brain barrier (BBB) renders GBM essentially incurable. In this paper, a brain‐targeting metastatic tumor cell membrane cloaked biomimetic nanomedicine that potently induces cascade chemodynamic and RNA interference (RNAi) is developed for combinational GBM therapy. The designed nanomedicines are constructed from metastatic melanoma cell membrane, charge‐conversional middle layer, and siRNA complexed polyethyleneimine xanthate (PEX). PEX chelates the abundant copper ions in tumor cells and then reacts with intracellular glutathione (GSH), resulting in GSH depletion and the reduction of Cu2+ to Cu+, followed by reacting with H2O2 and producing •OH via Fenton‐like reaction. Interestingly, the acidic conditions in endo/lysosomes accelerate siRNA release, inducing potent gene silencing of the anti‐apoptotic B‐cell lymphoma 2 (Bcl‐2), lead to multiple molecular cascades that readily induces GBM cell apoptosis. The results also demonstrate that this nanomedicine exhibits efficient BBB penetration via decreasing the tightness and interacting with the adhesion molecules in endothelial cells. More importantly, improved therapeutic efficacy with extended survival rates are achieved in both local melanoma and orthotopic GBM tumor models, suggesting that the chemodynamic and gene combinatorial therapy mediated by the biomimetic nanomedicines shows promising potential in treating GBM and brain metastases.
Zhang, F, Zhang, X, Li, Z, Yi, R, Li, Z, Wang, N, Xu, X, Azimi, Z, Li, L, Lysevych, M, Gan, X, Lu, Y, Tan, HH, Jagadish, C & Fu, L 2022, 'A New Strategy for Selective Area Growth of Highly Uniform InGaAs/InP Multiple Quantum Well Nanowire Arrays for Optoelectronic Device Applications', Advanced Functional Materials, vol. 32, no. 3, pp. 2103057-2103057.
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AbstractIII‐V semiconductor nanowires with quantum wells (QWs) are promising for ultra‐compact light sources and photodetectors from visible to infrared spectral region. However, most of the reported InGaAs/InP QW nanowires are based on the wurtzite phase and exhibit non‐uniform morphology due to the complex heterostructure growth, making it challenging to incorporate multiple‐QWs (MQW) for optoelectronic applications. Here, a new strategy for the growth of InGaAs/InP MQW nanowire arrays by selective area metalorganic vapor phase epitaxy is reported. It is revealed that {110} faceted InP nanowires with mixed zincblende and wurtzite phases can be achieved, forming a critical base for the subsequent growth of highly‐uniform, taper‐free, hexagonal‐shaped MQW nanowire arrays with excellent optical properties. Room‐temperature lasing at the wavelength of ≈1 µm under optical pumping is achieved with a low threshold. By incorporating dopants to form an n+‐i‐n+ structure, InGaAs/InP 40‐QW nanowire array photodetectors are demonstrated with the broadband response (400–1600 nm) and high responsivities of 2175 A W−1 at 980 nm outperforming those of conventional planar InGaAs photodetectors. The results show that the new growth strategy is highly feasible to achieve high‐quality InGaAs/InP MQW nanowires for the development of future optoelectronic devices and integrated photonic systems.
Zhang, G, Chen, J, Zhang, Z, Sun, M, Yu, Y, Wang, J & Cai, S 2022, 'Analysis of magnetorheological clutch with double cup-shaped gap excited by Halbach array based on finite element method and experiment', Smart Materials and Structures, vol. 31, no. 7, pp. 075008-075008.
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Abstract This work describes the magnetic analysis of an innovative double cup-shaped gap magnetorheological (MR) clutch featuring with three smart MR gels. Four kinds of Halbach array is used to excite the MR gel. The apparatus is designed by using a magneto/mechanical finite element method model, which is numerical calculated by COMSOL Multiphysics software. After describing the configuration, the transmittable torque in the designed MR clutch is derived based on the Bingham-Plastic field-dependent constitutive model of the MR gel. Considering the viscosity in the model building, such as the shear yield stress, which also various with change of magnetic flux density. The magnetic flux density distribution, the shear yield stress distribution, the dynamic viscosity distribution and the shear stress distribution inside the MR gel are obtained and carefully studied. Furthermore, the chain layer of internal cylindrical part, external cylindrical part, internal disc part and external disc part with lowest shear stress are found to calculate the transmission torque and slip torque. Then, the structure of the prototype is optimized based on multi-physics analysis. Finally, the optimal MR clutch is developed and the magneto-static torque is tested with detail analysis.
Zhang, G, Liu, B, Zhu, T, Zhou, A & Zhou, W 2022, 'Visual privacy attacks and defenses in deep learning: a survey', Artificial Intelligence Review, vol. 55, no. 6, pp. 4347-4401.
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Zhang, G, Zhang, Z, Sun, M, Yu, Y, Wang, J & Cai, S 2022, 'The Influence of the Temperature on the Dynamic Behaviors of Magnetorheological Gel', Advanced Engineering Materials, vol. 24, no. 9, pp. 2101680-2101680.
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Magnetorheological (MR) gel is a new generation of MR material, which can overcome disadvantages attaching MR fluid such as sedimentation. MR gel is formed by the soft magnetic particle suspension in the gel‐like matrix and its rheological properties are mainly controlled by the following two factors, i.e., magnetic field and temperature. Herein, MR gel based on polyurethane gel with a 60% weight fraction of carbonyl iron is prepared. The influence of temperature on the rheological characteristics of the MR gel sample is systematically investigated. Oscillatory shear test is used to investigate the impact of temperature on the modulus of the sample. It is observed that temperature has a significant effect on the viscoelastic properties of MR gel. The hysteresis responses activated by harmonic strain signal with frequencies of 0.1, 5, and 15 Hz and amplitude of 10% and 50% under five temperature levels are measured and employed to analyze the viscous and elastic properties of MR gel and the viscoelastic–plastic model is employed to capture the nonlinear hysteresis characteristics. From the analysis results, the viscoelastic–plastic model can predict the hysteresis properties of MR gel accuracy under various temperatures.
Zhang, H, An, N & Zhu, X 2022, 'Structural dynamic reliability analysis of super large-scale lattice domes during earthquakes using the stochastic finite element method', Soil Dynamics and Earthquake Engineering, vol. 153, pp. 107076-107076.
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For large-scale dome structures subjected to earthquake ground motions, the need for accurate and efficient approaches that account for uncertainties in design, material properties, loads, damping, and manufacturing processes has grown significantly. In uncertainty analyses, the theory of probability, uncertainty quantification, and reliability analysis approaches are commonly used. However, because of computing efficiency and capability, the space scale of dome structures is severely limited. As a result, the computational issues of the complex dynamic reliability that super-large-scale dome structures encounter during earthquakes are challenging. The uncertainties of the dynamic demands of a super-large-scale lattice dome with multiple variables are quantified using a stochastic finite element method (SFEM) with nonlinear time-history analysis developed in this paper, and this method can consider the randomness of variables in more detail for a structure. The dynamic reliability is investigated in depth using the efficient Latin hypercube sampling (LHS) technique to reduce the number of repetitive time-consuming calculations. To improve the efficiency and accuracy of the analysis, an optimization approach based on the genetic algorithm (GA) is used to obtain the probability of structural failure and the reliability index. The results show that these methods are highly efficient for super-large-scale structures. Finally, a sensitivity analysis based on variable randomness is conducted to further evaluate the effects of variables on structural performance using various evaluation methods. This paper provides an efficient solution for structural dynamic reliability problems of super-large-scale structures in the SFEM framework.
Zhang, H, Nguyen, H, Bui, X-N, Pradhan, B, Asteris, PG, Costache, R & Aryal, J 2022, 'A generalized artificial intelligence model for estimating the friction angle of clays in evaluating slope stability using a deep neural network and Harris Hawks optimization algorithm', Engineering with Computers, vol. 38, no. S5, pp. 3901-3914.
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In landslide susceptibility mapping or evaluating slope stability, the shear strength parameters of rocks and soils and their effectiveness are undeniable. However, they have not been studied for all-natural materials, as well as different locations. Therefore, this paper proposes a novel generalized artificial intelligence model for estimating the friction angle of clays from different areas/locations for evaluating slope stability or landslide susceptibility mapping, including the datasets from the UK, New Zealand, Indonesia, Venezuela, USA, Japan, and Italy. The robustness and consistency of the model’s prediction were checked by testing with various datasets having different geological and geomorphological setups. Accordingly, 162 observations from different areas/locations were collected from the locations and regions above for this aim. Subsequently, deep learning techniques were applied to develop the multiple layer perceptron (MLP) neural network model (i.e., DMLP model) with the goal of error reduction of the MLP model. Next, Harris Hawks optimization (HHO) algorithm was applied to boost the optimization of the DMLP model for predicting friction angle of clays aiming to get a better accuracy than those of the DMLP model, called HHO–DMLP model. A DMLP neural network without optimization of the HHO algorithm and two other conventional models (i.e., SVM and RF) were also employed to compare with the proposed HHO–DMLP model. The results showed that the proposed HHO–DMLP model predicted the friction angle of clays better than those of the other models. It can reflect the friction angle of clays with acceptable accuracy from different locations and regions (i.e., MSE = 12.042; RMSE = 3.470; R2 = 0.796; MAPE = 0.182; and VAF = 78.806). The DMLP model without optimization of the HHO algorithm provided slightly lower accuracy (i.e., MSE = 15.151; RMSE = 3.892; R2 = 0.738; MAPE = 0.202; and VAF = 73.431). Besides, two other conventional models (i.e., SVM and RF) p...
Zhang, H, Zhang, Y, Zhu, X, Wang, H & Song, Y 2022, 'Time-dependent performance of large-scale dome structures subjected to earthquakes using a machine learning-based evaluation method', Engineering Structures, vol. 273, pp. 115065-115065.
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Zhang, J, Chen, Z, Liu, Y, Wei, W & Ni, B-J 2022, 'Phosphorus recovery from wastewater and sewage sludge as vivianite', Journal of Cleaner Production, vol. 370, pp. 133439-133439.
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Developing feasible techniques to recover phosphorous (P) from wastewater and sewage sludge is urgent work to meet the huge demand for P resources and wastewater/sludge management. Recently, recovering P as value-added vivianite (Fe3(PO4)2·8H2O) has aroused great interest due to the convenient process operation, high recovery efficiency, and wide applications of vivianite. In this review, qualitative and quantitative detection methods of vivianite have been first analyzed, providing reference to test vivianite content and purity. Advanced technologies for P enrichment and vivianite production, including ion exchange, electrodialysis, capacitive de-ionization, membrane bioreactor, anaerobic fermentation, chemical precipitation, electrochemical crystallization, biomineralization, and anaerobic digestion are then comprehensively summarized. Mechanisms and representative applications of each technique are emphasized. Also, critical parameters (e.g., pH, Fe/P molar ratio, sulfate concentration, microorganisms, supersaturation index, organic matters, and seed crystals) affecting vivianite formation have been systematically analyzed. Furthermore, perspectives regarding fast detection of vivianite, strategies optimization, seed application, and vivianite commercialization have been proposed to guide the development of next-generation techniques for P recovery. This review is expected to provide fundamental insights into cleaner technologies synchronous recovering waste P and producing valued vivianite, as well as to stimulate future studies on circular economy-driven wastewater management.
Zhang, J, Cui, Q, Zhang, X, Ni, W, Lyu, X, Pan, M & Tao, X 2022, 'Online Optimization of Energy-Efficient User Association and Workload Offloading for Mobile Edge Computing', IEEE Transactions on Vehicular Technology, vol. 71, no. 2, pp. 1974-1988.
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Zhang, J, Hanjalic, A, Jain, R, Hua, X, Satoh, S, Yao, Y & Zeng, D 2022, 'Guest Editorial: Learning From Noisy Multimedia Data', IEEE Transactions on Multimedia, vol. 24, pp. 1247-1252.
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Zhang, J, Li, W, Yuan, L, Qin, L, Zhang, Y & Chang, L 2022, 'Shortest-Path Queries on Complex Networks: Experiments, Analyses, and Improvement.', Proc. VLDB Endow., vol. 15, no. 11, pp. 2640-2652.
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The shortest-path query, which returns the shortest path between two vertices, is a basic operation on complex networks and has numerous applications. To handle shortest-path queries, one option is to use traversal-based methods (e.g., breadth-first search); another option is to use extension-based methods, i.e., extending existing methods that use indexes to handle shortest-distance queries to support shortest-path queries. These two types of methods make different trade-offs in query time and space cost, but comprehensive studies of their performance on real-world graphs are lacking. Moreover, extension-based methods usually use extra attributes to extend the indexes, resulting in high space costs. To address these issues, we thoroughly compare the two types of methods mentioned above. We also propose a new extension-based approach, Monotonic Landmark Labeling (MLL), to reduce the required space cost while still guaranteeing query time. We compare the performance of different methods on ten large real-world graphs with up to 5.5 billion edges. The experimental results reveal the characteristics of various methods, allowing practitioners to select the appropriate method for a specific application.
Zhang, J, Lu, Y, Yang, Z, Zhu, X, Zheng, T, Liu, X, Tian, Y & Li, W 2022, 'Recognition of void defects in airport runways using ground-penetrating radar and shallow CNN', Automation in Construction, vol. 138, pp. 104260-104260.
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Zhang, J, Qin, X, Xiao, Y, Fei, R, Zang, Q, Xu, S, Bo, L, Li, H, Zhang, H & Zhong, Z 2022, 'Subspace cross representation measure for robust face recognition with few samples', Computers and Electrical Engineering, vol. 102, pp. 108162-108162.
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Zhang, J, Zhu, S, Bao, G, Liu, X & Wen, S 2022, 'Analysis and Design of Multivalued High-Capacity Associative Memories Based on Delayed Recurrent Neural Networks', IEEE Transactions on Cybernetics, vol. 52, no. 12, pp. 12989-13000.
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This article aims at analyzing and designing the multivalued high-capacity-associative memories based on recurrent neural networks with both asynchronous and distributed delays. In order to increase storage capacities, multivalued activation functions are introduced into associative memories. The stored patterns are retrieved by external input vectors instead of initial conditions, which can guarantee accurate associative memories by avoiding spurious equilibrium points. Some sufficient conditions are proposed to ensure the existence, uniqueness, and global exponential stability of the equilibrium point of neural networks with mixed delays. For neural networks with n neurons, m-dimensional input vectors, and 2k-valued activation functions, the autoassociative memories have (2k)n storage capacities and heteroassociative memories have min storage capacities. That is, the storage capacities of designed associative memories in this article are obviously higher than the 2n and min storage capacities of the conventional ones. Three examples are given to support the theoretical results.
Zhang, JA, Rahman, ML, Wu, K, Huang, X, Guo, YJ, Chen, S & Yuan, J 2022, 'Enabling Joint Communication and Radar Sensing in Mobile Networks—A Survey', IEEE Communications Surveys & Tutorials, vol. 24, no. 1, pp. 306-345.
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Mobile network is evolving from a communication-only network towards one with joint communication and radar/radio sensing (JCAS) capabilities, that we call perceptive mobile network (PMN). Radio sensing here refers to information retrieval from received mobile signals for objects of interest in the environment surrounding the radio transceivers, and it may go beyond the functions of localization, tracking, and object recognition of traditional radar. In PMNs, JCAS integrates sensing into communications, sharing a majority of system modules and the same transmitted signals. The PMN is expected to provide a ubiquitous radio sensing platform and enable a vast number of novel smart applications, whilst providing non-compromised communications. In this paper, we present a broad picture of the motivation, methodologies, challenges, and research opportunities of realizing PMN, by providing a comprehensive survey for systems and technologies developed mainly in the last ten years. Beginning by reviewing the work on coexisting communication and radar systems, we highlight their limits on addressing the interference problem, and then introduce the JCAS technology. We then set up JCAS in the mobile network context and envisage its potential applications. We continue to provide a brief review of three types of JCAS systems, with particular attention to their differences in design philosophy. We then introduce a framework of PMN, including the system platform and infrastructure, three types of sensing operations, and signals usable for sensing. Subsequently, we discuss required system modifications to enable sensing on current communication-only infrastructure. Within the context of PMN, we review stimulating research problems and potential solutions, organized under nine topics: performance bounds, waveform optimization, antenna array design, clutter suppression, sensing parameter estimation, resolution of sensing ambiguity, pattern analysis, networked sensing unde...
Zhang, JA, Wu, K, Huang, X, Guo, YJ, Zhang, D & Heath, RW 2022, 'Integration of Radar Sensing into Communications with Asynchronous Transceivers', IEEE Communications Magazine, vol. 60, no. 11, pp. 106-112.
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Clock asynchronism is a critical issue in integrating radar sensing into communication networks. It can cause ranging ambiguity and prevent coherent processing of discontinuous measurements in integration with asynchronous transceivers. Should it be resolved, sensing can be efficiently realized in communication networks, requiring few network infrastructure and hardware changes. This article provides a systematic overview of existing and potential new techniques for tackling this fundamental problem. We first review existing solutions, including using a finetuned global reference clock, and single-node-based and network-based techniques. We then examine open problems and research opportunities, offering insights into what may be better realized in each of the three solution areas.
Zhang, K, Fu, Y, Hao, D, Guo, J, Ni, B-J, Jiang, B, Xu, L & Wang, Q 2022, 'Fabrication of CN75/NH2-MIL-53(Fe) p-n heterojunction with wide spectral response for efficiently photocatalytic Cr(VI) reduction', Journal of Alloys and Compounds, vol. 891, pp. 161994-161994.
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In this study, p-type carbon nitride (CN75) nanoparticles were introduced to precursors of NH2-MIL-53(Fe). A CN75 and NH2-MIL-53(Fe) p-n heterojunction was formed by solvothermal reaction. It showed that tiny loading of CN75 onto NH2-MIL-53(Fe) would boost the separation, migration and transfer of photo-induced carriers effectively. Meanwhile, its spectral response was broadened, which draw in efficient photocatalytic performance together. As for photocatalytic reduction of Cr(VI), the rate constant on CN75/NH2-MIL-53(Fe) was ca 1.8 and 25.3 times that by NH2-MIL-53(Fe) and CN75 under visible light (λ ≥ 420 nm), respectively. Reduction rate of CN75/NH2-MIL-53(Fe) (0.1 g/L) reached about 100% within 15 min at pH 2. Good activity could also be observed even under red light (λ: 650–660 nm). Besides, CN75/NH2-MIL-53(Fe) exhibited high stability after 5 cyclic runs, and the leaching of Fe3+ can be greatly suppressed after loading CN75. Structural analysis proved that the MOFs framework was well maintained. Thus, this research paper would provide useful information about the construction and synthesis of efficient and steady Fe-MOFs based photocatalyst for environmental remediation.
Zhang, K, Song, X, Zhang, C & Yu, S 2022, 'Challenges and future directions of secure federated learning: a survey', Frontiers of Computer Science, vol. 16, no. 5, p. 165817.
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UNLABELLED: Federated learning came into being with the increasing concern of privacy security, as people's sensitive information is being exposed under the era of big data. It is an algorithm that does not collect users' raw data, but aggregates model parameters from each client and therefore protects user's privacy. Nonetheless, due to the inherent distributed nature of federated learning, it is more vulnerable under attacks since users may upload malicious data to break down the federated learning server. In addition, some recent studies have shown that attackers can recover information merely from parameters. Hence, there is still lots of room to improve the current federated learning frameworks. In this survey, we give a brief review of the state-of-the-art federated learning techniques and detailedly discuss the improvement of federated learning. Several open issues and existing solutions in federated learning are discussed. We also point out the future research directions of federated learning. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary material is available in the online version of this article at 10.1007/s11704-021-0598-z.
Zhang, L, Chang, X, Liu, J, Luo, M, Li, Z, Yao, L & Hauptmann, A 2022, 'TN-ZSTAD: Transferable Network for Zero-Shot Temporal Activity Detection', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PP, no. 3, pp. 1-14.
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An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large scale annotated videos for training. However, these methods are limited in real applications due to the unavailable videos about certain activity classes and the time-consuming data annotation. To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training still need to be detected. We design an end-to-end deep transferable network TN-ZSTAD as the architecture for this solution. On the one hand, this network utilizes an activity graph transformer to predict a set of activity instances that appear in the video, rather than produces many activity proposals in advance. On the other hand, this network captures the common semantics of seen and unseen activities from their corresponding label embeddings, and it is optimized with an innovative loss function that considers the classification property on seen activities and the transfer property on unseen activities together. Experiments on the THUMOS'14, Charades, and ActivityNet datasets show promising performance in terms of detecting unseen activities.
Zhang, L, Huang, S & Liu, W 2022, 'Enhancing Mixture-of-Experts by Leveraging Attention for Fine-Grained Recognition', IEEE Transactions on Multimedia, vol. 24, pp. 4409-4421.
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Zhang, L, Huang, S & Liu, W 2022, 'Learning sequentially diversified representations for fine-grained categorization', Pattern Recognition, vol. 121, pp. 108219-108219.
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Zhang, L, Shen, J, Zhang, J, Xu, J, Li, Z, Yao, Y & Yu, L 2022, 'Multimodal Marketing Intent Analysis for Effective Targeted Advertising', IEEE Transactions on Multimedia, vol. 24, pp. 1830-1843.
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Zhang, L, Sun, J, Zhang, Z, Peng, Z, Dai, X & Ni, B-J 2022, 'Polyethylene terephthalate microplastic fibers increase the release of extracellular antibiotic resistance genes during sewage sludge anaerobic digestion', Water Research, vol. 217, pp. 118426-118426.
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Microplastic fibers (MFs), as the most frequently detected microplastic shape in sewage sludge, have posed emerging concern for sludge treatment and disposal. However, the effect of MFs on antibiotic resistance genes (ARGs), especially extracellular ARGs (eARGs) during sludge treatment remains far from explicit. Therefore, this study investigated the potential impact of MFs on eARGs during sludge anaerobic digestion (AD), a commonly used sludge treatment method, through long-term operation. The qPCR results showed that both absolute and relative abundances of eARGs increased with the MFs exposure during sludge AD. The average absolute and relative abundances of eight tested eARGs in the AD reactor with the highest MFs dosage (170 items/gTS) were 1.70 and 2.15 times higher than those in the control AD reactor. The metagenomics results further comfirmed the increase of eARGs abundance during sludge anaerobic digestion after MFs exposure and the enhancement did not show significant selectivity. The identification of the potential hosts of eARGs suggested the host numbers of eARGs also increased with MFs exposure, which may suggest enhanced horizonal transformation as a result of increased eARGs. Further exploring the mechansims showed that the genes involved in type IV secretion system was upregulated after MFs exposure, suggesting the active release of eARGs was enhanced with MFs exposure. In contrast, the MFs may not affect the passive release of eARGs as its impact on cell membrance damage was insignificant. The enhanced eARGs in sludge AD process may further accelerate the transport of ARGs in environment, which should be considered during sludge treatment and disposal.
Zhang, L, Wang, S, Chang, X, Liu, J, Ge, Z & Zheng, Q 2022, 'Auto-FSL: Searching the Attribute Consistent Network for Few-Shot Learning', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 3, pp. 1213-1223.
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Prevailing deep methods for image recognition require massive labeled samples in each visual category for training. However, large amounts of data annotations are time-consuming, and some uncommon categories only have rare samples available. For this issue, we focus on more challenging few-shot learning (FSL) task, where just few labeled images are used in the training stage. Existing FSL models are constructed with various convolutional neural networks (CNNs), which are trained on an auxiliary base dataset and evaluated for new few-shot predictions on a novel dataset. The performance of these models is difficult to break through because of the domain shift between base and novel datasets and the monotonous network architectures. Considering that, we propose a novel automatic attribute consistent network called Auto-ACNet to overcome the above problems. On one hand, Auto-ACNet utilizes the attribute information about base and novel categories to guide the procedure of representation learning. It introduces the consistent and non-consistent subnets to capture the common and different attributes of image pair, which helps to mitigate the domain shift problem. On the other hand, the architecture of Auto-ACNet is searched with the popular neural architecture search (NAS) technique DARTS, for obtaining a superior FSL network automatically. And the DARTS’s search space is improved by adding the position-aware module to extract the attribute characteristics better. Extensive experimental results on two datasets indicate that the proposed Auto-ACNet achieves significant improvement over the state-of-the-art competitors in this literature.
Zhang, L, Wang, S, Liu, J, Lin, Q, Chang, X, Wu, Y & Zheng, Q 2022, 'MuL-GRN: Multi-Level Graph Relation Network for Few-Shot Node Classification', IEEE Transactions on Knowledge and Data Engineering, vol. PP, no. 99, pp. 1-1.
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Few-shot learning (FSL) that acquires new knowledge with little supervision, attracts much attention due to expensive cost of data annotation. Various meta-learning methods have made a great progress for few-shot problem in image and text data. In reality, data samples are not independent but rich in link relations. Large amounts of data exists in the form of graph structure such as citation, social, and biological networks. However, FSL study on graph data is still in its infancy because of the obstacle on extracting meta-knowledge from a meta node classification task. Current research just simply combines the FSL methods experienced in computer vision with node representation models together, but ignores the effect of rich links among support and query nodes in few-shot meta-task. For this issue, we propose a novel Multi-Level Graph Relation Network (MuL-GRN) for the challenging few-shot node classification. MuL-GRN extracts node embeddings through the popular graph neural networks (GNNs). And it includes a relation learning module to mine the deep node relations from three views, namely node-level, global subgraph-level, and local subgraph-level relations. For any two nodes, the node-level relation is computed on their node embeddings, global subgraph-level relation is measured on their subgraph embeddings, and the local subgraph-level relation is mined according to the pairwise node comparison information in their subgraphs. The three-view relation vectors are fused together with an interesting relation fusion module, which measures the importance of relation vector for the current few-shot classification task automatically. Extensive experiments on five real datasets show that MuL-GRN significantly outperforms existing state-of-the-art methods by a large margin.
Zhang, L, Xu, J, Gong, Y, Yu, L, Zhang, J & Shen, J 2022, 'Unsupervised Image and Text Fusion for Travel Information Enhancement', IEEE Transactions on Multimedia, vol. 24, pp. 1415-1425.
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Zhang, M, Viscarra Rossel, RA, Zhu, Q, Leys, J, Gray, JM, Yu, Q & Yang, X 2022, 'Dynamic Modelling of Water and Wind Erosion in Australia over the Past Two Decades', Remote Sensing, vol. 14, no. 21, pp. 5437-5437.
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Soil erosion caused by water and wind is a complicated natural process that has been accelerated by human activity. It results in increasing areas of land degradation, which further threaten the productive potential of landscapes. Consistent and continuous erosion monitoring will help identify the location, magnitude, and trends of soil erosion. This information can then be used to evaluate the impact of land management practices and inform programs that aim to improve soil conditions. In this study, we applied the Revised Universal Soil Loss Equation (RUSLE) and the Revised Wind Erosion Equation (RWEQ) to simulate water and wind erosion dynamics. With the emerging earth observation big data, we estimated the monthly and annual water erosion (with a resolution of 90 m) and wind erosion (at 1 km) from 2001 to 2020. We evaluated the performance of three gridded precipitation products (SILO, GPM, and TRMM) for monthly rainfall erosivity estimation using ground-based rainfall. For model validation, water erosion products were compared with existing products and wind erosion results were verified with observations. The datasets we developed are particularly useful for identifying finer-scale erosion dynamics, where more sustainable land management practices should be encouraged.
Zhang, Q, Cao, L, Shi, C & Niu, Z 2022, 'Neural Time-Aware Sequential Recommendation by Jointly Modeling Preference Dynamics and Explicit Feature Couplings', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 10, pp. 5125-5137.
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In recommendation, both stationary and dynamic user preferences on items are embedded in the interactions between users and items (e.g., rating or clicking) within their contexts. Sequential recommender systems (SRSs) need to jointly involve such context-aware user-item interactions in terms of the couplings between the user and item features and sequential user actions on items over time. However, such joint modeling is non-trivial and significantly challenges the existing work on preference modeling, which either only models user-item interactions by latent factorization models but ignores user preference dynamics or only captures sequential user action patterns without involving user/item features and context factors and their coupling and influence on user actions. We propose a neural time-aware recommendation network (TARN) with a temporal context to jointly model 1) stationary user preferences by a feature interaction network and 2) user preference dynamics by a tailored convolutional network. The feature interaction network factorizes the pairwise couplings between non-zero features of users, items, and temporal context by the inner product of their feature embeddings while alleviating data sparsity issues. In the convolutional network, we introduce a convolutional layer with multiple filter widths to capture multi-fold sequential patterns, where an attentive average pooling (AAP) obtains significant and large-span feature combinations. To learn the preference dynamics, a novel temporal action embedding represents user actions by incorporating the embeddings of items and temporal context as the inputs of the convolutional network. The experiments on typical public data sets demonstrate that TARN outperforms state-of-the-art methods and show the necessity and contribution of involving time-aware preference dynamics and explicit user/item feature couplings in modeling and interpreting evolving user preferences.
Zhang, Q, Jia, X, Li, T, Shao, M, Yu, Q & Wei, X 2022, 'Intensification of water storage deficit in topsoil but not deep soil in a semi-humid forest after excluding precipitation for two years', Journal of Hydrology, vol. 605, pp. 127374-127374.
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Zhang, Q, Zhou, D, Fang, G, Lu, H, Zeng, J & Gu, Z 2022, 'Cell‐Derived Biomimetic 2D Nanoparticles to Improve Cell‐Specific Targeting and Tissue Penetration for Enhanced Magnetic Resonance Imaging', Advanced Materials Interfaces, vol. 9, no. 7, pp. 2101914-2101914.
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AbstractA critical challenge of nanoparticle‐based imaging diagnosis of cancers is limited accumulation of nanoparticles at the biological target. Here, a cancer cell derived membrane coated 2D layered double hydroxides nanoparticle (CM‐PEG/MnLDH) has been developed to realize both cell‐specific targeting and deep tissue penetration for enhanced magnetic resonance imaging (MRI) of tumors. Using 4T1 breast cancer cells as a model, the biomimetic CM‐PEG/MnLDH exhibited excellent cell‐specific targeting ability with significantly higher accumulation in homologous cancer cells than the nonhomologous cells. Using 3D tumor spheroid cultures, the CM‐PEG/MnLDH demonstrated highly selective binding and deep tissue penetration ability. In vivo imaging revealed that intravenous injection of CM‐PEG/MnLDH provides clear MRI of tumor tissues in a 4T1 mouse model. Particularly, the CM‐PEG/MnLDH showed enhanced imaging performance in the central region of tumors, owing to the cell homing and tissue penetration ability of the biomimetic nanoparticle. In vivo biosafety evaluation of the CM‐PEG/MnLDH showed a high degree of biocompatibility, signifying its suitability for further clinical applications. The biomimetic CM‐PEG/MnLDH nanoparticle featured with cell‐specific targeting ability and deep tissue penetration is a promising MRI contrast agent for precise cancer diagnosis.
Zhang, R, Liu, X, Chen, R, Wang, Z, Lin, W, Ngo, HH, Nan, J, Li, G, Ma, J & Ding, A 2022, 'Environmental and economic performances of incorporating Fenton-based processes into traditional sludge management systems', Journal of Cleaner Production, vol. 364, pp. 132613-132613.
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Municipal and industrial wastewater treatment plants produce a tremendous amount of sludge containing organic and toxic components. One of the advanced oxidation processes (AOP) - Fenton process has demonstrated great prospect in reduction of sludge organics and toxicity. Fenton pretreatment could ameliorate the sludge dewaterability and biodegradability for anaerobic digestion (AD) process, and enhance the sludge lower heating value for incineration process, thus stimulating sludge dewatering, reduction and energy recovery. However, doubts remain about whether the incorporation of the Fenton process into the traditional sludge management systems brings environmental benefits. Hence, a life cycle environmental impact calculation model was established for sludge with various organic contents (60%, 70%, 80%) under the effect of Fenton and US/UV/Electro-Fenton processes. Noteworthy mitigation of environmental load was observed for the Fenton process coupled with incineration system, which involves high dewatering demand. Conversely, as for the AD system with high biomass transformation rate, Fenton process failed to attain the assumed promotion of environmental benefit. Hydrogen peroxide (H2O2) prominently attributed to the weakness of Fenton process combined with AD (F-AD) scenario, compared with the AD scenario in terms of environmental impact. Summarily, the F-AD scenario acts as the preponderant system when weighing up the pros and cons of environmental impact, energy balance and life cycle cost. Contrary to the mainstream view, the proven technical advantages of Fenton process cannot compensate for the additional environmental loads in the life cycle of sludge. It provides valuable reflection for environmental managers and scholars that we should be more cautious in the application of cutting-edge technologies.
Zhang, R, Xu, L, Yu, Z, Shi, Y, Mu, C & Xu, M 2022, 'Deep-IRTarget: An Automatic Target Detector in Infrared Imagery Using Dual-Domain Feature Extraction and Allocation', IEEE Transactions on Multimedia, vol. 24, pp. 1735-1749.
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Recently, convolutional neural networks (CNNs) have brought impressive improvements for object detection. However, detecting targets in infrared images still remains challenging, because the poor texture information, low resolution and high noise levels of the thermal imagery restrict the feature extraction ability of CNNs. In order to deal with these difficulties in the feature extraction, we propose a novel backbone network named Deep-IRTarget, composing of a frequency feature extractor, a spatial feature extractor and a dual-domain feature resource allocation model. Hypercomplex Infrared Fourier Transform is developed to calculate the infrared intensity saliency by designing hypercomplex representations in the frequency domain, while a convolutional neural network is invoked to extract feature maps in the spatial domain. Features from the frequency domain and spatial domain are stacked to construct Dual-domain features. To efficiently integrate and recalibrate them, we propose a Resource Allocation model for Features (RAF). The well-designed channel attention block and position attention block are used in RAF to respectively extract interdependent relationships among channel and position dimensions, and capture channel-wise and position-wise contextual information. Extensive experiments are conducted on three challenging infrared imagery databases. We achieve 10.14%, 9.1% and 8.05% improvement on mAP scores, compared to the current state of the art method on MWIR, BITIR and WCIR respectively.
Zhang, S, Lan, P, Li, H-C, Tong, C-X & Sheng, D 2022, 'Physics-informed neural networks for consolidation of soils', Engineering Computations, vol. 39, no. 7, pp. 2845-2865.
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PurposePrediction of excess pore water pressure and estimation of soil parameters are the two key interests for consolidation problems, which can be mathematically quantified by a set of partial differential equations (PDEs). Generally, there are challenges in solving these two issues using traditional numerical algorithms, while the conventional data-driven methods require massive data sets for training and exhibit negative generalization potential. This paper aims to employ the physics-informed neural networks (PINNs) for solving both the forward and inverse problems.Design/methodology/approachA typical consolidation problem with continuous drainage boundary conditions is firstly considered. The PINNs, analytical, and finite difference method (FDM) solutions are compared for the forward problem, and the estimation of the interface parameters involved in the problem is discussed for the inverse problem. Furthermore, the authors also explore the effects of hyperparameters and noisy data on the performance of forward and inverse problems, respectively. Finally, the PINNs method is applied to the more complex consolidation problems.FindingsThe overall results indicate the excellent performance of the PINNs method in solving consolidation problems with various drainage conditions. The PINNs can provide new ideas with a broad application prospect to solve PDEs in the field of geotechnical engineering, and also exhibit a certain degree of noise resistance for estimating the soil parameters.Originality/valueThis study presents the potential application of PINNs for the consolidation of soils. S...
Zhang, S, Li, X, Shi, J, Sivakumar, M, Luby, S, O'Brien, J & Jiang, G 2022, 'Analytical performance comparison of four SARS-CoV-2 RT-qPCR primer-probe sets for wastewater samples', Science of The Total Environment, vol. 806, no. Pt 2, pp. 150572-150572.
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Current studies have confirmed the feasibility of SARS-CoV-2 RNA detection by RT-qPCR assays in wastewater samples as an effective surveillance tool of COVID-19 prevalence in a community. Analytical performance of various RT-qPCR assays has been compared against wastewater samples based on the positive ratio. However, there is no systematic comparison work has been conducted for both analytical sensitivity and quantitative reliability against wastewater, which are essential factors for WBE. In this study, the detection performance of four RT-qPCR primer-probe sets, including CCDC-N, CDC-N1, N-Sarbeco, and E-Sarbeco, was systematically evaluated with pure synthetized plasmids, spiked wastewater mocks and raw wastewater samples. In addition to confirm RT-qPCR results, Nanopore sequencing was employed to delineate at molecular level for the analytical sensitivity and reproducibility of those primer-probe sets. CCDC-N showed high sensitivity and the broadest linearity range for wastewater samples. It was thus recommended to be the most efficient tool in the quantitative analysis of SARS-CoV-2 in wastewater. CDC-N1 had the highest sensitivity for real wastewater and thus would be suitable for the screening of wastewater for the presence of SARS-CoV-2. When applying the primer-probe sets to wastewater samples collected from different Australian catchments, increased active clinical cases were observed with the augment of SARS-CoV-2 RNA quantified by RT-qPCR in wastewater in low prevalence communities.
Zhang, S, Zhang, C, Zhang, S & Yu, JJQ 2022, 'Attention-Driven Recurrent Imputation for Traffic Speed', IEEE Open Journal of Intelligent Transportation Systems, vol. 3, pp. 723-737.
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Zhang, SS, Ke, Y, Chen, E, Biscaia, H & Li, WG 2022, 'Effect of load distribution on the behaviour of RC beams strengthened in flexure with near-surface mounted (NSM) FRP', Composite Structures, vol. 279, pp. 114782-114782.
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Debonding failures of FRP have been frequently observed in laboratory tests of reinforced concrete (RC) beams flexurally-strengthened with near-surface mounted (NSM) fibre-reinforced polymer (FRP). A number of numerical and theoretical studies have been carried out to predict debonding failures in NSM FRP-strengthened beams, and several strength models have also been proposed. The existing studies, however, were all based on the scenario of a simply supported beam tested under one or two-point loading, while the influence of load distribution has not yet been investigated. This paper presents the first ever study into the effect of load distribution on the behaviour of NSM FRP-strengthened RC beams. A series of large-scale RC beams flexurally-strengthened with NSM FRP strips were first tested under different load uniformities; then a finite element (FE) model, which can give close predictions to the behaviour of such strengthened beams, was developed; finally, the proposed FE model was utilized to investigate the influence of bond length of NSM FRP on the load uniformity effect. It was found that the load uniformity has a significant effect on the beam behaviour, and the degree of this effect varies with the bond length of NSM FRP.
Zhang, T, Du, J & Guo, YJ 2022, 'High-Tc Superconducting Microwave and Millimeter Devices and Circuits—An Overview', IEEE Journal of Microwaves, vol. 2, no. 3, pp. 374-388.
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Zhang, T, Jin, B & Jia, W 2022, 'An anchor-free object detector based on soften optimized bi-directional FPN', Computer Vision and Image Understanding, vol. 218, pp. 103410-103410.
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We propose an anchor-free object detector that combines a weighted bi-directional Feature Pyramid Network (BiFPN) and Soft Anchor Point Detector to address the object detection problem in a pixel-wise paradigm. The current mainstream object detection methods are anchor-based, which require to set hyper parameters such as scale and aspect ratio. This requires strong prior knowledge and can be difficult to design. Therefore, we propose an anchor-free detector that completely avoids the complex calculations and all the hyper parameters related to the anchor box by eliminating the predefined set of anchor boxes in an anchor-free way. Anchor-free detectors are essentially dense prediction methods. Although the huge solution space can yield high recall, simple anchor-free methods tend to return too many false positives, which leads to the problem of semantic ambiguity caused by the high overlap of object centers. Therefore, we propose BiFPN to alleviate the impact of high overlap which also effectively addresses the problems related to multi-scale features. Moreover, in order to utilize the power of feature pyramid better, we tackle the issues with a novel training strategy that involves two soften optimization techniques, i.e., soft-weighted anchor points and soft-selected pyramid levels. This training strategy further re-weights the quality of the detection results to make our detection results more stable.
Zhang, T, Zhang, H, Huang, X, Suzuki, H, Pathikulangara, J, Smart, K, Du, J & Guo, J 2022, 'A 245 GHz Real-Time Wideband Wireless Communication Link with 30 Gbps Data Rate', Photonics, vol. 9, no. 10, pp. 683-683.
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This paper presents a 245 GHz wireless communications system with a data rate of 30 Giga bits per second (Gbps) at a 1.2 m distance, which proves the potential for future high-speed communications beyond 5G technology. The system consists of low-complexity and real-time base-band modules to provide the high-speed wideband signal processing capability. Multi-channel base-band signals are combined and converted to 15.65 ± 6.25 GHz wideband intermediate frequency (IF) signals. A novel 245 GHz waveguide bandpass filter (BPF) with low loss and high selectivity is designed and applied to a terahertz (THz) front-end for image rejection and noise suppression. Configuration of the base-band, IF, and THz front-end modules is also given in detail. The 245 GHz wireless communication link is demonstrated over a distance of 1.2 m.
Zhang, T, Zhu, T, Liu, R & Zhou, W 2022, 'Correlated data in differential privacy: Definition and analysis', Concurrency and Computation: Practice and Experience, vol. 34, no. 16.
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SummaryDifferential privacy is a rigorous mathematical framework for evaluating and protecting data privacy. In most existing studies, there is a vulnerable assumption that records in a dataset are independent when differential privacy is applied. However, in real‐world datasets, records are likely to be correlated, which may lead to unexpected data leakage. In this survey, we investigate the issue of privacy loss due to data correlation under differential privacy models. Roughly, we classify existing literature into three lines: (1) using parameters to describe data correlation in differential privacy, (2) using models to describe data correlation in differential privacy, and (3) describing data correlation based on the framework of Pufferfish. First, a detailed example is given to illustrate the issue of privacy leakage on correlated data in real scenes. Then our main work is to analyze and compare these methods, and evaluate situations that these diverse studies are applied. Finally, we propose some future challenges on correlated differential privacy.
Zhang, W, Liu, T, Brown, A, Ueland, M, Forbes, SL & Su, SW 2022, 'The Use of Electronic Nose for the Classification of Blended and Single Malt Scotch Whisky', IEEE Sensors Journal, vol. 22, no. 7, pp. 7015-7021.
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As with any profitable industry, the whisky market is subject to fraudulent activity, including adulteration. An expert can identify the differences between whiskies, but it is difficult for the majority of consumers to differentiate fraudulent beverages. Complex chemical and analytical analyses have been able to detect the differences between whiskies; however, this type of analysis is time-consuming, complex, requires trained professionals, and can only be conducted in the laboratory. A rapid and real-time assessment of whisky quality could prove beneficial to wholesalers and consumers. The odour of whiskies can be used to identify their brands, regions and styles, as thus has the potential for quality assessment and fraudulent detection. One type of technology used for real-time odour analysis is an electronic nose (e-nose). This study investigates the capability of a new e-nose prototype (called NOS.E) developed by our team to identify the differences between six whiskies with respect to their brand names, regions, and styles. This study investigates the capability of a new e-nose prototype (called NOS.E) developed by our team in identifying the differences among whiskies. Ensemble of several classifiers is adopted to improve the classification accuracy of the system. The proposed e-nose solution was verified by a field testing displayed at the CEBIT Australia 2019 trade show, by reaching an accuracy of 96.15%, 100%, and 92.31% in brand name, region, and style classification, respectively. Confirmation of the NOS.E findings was further carried out using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC $\times $ GC-TOFMS).
Zhang, W, Wang, D, Yu, S, He, H & Wang, Y 2022, 'Repeatable Multi-Dimensional Virtual Network Embedding in Cloud Service Platform', IEEE Transactions on Services Computing, vol. 15, no. 6, pp. 3499-3512.
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Virtual network embedding (VNE) can effectively deploy virtual networks (VNs) onto shared substrate network (SN) resources. However, with the consistent changing scalability and diversity demands of VNs, traditional VNE methods prove to be a challenging task for current cloud service platforms. Thus, we model a repeatable multi-dimensional virtual network embedding (RMD-VNE) problem for implementing multi-dimensional virtual networks (MD-VNs) that involves real servers, virtual machines, containers, and network simulators. The MD-VN is preprocessed and embedded via a heuristic method denoted as ReMiDvne. Following its transformation for the containers and simulation networks, the MD-VN topology undergoes a process of coarsening, partitioning, and uncoarsening. ReMiDvne then applies a topology-aware repeatable embedding solution to complete the embedding stage. Experimental results demonstrate that ReMiDvne outperforms seven baseline approaches through small-, 1,000- and 10,000-scale VNE simulation experiments. Remarkably, ReMiDvne improves the average rates of acceptance ratio, revenue, and revenue-cost ratio by up to 40.45%, 40.45%, and 299.03%, respectively, and reduces the average rate of cost by up to 64.16%. Furthermore, real-world VNE experiments are conducted based on the OpenStack platform. The results reveal the ability of ReMiDvne to efficiently reduce communication costs by up to 45.93% and 63.43% for download and upload, respectively.
Zhang, X & Far, H 2022, 'Effects of dynamic soil-structure interaction on seismic behaviour of high-rise buildings', Bulletin of Earthquake Engineering, vol. 20, no. 7, pp. 3443-3467.
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Zhang, X, Ekanayake Weeramange, C, Hughes, BGM, Vasani, S, Liu, ZY, Warkiani, ME, Hartel, G, Ladwa, R, Thiery, JP, Kenny, L & Punyadeera, C 2022, 'Application of circulating tumour cells to predict response to treatment in head and neck cancer', Cellular Oncology, vol. 45, no. 4, pp. 543-555.
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Abstract Background Local recurrence and metastasis remain the major causes of death in head and neck cancer (HNC) patients. Circulating tumour cells (CTCs) are shed from primary and metastatic sites into the circulation system and have been reported to play critical roles in the metastasis and recurrence of HNC. Here, we explored the use of CTCs to predict the response to treatment and disease progression in HNC patients. Methods Blood samples were collected at diagnosis from HNC patients (n = 119). CTCs were isolated using a spiral microfluidic device and were identified using immunofluorescence staining. Correlation of baseline CTC numbers to 13-week PET-CT data and multidisciplinary team consensus data were conducted. Results CTCs were detected in 60/119 (50.4%) of treatment naïve HNC patients at diagnosis. Baseline CTC numbers were higher in stage III vs. stage I-II p16-positive oropharyngeal cancers (OPCs) and other HNCs (p = 0.0143 and 0.032, respectively). In addition, we found that baseline CTC numbers may serve as independent predictors of treatment response, even after adjusting for other conventional prognostic factors. CTCs were detected in 10 out of 11 patients exhibiting incomplete treatment responses. Conclusions We found that baseline CTC numbers are correlated with treatment response in patients with HNC. The expression level of cell-surface vimentin (CSV) on CTCs was significantly higher in patients with persistent or progressive disease, thus providing additional prognostic information...
Zhang, X, Huang, J, Cheng, X, Chen, H, Liu, Q, Yao, P, Ngo, HH & Nghiem, LD 2022, 'Mitigation of reverse osmosis membrane fouling by electrochemical-microfiltration- activated carbon pretreatment', Journal of Membrane Science, vol. 656, pp. 120615-120615.
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Zhang, X, Li, Z-X, Shi, Y, Wu, C & Li, J 2022, 'Fragility analysis for performance-based blast design of FRP-strengthened RC columns using artificial neural network', Journal of Building Engineering, vol. 52, pp. 104364-104364.
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In this paper, fragility analysis for performance-based blast design of FRP-strengthened RC columns is carried out. The blast intensity levels, performance levels and performance objectives of the RC columns are defined. A simplified probabilistic risk assessment framework incorporating the performance-based design concept and fragility analysis is established. Since fragility analysis is the most important but time-consuming process of probabilistic assessment risk, an artificial neural network (ANN) based fragility analysis framework is proposed to improve its computational efficiency. Based on the rapid fragility analysis method, fragility curves of several typical RC columns with or without FRP strengthening are calculated to analyze their damage probabilities. This study provides avenues for engineers to estimate the failure probabilities of RC columns with or without FRP strengthening under blast loads and make decisions quickly.
Zhang, X, Sui, G, Wang, Z, Ngo, HH, Guo, W, Wen, H, Zhang, D, Wang, X & Zhang, J 2022, 'Effective fluorine removal using mixed matrix membrane based on polysulfone: adsorption performance and diffusion behavior', Water Science and Technology, vol. 85, no. 11, pp. 3196-3207.
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Abstract Fluorine is one of the essential trace elements for human life activities, but excessive intake of fluoride poses a great risk to people's health. In this paper, a series of mixed matrix membrane (MMM)-based polysulfone for removing fluoride were prepared by phase inversion, and their properties, adsorption capacity, adsorption isotherms, adsorption kinetics of fluoride ions, and mechanism were all investigated. The results confirmed that the MMM contained a large number of hydroxyl and aluminum functional groups due to resin being added. The MMM exhibited the best fluorine ion adsorption capacity of 2.502 mg/g at a pH of 6 with the initial concentration of 6 mg/L. As well, adsorption kinetics of fluorine ion on MMM followed the pseudo-second-order model, while the adsorption behavior of fluorine ion on MMM was well simulated by the Langmuir isotherm model. The adsorption capacity of MMM remained stable after six cycles and the regeneration efficiency was still above 80%, resulting in a long-term stability adequate for fluorine ion removal. Complexation and ion exchange played a key role in the fluorine ion adsorption of MMM. These results indicated the MMM as novel type of absorbent had an excellent capacity for removing fluoride.
Zhang, X, Wang, H, Yu, J, Chen, C, Wang, X & Zhang, W 2022, 'Polarity-based graph neural network for sign prediction in signed bipartite graphs', World Wide Web, vol. 25, no. 2, pp. 471-487.
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AbstractAs a fundamental data structure, graphs are ubiquitous in various applications. Among all types of graphs, signed bipartite graphs contain complex structures with positive and negative links as well as bipartite settings, on which conventional graph analysis algorithms are no longer applicable. Previous works mainly focus on unipartite signed graphs or unsigned bipartite graphs separately. Several models are proposed for applications on the signed bipartite graphs by utilizing the heuristic structural information. However, these methods have limited capability to fully capture the information hidden in such graphs. In this paper, we propose the first graph neural network on signed bipartite graphs, namely Polarity-based Graph Convolutional Network (PbGCN), for sign prediction task with the help of balance theory. We introduce the novel polarity attribute to signed bipartite graphs, based on which we construct one-mode projection graphs to allow the GNNs to aggregate information between the same type nodes. Extensive experiments on five datasets demonstrate the effectiveness of our proposed techniques.
Zhang, X, Wang, Z, Huang, J, Chen, H, Liu, Q, Yao, P, Ngo, HH & Nghiem, LD 2022, 'A novel membrane photo-electro oxidizer for advanced treatment of coal processing wastewater: Fouling control and permeate quality', Journal of Cleaner Production, vol. 378, pp. 134573-134573.
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Zhang, X, Xia, W, Wang, X, Liu, J, Cui, Q, Tao, X & Liu, RP 2022, 'The Block Propagation in Blockchain-Based Vehicular Networks', IEEE Internet of Things Journal, vol. 9, no. 11, pp. 8001-8011.
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Zhang, X, Xie, W, Li, Y, Lei, J, Du, Q & Yang, G 2022, 'Rank-Aware Generative Adversarial Network for Hyperspectral Band Selection', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-12.
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Zhang, X, Xu, Z, Fan, L, Yu, S & Qu, Y 2022, 'Near-Optimal Energy-Efficient Algorithm for Virtual Network Function Placement', IEEE Transactions on Cloud Computing, vol. 10, no. 1, pp. 553-567.
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To accommodate heterogeneous and sophisticated network services, Network Function Virtualization (NFV) is invented as a hopeful networking technology. The most distinct feature of NFV is that it separates network functions from physical hardware. In the NFV architecture, various types of Virtual Network Functions (VNFs) are placed on specific software-based middleboxes by telecom providers. Traffic traverses through a sequence of Virtual Network Functions (VNFs) in pre-defined order, which is named as Service Function Chain (SFC). However, how to effectively place VNFs at different locations and steer SFC requests while minimizing energy consumption is still an open problem. Accordingly, we investigate on the joint optimization of VNF placement and traffic steering for energy efficiency in telecom networks. We first present the power consumption model in NFV-enabled telecom networks, and then formulate the studied problem as an Integer Linear Programming (ILP) model. Since the problem is proved as NP-hard, we design a polynomial algorithm that can achieve near-optimal performances based on the Markov approximation technique. In addition, our algorithm can be extended to an online version to serve dynamic arriving SFC requests. The online algorithm achieves a near-optimal long-term averaged performance. Extensive simulation results show that compared with the benchmark algorithms, in the offline and online scenario, our algorithm can reduce up to 14.08 and 13.72 percent power consumption in telecom networks, respectively.
Zhang, X, Yang, Y, Hao Ngo, H, Guo, W, Long, T, Wang, X, Zhang, J & Sun, F 2022, 'Enhancement of urea removal from reclaimed water using thermally modified spent coffee ground biochar activated by adding peroxymonosulfate for ultrapure water production', Bioresource Technology, vol. 349, pp. 126850-126850.
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Zhang, X, Yang, Y, Hao Ngo, H, Guo, W, Sun, F, Wang, X, Zhang, J & Long, T 2022, 'Urea removal in reclaimed water used for ultrapure water production by spent coffee biochar/granular activated carbon activating peroxymonosulfate and peroxydisulfate', Bioresource Technology, vol. 343, pp. 126062-126062.
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Zhang, X, Zhang, W, Zhang, L, Huang, Z, Hu, J, Gao, M, Pan, H & Liu, Y 2022, 'Single-pot solvothermal strategy toward support-free nanostructured LiBH4 featuring 12 wt% reversible hydrogen storage at 400 °C', Chemical Engineering Journal, vol. 428, pp. 132566-132566.
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Lithium borohydride (LiBH4) exhibits poor hydrogen storage reversibility because of phase separation between LiH and B due to foaming during thermal dehydrogenation. Herein, we report that by synthesizing nanostructured LiBH4 without any supports, the foaming and phase separation can be effectively suppressed, and consequently, the hydrogen storage reversibility of LiBH4 can be considerably improved. Using a facile single-pot solvothermal approach, a hierarchical porous nanostructured LiBH4 composed of 50–60 nm-sized primary nanoparticles is synthesized. The resulting neat nano-LiBH4 reversibly desorbs and absorbs approximately 12 wt% of H at 400 °C and under 100 bar H2. The superior hydrogen storage performance is attributed to the effective inhibition of foaming upon heating. The formation of LiH and B prior to melting, which can be associated with the largely reduced particle sizes and porous agglomeration structure, plays a crucial role in suppressing foaming. Our findings offer a new strategy for the preparation of nanoscaled freestanding borohydrides, and also important insights into the development of highly reversible metal borohydrides for hydrogen storage applications.
Zhang, XL, Zhang, X, Zhang, LC, Huang, ZG, Fang, F, Hu, JJ, Yang, YX, Gao, MX, Pan, HG & Liu, YF 2022, 'Ultrafast hydrogenation of magnesium enabled by tetragonal ZrO2 hierarchical nanoparticles', Materials Today Nano, vol. 18, pp. 100200-100200.
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Transition metal catalysts are particularly effective in improving the reaction kinetics of light metal hydrides for reversible hydrogen storage. Herein, tetragonal ZrO2 hierarchical nanoparticles (nano-ZrO2) composed of primary particles of ∼4 nm in diameter are successfully synthesized by a facile one-pot solvothermal process. The unique hierarchical structure features homogeneous distributions of in situ formed multivalent Zr-based species, which allow superior catalytic activity for hydrogen storage in MgH2. The MgH2+10 wt% nano-ZrO2 starts releasing H2 at 163 °C after one activation, which is 107 °C lower than additive-free MgH2, and 50 °C lower than that of bulk ZrO2-doped MgH2. At 230 °C, 5.9 wt% of H is rapidly liberated within 20 min from the nano-ZrO2-containing MgH2. More importantly, the material shows superior hydrogenation kinetics compared with all reported catalyst-modified MgH2. The nano-ZrO2-containing Mg took up 4.0 wt% of H in only 12 s at 100 °C under 50 bar H2, 400 times faster than the bulk-ZrO2-modified sample. Even at 50 °C, approximately 1.8 wt% H was absorbed within 1 min. Our findings provide useful insights into the design and development of high-performance catalysts toward solid-state hydrogen storage materials.
Zhang, Y, Afroz, S, Nguyen, QD, Kim, T, Nguyen, D, Castel, A, Nairn, J & Gilbert, RI 2022, 'Autogenous shrinkage of fly ash and ground granulated blast furnace slag concrete', Magazine of Concrete Research, pp. 1-13.
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Supplementary cementitious materials (SCMs) are widely used to reduce the cement content to achieve economic and environmental objectives. As a result, understanding the shrinkage of blended cement-based concrete is essential. In total, 21 concrete mixes were produced with type general purpose cement and with cement replacements of 30% by fly ash, 40% and 60% by ground granulated blast furnace slag (GGBFS). The concrete compressive strength ranged from 25 MPa to 100 MPa. Experimental results were also compared with the predictions by models. Additional tests on pastes with the same SCM content were conducted to investigate both autogenous and chemical shrinkage in relation to their time-dependent pore structure refinement. For concretes with strength below 50 MPa, no significant difference in autogenous shrinkage could be observed between the different blends up to 28 days. However, the autogenous shrinkage of GGBFS concrete increased significantly after 28 days, being about 50% higher than all other concretes at 100 days. This late increase in autogenous shrinkage between 28 and 100 days can be attributed to pore refinement processes. No clear difference was observed for GGBFS concretes with strength greater than 50 MPa. Autogenous shrinkage of fly ash concretes was overall equivalent to that of reference concretes.
Zhang, Y, Huang, R, Zhou, Y, Zhou, T, Tao, C, Huang, Y & Qian, Y 2022, 'Effects of turbulence intensity and n-pentanol concentration on droplet evaporation and auto-ignition', Fuel, vol. 322, pp. 124177-124177.
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Evaporation and auto-ignition characteristics of an n-pentanol-diesel droplet are investigated under a high-temperature (900 K) and turbulent environment. Turbulence intensity and n-pentanol mass fraction are varied between 0 and 0.527 m/s and 0–50%, respectively. Droplet evaporation is controlled by the gas temperature, which is affected by turbulent transport and chemical reactions. Diesel vapor accumulates around the droplet at all turbulence intensities, whereas strong turbulence facilitates the transport of n-pentanol vapor to far areas. Turbulence intensity has little impact on the droplet temperature and evaporation rate of pure diesel. The addition of n-pentanol reduces both the evaporation rate and droplet temperature at a high turbulence intensity of 0.527 m/s, but it has little influence on droplet temperature in a static environment. The vapor distribution determines the chemical activity of gas phase around the droplet, and consequently the auto-ignition characteristics. With the increase of n-pentanol concentration, the auto-ignition delay firstly decreases and then increases under low turbulence intensities (0–0.264 m/s), while it monotonically increases under a high turbulence intensity (0.395 and 0.527 m/s). The auto-ignition delay firstly decreases and then increases with the increase of turbulence intensity, regardless of n-pentanol concentration.
Zhang, Y, Huang, Y, Chiavetta, D & Porter, AL 2022, 'An introduction of advanced tech mining: Technical emergence indicators and measurements', Technological Forecasting and Social Change, vol. 182, pp. 121855-121855.
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Zhang, Y, Li, B, Wu, J, Liu, B, Chen, R & Chang, J 2022, 'Efficient and Privacy-Preserving Blockchain-Based Multifactor Device Authentication Protocol for Cross-Domain IIoT', IEEE Internet of Things Journal, vol. 9, no. 22, pp. 22501-22515.
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Zhang, Y, Liu, F, Fang, Z, Yuan, B, Zhang, G & Lu, J 2022, 'Learning From a Complementary-Label Source Domain: Theory and Algorithms', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 12, pp. 7667-7681.
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Zhang, Y, Silva, DM, Young, P, Traini, D, Li, M, Ong, HX & Cheng, S 2022, 'Understanding the effects of aerodynamic and hydrodynamic shear forces on Pseudomonas aeruginosa biofilm growth', Biotechnology and Bioengineering, vol. 119, no. 6, pp. 1483-1497.
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AbstractBiofilms are communities of bacterial cells encased in a self‐produced polymeric matrix and exhibit high tolerance towards environmental stress. Despite the plethora of research on biofilms, most biofilm models are produced using mono‐interface culture in static flow conditions, and knowledge of the effects of interfaces and mechanical forces on biofilm development remains fragmentary. This study elucidated the effects of air–liquid (ALI) or liquid–liquid (LLI) interfaces and mechanical shear forces induced by airflow and hydrodynamic flow on biofilm growing using a custom‐designed dual‐channel microfluidic platform. Results from this study showed that comparing biofilms developed under continuous nutrient supply and shear stresses free condition to those developed with limited nutrient supply, ALI biofilms were four times thicker, 60% less permeable, and 100 times more resistant to antibiotics, while LLI biofilms were two times thicker, 20% less permeable, and 100 times more resistant to antibiotics. Subjecting the biofilms to mechanical shear stresses affected the biofilm structure across the biofilm thickness significantly, resulting in generally thinner and denser biofilm compared to their controlled biofilm cultured in the absence of shear stresses, and the ALI and LLI biofilm's morphology was vastly different. Biofilms developed under hydrodynamic shear stress also showed increased antibiotic resistance. These findings highlight the importance of investigating biofilm growth and its mechanisms in realistic environmental conditions and demonstrate a feasible approach to undertake this study using a novel platform.
Zhang, Y, Wang, M, Saberi, M & Chang, E 2022, 'Analysing academic paper ranking algorithms using test data and benchmarks: an investigation', Scientometrics, vol. 127, no. 7, pp. 4045-4074.
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AbstractResearch on academic paper ranking has received great attention in recent years, and many algorithms have been proposed to automatically assess a large number of papers for this purpose. How to evaluate or analyse the performance of these ranking algorithms becomes an open research question. Theoretically, evaluation of an algorithm requires to compare its ranking result against a ground truth paper list. However, such ground truth does not exist in the field of scholarly ranking due to the fact that there does not and will not exist an absolutely unbiased, objective, and unified standard to formulate the impact of papers. Therefore, in practice researchers evaluate or analyse their proposed ranking algorithms by different methods, such as using domain expert decisions (test data) and comparing against predefined ranking benchmarks. The question is whether using different methods leads to different analysis results, and if so, how should we analyse the performance of the ranking algorithms? To answer these questions, this study compares among test data and different citation-based benchmarks by examining their relationships and assessing the effect of the method choices on their analysis results. The results of our experiments show that there does exist difference in analysis results when employing test data and different benchmarks, and relying exclusively on one benchmark or test data may bring inadequate analysis results. In addition, a guideline on how to conduct a comprehensive analysis using multiple benchmarks from different perspectives is summarised, which can help provide a systematic understanding and profile of the analysed algorithms.
Zhang, Y, Xu, X, Fang, J, Huang, W & Wang, J 2022, 'Load characteristics of triangular honeycomb structures with self-similar hierarchical features', Engineering Structures, vol. 257, pp. 114114-114114.
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Zhang, Y, Yu, X, Lu, X & Liu, P 2022, 'Pro-UIGAN: Progressive Face Hallucination From Occluded Thumbnails', IEEE Transactions on Image Processing, vol. 31, pp. 3236-3250.
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Zhang, Y, Yuen, K-V, Mousavi, M & Gandomi, AH 2022, 'Timber damage identification using dynamic broad network and ultrasonic signals', Engineering Structures, vol. 263, pp. 114418-114418.
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Timber has been widely utilized as a type of green material in the construction industry. However, the anisotropic and highly heterogeneous nature of timber increases the difficulty of damage identification, which is critical for maintaining structures in which it is used. In this paper, we propose a timber damage identification dynamic broad network, namely TimberNet, that can quickly realize damage identification via a one-shot calculation. Ultrasonic signals are fed into the dynamic network to automatically extract features for damage identification, avoiding excessive artificial involvement in feature selection. Furthermore, the proposed method allows incremental updating of the damage detection model and greatly reduces the updating time and computational cost. Comparison studies with some well-known algorithms demonstrated that the damage identification accuracy of TimberNet is about 30% higher than that of the Naïve Bayes classifier. Moreover, its training efficiency and inference speed are 12 times and 2.1 times greater than those of the one-dimensional convolutional neural network (1DCNN), respectively. Finally, a series of validation experiments indicates the robustness of the proposed method in timber damage identification.
Zhang, Y-D 2022, 'Preface', Recent Patents on Engineering, vol. 16, no. 1.
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Zhang, Y-F, Zheng, J, Jia, W, Huang, W, Li, L, Liu, N, Li, F & He, X 2022, 'Deep RGB-D Saliency Detection Without Depth', IEEE Transactions on Multimedia, vol. 24, no. 99, pp. 755-767.
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The existing saliency detection models based on RGB colors only leverage appearance cues to detect salient objects. Depth information also plays a very important role in visual saliency detection and can supply complementary cues for saliency detection. Although many RGB-D saliency models have been proposed, they require to acquire depth data, which is expensive and not easy to get. In this paper, we propose to estimate depth information from monocular RGB images and leverage the intermediate depth features to enhance the saliency detection performance in a deep neural network framework. Specically, we rst use an encoder network to extract common features from each RGB image and then build two decoder networks for depth estimation and saliency detection, respectively. The depth decoder features can be fused with the RGB saliency features to enhance their capability. Furthermore, we also propose a novel dense multiscale fusion model to densely fuse multiscale depth and RGB features based on the dense ASPP model. A new global context branch is also added to boost the multiscale features. Experimental results demonstrate that the added depth cues and the proposed fusion model can both improve the saliency detection performance. Finally, our model not only outperforms state-of-the-art RGB saliency models, but also achieves comparable results compared with state-of-the-art RGB-D saliency models.
Zhang, Y-T, Wei, W, Wang, C & Ni, B-J 2022, 'Microbial and physicochemical responses of anaerobic hydrogen-producing granular sludge to polyethylene micro(nano)plastics', Water Research, vol. 221, pp. 118745-118745.
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Micro(nano)plastics is an emerging contaminant in wastewater that has showed significant impacts on various biological treatment processes. Nevertheless, the underlying effects of micro(nano)plastics with different concentrations and sizes on the anaerobic hydrogen-producing granular sludge (HPG) were still unclear. This work firstly attempted to illustrate the microbial and physicochemical responses of HPG to a shock load of polyethylene microplastics (PE-MPs) with varied concentrations and sizes. The results revealed that the PE-MPs inhibitory effect on hydrogen production by HPG was both concentration- and size-dependent. Specifically, the increase of PE-MPs concentration and the decline of PE-MPs size to nano-sized plastics (NPs) significantly decreased the hydrogen yield, downgraded to 79.9 ± 2.6% and 63.0 ± 3.9% (p = 0.001, and 0.0002) of control, respectively, at higher MPs concentration and the smaller MPs size (i.e., NPs). The higher PE-MPs concentration and PE-NPs also suppressed extracellular polymeric substances (EPS) generation more severely. The critical bio-processes involved in hydrogen production were disturbed by PE-MPs, with the extent of negative impacts depending on the dosage and size of PE-MPs. These adverse impacts further manifested as granule disintegration and loss of cellular activity. Mechanism analysis highlighted the roles of oxidative stress, leachate released from PE-MPs, interaction between PE-NPs and granules inducing physical crushing of HPG that led to possible direct contact between cells and toxic substances.
Zhang, Y-T, Wei, W, Wang, C & Ni, B-J 2022, 'Understanding and mitigating the distinctive stresses induced by diverse microplastics on anaerobic hydrogen-producing granular sludge', Journal of Hazardous Materials, vol. 440, pp. 129771-129771.
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This work comparatively studied the different stress responses of anaerobic hydrogen-producing granular sludge (HPG) to several typical MPs in wastewater, i.e., polyethylene (PE), polyethylene terephthalate (PET), and polyvinyl chloride (PVC) MPs. A new approach to mitigating the inhibition caused by MPs based on biochar was then proposed. The results displayed that microbe in HPG had diverse tolerances to PE-MPs, PET-MPs and PVC-MPs, with the hydrogen production downgraded to 82.0 ± 3.2 %, 72.3 ± 2.5 % and 66.6 ± 2.3 % (p < 0.05) of control respectively, due to the distinct leachates toxicities and oxidative stress level induced by different MPs. The discrepant mitigation reflected in the hydrogen yields of biochar-based HPGs raised back to 88.7 ± 1.4 %, 85.3 ± 3.8 % and 88.5 ± 3.5 % of control. The MPs induced disintegrated granule morphology, fragile microbial viability and impaired defensive function of extracellular polymeric substances were restored by biochar. The effective mitigation was revealed to be due to the strong adsorption of MPs by biochar, reducing direct contact between microbes and MPs. Biochar addition also enhanced protection for HPG by increasing EPS secretion and weakened the oxidative damage to anaerobes induced by MPs. Biochar manifested the disparate adsorption properties of three MPs. The most superior mitigation in HPG contaminated by PVC-MPs was attributed to the strongest affinity of biochar to PVC-MPs and effective alleviation of PVC leachates toxicity.
Zhang, Z, Jiang, S, Huang, C & Da Xu, RY 2022, 'Unsupervised Clothing Change Adaptive Person ReID', IEEE Signal Processing Letters, vol. 29, pp. 304-308.
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Zhang, Z, Li, X, Liu, H, Zamyadi, A, Guo, W, Wen, H, Gao, L, Nghiem, LD & Wang, Q 2022, 'Advancements in detection and removal of antibiotic resistance genes in sludge digestion: A state-of-art review', Bioresource Technology, vol. 344, no. Pt A, pp. 126197-126197.
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Sludge from wastewater treatment plants can act as a repository and crucial environmental provider of antibiotic resistance genes (ARGs). Over the past few years, people's knowledge regarding the occurrence and removal of ARGs in sludge has broadened remarkably with advancements in molecular biological techniques. Anaerobic and aerobic digestion were found to effectively achieve sludge reduction and ARGs removal. This review summarized advanced detection and removal techniques of ARGs, in the last decade, in the sludge digestion field. The fate of ARGs due to different sludge digestion strategies (i.e., anaerobic and aerobic digestion under mesophilic or thermophilic conditions, and in combination with relevant pretreatment technologies (e.g., thermal hydrolysis pretreatment, microwave pretreatment and alkaline pretreatment) and additives (e.g., ferric chloride and zero-valent iron) were systematically summarized and compared in this review. To date, this is the first review that provides a comprehensive assessment of the state-of-the-art technologies and future recommendations.
Zhang, Z, Liao, S, Yang, Y, Che, W & Xue, Q 2022, 'Low-Profile and Shared Aperture Dual-Polarized Omnidirectional Antenna by Reusing Structure of Annular Quasi-Dipole Array', IEEE Transactions on Antennas and Propagation, vol. 70, no. 9, pp. 8590-8595.
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A low-profile and shared aperture dual-polarized (DP) omnidirectional antenna by reusing the structure of an annular quasi-dipole array is presented. The proposed antenna consists of a center-fed circular patch antenna, an annular quasi-dipole array, a wideband one-to-four power divider integrated with the center-fed circular patch antenna, and two annular strips. The center-fed circular patch antenna is utilized as the driven element for the vertically polarized (VP) radiation. The annular quasi-dipole array fed by the one-to-four power divider produces horizontally polarized (HP) radiation. Simultaneously, it also acts as the VP director to improve VP gain and bandwidth. Besides, the two annular strips enhance the HP omni-directivity and gain. In this way, the VP and HP share the same aperture to improve the profile utilization rate effectively. Due to the symmetry of the basic antenna structure, stable DP donut patterns are obtained within the entire operating bandwidth. For demonstration, a 4.1-5.0 GHz prototype is designed, fabricated, and measured. Under a low profile of 0.13λ0 , the prototype realizes a-10 dB overlapped bandwidth of 20%, high port isolation of above 35 dB, and stable DP donut patterns with horizontal gain larger than 1.8 dBi.
Zhang, Z, Ni, B-J, Zhang, L, Liu, Z, Fu, W, Dai, X & Sun, J 2022, 'Medium-chain fatty acids production from carbohydrates-rich wastewater through two-stage yeast biofilm processes without external electron donor addition: Biofilm development and pH impact', Science of The Total Environment, vol. 828, pp. 154428-154428.
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The production of medium-chain fatty acids (MCFAs) is considered promising for carbon resource recovery from waste streams. However, a large quantity of external electron donors are often required, causing great cost and environmental impact. Therefore, in this study, a two-stage technology was developed to produce MCFAs from carbohydrate-rich wastewater without external electron donor addition, with the biofilm development and pH impact being explored. Stage I aimed at converting organics into ethanol and a yeast biofilm reactor is innovatively applied. The results showed that the yeast biofilm could quickly form on carriers with steady-state thickness reaching 50-200 μm. However, the attachment of yeast biofilm was weak at the initial stage so that the violent turbulence should be avoided during operation. The polyurethane foam was the most suitable for yeast biofilm development among the tested carriers, as evidenced by the highest ethanol production, accounting for 74.2% of soluble organics. The Nakaseomyces was the main fungal genus in the steady-state biofilm, while lactic acid bacteria were also developed, resulting in lactate and acetate production. In Stage II, the yeast biofilm reactor effluent was applied for MCFA production at different pH (5-8). However, the MCFA production selectivity was significantly affected by pH, with 65.2% at pH of 5 but decreasing substantially to 3.0% at pH of 8. Both the microbial and electron transfer efficiency analysis suggested that mildly acidic pH can promote the electron transfer from ethanol toward the chain elongation process instead of its excessive oxidation. Thus, if conditions of online extraction or microbial tolerance permit, a lower pH should be recommended for Stage II in the developed technology as well as other ethanol-based MCFA production process. This is a conceptual study that eliminated external electron donor addition in MCFAs production and provide a sustainable and reliable way in carbon...
Zhang, Z, Wu, Q, Wang, Y & Chen, F 2022, 'Exploring Pairwise Relationships Adaptively From Linguistic Context in Image Captioning', IEEE Transactions on Multimedia, vol. 24, pp. 3101-3113.
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For image captioning, recent works start to focus on exploring visual relationships for generating high-quality interactive words (i.e. verbs and prepositions). However, many existing works only focus on semantic level by analysing the feature similarity between objects in the visual domain but ignore the linguistic context included in the caption decoder. When captioning is being carried out, the entity words can be inferred based on visual information of objects. The interactive words representing the relationships between entity words can only be inferred based on high-level language meaning generated in the process of captioning decoding. Such high-level language meaning is called linguistic context, which refers to the relational context between words or phrases in the caption sentences. The linguistic context can be used as strong guidance to explore related visual relationships between different objects effectively. To achieve this, we propose a novel context-adaptive attention module that is strongly driven by the linguistic context from the caption decoder. In this module, a novel design of visual relationship attention is proposed based on a bilinear self-attention model to explore related visual relationships and encode more discriminative features under the linguistic context. It works parallelly with visual region attention. To achieve the adaptive process of attending to related visual relationships for generating interactive words or related visual objects for entity words, an attention modulator is integrated as an attention channel controller responding to the changing linguistic context of the caption decoder dynamically. To take full advantage of the linguistic context in the caption, an additional interaction dataset is extracted from the COCO caption datasets and COCO Entities dataset to supervise the training of the proposed context-adaptive attention module explicitly. Demonstrated by experiments on MSCOCO caption dataset, it is e...
Zhang, Z, Xu, T & Castel, A 2022, 'Damage of non-steam-cured UHPC under axial compression with and without short-term sustained loading history', Structures, vol. 38, pp. 1066-1078.
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The damage of non-steam-cured ultra-high performance concrete (UHPC) under short-term sustained compression was experimentally investigated. The UHPC specimens were tested under monotonic loading, cyclic loading without a sustained loading history, or cyclic loading with a sustained loading history. The influence of the sustained stress levels and loading age of UHPC was elucidated. The stress-strain history of the specimens was measured. The effect of damage due to the short-term sustained loading history on the mechanical properties of UHPC was analyzed. The fraction of the sustained strains contributing to the damage was calibrated. The micro-cracking propagation within the UHPC is imagined based on non-destructive testing using an ultrasonic wave method and by assessing the nominal Poisson's ratio. The interface between steel fibers and matrix was observed by Scanning Electron Microscope (SEM). The damage occurred for the UHPC specimens under short-term sustained compression when the sustained stress was higher than 0.7fc′. Both the compressive strength and the elastic modulus of the specimens were reduced. When the sustained stress was greater than or equal to 0.8fc′, the elastic modulus of the UHPC specimens measured during the reloading phase was considerably reduced. The specimen group subjected to a sustained stress of 0.9fc′ with a duration of 128 s experienced a reduction of about 30% in elastic modulus. The damage is statistically independent of the loading age of the UHPC. Damage is characterized by micro-cracking propagation within the UHPC specimens. Microstructural analysis of the interface between steel fibers and matrix shows that under high sustained stress, the bond between the steel fibers and the concrete matrix was impaired by the creep effect and could not prevent the propagation of micro-cracks.
Zhao, E, Walker, PD & Surawski, NC 2022, 'Emissions life cycle assessment of diesel, hybrid and electric buses', Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 236, no. 6, pp. 1233-1245.
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This paper applies a case study approach for Australia and calculates the equipment life cycle assessment of diesel, hybrid and electric buses. This study prepared the assessment according to the procedures and methodologies outlined in the ISO 14040:2006 Environmental Management – Life Cycle Assessment. The authors have chosen three bus models currently in service in the Australian bus fleet to serve as a baseline model for comparison. The amount of greenhouse gas emissions were calculated from the production, assembly, transportation, maintenance and disposal phases. The results in this study show that the electric bus has a higher total environmental impact than the diesel and hybrid bus, mainly due to the manufacturing of the lithium-ion battery. The results also show that the electric bus has a higher environmental impact than the diesel and hybrid bus (18.2% and 14.7% higher, respectively), albeit specific to the product life cycle and without including operation emissions. However, there are many opportunities to reduce product life cycle emissions, such as improvement in manufacturing efficiency, developing new battery technology and production in regions with low carbon-intense grid-mixes.
Zhao, F, Cao, S, Luo, Q, Li, L & Ji, J 2022, 'Practical design of the QZS isolator with one pair of oblique bars by considering pre-compression and low-dynamic stiffness', Nonlinear Dynamics, vol. 108, no. 4, pp. 3313-3330.
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Various quasi-zero stiffness (QZS) vibration isolators have been developed by using the structures of oblique springs and bars. Towards a practical design, this paper further theoretically and experimentally studies the static and dynamic force of the QZS isolator with one pair of oblique bars by considering pre-compression of horizontal springs and producing an extremely low-dynamic stiffness. By designing the new parameter configuration, two simple formulations are derived on the basis of two QZS conditions to design an improved QZS isolator with a constant low-dynamic stiffness in a wide region around the static equilibrium position. A detailed comparison between the proposed and the existing isolators is made to show the significant improvement on isolation performance. On the basis of the derived formulations, a prototype is fabricated and tested to verify the theoretical formulations and constant low-dynamic stiffness. The experimental results show that the designed QZS isolator can achieve a much wider QZS region to isolate vibration in a larger frequency band and demonstrate a lower displacement transmissibility for the external excitation.
Zhao, H, Hu, Y, Tang, Z, Wang, K, Li, Y & Li, W 2022, 'Deterioration of concrete under coupled aggressive actions associated with load, temperature and chemical attacks: A comprehensive review', Construction and Building Materials, vol. 322, pp. 126466-126466.
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Concrete infrastructure subjected to mechanical load or surrounding aggressive environments experiences gradual deterioration of performance. Although many studies have conducted on durability of concrete under single aggressive condition, the related research results have already been well-summarized. The concrete deterioration under coupled actions is severer and more complicated than that under single aggressive conditions. However, few studies have reviewed the studies on this topic. This paper comprehensively reviews the research outcomes of the performance of concrete under various coupled aggressive actions, such as high temperature and impact load, freeze–thaw and fatigue loading, alkali-silica reaction and compression, sulfate attack and dry-wet cycling, chloride penetration and carbonation, as well as acid corrosion and high temperature exposure, and so on. The testing methods, interaction of two aggressive conditions, and strengthening measures have been discussed and summarized correspondingly. The results indicate that the performance degradation under the coupled mechanical load and aggressive environments are much severer compared to the single aggressive environment. The properties of concrete are significantly affected by the severity of coupled aggressive actions, and can be improved at the early-age stage. The experimental methods also have higher impact on the performance of concrete due to different mechanisms, but the difference between under and after high temperature exposures is relatively lower. Finally, some enhancement methods proposed in this review, such as adding fly ash, silica fume, slag, fibers or air-entraining agents, are effective in improving the durability of concrete under coupled aggressive environments.
Zhao, H, Zhou, T, Long, G, Jiang, J & Zhang, C 2022, 'Extracting Local Reasoning Chains of Deep Neural Networks', Transactions on Machine Learning Research, vol. 2022-November.
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We study how to explain the main steps of inference that a pre-trained deep neural net (DNN) relies on to produce predictions for a (sub)task and its data. This problem is related to network pruning and interpretable machine learning with the following highlighted differences: (1) fine-tuning of any neurons/filters is forbidden; (2) we target a very high pruning rate, e.g., ≥ 95%, for better interpretability; (3) the interpretation is for the whole inference process on a few data of a task rather than for individual neurons/filters or a single sample. In this paper, we introduce NeuroChains to extract the local inference chains by optimizing differentiable sparse scores for the filters and layers, which reflects their importance in preserving the outputs on a few data drawn from a given (sub)task. Thereby, NeuroChains can extract an extremely small sub-network composed of critical filters exactly copied from the original pre-trained DNN by removing the filters/layers with small scores. For samples from the same class, we can then visualize the inference pathway in the pre-trained DNN by applying existing interpretation techniques to the retained filters and layers. It reveals how the inference process stitches and integrates the information layer by layer and filter by filter. We provide detailed and insightful case studies together with several quantitative analyses over thousands of trials to demonstrate the quality, sparsity, fidelity and accuracy of the interpretation. In extensive empirical studies on VGG, ResNet, and ViT, NeuroChains significantly enriches the interpretation and makes the inner mechanism of DNNs more transparent.
Zhao, J, Li, H, Qu, L, Zhang, Q, Sun, Q, Huo, H & Gong, M 2022, 'DCFGAN: An adversarial deep reinforcement learning framework with improved negative sampling for session-based recommender systems', Information Sciences, vol. 596, pp. 222-235.
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Zhao, J, Zhang, JA, Li, Q, Zhang, H & Wang, X 2022, 'Recursive constrained generalized maximum correntropy algorithms for adaptive filtering', Signal Processing, vol. 199, pp. 108611-108611.
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Thanks to the ability of preventing the accumulation of errors, constrained adaptive filtering (CAF) algorithms have been widely applied. However, in practice, non-Gaussian noise may significantly degrade the filtering performance of CAFs derived from the second-order signal statistics. In this paper, we propose several constrained generalized maximum correntropy (CGMC) algorithms to overcome this problem, inspired by the robustness and flexibility of GMC to non-Gaussian noises. We first introduce a CGMC algorithm based on the gradient method. To improve its convergence rate with correlated inputs, we further propose a recursive CGMC (RCGMC) algorithm. For RCGMC, we conduct the convergence analysis, and characterize the theoretical transient mean square deviation (MSD) performance. Furthermore, we derive a low-complexity version of RCGMC by using the weighting method and the leading dichotomous coordinate descent (DCD) algorithm. Simulation results demonstrate the effectiveness of our proposed algorithms in non-Gaussian noise environment, and the consistency between the analytical and simulation results.
Zhao, J, Zhang, JA, Li, Q, Zhang, H & Wang, X 2022, 'Recursive Maximum Correntropy Algorithms for Second-Order Volterra Filtering', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 4, pp. 2336-2340.
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As a special case of the Volterra system, the second-order Volterra (SOV) filter is very efficient for nonlinear system identification. The improved correntorpy based on the generalized Gaussian density function has been proven robust against impulsive noise. In this brief, we propose several SOV filters based on a recursive maximum correntropy (RMC) algorithm for nonlinear system identification. We first introduce a basic RMC algorithm, which faces a trade-off between filtering accuracy and tracking capability due to the use of a fixed forgetting factor (FFF). Two RMCs with variable FF (VFF) are further proposed to enhance the tracking ability. Simulation results demonstrate that our proposed algorithms outperform existing ones in impulsive noise environments and/or in time-varying systems.
Zhao, J, Zhang, JA, Zhang, H & Li, Q 2022, 'Generalized correntropy induced metric based total least squares for sparse system identification', Neurocomputing, vol. 467, pp. 66-72.
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The total least squares (TLS) method has been successfully applied to system identification in the errors-in-variables (EIV) model, which can efficiently describe systems where input–output pairs are contaminated by noise. In this paper, we propose a new gradient-descent TLS filtering algorithm based on the generalized correntropy induced metric (GCIM), called as GCIM-TLS, for sparse system identification. By introducing GCIM as a penalty term to the TLS problem, we can achieve improved accuracy of sparse system identification. We also characterize the convergence behaviour analytically for GCIM-TLS. To reduce computational complexity, we use the first-order Taylor series expansion and further derive a simplified version of GCIM-TLS. Simulation results verify the effectiveness of our proposed algorithms in sparse system identification.
Zhao, J, Zhang, T, Sun, Q, Huo, H & Gong, M 2022, 'A novel initialization method of fixed point continuation for recommendation systems', Expert Systems with Applications, vol. 210, pp. 118346-118346.
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Zhao, J, Zheng, S, Huo, H, Gong, M, Zhang, T & Qu, L 2022, 'Fast weighted CP decomposition for context-aware recommendation with explicit and implicit feedback', Expert Systems with Applications, vol. 204, pp. 117591-117591.
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Zhao, L, Chen, Z, Wang, H, Li, L, Mao, X, Li, Z, Zhang, J & Wu, D 2022, 'An Improved Deadbeat Current Controller of PMSM Based on Bilinear Discretization', Machines, vol. 10, no. 2, pp. 79-79.
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Based on the bilinear discretization mathematical model of permanent magnet synchronous motor (PMSM), an improved incremental deadbeat current prediction control algorithm is proposed. Aiming at the system instability caused by the forward Euler discretization method, this paper combines the deadbeat current prediction control and the improved bilinear discretization method to improve the system stability. Further, the proposed controller considers the two-beat delay of a digital system to make the mathematical model more accurate. Moreover, the proposed bilinear discretization predictive current controller is not affected by the permanent magnet flux of the motor. Then, the system stability conditions of the proposed controller are analyzed. The simulation and experimental results verify the feasibility and effectiveness of the proposed method.
Zhao, L-H, Wen, S, Li, C, Shi, K & Huang, T 2022, 'A Recent Survey on Control for Synchronization and Passivity of Complex Networks', IEEE Transactions on Network Science and Engineering, vol. 9, no. 6, pp. 4235-4254.
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Over the past few years, dynamical behaviors of complex networks (CNs) and complex networks with multi-weights (CNMWs) have attracted considerable attention on account of their successfully applying in many distinct fields. This paper attempts to introduce some recent progress in the dynamical behaviors (including synchronization and passivity) for CNs and CNMWs under various control approaches. Firstly, we focus on synchronization, passivity, and their extensions for CNs and CNMWs. Secondly, some control methods, such as adaptive control, pinning control, and impulsive control, used in CNs and CNMWs are also discussed, where some of the existing results along these topics are presented in a tutorial-like fashion. Thirdly, we introduce several applications of synchronization in CNs and CNMWs, especially complex human networks and urban public traffic networks. These existing results are summarized and some interesting future investigations are also highlighted in end.
Zhao, L-H, Wen, S, Xu, M, Shi, K, Zhu, S & Huang, T 2022, 'PID Control for Output Synchronization of Multiple Output Coupled Complex Networks', IEEE Transactions on Network Science and Engineering, vol. 9, no. 3, pp. 1553-1566.
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This article attempts to address output synchronization and $\mathcal {H}_{\infty }$ output synchronization problems for multiple output coupled complex networks (MOCCNs) under proportional-derivative (PD) and proportional-integral (PI) controllers. Firstly, two classes of MOCCNs without and with external disturbances are separately put forward. Secondly, based on the PD and PI control schemes, several output synchronization criteria for MOCCNs are formulated by using the Lyapunov functional method and inequality techniques. Thirdly, $\mathcal {H}_{\infty }$ output synchronization for MOCCNs is also studied with the help of the PD and PI controllers. Finally, two numerical examples are separately presented to demonstrate the validity of acquired theoretical results.
Zhao, P, Lu, W, Wang, S, Peng, X, Jian, P, Wu, H & Zhang, W 2022, 'Multi-granularity interaction model based on pinyins and radicals for Chinese semantic matching', World Wide Web, vol. 25, no. 4, pp. 1703-1723.
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Zhao, R, Tao, M, Wu, C, Li, X & Wang, S 2022, 'Study on size and load rate effect of dynamic fragmentation and mechanical properties of marble sphere', Engineering Failure Analysis, vol. 142, pp. 106814-106814.
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Impact-induced fragmentation of rock blocks is a frequent phenomenon during rockfall, mining, geotechnical engineering. In order to obtain the fragmentation characteristics of rocks under the action of impact, the marble spheres with diameters of 30, 35, 40, 45, and 50 mm were used for paired-impact using the Split Hopkinson Pressure Bar (SHPB) tests, at the impact velocities of 4.5, 7.4 and 11.2 m/s, respectively, and the corresponding load and failure characteristics of each sample were examined. The experimental results revealed that the peak loading capacity and fragmentations of the rock sphere was influenced by the sample size and impact velocity. The finite element method (FEM) simulation by LS-DYNA was carried out to reveal the failure mechanism of marble spheres under paired-impact. Finally, a theoretical model was proposed to evaluating the breakage of rock block under impact. The results can be used to guide rock breakage in mines and rockfall hazard protection.
Zhao, S & Burnett, IS 2022, 'Evolutionary array optimization for multizone sound field reproduction', The Journal of the Acoustical Society of America, vol. 151, no. 4, pp. 2791-2801.
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Multizone sound field reproduction aims to generate personal sound zones in a shared space with multiple loudspeakers. Traditional multizone sound field reproduction methods have focused on optimizing the source strengths given a preset array configuration. Recently, however, various methods have explored optimization of the loudspeaker locations. These can be categorized into sparse regularization and iterative methods with existing studies based on numerical simulations and mostly aiming at single-zone sound field reproduction. In this paper, unique experiments compare the state-of-the-art loudspeaker placement optimization methods by selecting a smaller number of loudspeakers from the candidates uniformly placed along a circle. An evolutionary array optimization scheme is proposed and shown to outperform the best existing methods in terms of mean square error in the bright zone and acoustic contrast between the bright and dark zones at frequencies below 1 kHz. The proposed evolutionary optimization scheme is simple, flexible, and can be extended to broadband optimization and other cost functions.
Zhao, S, Dou, P, Sun, N, Shon, HK & He, T 2022, 'Fabrication of dialyzer membrane-based forward osmosis modules via vacuum-assisted interfacial polymerization for the preparation of dialysate', Journal of Membrane Science, vol. 659, pp. 120814-120814.
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Zhao, S, Li, S, Liu, H, Jiang, J, Wang, M, Liu, H, Wang, W & Wang, Z 2022, 'Quadruple hydrogen bond motif-toughened polybenzoxazine with improved comprehensive performances', Advanced Composites and Hybrid Materials, vol. 5, no. 4, pp. 3057-3067.
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Zhao, S, Zhu, Q, Cheng, E & Burnett, IS 2022, 'A room impulse response database for multizone sound field reproduction (L)', The Journal of the Acoustical Society of America, vol. 152, no. 4, pp. 2505-2512.
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This letter introduces a database of Room Impulse Responses (RIRs) measured in seven different rooms for multizone sound field reproduction research in various acoustic environments. A circular array of 60 loudspeakers was installed in each room, with two microphone arrays placed sequentially in five different zones inside the loudspeaker array. A total of 260 400 RIRs were measured to establish the database. As a demonstration application of the database for multizone sound field reproduction, simulations were performed on the pressure matching and acoustic contrast control methods to investigate how a system optimized with the RIRs measured in one room would perform in other rooms.
Zhao, T, Wang, S, Li, Y, Jia, C, Su, Z, Hao, D, Ni, B, Zhang, Q & Zhao, C 2022, 'Heterostructured V‐Doped Ni2P/Ni12P5 Electrocatalysts for Hydrogen Evolution in Anion Exchange Membrane Water Electrolyzers', Small, vol. 18, no. 40, pp. e2204758-2204758.
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AbstractRegulating the electronic structure and intrinsic activity of catalysts’ active sites with optimal hydrogen intermediates adsorption is crucial to enhancing the hydrogen evolution reaction (HER) in alkaline media. Herein, a heterostructured V‐doped Ni2P/Ni12P5 (V–Ni2P/Ni12P5) electrocatalyst is fabricated through a hydrothermal treatment and controllable phosphidation process. In comparison with pure‐phase V–Ni2P, in/ex situ characterizations and theoretical calculations reveal a redistribution of electrons and active sites in V–Ni2P/Ni12P5 due to the V doping and heterointerfaces effect. The strong coupling between Ni2P and Ni12P5 at the interface leads to an increased electron density at interfacial Ni sites while depleting at P sites, with V‐doping further promoting the electron accumulation at Ni sites. This is accompanied by the change of active sites from the anionic P sites to the interfacial Ni–V bridge sites in V–Ni2P/Ni12P5. Benefiting from the interface electronic structure, increased number of active sites, and optimized H‐adsorption energy, the V‐Ni2P/Ni12P5 exhibits an overpotential of 62 mV to deliver 10 mA cm–2 and excellent long‐term stability for HER. The V–Ni2P/Ni12P5 catalyst is applied for anion exchange membrane water electrolysis to deliver superior performance with a current density of 500 mA cm–2 at a cell voltage of 1.79 V and excellent durability.
Zhao, X, Liu, Y, Wang, Z, Wu, K, Dissanayake, G & Liu, Y 2022, 'TG: Accurate and Efficient RGB-D Feature With Texture and Geometric Information', IEEE/ASME Transactions on Mechatronics, vol. 27, no. 4, pp. 1973-1981.
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Zhao, X, Peng, X, Niu, K, Li, H, He, L, Yang, F, Wu, T, Chen, D, Zhang, Q, Ouyang, M, Guo, J & Pan, Y 2022, 'A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy', Frontiers in Neuroinformatics, vol. 16, p. 771965.
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Magnetoencephalography is a noninvasive neuromagnetic technology to record epileptic activities for the pre-operative localization of epileptogenic zones, which has received increasing attention in the diagnosis and surgery of epilepsy. As reported by recent studies, pathological high frequency oscillations (HFOs), when utilized as a biomarker to localize the epileptogenic zones, result in a significant reduction in seizure frequency, even seizure elimination in around 80% of cases. Thus, objective, rapid, and automatic detection and recommendation of HFOs are highly desirable for clinicians to alleviate the burden of reviewing a large amount of MEG data from a given patient. Despite the advantage, the performance of existing HFOs rarely satisfies the clinical requirement. Consequently, no HFOs have been successfully applied to real clinical applications so far. In this work, we propose a multi-head self-attention-based detector for recommendation, termed MSADR, to detect and recommend HFO signals. Taking advantage of the state-of-the-art multi-head self-attention mechanism in deep learning, the proposed MSADR achieves a more superior accuracy of 88.6% than peer machine learning models in both detection and recommendation tasks. In addition, the robustness of MSADR is also extensively assessed with various ablation tests, results of which further demonstrate the effectiveness and generalizability of the proposed approach.
Zhao, Y, Chen, J, Zhang, J, Wu, D, Blumenstein, M & Yu, S 2022, 'Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks', Concurrency and Computation: Practice and Experience, vol. 34, no. 7.
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SummaryIn the age of the Internet of Things (IoT), large numbers of sensors and edge devices are deployed in various application scenarios; Therefore, collaborative learning is widely used in IoT to implement crowd intelligence by inviting multiple participants to complete a training task. As a collaborative learning framework, federated learning is designed to preserve user data privacy, where participants jointly train a global model without uploading their private training data to a third party server. Nevertheless, federated learning is under the threat of poisoning attacks, where adversaries can upload malicious model updates to contaminate the global model. To detect and mitigate poisoning attacks in federated learning, we propose a poisoning defense mechanism, which uses generative adversarial networks to generate auditing data in the training procedure and removes adversaries by auditing their model accuracy. Experiments conducted on two well‐known datasets, MNIST and Fashion‐MNIST, suggest that federated learning is vulnerable to the poisoning attack, and the proposed defense method can detect and mitigate the poisoning attack.
Zhao, Y, Liu, B, Zhu, T, Ding, M & Zhou, W 2022, 'Private‐encoder: Enforcing privacy in latent space for human face images', Concurrency and Computation: Practice and Experience, vol. 34, no. 3.
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SummaryThe explosive growth of various computer vision technologies generates a tremendous amount of visual data online every day. In addition to bringing convenience and revolutionizing our daily life, image data also reveal a wide range of sensitive information and pose unprecedented privacy leakage risks. Particularly, in the case of photos contain human faces, people can easily access those face images on social media without any consent, and the misuse of personal information could cause serious privacy violation to individuals. Therefore, it is essential to consider sanitizing people's identity information when using images containing human faces. As a result, there has been rapid development in the area of facial anonymization, also called image de‐identification. However, due to the emergence of numerous deep‐learning based attacks, traditional anonymization methods such as blurring and mosaic are weak and ineffective to protect individual's privacy in face images. To respond to this challenge, this article proposes a novel de‐identification method that utilizes a deep neural network. The proposed framework encompasses two modules: encoder network and generator network. The encoder transforms a face image into a high‐semantic latent vector of codes, which will be de‐identified according to the differential privacy criterion. The generator leverages the unconditional generative adversarial network to synthesize high‐quality images based on the modified latent codes from the encoder. Extensive experimental results indicate that our proposed model can protect image privacy while keeping the processed image visual realistic.
Zhao, Y, Ngo, HH & Yu, X 2022, 'Phytohormone-like small biomolecules for microalgal biotechnology', Trends in Biotechnology, vol. 40, no. 9, pp. 1025-1028.
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Microalgae are highly adaptable to abiotic stress and produce valuable metabolites, but microalgal commercialization is still difficult because of minimal yields. The application of phytohormone-like small biomolecules is effective in simultaneously improving the productivity of valuable microalgal biomass-derived metabolites and stress tolerance. This represents a significant opportunity for microalgal biotechnology.
Zhao, Z, Cao, L & Lin, K-Y 2022, 'Revealing the Distributional Vulnerability of Discriminators by Implicit Generators', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PP, no. 99, pp. 1-13.
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Zhen, J, Zhao, Y, Yu, X, Guo, W, Qiao, Z, Ismail, S & Ni, S-Q 2022, 'Feasibility of Partial Nitrification Combined with Nitrite-Denitrification Phosphorus Removal and Simultaneous Nitrification–Endogenous Denitrification for Synchronous Chemical Oxygen Demand, Nitrogen, and Phosphorus Removal', ACS ES&T Water, vol. 2, no. 6, pp. 1119-1131.
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Zheng, G, Zhang, L, Wang, E, Yao, R, Luo, Q, Li, Q & Sun, G 2022, 'Investigation into multiaxial mechanical behaviors of Kelvin and Octet-B polymeric closed-cell foams', Thin-Walled Structures, vol. 177, pp. 109405-109405.
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As a class of effective lightweight energy absorption materials, periodic closed-cell foams have been widely applied in engineering, in which the Kelvin and Octet-B foams have demonstrated great value in the research of multiaxial mechanical characteristics. For this reason, this study aims to develop a series of realistic finite element analysis (FEA) models for investigating their uniaxial, compression-shear, and arbitrary triaxial compression performance. Under uniaxial loading conditions, the mechanical responses and deformation modes of the two foams are compared and analyzed with different densities. The influence of different loading angles is also considered under compressive-shear loading. The deformation pattern of foams subject to equal biaxial and hydrostatic loading are compared with uniaxial compression. Based on sufficient simulation data, the initial yield surfaces of the two foams are plotted in the von Mises and mean stress plane, and fitted by three theoretical yield criteria characterized in terms of quadratic functions. It is found that the Miller criterion can better describe the initial yield surface shape of Kelvin foams than the yield models of Deshpande–Fleck and Zhang et al.; while the above yielding models are all of high fitting accuracy for the Octet-B foam. Further, the ability to resist initial yield of the Kelvin foam has proven superior to Octet-B foams by calculating the curve integration. The study is anticipated to provide new insights into novel design and extensive applications of periodic closed-cell foam materials in practice.
Zheng, H, Guan, R, Liu, Q, Ou, K, Li, D-S, Fang, J, Fu, Q & Sun, Y 2022, 'A flexible supercapacitor with high capacitance retention at an ultra-low temperature of -65.0°C', Electrochimica Acta, vol. 424, pp. 140644-140644.
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Zheng, J, Dong, Q, Wang, X, Zhang, Y, Ma, W & Ma, Y 2022, 'Efficient processing of k-regret minimization queries with theoretical guarantees', Information Sciences, vol. 586, pp. 99-118.
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Zheng, J, Li, K, Mhaisen, N, Ni, W, Tovar, E & Guizani, M 2022, 'Exploring Deep-Reinforcement-Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Preserving EdgeIoT', IEEE Internet of Things Journal, vol. 9, no. 21, pp. 21099-21110.
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Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile-edge computing-based Internet of Things (EdgeIoT). On the one hand, the learning accuracy of FL can be improved by selecting the IoT devices with large data sets for training, which gives rise to a higher energy consumption. On the other hand, the energy consumption can be reduced by selecting the IoT devices with small data sets for FL, resulting in a falling learning accuracy. In this article, we formulate a new resource allocation problem for privacy-preserving EdgeIoT to balance the learning accuracy of FL and the energy consumption of the IoT device. We propose a new FL-enabled twin-delayed deep deterministic policy gradient (FL-DLT3) framework to achieve the optimal accuracy and energy balance in a continuous domain. Furthermore, long short-term memory (LSTM) is leveraged in FL-DLT3 to predict the time-varying network state while FL-DLT3 is trained to select the IoT devices and allocate the transmit power. Numerical results demonstrate that the proposed FL-DLT3 achieves fast convergence (less than 100 iterations) while the FL accuracy-to-energy consumption ratio is improved by 51.8% compared to the existing state-of-the-art benchmark.
Zheng, J, Tian, H, Ni, W, Ni, W & Zhang, P 2022, 'Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-Aided Over-the-Air Federated Learning', IEEE Transactions on Wireless Communications, vol. 21, no. 12, pp. 10964-10980.
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Zheng, Y, Luo, Z, Wang, Y, Li, Z, Qu, J & Zhang, C 2022, 'Optimized high thermal insulation by the topological design of hierarchical structures', International Journal of Heat and Mass Transfer, vol. 186, pp. 122448-122448.
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Zheng, Y, Yu, X, Liu, M & Zhang, S 2022, 'Single-Image Deraining via Recurrent Residual Multiscale Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 3, pp. 1310-1323.
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Zheng, Z, Lin, M, Lu, W, Huang, P, Zheng, Y, Zhang, X, Yan, L, Wang, W, Lawson, T, Shi, B, Chen, S & Liu, Y 2022, 'The Efficient Regeneration of Corneal Nerves via Tunable Transmembrane Signaling Channels Using a Transparent Graphene‐Based Corneal Stimulation Electrode', Advanced Healthcare Materials, vol. 11, no. 10.
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AbstractThe efficient regeneration of corneal nerves is of limited success in the field of ophthalmology. This work reports the use of a non‐invasive electrical stimulation technique that uses a transparent graphene‐based corneal stimulation electrode and that can achieve efficient regeneration of corneal nerves. The corneal stimulation electrode is prepared using electroactive nitrogen‐containing conducting polymers such as polyaniline functionalized graphene (PAG). This composite can carry a high capacitive current. It can be used to tune transmembrane signaling pathways including calcium channels and the MAPK signaling pathway. Tuning can lead to the efficient regeneration of corneal damaged nerves after the surgery of laser in‐situ keratomileusis (LASIK). The composite and its application reported have the potential to provide a new way to treat nerve‐related injuries.
Zhong, D, Shivakumara, P, Nandanwar, L, Pal, U, Blumenstein, M & Lu, Y 2022, 'Local Resultant Gradient Vector Difference and Inpainting for 3D Text Detection in the Wild', International Journal of Pattern Recognition and Artificial Intelligence, vol. 36, no. 08, p. 2253005.
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Three-dimensional (3D) text appearing in natural scene images is common due to 3D cameras and the capture of text from different angles, which presents new problems for text detection. This is because of the presence of depth information, shadows, and decorative characters in the images. In this work, we consider those images where 3D text appears with depth, as well as shadow information for text detection. We propose a novel method based on local resultant gradient vector difference (LRGVD), inpainting and a deep learning model for detecting 3D as well as two-dimensional (2D) texts in natural scene images. The boundary of components that are invariant to the above challenges is detected by exploring LRGVD. The LRGVD uses gradient magnitude and direction in a novel way for detecting the boundary of the components. Further, we propose an inpainting method in a new way for restoring the character background information using boundaries. For a given region and the input image, the inpainting method divides the whole image into planes and then propagates the values in the planes into the missing region based on posterior probabilities and neighboring information. This results in text regions with false positives. Then, the differential binarization network (DB-Net) is proposed for detecting text irrespective of orientation, background, 3D or 2D, etc. Experiments conducted on our 3D text images and standard datasets of natural scene text images, namely ICDAR 2019 MLT, ICDAR 2019 ArT, DAST1500, Total-Text and SCUT-CTW1500, show that the proposed method is effective in detecting 3D and 2D texts in the images.
Zhong, J, Kirby, R, Karimi, M & Zou, H 2022, 'A spherical wave expansion for a steerable parametric array loudspeaker using Zernike polynomials', The Journal of the Acoustical Society of America, vol. 152, no. 4, pp. 2296-2308.
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A steerable parametric array loudspeaker (PAL) can electronically steer highly directional audio beams in the desired direction. The challenge of modelling a steerable PAL is to obtain the audio sound pressure in both near and far fields with a low computational load. To address this issue, an extension of the spherical wave expansion is proposed in this paper. The steerable velocity profile on the radiation surface is expanded as Zernike polynomials which are an orthogonal and form a complete set over a unit circle. An expression for the radiated audio sound is then obtained using a superposition of Zernike modes. Compared to the existing methods, the proposed expansion is computationally efficient and provides a rigorous transformation of the quasilinear solution of the Westervelt equation without paraxial approximations. The proposed expansion is further extended to accommodate local effects by using an algebraic correction to the Westervelt equation. Numerical results for steering single and dual beams are presented and discussed. It is shown that the single beam can be steered in the desired direction in both near and far fields. However, dual beams cannot be well separated in the near field, which cannot be predicted by the existing far field models.
Zhong, J, Kirby, R, Karimi, M, Zou, H & Qiu, X 2022, 'Scattering by a rigid sphere of audio sound generated by a parametric array loudspeaker', The Journal of the Acoustical Society of America, vol. 151, no. 3, pp. 1615-1626.
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This work investigates the scattering by a rigid sphere of audio sound generated by a parametric array loudspeaker (pal). A computationally efficient method utilizing a spherical harmonic expansion is developed to calculate the quasilinear solution of audio sound fields based on both Kuznetsov and Westervelt equations. The accuracy of using the Westervelt equation is examined, and the rigid sphere scattering effects are simulated with the proposed method. It is found the results obtained using the Westervelt equation are inaccurate near the sphere at low frequencies. Contrary to conventional loudspeakers, the directivity of the audio sound generated by a pal severely deteriorates behind a sphere, as the ultrasounds maintaining the directivity of the audio sound are almost completely blocked by the sphere. Instead, the ultrasounds are reflected and generate audio sound on the front side of the sphere. It means that a listener in front of the pal will hear the audio sound scattered back after introducing the sphere as if it is reflected by the sphere. The experiment results are also presented to validate the numerical results.
Zhong, J, Zhuang, T, Kirby, R, Karimi, M, Qiu, X, Zou, H & Lu, J 2022, 'Low Frequency Audio Sound Field Generated by a Focusing Parametric Array Loudspeaker', IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 30, pp. 3098-3109.
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Zhong, J, Zhuang, T, Kirby, R, Karimi, M, Zou, H & Qiu, X 2022, 'Quiet zone generation in an acoustic free field using multiple parametric array loudspeakers', The Journal of the Acoustical Society of America, vol. 151, no. 2, pp. 1235-1245.
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This paper investigates the feasibility of remotely generating a quiet zone in an acoustic free field using multiple parametric array loudspeakers (PALs). A primary sound field is simulated using point monopoles located randomly in a two-dimensional plane, or three-dimensional (3D) space, whereas the secondary sound field is generated by multiple PALs uniformly distributed around the circumference of a circle sitting on the same plane as the primary sources, or on the surface of a sphere for 3D space. A quiet zone size is defined as the diameter of the maximal circular zone within which the noise reduction is greater than 10 dB. The size of this quiet zone is found to be proportional to 0.19λN for N secondary sources with a wavelength λ when the primary and secondary sources are in the same plane, whereas it is found to be 0.55λN1/2 for the 3D case. The size of the quiet zones generated by PALs is similar to that observed with traditional omnidirectional loudspeakers; however, the effects of using PALs on the sound field outside the target zone is much smaller due to their sharp radiation directivity and slow decay rate along the propagation distance. Experimental results are also presented to validate these numerical simulations.
Zhong, Y, Bi, T, Wang, J, Zeng, J, Huang, Y, Jiang, T, Wu, Q & Wu, S 2022, 'Empowering the V2X Network by Integrated Sensing and Communications: Background, Design, Advances, and Opportunities', IEEE Network, vol. 36, no. 4, pp. 54-60.
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To enable next-generation connected autonomous vehicles (CAVs), the future Vehicle-to-everything (V2X) network is expected to provide centimeter-accurate localization service while attaining low-latency transmissions in high-mobility environments. Nevertheless, these unprecedented requirements are far beyond the capabilities of 5G vehicular networks. Given the above evolution trend, a natural idea is thus to design a joint system architecture that combines both communications and sensing subsystems. To this end, research efforts toward integrated sensing and communications (ISAC) for the V2X network are well underway. It is our belief that ISAC should facilitate both sensing and communication via a single system in a spectrum-/energy-/cost-efficient way. Moreover, it can also improve the performance of both functionalities with mutual assistance, which is also essential to enable CAV's mission-critical services for 6G and beyond V2X. In this article, we first provide a brief historical overview of V2X and ISAC. In particular, we analyze the forces driving the usage of ISAC in V2X. Then we introduce three ISAC design schemes based on their underlying systems. We also survey state-of-the-art enabling technologies by reviewing recent developments of ISAC-assisted beamforming technologies in vehicular networks. Finally, we shed light on some potential challenges and research directions.
Zhou, F, Li, S, Ouyang, L, Liu, J, Liu, J, Huang, Z & Zhu, M 2022, 'Facile synthesis of black phosphorene via a low melting media assisted ball milling', Chemical Engineering Journal, vol. 444, pp. 136593-136593.
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Few-layer phosphorene (FLP) has attracted strong research interest due to its extraordinary physical and chemical properties. However, an efficient and rapid fabrication of high-quality FLP is still unavailable. Herein, a simple and efficient low melting media-assisted ball milling (LMMBM) approach is developed to prepare FLP in large quantities. The phase change of the LMM at higher temperatures leads to FLP with larger lateral dimensions compared to the ones obtained via dry solid state ball milling. Transmission electron microscope (TEM) studies indicated that liquid facilitates the slipping/curling of black phosphorus (BP) layers under the shearing force generated during ball milling. When used as an anode in Lithium-ion batteries, the FLP-C composite exhibits high initial Coulombic efficiency, stable cycling and rate capacity. This LMMBM approach can be adopted for mass production of other two-dimensional materials from their bulk counterparts.
Zhou, F, Xiao, W-Z, Zhou, K-Y, Zhuang, J-L, Zhang, X, Liu, Y-D, Ni, B-J, Shapleigh, JP, Zhou, M, Luo, X-Z & Li, W 2022, 'Performance characteristics and community analysis of a single-stage partial nitritation, anammox and denitratation (SPANADA) integrated process for treating low C/N ratio wastewater', Chemical Engineering Journal, vol. 433, pp. 134452-134452.
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This study describes the development of an air-lift internal circulation reactor that integrates partial nitrification, anammox and denitratation (SPANADA) into a single stage bioprocess for the treatment of low C/N coal gasification wastewater. During 245 days of operation, the compartmental fluidized bed reactor achieved a total inorganic nitrogen (TIN) removal efficiency of 91.4%. Reads-based metatranscriptomic analysis found the expression of the amoA and hao genes essential for nitritation and the hzsA and hdh genes essential for anammox increased dramatically as reactor performance improved and stabilized. Another notable trend was that the total expression of the napA and narG genes, essential for denitratation, was 3-fold higher than the combined reads for nirK and nirS whose products, nitrite reductases, would lower nitrite levels reducing available substrate for anammox. Analysis of metagenome-assembled genomes revealed members of Nitrosomonas and Candidatus Brocadia, were the dominant genera of ammonium-oxidizing bacteria and anammox bacteria, respectively, and accounted for 5.5% and 10.0% of the total reads in the transcriptome. For denitratation, Thiolinea, whose only relevant gene involved in N-metabolism is narG, accounted for 8.5% of the total reads in the transcriptome and, remarkably, 84.1% of narG expression. Mass balance confirmed anammox was the dominant nitrogen removal pathway, accounting for 67.0% of the TIN removed. Nitritation and denitratation accounted for 82.7% and 17.3% of the nitrite production, respectively. The analysis reported demonstrates the development of a novel and effective one-stage nitrogen removal alternative for low C/N wastewater treatment and also helps gain insight into the underlying microbial interactions.
Zhou, H, Qu, X, Yang, Y, KC, A, Liu, X, Yang, C, Wang, A, Huang, Y, Sun, J, Huang, X & Wang, Y 2022, 'Relationship between moderate to late preterm, diet types and developmental delay in less-developed rural China', Nutritional Neuroscience, vol. 25, no. 1, pp. 70-79.
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Aim: To measure the development of moderate to late preterm children by Ages and Stages Questionnaires (ASQ) and explore the relationship between moderate to late preterm, diet types and development delay in less-developed rural China.Methods: Data were collected from a cross-sectional community-based survey, which recruited 1748 children aged 1-59 months in eight counties of China. Caregivers of these children completed the Chinese version of ASQ-3 (ASQ-C) while physical examination and questionnaires on socio-demographic characteristics were conducted. Multivariate logistic regressions were used to analyze the association between moderate to late preterm and suspected developmental delay, as well as the association between diet types and suspected developmental delay. Consumption of certain food types was compared between moderate to late preterm and full-term children.Results: The prevalence of suspected overall developmental delay was 31.3% in the moderate to the late preterm group, compared with 21.6% in the full-term group. Moderate to late preterm birth was not associated with total suspected developmental delay and developmental delay in all the domains of ASQ, except for fine motor (OR = 2.43 95% C.I.: 1.04-5.56). The intake of vegetables and fruits had a protective influence on developmental delay in fine motor function, and moderate to late preterm children had lower relative consumption of fruits and vegetables than full-term children.Conclusion: Moderate to late preterm children in rural China showed an increased likelihood of developmental delay in fine motor function. Future interventions to improve the intake of vegetables and fruits in moderate to late preterm children are recommended.
Zhou, I, Lipman, J, Abolhasan, M & Shariati, N 2022, 'Minute-wise frost prediction: An approach of recurrent neural networks', Array, vol. 14, pp. 100158-100158.
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Frost events incur substantial economic losses to farmers. These events could induce damage to plants and crops by damaging the cells. In this article, a recurrent neural network-based method, automating the frost prediction process, is proposed. The recurrent neural network-based models leveraged in this article include the standard recurrent neural network, long short-term memory, and gated recurrent unit. The proposed method aims to increase the prediction frequency from once per 12–24 h for the next day or night events to minute-wise predictions for the next hour events. To achieve this goal, datasets from NSW and ACT of Australia are obtained. The experiments are designed considering the scene of deploying the model to the Internet of Things systems. Factors such as model processing speed, long-term error and data availability are reviewed. After model construction, there are three experiments. The first experiment tests the errors between different model types. The second and third experiments test the effect of sequence length on error and performance for recurrent neural network-based models. All tests introduce artificial neural network models as the baseline. Also, all tests for model error are conducted in two rounds with testing datasets from the current year (2016) and next year (2017). As a result, recurrent neural network-based models are more suitable for short-term deployment with a smaller sequence length. In contrast, artificial neural network models demonstrate a lower error over the long term with faster processing time. With the results presented, the limitations of the proposed method are discussed.
Zhou, J, Chen, C, Armaghani, DJ & Ma, S 2022, 'Developing a hybrid model of information entropy and unascertained measurement theory for evaluation of the excavatability in rock mass', Engineering with Computers, vol. 38, no. 1, pp. 247-270.
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Zhou, J, Chen, C, Du, K, Jahed Armaghani, D & Li, C 2022, 'A new hybrid model of information entropy and unascertained measurement with different membership functions for evaluating destressability in burst-prone underground mines', Engineering with Computers, vol. 38, no. S1, pp. 381-399.
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Zhou, J, Huang, S, Zhou, T, Armaghani, DJ & Qiu, Y 2022, 'Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential', Artificial Intelligence Review, vol. 55, no. 7, pp. 5673-5705.
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Zhou, J, Zhu, S, Qiu, Y, Armaghani, DJ, Zhou, A & Yong, W 2022, 'Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm', Acta Geotechnica, vol. 17, no. 4, pp. 1343-1366.
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Zhou, L, Barthe, G, Strub, P-Y, Liu, J & Ying, M 2022, 'CoqQ: Foundational Verification of Quantum Programs.', CoRR, vol. abs/2207.11350.
Zhou, L, Yu, N, Ying, S & Ying, M 2022, 'Quantum earth mover’s distance, a no-go quantum Kantorovich–Rubinstein theorem, and quantum marginal problem', Journal of Mathematical Physics, vol. 63, no. 10, pp. 102201-102201.
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The quantum coupling of two given quantum states denotes the set of bipartite states whose marginal states are these given two states. In this paper, we provide tight inequalities to describe the structure of quantum coupling. These inequalities directly imply that the trace distance between two quantum states cannot be determined by the quantum analog of the earth mover’s distance, thus ruling out the equality version of the quantum Kantorovich–Rubinstein theorem for trace distance even in the finite-dimensional case. In addition, we provide an inequality that can be regarded as a quantum generalization of the Kantorovich–Rubinstein theorem. Then, we generalize our inequalities and apply them to the three tripartite quantum marginal problems. Numerical tests with a three-qubit system show that our criteria are much stronger than the known criteria: the strong subadditivity of entropy and the monogamy of entanglement.
Zhou, T, Li, X, Zhang, Q, Dong, S, Liu, H, Liu, Y, Chaves, AV, Ralph, PJ, Ruan, R & Wang, Q 2022, 'Ecotoxicological response of Spirulina platensis to coexisted copper and zinc in anaerobic digestion effluent', Science of The Total Environment, vol. 837, pp. 155874-155874.
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Copper ion (Cu2+) and zinc ion (Zn2+) are widely co-existent in anaerobic digestion effluent as typical contaminants. This work aims to explore how Cu2+-Zn2+ association affects physiological properties of S. platensis using Schlösser medium (SM) and sterilized anaerobic digestion effluent (SADE). Microalgae cells viability, biochemical properties, uptake of Cu2+ and Zn2+, and risk assessment associated with the biomass reuse as additives to pigs were comprehensively assessed. Biomass production ranged from 0.03 to 0.28 g/L in SM and 0.63 to 0.79 g/L in SADE due to the presence of Cu2+ and Zn2+. Peak value of chlorophyll-a and carotenoid content during the experiment decreased by 70-100% and 40-100% in SM, and by 70-77% and 30-55% in SADE. Crude protein level reduced by 4-41% in SM and by 65-75% in SADE. The reduction ratio of these compounds was positively related to the Cu2+ and Zn2+ concentrations. Maximum value of saturated and unsaturated fatty acids was both obtained at 0.3 Cu + 2.0 Zn (50.8% and 22.8%, respectively) and 25% SADE reactors (33.8% and 27.7%, respectively). Uptake of Cu in biomass was facilitated by Zn2+ concentration (> 4.0 mg/L). Risk of S. platensis biomass associated with Cu2+ was higher than Zn2+. S. platensis from SM (Cu2+ ≤ 0.3 mg/L and Zn2+ ≤ 4.0 mg/L) and diluted SADE (25% and 50% SADE) reactors could be used as feed additives without any risk (hazard index <1), which provides sufficient protein and fatty acids for pig consumption. These results revealed the promising application of using S. platensis for bioremediation of Cu2+ and Zn2+ in anaerobic digestion effluent and harvesting biomass for animal feed additives.
Zhou, W-H, Vijayan, MK, Wang, X-W, Lu, Y-H, Gao, J, Jiao, Z-Q, Ren, R-J, Chang, Y-J, Shen, Z-S, Rohde, PP & Jin, X-M 2022, 'Reducing circuit complexity in optical quantum computation using 3D architectures', Optics Express, vol. 30, no. 18, pp. 32887-32887.
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Integrated photonic architectures based on optical waveguides are one of the leading candidates for the future realisation of large-scale quantum computation. One of the central challenges in realising this goal is simultaneously minimising loss whilst maximising interferometric visibility within waveguide circuits. One approach is to reduce circuit complexity and depth. A major constraint in most planar waveguide systems is that beamsplitter transformations between distant optical modes require numerous intermediate SWAP operations to couple them into nearest neighbour proximity, each of which introduces loss and scattering. Here, we propose a 3D architecture which can significantly mitigate this problem by geometrically bypassing trivial intermediate operations. We demonstrate the viability of this concept by considering a worst-case 2D scenario, where we interfere the two most distant optical modes in a planar structure. Using femtosecond laser direct-writing technology we experimentally construct a 2D architecture to implement Hong-Ou-Mandel interference between its most distant modes, and a 3D one with corresponding physical dimensions, demonstrating significant improvement in both fidelity and efficiency in the latter case. In addition to improving fidelity and efficiency of individual non-adjacent beamsplitter operations, this approach provides an avenue for reducing the optical depth of circuits comprising complex arrays of beamsplitter operations.
Zhou, X, Feng, Y & Li, S 2022, 'Quantum Circuit Transformation: A Monte Carlo Tree Search Framework', ACM Transactions on Design Automation of Electronic Systems, vol. 27, no. 6, pp. 1-27.
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In the noisy intermediate-scale quantum era, quantum processing units suffer from, among others, highly limited connectivity between physical qubits. To make a quantum circuit effectively executable, a circuit transformation process is necessary to transform it, with overhead cost the smaller the better, into a functionally equivalent one so that the connectivity constraints imposed by the quantum processing unit are satisfied. Although several algorithms have been proposed for this goal, the overhead costs are often very high, which degenerates the fidelity of the obtained circuits sharply. One major reason for this lies in that, due to the high branching factor and vast search space, almost all of these algorithms only search very shallowly, and thus, very often, only (at most) locally optimal solutions can be reached. In this article, we propose a Monte Carlo Tree Search (MCTS) framework to tackle the circuit transformation problem, which enables the search process to go much deeper. The general framework supports implementations aiming to reduce either the size or depth of the output circuit through introducing SWAP or remote CNOT gates. The algorithms, called MCTS-Size and MCTS-Depth , are polynomial in all relevant parameters. Empirical results on extensive realistic circuits and IBM Q Tokyo show that the MCTS-based algorithms can reduce the size (respectively, depth) overhead by, on average, 66% (respectively, 84%) when compared with t \( \left| {\mathrm{ket}} \right\rangle \) , an industrial-level compiler.
Zhou, Y, Fu, Y, Luo, Z, Hu, M, Wu, D, Sheng, QZ & Yu, S 2022, 'The Role of Communication Time in the Convergence of Federated Edge Learning', IEEE Transactions on Vehicular Technology, vol. 71, no. 3, pp. 3241-3254.
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Federated Edge Learning (FEL) enables a massive number of edge devices (e.g. smart phones) to train machine learning models collaboratively. Due to the inherent unreliability of participating edge devices and the unpredictable network conditions, the final model accuracy is largely determined by the communication time between edge devices and the parameter server (PS). However, there exists very limited work to quantify the influence of the communication time in FEL and show how to minimize its negative impacts from a theoretic perspective. In this paper, we are among the first to develop a formal model of the communication time in FEL and its influence on the final model accuracy. In our work, the set of edge devices involved in each global iteration is defined as the ECP (Engaged Client Pool). We model the communication time cost as a function with respect to the response time distribution of individual devices and the ECP size. By incorporating communication time cost into the convergence rate analysis, we propose the ECPA and H-ECPA algorithms to automatically adjust the size of the ECP so as to maximize the model accuracy in both homogeneous and heterogeneous networks. We also analyze how the tail shape of response time affects the convergence rate, and prove that the heavy tail shape can significantly lower the model accuracy. Finally, we conduct extensive experiments with real datasets, and the results confirm the correctness of our analysis and demonstrate the superiority of our proposed algorithms.
Zhou, Y, Liu, J, Wang, JH, Wang, J, Liu, G, Wu, D, Li, C & Yu, S 2022, 'USST: A two-phase privacy-preserving framework for personalized recommendation with semi-distributed training', Information Sciences, vol. 606, pp. 688-701.
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Personalized recommendations are becoming indispensable for assisting online users in discovering items of interest. However, existing recommendation algorithms rely heavily on the collection of personal information, which poses significant privacy concerns to users. In this paper, we propose a two-phase privacy-preserving framework called user sampling and semi-distributed training (USST) for personalized recommendations, which can protect user privacy while ensuring high recommendation accuracy. In the USST framework, rather than directly training the model with all user records, a shared model is first trained with a small set of records contributed by sampled users (e.g., paid users and volunteers). This shared model is then distributed to each user, who further trains a personalized model using personal information. Thus, the USST guarantees that all unsampled users never disclose their private information. To validate the effectiveness and practicality of USST, we designed two USST-based privacy-preserving recommendation algorithms, USST-SVD and USST-NCF based on SVD and NCF algorithms, respectively. We conducted evaluations using MovieLens and Netflix Prize datasets, and the results show that, using only 20% of sampled users’ records, the recommendation accuracy of USST-based algorithms is very close to that of all users’ records. Thus, USST can significantly improve the level of privacy protection in recommender systems.
Zhou, Y, Shang, Y, Cao, Y, Li, Q, Zhou, C & Xu, G 2022, 'API-GNN: attribute preserving oriented interactive graph neural network', World Wide Web, vol. 25, no. 1, pp. 239-258.
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AbstractAttributed graph embedding aims to learn node representation based on the graph topology and node attributes. The current mainstream GNN-based methods learn the representation of the target node by aggregating the attributes of its neighbor nodes. These methods still face two challenges: (1) In the neighborhood aggregation procedure, the attributes of each node would be propagated to its neighborhoods which may cause disturbance to the original attributes of the target node and cause over-smoothing in GNN iteration. (2) Because the representation of the target node is derived from the attributes and topology of its neighbors, the attributes and topological information of each neighbor have different effects on the representation of the target node. However, this different contribution has not been considered by the existing GNN-based methods. In this paper, we propose a novel GNN model named API-GNN (Attribute Preserving Oriented Interactive Graph Neural Network). API-GNN can not only reduce the disturbance of neighborhood aggregation to the original attribute of target node, but also explicitly model the different impacts of attribute and topology on node representation. We conduct experiments on six public real-world datasets to validate API-GNN on node classification and link prediction. Experimental results show that our model outperforms several strong baselines over various graph datasets on multiple graph analysis tasks.
Zhou, Y, Sun, Y, Wang, S, Jamil Mahfoud, R, Haes Alhelou, H, Hatziargyriou, N & Siano, P 2022, 'Performance Improvement of Very Short-term Prediction Intervals for Regional Wind Power Based on Composite Conditional Nonlinear Quantile Regression', Journal of Modern Power Systems and Clean Energy, vol. 10, no. 1, pp. 60-70.
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Zhou, Y, Wang, J, Li, Z & Lu, H 2022, 'Short-term photovoltaic power forecasting based on signal decomposition and machine learning optimization', Energy Conversion and Management, vol. 267, pp. 115944-115944.
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Owing to the continuous increase in the proportion of solar generation accounting for the total global generation, real-time management of solar power has become indispensable. Moreover, accurate prediction of photovoltaic power is emerging as an important link to support grid operations and reflect real-life scenarios. Various studies have led to the design of several forecasting models. Nevertheless, most predictors do not focus on the effects of the factors of photovoltaic modules on the forecast results. To fill this gap, in this paper, a novel multivariable hybrid prediction system combining signal decomposition, artificial intelligence models, deep learning models, and a swarm intelligence optimization strategy is proposed. This system fully utilizes independent variable features (including the module temperature) to efficiently enhance the precision and efficiency of photovoltaic forecasting. In particular, it is proved that a Pareto-optimal solution can be obtained using the designed system. Using three datasets obtained from Safi-Morocco, the presented system is verified by comparative experiments, and its remarkable advantages in terms of forecasting are demonstrated. Specifically, using the three datasets, the symmetric mean absolute percentage errors obtained by the presented forecast system are 2.129%, 2.335%, and 3.654%, respectively, which are significantly lower than those achieved with other comparison models. Furthermore, a comprehensive and rational evaluation methodology is employed to assess the predictive capability of the developed system. The evaluation results show that the system is effective in improving the forecasting efficiency and outperforms other benchmark models.
Zhou, Y, Wang, J, Lu, H & Zhao, W 2022, 'Short-term wind power prediction optimized by multi-objective dragonfly algorithm based on variational mode decomposition', Chaos, Solitons & Fractals, vol. 157, pp. 111982-111982.
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Short-term wind power prediction has a considerable effect on improving the productivity of wind energy systems and increasing economic benefits. In recently years, various wind velocity predictive models have been designed to raise the prediction effect. However, numerous predictive systems are limited by single type, and many ordinary predictive systems ignore the advantage of optimized parameters and the significance of data preparation, which bring about the lower predictive precision. To fill this gap, in this article, a novel predictive system is come up, which is on the basis of data denoising strategy, statistical predictive systems, artificial intelligence forecasting system and multi-objective optimization strategy. After using the data denoising strategy for denoising, the reconstructed data is used for the forecasting of different sub-systems, to obtain stable forecasting results, multi-objective dragonfly algorithm is used to estimate the weight coefficient of sub-systems. To evaluate the availability of the designed predictive system, five wind velocity datasets from different wind farms are used for the purpose of a case research. According four experiments and four analyses, it can be concluded that the designed combined system has a well predictive effect in short-term wind speed prediction. And it is in favor of grid regulation and operation.
Zhu, C, Ye, D, Zhu, T & Zhou, W 2022, 'Time-optimal and privacy preserving route planning for carpool policy', World Wide Web, vol. 25, no. 3, pp. 1151-1168.
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AbstractTo alleviate the traffic congestion caused by the sharp increase in the number of private cars and save commuting costs, taxi carpooling service has become the choice of many people. Current research on taxi carpooling services has focused on shortening the detour distances. While with the development of intelligent cities, efficiently match passengers and vehicles and planning routes become urgent. And the privacy between passengers in the taxi carpooling service also needs to be considered. In this paper, we propose a time-optimal and privacy-preserving carpool route planning system via deep reinforcement learning. This system uses the traffic information around the carpooling vehicle to optimize passengers’ travel time, not only to efficiently match passengers and vehicles but also to generate detailed route planning for carpooling vehicles. We conducted experiments on an Internet of Vehicles simulator CARLA, and the results demonstrate that our method is better than other advanced methods and has better performance in complex environments.
Zhu, G-A, Su, B-R, Liu, H-Y, Jiang, Q-P & Liu, H 2022, 'Acoustic Emission Characteristics of Fracture and Damage in Coal Samples under Overstress Loading and Unloading Paths', Lithosphere, vol. 2022, no. Special 11.
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Abstract As coal mining depth is over 1000 m, an overstressed effect is commonly observed around working face which shows a higher stress than the ultimate strength of the coal mass. Aiming at the problem of dynamic disasters induced by overstress loading and loading, a true triaxial AE monitoring unit was designed to systematically investigate the acoustic emission (AE) characteristics of fracture and damage. The AE results show that the ring counts of coal sample under true triaxial loading are influenced by three factors: loading level, individual differences, and loading rates. When under lower true triaxial loads, the coal sample damage represented by ring counts appears largely in loading phase, and the quantity is few. When true triaxial loads increase, the number of ring counts, especially in packing phase, increases significantly, which indicates that the damage process is transferred from loading phase to packing phase. The study on evolution laws of microcracks, wave velocity, and energy indicates that, during the initially true triaxial loads, the primary microcracks and the initial loading on primary dense areas contributes to a small amount of lower and higher velocity regions in the sample. Along with the increase of true triaxial loading, the closure of the primary microcracks and the fracture of the dense area result in a transfer and expansion of higher velocity regions and the formation of velocity anomaly regions. When approaching failure, the macrocracks throughout the sample present a large area of lower velocity band. The formation and closure of cracks, the fast transfer of high velocity region, and the velocity anomaly region indicate the unstable status of the sample. During the packing phase of true triaxial loads, the cracks are compacted, and the inner structures are thoroughly damaged in the sample, which eventually presents a continuous damage in a form of the shrink of the l...
Zhu, H, Ansari, M & Guo, YJ 2022, 'Wideband Beam-Forming Networks Utilizing Planar Hybrid Couplers and Phase Shifters', IEEE Transactions on Antennas and Propagation, vol. 70, no. 9, pp. 7592-7602.
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Zhu, H, Zhang, T & Guo, YJ 2022, 'Wideband Hybrid Couplers With Unequal Power Division/Arbitrary Output Phases and Applications to Miniaturized Nolen Matrices', IEEE Transactions on Microwave Theory and Techniques, vol. 70, no. 6, pp. 3040-3053.
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Zhu, J, Li, J, Chapman, EC, Shi, H, Ciocan, CM, Chen, K, Shi, X, Zhou, J, Sun, P, Zheng, Y & Rotchell, JM 2022, 'Gonadal Atresia, Estrogen-Responsive, and Apoptosis-Specific mRNA Expression in Marine Mussels from the East China Coast: A Preliminary Study', Bulletin of Environmental Contamination and Toxicology, vol. 108, no. 6, pp. 1111-1117.
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AbstractThis preliminary survey analysed mussel atresia incidences, estrogen-responsive and apoptotic-specific molecular end points, and aqueous and gonadal levels of selected estrogens from the East China coast. Estrogen levels were low (e.g. < LOD-28.36 ng/L, < LOD-3.88 ng/g wet weight of tissue for BPA) relative to worldwide freshwater environments, but high oocyte follicle atresia incidences (up to 26.6%) occurred at selected sites. Expression of estrogen-responsive ER2 was significantly increased in males relative to females at sites with high atresia incidences in females. A second estrogen-responsive gene, V9, was significantly increased at two sites in April in females relative to males; the opposite was true for the remaining two sites. Apoptosis-specific genes (Bcl-2, fas) showed elevated expression in males relative to females at the site with the highest atresia incidence. These results provide coastal estrogen levels and the utility of several estrogen-specific molecular-level markers for marine mussels.
Zhu, J, Yang, Y, Hou, Z, Liao, S & Xue, Q 2022, 'Dual-Band Aperture-Shared High Gain Antenna for Millimeter-Wave Multi-Beam and Sub-6 GHz Communication Applications', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4848-4853.
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This communication presents a dual-band aperture-shared high gain antenna for mm-wave multi-beam and Sub-6 GHz applications by integrating transmitarray into Fresnel zone plate (FZP) lens. The transmistarray consists of two sets of unit cells (UCs), which can provide high transmission magnitude and [-π, π] phase tuning at the mm-wave frequency band. Meanwhile, they show distinct transmission features at the Sub-6 GHz band (one set of UC allows the electromagnetic (EM) wave to pass while the other prohibits the EM wave from passing). Then, by properly arranging the two kinds of UCs into the transparent and opaque region of the FZP lens, respectively, the Sub-6 GHz FZP lens and the mm-wave band transmitarray are integrated into the same aperture seamlessly. A waveguide-integrated patch antenna is adopted as the feed. The patch antenna operates at the Sub-6 GHz band and three open waveguide antennas are used for the mm-wave multi-beam radiation. The proposed antenna achieves high gain at both bands with a reused radiating aperture. Mm-wave multi-beam radiation is obtained without an extra feeding network. For demonstration purposes, the central frequencies of the Sub-6 GHz and the mm-wave bands are selected at 6-GHz and 27-GHz, respectively. An antenna prototype is fabricated and experimentally verified.
Zhu, J, Yang, Y, Lai, J & Nulman, J 2022, 'Additively Manufactured Polarization Insensitive Broadband Transmissive Metasurfaces for Arbitrary Polarization Conversion and Wavefront Shaping', Advanced Optical Materials, vol. 10, no. 21, pp. 2200928-2200928.
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AbstractArbitrarily manipulating the polarization and wavefront of electromagnetic waves using ultrathin and highly efficient devices is desirable for many systems. Ultrathin broadband polarization‐insensitive transmissive metasurfaces, which can achieve the same polarization conversion and wavefront shaping for arbitrary linearly polarized incident waves, are proposed. The meta‐atom is composed of a transmitting antenna and a receiving antenna with a group of phase delay lines connecting them. The rotation of the transmitting antenna introduces the polarization conversion, and the different lengths of the transmission lines provide [−π, π] phase shift for wavefront shaping. Both the polarization conversion and wavefront shaping functions are polarization insensitive. Differential feeding is applied to both the transmitting and the receiving patch antennas to improve the transmission efficiency. The prototypes are conveniently printed by using the additive manufacturing technique. Two polarization‐insensitive metasurfaces are designed and measured to demonstrate. One of them produces vortex beams with 90° polarization conversion, and the other achieves anomalous refraction with 45° polarization conversion.
Zhu, J, Yang, Y, Liao, S & Xue, Q 2022, 'Dual-Band Antenna Hybridizing Folded Transmitarray and Folded Reflectarray', IEEE Transactions on Antennas and Propagation, vol. 70, no. 4, pp. 3070-3075.
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This communication proposes a dual-band antenna hybridizing a folded reflectarray antenna (FRA) and a folded transmitarray antenna (FTA). First, a polarization-rotation transmitarray is designed based on a new Fabry-Perot (FP)-liked phasing element. Unlike its counterparts, this new FP-liked phasing element twists the polarization of electromagnetic (EM) wave and provides sufficient phase tuning range only in the operating band of FTA. In the meantime, in the band of FRA, the transmitarray acts as the polarizing grid, which reflects one polarization while allows the orthogonally polarized one to pass without affecting its polarization and phase. Second, a polarization-rotation-ground-integrated (PRGI) reflectarray is designed. The reflectarray collimates the EM wave in the band of FRA and twists its polarization by 90°, forming the folded reflectarray. Meanwhile, it also twists the polarization of the EM wave in the band of FTA. By hybridizing the FTA and FRA sharing the same aperture, the proposed design achieves dual-band high gain radiation. To the best of our knowledge, this is the first time that the FRA and FTA are combined to form a shared aperture antenna, which inherits the advantages of both FRA and FTA, such as high gain, low profile, and reduced blockage effect of the feed. An antenna prototype is fabricated and measured to verify the idea.
Zhu, J, Yang, Y, Liao, S, Li, S & Xue, Q 2022, 'Dual-Band Aperture-Shared Fabry–Perot Cavity-Integrated Patch Antenna for Millimeter-Wave/Sub-6 GHz Communication Applications', IEEE Antennas and Wireless Propagation Letters, vol. 21, no. 5, pp. 868-872.
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This letter presents a dual-band antenna with a large frequency ratio of 11.7 (2.4 GHz/28 GHz) by integrating an mm-wave Fabry-Perot cavity (FPC) antenna into a sub-6 GHz patch antenna. The patch with periodic slots functions as both the 2.4 GHz radiator and the partially reflective surface (PRS) of the 28 GHz FPC antenna. By properly tuning the length of the periodic slot, the PRS's reflection can be easily adjusted. As the periodic slot's length and width operating at 28 GHz are much smaller than the wavelength at 2.4 GHz, periodic slot operating at 28 GHz have little impact on the radiation of the patch. Furthermore, because of the Fabry-Perot resonance, the antenna can have a peak gain reaching 15 dBi at 28 GHz band with an easy feeding structure. For demonstration, a prototype is fabricated and experimentally verified. Note that the frequency ratio is not limited to the proposed design (11.7 for demonstration). It can be easily adjusted based on the same principle.
Zhu, L, Fan, H, Luo, Y, Xu, M & Yang, Y 2022, 'Temporal Cross-Layer Correlation Mining for Action Recognition', IEEE Transactions on Multimedia, vol. 24, pp. 668-676.
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Neighboring frames are more correlated compared to frames from further temporal distances. In this paper, we aim to explore the temporal correlations among neighboring frames and exploit cross-layer multi-scale features for action recognition. First, we present a Temporal Cross-Layer Correlation (TCLC) framework for temporal correlation learning. The unified framework uncovers both local and global structures from video data, enabling a better exploration of temporal context and assisting cross-layer spatio-temporal feature learning. Second, we propose a novel cross-layer attention and a center-guided attention mechanism to integrate features with contextual knowledge from multiple scales. Our method is a two-stage process for effective cross-layer feature learning. The first stage incorporates the cross-layer attention module to decide the importance weight of the convolutional layers. The second stage leverages the center-guided attention mechanism to aggregate local features from each layer for the generation of a final video representation. We leverage global centers to extract shared semantic knowledge among videos. We evaluate TCLC on three action recognition datasets, i.e., UCF-101, HMDB-51 and Kinetics. Our experimental results demonstrate the superiority of our proposed temporal correlation mining method.
Zhu, P, Pan, X, Shen, Y, Huang, X, Yu, F, Wu, D, Feng, Q, Zhou, J & Li, X 2022, 'Biodegradation and potential effect of ranitidine during aerobic composting of human feces', Chemosphere, vol. 296, pp. 134062-134062.
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Ranitidine is widely concerned due to it is mainly related to the transformation into highly toxic carcinogenic products and non-readily biodegradable characteristics in aquatic environment. In this study, biodegradation of ranitidine during rural human feces (HF) aerobic composting was investigated. Results show that both levels of ranitidine are quickly removed in the first-3-day composting. The microorganisms play a vital role in the ranitidine degradation, especially for Firmicutes at the thermophilic period. The effect of ranitidine on the aerobic composting was further analyzed under the normal content (10 mg/kg) and high content (100 mg/kg). The 10 mg/kg ranitidine quickens temperature rise and organic matter degradation of the composting, while the 100 mg/kg ranitidine produces inhibiting effects. However, the effects only occur in the early stage of composting, and then tend to disappear with the removal of ranitidine. Fluorescence spectra confirm that humification and aromatization of dissolved organic matters (DOMs) in the substrates are fastened in 10 mg/kg group, while delayed in 100 mg/kg group. Metagenomic analysis reveals that relative abundances of Firmicutes and sequences related to carbohydrates metabolism increase in the groups mixed with the ranitidine at the early period. The findings provide the first new and systematical insights into degradation characteristics and potential effect of ranitidine during the rural HF composting.
Zhu, P, Shen, Y, Li, X, Liu, X, Qian, G & Zhou, J 2022, 'Feeding preference of insect larvae to waste electrical and electronic equipment plastics', Science of The Total Environment, vol. 807, no. Pt 3, pp. 151037-151037.
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Waste electrical and electronic equipment (WEEE) plastics not only pollute the environment, but are challenging to treat in an environmentally friendly manner. Biodegradation by insect larvae is potentially an eco-friendly method to treat WEEE plastics, but information about the feeding preference of insect larvae to WEEE plastics is lacking. In this study, a total of nine WEEE and pristine plastics were chosen to feed larvae of the following two insect species, i.e. Galleria mellonella and Tenebrio molitor. G. mellonella larvae significantly favor corresponding pristine plastics compared to two types of WEEE plastics, waste rigid polyurethane (RPU) and waste polystyrene (PS). One possible explanation is the increased chlorine or metals in the WEEE plastics measured using X-ray fluorescence spectrometer analysis. Scanning electron microscopy and Fourier transform infrared spectroscopy show that the destruction of physical structures and changes in surface functional groups were found in the two types of WEEE plastics in the larval frass, implying that the larvae partly biodegraded the plastics. Meanwhile, the powdered waste high impact polystyrene plastics (WHIPS) were ingested, but not the lumpy ones, indicating that the consumption by G. mellonella larvae is improved by the WHIPS physical modification. In addition, G. mellonella larvae presented the following decreasing preference for pristine plastics under individual-plastic-fed mode: RPU > phenol-formaldehyde resin > polyethylene (PE) > polypropylene > PS ≈ polyvinyl chloride; this is possibly due to differences in physical properties and chemical structures of the plastics; feeding preference of the larvae under multiple-plastics-fed mode is relatively consistent to that under individual-plastic-fed mode. Interestingly, the consumption by G. mellonella larvae of PE is higher than that of PS, while T. molitor larvae showed the opposite trend, implying that insect larvae have different plastics pref...
Zhu, R, Li, G, Wang, P, Xu, M & Yu, S 2022, 'DRL-Based Deadline-Driven Advance Reservation Allocation in EONs for Cloud–Edge Computing', IEEE Internet of Things Journal, vol. 9, no. 21, pp. 21444-21457.
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The ongoing roll-out of cloud-edge computing and Internet of Things (IoT) has been simulating the boom of new advance reservation (AR) services, such as bulk-data migration and virtual machine backup, driving the development of substrate elastic optical networks (EONs). These AR requests are initial-delay-insensitive if they are guaranteed to be completed before a predefined deadline. Therefore, the routing, modulation, and spectrum assignment (RMSA) problem is extended to the time-spectrum domain rather than the single spectrum domain. Traditional heuristic RMSA algorithms follow static procedures under handcrafted rules and assumptions, and thus cannot be optimized automatically. To solve this problem, we propose a deep reinforced deadline-driven allocation (DRDA) algorithm. To the best of our knowledge, this work is the first to leverage deep reinforcement learning (DRL) methods to solve the AR resource allocation problem. Moreover, compared with the single experiment scenario of many existing works, the DRDA algorithm is evaluated in both static and dynamic scenarios. Simulation results show that our DRDA algorithm outperforms the other leading algorithms in both static scenario and dynamic scenario.
Zhu, S, Zhang, YX & Lee, CK 2022, 'A new finite element procedure for simulation of flexural fatigue behaviours of hybrid engineered cementitious composite beams', Engineering Structures, vol. 269, pp. 114839-114839.
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Zhu, S, Zhang, YX & Lee, CK 2022, 'Experimental investigation of flexural behaviours of hybrid engineered cementitious composite beams under static and fatigue loading', Engineering Structures, vol. 262, pp. 114369-114369.
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Zhu, T, Ye, D, Wang, W, Zhou, W & Yu, PS 2022, 'More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 6, pp. 2824-2843.
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CCBY Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However, alongside all its advancements, problems have also emerged, such as privacy violations, security issues and model fairness. Differential privacy, as a promising mathematical model, has several attractive properties that can help solve these problems, making it quite a valuable tool. For this reason, differential privacy has been broadly applied in AI but to date, no study has documented which differential privacy mechanisms can or have been leveraged to overcome its issues or the properties that make this possible. In this paper, we show that differential privacy can do more than just preserve privacy. It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI. With a focus on regular machine learning, distributed machine learning, deep learning, and multi-agent systems, the purpose of this article is to deliver a new view on many possibilities for improving AI performance with differential privacy techniques.
Zhu, W, Tuan, HD, Dutkiewicz, E & Hanzo, L 2022, 'Collaborative Beamforming Aided Fog Radio Access Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 7, pp. 7805-7820.
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The success of fog radio access networks (F-RANs) is critically dependent on the potential quality of service (QoS) that they can offer to users in the face of capacity-constrained fronthaul links and limited caches at their remote radio heads (RRHs). In this context, the collaborative beamforming design is very challenging, since it constitutes a large-dimensional nonlinearly constrained optimization problem. The paper develops a new technique for tackling these critical challenges in fog computing. We show that all the associated constraints can be efficiently dealt with maximizing the geometric mean (GM) of the user throughputs (GM-throughput) subject to the affordable total transmit power constraints. To elaborate, the GM-throughput maximization judiciously exploits the fronthaul links and the RRHs' caches by relying on our novel algorithm, which evaluates low-complexity closed-form expressions in each of its iterations. The problem of F-RAN energy-efficiency is also addressed while maintaining the target throughput. Numerical examples are provided for quantifying the efficiency of the proposed algorithms.
Zhu, Y, Shen, S, Ouyang, L, Liu, J, Wang, H, Huang, Z & Zhu, M 2022, 'Effective synthesis of magnesium borohydride via B-O to B-H bond conversion', Chemical Engineering Journal, vol. 432, pp. 134322-134322.
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Magnesium borohydride (Mg(BH4)2) is widely regarded as a promising hydrogen storage material due to its high capacity; however, it is still challenging to synthesize Mg(BH4)2 with low cost. Traditionally, Mg(BH4)2 has been mainly produced using other borohydride as the starting materials via exchange reactions. Herein, we report an economical method to synthesize Mg(BH4)2 by converting B-O bonds in widely available borates or boric acid to B-H. The borates or boric acid is ball-milled with MgH2 under ambient conditions to form Mg(BH4)2 with high yield (>80%). Mg(BH4)2 was also successfully generated by reacting low-cost Mg with boric acid. Compared with previous approaches, this method avoids expensive boron sources such as LiBH4, NaBH4, and B2H6, and does not require high pressure H2 gas and high temperatures, and therefore significantly reduces costs. This method could be an alternative to the current Mg(BH4)2 synthesis processes.
Zhu, Y, Wang, Z, Chen, C & Dong, D 2022, 'Rule-Based Reinforcement Learning for Efficient Robot Navigation With Space Reduction', IEEE/ASME Transactions on Mechatronics, vol. 27, no. 2, pp. 846-857.
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Zhu, Y, Zhang, Q, Qin, L, Chang, L & Yu, JX 2022, 'Cohesive Subgraph Search Using Keywords in Large Networks.', IEEE Trans. Knowl. Data Eng., vol. 34, no. 1, pp. 178-191.
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IEEE Keyword search problem has been widely studied to retrieve relevant substructures from graphs. However, existing approaches aim at finding compact trees/subgraphs containing the keywords, and ignore density to evaluate how strongly and stablely the keyword nodes are connected. In this paper, we study the problem of finding cohesive subgraph containing query keywords with high density and compactness, and formulate it as minimal dense truss search problem based on k-truss model. However, unlike ${k}$-truss based community search that can be efficiently done by local search from a given set of nodes, this problem is nontrivial as the keyword nodes to be included in the retrieved substructure is previously unknown. To tackle this problem, we first design a novel hybrid KT-Index that keeps the keyword and truss information compactly to support efficient search of dense truss with the maximum trussness ${G_{den}}$. Then, we develop a novel refinement approach to extract minimal dense truss from ${G_{den}}$, by checking each node at most once based on the anti-monotonicity property of k-truss, together with several optimization strategies including batch based deletion, early-stop based deletion, and local exploration. Extensive experimental studies on real-world networks validated the effectiveness and efficiency of our approaches.
Zhu, Y-Y, Liu, Y, Xu, J & Ni, B-J 2022, 'Three-dimensional excitation-emission matrix (EEM) fluorescence approach to probing the binding interactions of polystyrene microplastics to bisphenol A', Journal of Hazardous Materials Advances, vol. 5, pp. 100046-100046.
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Zhuang, J-L, Sun, X, Zhao, W-Q, Zhang, X, Zhou, J-J, Ni, B-J, Liu, Y-D, Shapleigh, JP & Li, W 2022, 'The anammox coupled partial-denitrification process in an integrated granular sludge and fixed-biofilm reactor developed for mainstream wastewater treatment: Performance and community structure', Water Research, vol. 210, pp. 117964-117964.
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This study describes an integrated granular sludge and fixed-biofilm (iGB) reactor innovatively designed to carry out the anammox/partial-denitrification (A/PD) process for nitrogen removal with mainstream municipal wastewater. The iGB-A/PD reactor consists of anammox granules inoculated in the lower region of reactor and an acclimated fixed-biofilm positioned in the upper region. Compared to the other reported A/PD systems for mainstream wastewater treatment, this iGB-A/PD reactor is notable due to its higher quality effluent with a total inorganic nitrogen (TIN) of ∼3 mg•L-1 and operation at a high nitrogen removal rate (NRR) of 0.8 ± 0.1 kg-N•m-3•d-1. Reads-based metatranscriptomic analysis found that the expression values of hzsA and hdh, key genes associated with anammox, were much higher than other functional genes on nitrogen conversion, confirming the major roles of the anammox bacteria in nitrogen bio-removal. In both regions of the reactor, the nitrate reduction genes (napA/narG) had expression values of 56-99 RPM, which were similar to that of the nitrite reduction genes (nirS/nirK). The expression reads from genes for dissimilatory nitrate reduction to ammonium (DNRA), nrfA and nirB, were unexpectedly high, and were over the half of the levels of reads from genes required for nitrate reduction. Kinetic assays confirmed that the granules had an anammox activity of 16.2 g-NH4+-N•kg-1-VSS•d-1 and a nitrate reduction activity of 4.1 g-N•kg-1-VSS•d-1. While these values were changed to be 4.9 g- NH4+-N•kg-1-VSS•d-1and 4.3 g-N•kg-1-VSS•d-1 respectively in the fixed-biofilm. Mass flux determination found that PD and DNRA was responsible for ∼50% and ∼25% of nitrate reduction, respectively, in the whole reactor, consistent with high effluent quality and treatment efficiency via a nitrite loop. Metagenomic binning analysis revealed that new and unidentified anammox species, affiliated with Candidatus Brocadia, were the dominant anammox organisms. Myx...
Zogan, H, Razzak, I, Wang, X, Jameel, S & Xu, G 2022, 'Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media', World Wide Web, vol. 25, no. 1, pp. 281-304.
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AbstractThe ability to explain why the model produced results in such a way is an important problem, especially in the medical domain. Model explainability is important for building trust by providing insight into the model prediction. However, most existing machine learning methods provide no explainability, which is worrying. For instance, in the task of automatic depression prediction, most machine learning models lead to predictions that are obscure to humans. In this work, we propose explainable Multi-Aspect Depression Detection with Hierarchical Attention Network MDHAN, for automatic detection of depressed users on social media and explain the model prediction. We have considered user posts augmented with additional features from Twitter. Specifically, we encode user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words’ importance, and capture semantic sequence features from the user timelines (posts). Our hierarchical attention model is developed in such a way that it can capture patterns that leads to explainable results. Our experiments show that MDHAN outperforms several popular and robust baseline methods, demonstrating the effectiveness of combining deep learning with multi-aspect features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. MDHAN achieves excellent performance and ensures adequate evidence to explain the prediction.
Zohurul Islam, M, Nath Mondal, R & C. Saha, S 2022, 'Impacts of Rotation on Unsteady Fluid Flow and Energy Distribution through a Bending Duct with Rectangular Cross Section', Energy Engineering, vol. 119, no. 2, pp. 453-472.
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A depth understanding of fluid flow past a curved duct having rectangular cross-section with different aspect ratios (l) are essential for various engineering applications such as in chemical, mechanical, bio-mechanical and bio-medical engineering. So highly ambitious researchers have given significant attention to study new characteristics of fluid flow in a curved duct. The flow characterization in the rectangular duct has been studied over a wide range of numerical and selective experimental studies. However, proper knowledge with the effects of Coriolis force for different aspect ratios is important for better understanding of the transitional behaviour and the subsequent heat generation, which is required to improve further. The purpose of this study is to reveal insight into the transitional flow pattern and heat transfer in a curved rectangular domain. The Navier-Stokes equations are solved using the spectral method, while the Crank-Nicolson method is used to solve the energy equation. An in-house FORTRAN code is developed to get the numerical solution. For post-processing purposes, Tecplot-360 and Ghost-script tools are used. The present study exposes development of Dean vortices that affect heat generation as well as thermal enhancement in the flow with underlying the flow controlling parameters, the Dean number (Dn), the Grashof number (Gr) and the Taylor number (Tr). Time-dependent results followed by phase spaces show that transient flow undergoes in the scenario ‘chaotic → multi-periodic→ periodic → steady-state’ generating 2-to 8-vortices for the periodic/multi-periodic flow at 2000 ≤ Tr ≤ 2205 for l = 2, whereas similar sort of flow is observed in the range of 3100 ≤ Tr ≤ 3195 for l = 3. More complicated 4-to 13-vortex solutions are obtained for the chaotic flow regime at l = 2 in the range of 0 ≤ Tr < 2200 and at l = 3 in the range of 0 ≤ Tr < 3100. The chaotic flow that occurs at the certain range of Tr proficiently intensifies the heat...
Zou, L, Yang, P, Herold, F, Liu, W, Szabo, A, Taylor, A, Sun, J & Ji, L 2022, 'The Contribution of BMI, Body Image Inflexibility, and Generalized Anxiety to Symptoms of Eating Disorders and Exercise Dependence in Exercisers', International Journal of Mental Health Promotion, vol. 24, no. 6, pp. 811-823.
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Zou, P, Liu, J, Huang, Z, Hu, R & Ouyang, L 2022, 'Phenylphosphonic acid as a grain-refinement additive for a stable lithium metal anode', Chemical Communications, vol. 58, no. 91, pp. 12724-12727.
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The increased overpotential due to the complexation between phenylphosphonic acid and Li ions can reduce the grain size, boost nucleation rates, and prevent the formation of Li dendrites.
Zou, S, Wang, W, Ni, W, Wang, L & Tang, Y 2022, 'Efficient Orchestration of Virtualization Resource in RAN Based on Chemical Reaction Optimization and Q-Learning', IEEE Internet of Things Journal, vol. 9, no. 5, pp. 3383-3396.
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Zou, Y, Long, Y, Gong, S, Hoang, DT, Liu, W, Cheng, W & Niyato, D 2022, 'Robust Beamforming Optimization for Self-Sustainable Intelligent Reflecting Surface Assisted Wireless Networks', IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 2, pp. 856-870.
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We focus on an intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) system where the IRS sustains its operations by harvesting energy from the access point (AP) in the power splitting (PS) protocol. We aim to minimize the AP's transmit power subject to the receivers' signal-to-noise ratio (SNR) and the IRS's energy budget constraints. A two-stage optimization framework is proposed to jointly optimize the AP's active beamforming, the IRS's passive beamforming, and the reflection amplitude. Given the reflection amplitude, we employ alternating optimization to update the beamforming strategies. Then, we determine the lower and upper bounds of the reflection amplitude in closed-form expressions, which help to update the reflection amplitude in a bisection method. We further extend our study to the robust case with uncertain channels. Our analysis reveals that the robust counterpart can be solved by the same optimization framework. Extensive simulations reveal that our algorithm is efficacy to balance the IRS's energy budget and the receiver's SNR performance. With uncertain channel information, a larger size of the IRS does not always ensure a higher performance improvement to information transmissions.
Zou, Y, Sun, X, Yang, Q, Zheng, M, Shimoni, O, Ruan, W, Wang, Y, Zhang, D, Yin, J, Huang, X, Tao, W, Park, JB, Liang, X-J, Leong, KW & Shi, B 2022, 'Blood-brain barrier–penetrating single CRISPR-Cas9 nanocapsules for effective and safe glioblastoma gene therapy', Science Advances, vol. 8, no. 16, p. eabm8011.
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We designed a unique nanocapsule for efficient single CRISPR-Cas9 capsuling, noninvasive brain delivery and tumor cell targeting, demonstrating an effective and safe strategy for glioblastoma gene therapy. Our CRISPR-Cas9 nanocapsules can be simply fabricated by encapsulating the single Cas9/sgRNA complex within a glutathione-sensitive polymer shell incorporating a dual-action ligand that facilitates BBB penetration, tumor cell targeting, and Cas9/sgRNA selective release. Our encapsulating nanocapsules evidenced promising glioblastoma tissue targeting that led to high PLK1 gene editing efficiency in a brain tumor (up to 38.1%) with negligible (less than 0.5%) off-target gene editing in high-risk tissues. Treatment with nanocapsules extended median survival time (68 days versus 24 days in nonfunctional sgRNA-treated mice). Our new CRISPR-Cas9 delivery system thus addresses various delivery challenges to demonstrate safe and tumor-specific delivery of gene editing Cas9 ribonucleoprotein for improved glioblastoma treatment that may potentially be therapeutically useful in other brain diseases.
Zou, Y, Wang, Y, Xu, S, Liu, Y, Yin, J, Lovejoy, DB, Zheng, M, Liang, X, Park, JB, Efremov, YM, Ulasov, I & Shi, B 2022, 'Brain Co‐Delivery of Temozolomide and Cisplatin for Combinatorial Glioblastoma Chemotherapy', Advanced Materials, vol. 34, no. 33.
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AbstractGlioblastoma (GBM) is an intractable malignancy with high recurrence and mortality. Combinatorial therapy based on temozolomide (TMZ) and cisplatin (CDDP) shows promising potential for GBM therapy in clinical trials. However, significant challenges include limited blood–brain‐barrier (BBB) penetration, poor targeting of GBM tissue/cells, and systemic side effects, which hinder its efficacy in GBM therapy. To surmount these challenges, new GBM‐cell membrane camouflaged and pH‐sensitive biomimetic nanoparticles (MNPs) inspired by the fact that cancer cells readily pass the BBB and localize with homologous cells, are developed. This study's results show that MNPs can efficiently co‐load TMZ and CDDP, transport these across the BBB to specifically target GBM. Incorporation of pH‐sensitive polymer then allows for controlled release of drug cargos at GBM sites for combination drug therapy. Mice bearing orthotopic U87MG or drug‐resistant U251R GBM tumor and treated with MNPs@TMZ+CDDP show a potent anti‐GBM effect, greatly extending the survival time relative to mice receiving single‐drug loaded nanoparticles. No obvious side effects are apparent in histological analyses or blood routine studies. Considering these results, the study's new nanoparticle formulation overcomes multiple challenges currently limiting the efficacy of combined TMZ and CDDP GBM drug therapy and appears to be a promising strategy for future GBM combinatorial chemotherapy.
Zuo, S, Wang, D, Zhang, Y & Luo, Q 2022, 'Design and testing of a parabolic cam-roller quasi-zero-stiffness vibration isolator', International Journal of Mechanical Sciences, vol. 220, pp. 107146-107146.
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Circular cam-roller (CCR) quasi-zero stiffness (QZS) vibration isolators have been extensively studied. As the CCR isolator can only achieve low stiffness in a very small range and cannot withstand excitation with large amplitude, study on other cam profiles has become interests. This paper investigates a parabolic cam-roller (PCR) QZS vibration isolator. Theoretical formulations of the PCR QZS isolator are derived in detail and the condition of PCR isolator outperforming CCR one is obtained. Design parameters are analyzed and then the optimal design is presented. A prototype of the PCR QZS vibration isolator is fabricated and tested; the corresponding CCR QZS and linear isolators are also experimentally evaluated. In comparison with CCR QZS isolators, the proposed PCR QZS isolators can withstand force and displacement excitations with larger amplitudes in the QZS region. The experimental results validate the present formulation and show that vibration isolation performance of the proposed PCR QZS isolator is much better than that of the CCR QZS isolator and that of the corresponding linear isolator. In comparison with the CCR QZS isolator, the present PCR QZS isolator can have lower stiffness in a wider region around the equilibrium position and lower transmissibility.
Zuo, S, Xiao, Y, Chang, X & Wang, X 2022, 'Vision transformers for dense prediction: A survey', Knowledge-Based Systems, vol. 253, pp. 109552-109552.
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Transformers have demonstrated impressive expressiveness and transfer capability in computer vision fields. Dense prediction is a fundamental problem in computer vision that is more challenging to solve than general image-level prediction tasks. The inherent properties of transformers enable them to process feature representations with stable and relatively high resolution, which precisely satisfies the demands of dense prediction tasks for finer-grained and more globally coherent predictions. Furthermore, compared to convolutional networks, transformer methods require minimal inductive bias and permit long-range information interaction. These strengths have contributed to exciting advancements in dense prediction tasks that apply transformer networks. This survey aims to provide a comprehensive overview of transformer models with a specific focus on dense prediction. In this survey, we provide a well-rounded view of state-of-the-art transformer-based approaches, explicitly emphasizing pixel-level prediction tasks. We generally consider transformer variants from the network architecture perspective. We further propose a novel taxonomy to organize these models according to their constructions. Subsequently, we examine various specific optimization strategies to tackle certain bottleneck problems in dense prediction tasks. We explore the commonalities and differences among these works and provide multiple horizontal comparisons from the experimental point of view. Finally, we summarize several stubborn problems that continue to impact visual transformers and outline some possible development directions.
Zuo, W, Zhang, Y, E, J, Huang, Y, Li, Q, Zhou, K & Zhang, G 2022, 'Effects of multi-factors on performance of an improved multi-channel cold plate for thermal management of a prismatic LiFePO4 battery', Energy, vol. 261, pp. 125384-125384.
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In this work, an improved multi-channel cold plate is designed based on a traditional multi-channel cold plate for enhancing its performance. Taguchi experimental design, grey relational analysis and analysis of variance are applied to investigate the effects of multi-factors on the performance of the improved multi-channel cold plate. In addition, j/f factor is employed to evaluate the overall performance of the improved multi-channel cold plate. Results suggest that j/f factor of the improved multi-channel cold plate is the highest when the mass flow rate, channel number, ambient temperature and oblique angle are 0.4 g/s, 6, 35 °C and 45°, respectively. Moreover, the impact of multi-factors on the j/f factor is ranked as: ambient temperature > channel number = oblique angle > mass flow rate > numerical error. Finally, mass flow rate, channel number and ambient temperature dominate the pressure drop, heat transfer coefficient and j/f factor of the improved multi-channel cold plate, respectively. This work provides a significant reference and valuable guidance for designing improved multi-channel cold plates.
Zuo, Y, Wang, H, Fang, Y, Huang, X, Shang, X & Wu, Q 2022, 'MIG-Net: Multi-Scale Network Alternatively Guided by Intensity and Gradient Features for Depth Map Super-Resolution', IEEE Transactions on Multimedia, vol. 24, pp. 3506-3519.
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The studies of previous decades have shown that the quality of depth maps can be significantly lifted by introducing the guidance from intensity images describing the same scenes. With the rising of deep convolutional neural network, the performance of guided depth map super-resolution is further improved. The variants always consider deep structure, optimized gradient flow and feature reusing. Nevertheless, it is difficult to obtain sufficient and appropriate guidance from intensity features without any prior. In fact, the features in gradient domain, e.g., edges, present strong correlations between the intensity image and the corresponding depth map. Therefore, the guidance in gradient domain can be more efficiently explored. In this paper, the depth features are iteratively upsampled by 2$\times$. In each upsampling stage, the low-quality depth features and the corresponding gradient features are iteratively refined by the guidance from the intensity features via two parallel streams. Then, to make full use of depth features in pixel and gradient domains, the depth features and gradient features are alternatively complemented with each other. Compared with state-of-the-art counterparts, the sufficient experimental results show improvements according to the objective and subjective assessments.
Zurita, G, Mulet-Forteza, C, Merigo, J, Lobos-Ossandon, V & Ogata, H 2022, 'A Bibliometric Overview of the IEEE Transactions on Learning Technologies', IEEE Transactions on Learning Technologies, vol. 15, no. 6, pp. 656-672.
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A, A & Chakraborty, S 1970, 'Hidden and Face-like Object Detection using Deep Learning Techniques - An Empirical Study', 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, pp. 1-8.
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Abahussein, S, Cheng, Z, Zhu, T, Ye, D & Zhou, W 1970, 'Privacy-Preserving in Double Deep-Q-Network with Differential Privacy in Continuous Spaces', Springer International Publishing, pp. 15-26.
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Abhijith, V, Hossain, MJ, Lei, G & Sreelekha, PA 1970, 'A Hybrid Excited Switched Reluctance Motor for Torque Enhancement Without Permanent Magnet Behavior in Electric Vehicle Applications', 2022 IEEE 10th Power India International Conference (PIICON), 2022 IEEE 10th Power India International Conference (PIICON), IEEE, pp. 1-5.
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Accordini, D, Cagno, E, Galli, A & Trianni, A 1970, 'The Role of Contextual Characteristics in the Adoption of Energy Efficiency Measures in Electric Motors Systems: An Exploratory Analysis'.
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Adak, C, Sharma, P & Chanda, S 1970, 'DAZeTD: Deep Analysis of Zones in Torn Documents', Springer International Publishing, pp. 515-529.
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Ahadi, A & Mathieson, L 1970, 'A Bibliometrics Analysis of Australasian Computing Education Conference Proceedings', Proceedings of the 24th Australasian Computing Education Conference, ACE '22: Australasian Computing Education Conference, ACM, pp. 1-9.
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Ahadi, A, Kitto, K, Rizoiu, MA & Musial, K 1970, 'Skills Taught vs Skills Sought: Using Skills Analytics to Identify the Gaps between Curriculum and Job Markets', Proceedings of the 15th International Conference on Educational Data Mining Edm 2022, International Conference on Educational Data Mining, International Educational Data Mining Society, Durham, pp. 538-542.
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Higher education often aims to create job-ready graduates. Thus, the skills and knowledge taught in professional degrees are expected to align with the needs of the labor market. However, the dynamic nature of the job market makes it challenging to ensure that this alignment occurs. In this study, we show how Skills Analytics can be used to identify critical skills in the workforce, mapping these to the curriculum offerings of a university. This enables us to identify skill gaps between what is taught and what is needed in the job market. Methods are presented that allow universities to test the alignment of their curriculum offerings with the job market. Where gaps are identified, this would enable universities to update their curriculum more rapidly to produce graduates equipped with up-to-date skills required by the local job market. Our contributions include: a new method for ranking skills in curricula based on their relative importance in the job market; and proof of concept methods to find skills gaps between curriculum offerings and an identified job market that can lead to curriculum redesign and enhancements.
Ahmed, F, Afzal, MU, Hayat, T, Thalakotuna, D & Esselle, KP 1970, 'Near-Field Phase Transforming Structures for High-Performance Antenna Systems', 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/USNC-URSI), IEEE.
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Ahmed, F, Afzal, MU, Singh, K, Hayat, T & Esselle, KP 1970, 'Highly Transparent Fully Metallic 1-Bit Coding Metasurfaces for Near-Field Transformation', 2022 16th European Conference on Antennas and Propagation (EuCAP), 2022 16th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Ahmed, F, Ferdows, R, Islam, MR & Kamal, ARM 1970, 'DeepVis: A Visual Interactive System for Exploring Performance of Deep Learning Models', 2022 10th International Conference on Information and Education Technology (ICIET), 2022 10th International Conference on Information and Education Technology (ICIET), IEEE, Matsue, Japan, pp. 398-402.
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Nowadays deep learning DL models have been an emerging technology because of their performances and wide acceptance in various fields However in most cases the performance analysis of DL models is not viable to understand how they predict because they are inherently considered black boxes and different models have different performance rates Ad ditionally due to a lack of highly technical expertise and domain knowledge people struggle to choose a proper model for their work Therefore to understand and improve the performance of the D L model careful selection of model layer epoch optimizer hyperparameter tuning and model visualization is essential In this paper we design an interactive visualization system named DeepVis with a wide range of performance evaluation methods that assist the non expert in adopting an appropriate model Finally we demonstrate use cases and expert opinion using a publicly available dataset to validate the usability and effectiveness of Deep Vis
Ahmed, F, Hayat, T, Afzal, MU & Esselle, KP 1970, 'All-Dielectric Phase Correcting Surface Using Fused Deposition Modeling Technique', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 77-78.
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Ahmed, F, Singh, K, Esselle, KP & Thalakotuna, D 1970, 'Metasurface-Driven Beam Steering Antenna for Satellite Communications', 2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA), 2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA), IEEE, pp. 1-5.
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Ahmed, F, Singh, K, Hayat, T, Afzal, MU & Esselle, KP 1970, 'Ku-band Metallic Metasurfaces for High-Power Microwave Applications', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 1-2.
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Metallic metasurfaces operating at Ku-band and suitable for high-power microwave applications are presented. They are made of cheap off-the-shelf metal sheets and synthesized based on the near-field phase transformation principle. Two pairs of non-uniform slots are etched in the center of the thin metal sheet, and such identical layers are stacked to form the phase-shifting cell. Slots' lengths can control the full 360° phase range with high transmission efficiency. Cells are strategically arranged to form the near-field phase transforming metasurfaces (NF-PTMs). They are innovatively applied to enhance the antenna gain by two-fold and steer the antenna beam within the 104° large conical space. In addition, the proposed NF-PTMs have a power handling capability of 1.9 GW level.
Alakhtar, R, Ferguson, S & Alsobhi, H 1970, 'User Expectations When Augmented Reality Mediates Historical Artifacts', Springer International Publishing, pp. 334-344.
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Alalmaie, AZ, Nanda, P & He, X 1970, 'Zero Trust-NIDS: Extended Multi-View Approach for Network Trace Anonymization and Auto-Encoder CNN for Network Intrusion Detection', 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), IEEE, Wuhan, China, pp. 449-456.
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Alam, M, Lu, D & Siwakoti, YP 1970, 'A Novel Non-Isolated Three-Port Converter for Battery Management Systems', 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), IEEE, pp. 1-5.
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Alam, MM, Aljarajreh, H, Farhangi, M, Dylan D.-C., L & Siwakoti, YP 1970, 'A Novel DC/DC Three Port Converter with Fault-Tolerant Ability', 2022 5th International Conference on Power Electronics and their Applications (ICPEA), 2022 5th International Conference on Power Electronics and their Applications (ICPEA), IEEE, Hail, Saudi Arabia, pp. 1-7.
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This article proposes a novel three-port converter with a fault-tolerant (FT) capability to reconFigure automatically under different switching fault conditions, i.e., open circuit fault (OCF) or short circuit fault (SCF) of the power transistors, and to continue achieving different control objectives such as effective battery charging, maximum power point tracking (MPPT) and output voltage regulation. The proposed fault-tolerant design with a one-level redundancy structure enhances the reliability of traditional three port converters (TPCs), but it also addresses the component failure while different modes of operations using fewer components by incorporating dual-input single-inductor structure into the converter design. Simulation results are presented to explain the performance of the proposed converter during different fault conditions and the reconfiguration mechanism during these conditions.
Alanazi, F & Gay, V 1970, 'e-Health Care Development in Saudi Arabia: Challenges and Problems in e-Health Systems', Proceedings of the Information Systems Education Conference Isecon, pp. 154-165.
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This systematic review aimed to identify the challenges and problems facing e-health in Saudi Arabia. This information is essential for subsequent identification of e-health modelling requirements and e-health opportunities in the country. A search in Google Scholar using the topic as the search term generated 19 papers for review. The results are presented as abstracted findings of each paper (Supplementary Material) and categorisation by topic, type, and research methods. Analysis of the tabulated data showed that 10 papers dealt explicitly with the topic of this review, that is, problems, challenges and barriers in e-health. The remaining 9 addressed other topics, but included discussion of barriers, problems or challenges. There were 8 conference papers and 11 journal articles. Surveys (10) were the most frequently used (10) research method. Some studies used more than one method. In relation to specific problems, barriers or challenges, 29 papers discussed technological issues, 20 were related to ICT infrastructure and 13 identified organisational and psychosocial factors. This report discusses these results and makes three recommendations.
Alanazi, F, Gay, V & Alturki, R 1970, 'A Model for a Mobile-enabled e-Health System in Saudi Arabia for the Self-management of Diabetes', Proceedings of the Information Systems Education Conference Isecon, pp. 137-153.
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This paper prescribes the design requirements for a mobile-enabled e-health system for the self-management of diabetes by Saudi diabetes patients. The findings from a survey and a focus group were integrated to achieve this. The requirements, challenges and problems were identified and were supported by published works on the topic. The findings showed that since a variety of stakeholders are involved in such an ecosystem, it is imperative to ensure smooth coordination and an improvement in the outreach of public health campaigns. The findings thus far have highlighted the demographic groups to be targeted for designing and implementing targeted interventions to tackle diabetes in Saudi Arabia. Doing this would require interventions in the healthcare system, hospital and home-based management, and targeted patient interventions. The finer aspects of the system design need to be determined based on similar successful models and expert opinions. Some comments on the boundaries of this research are also provided.
Alfaro, FG, Jacinto, DV, Flores, AE, Alvarez, JC & Trianni, A 1970, 'Lean Service-inventory Management Integrated Model to Improve the Service Level in a Metalworking Company', 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), IEEE, pp. 1551-1555.
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The importance of the metalworking sector in Peru is reflected in the high demand for machinery, equipment and structures in various economic sectors such as industry, construction, mining, transportation, among others. This makes it a generator of large productive links and employment. Today, the struggle to follow a quality standard that guarantees the production of these goods persists, as well as the effort to improve the customer's perception of the service offered. This study proposes an integrated model to increase the service level of a metalworking company through the use of Lean Service and Inventory Management tools. The results show a 10.13% increase in the service level, thanks to the implementation of engineering concepts such as 5s, Kaizen and Inventory Management.
Alharbi, M & Hussain, FK 1970, 'A Systematic Literature Review of Blockchain Technology for Identity Management', Springer International Publishing, pp. 345-359.
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Alharbi, M & Hussain, FK 1970, 'Blockchain-Based Identity Management for Personal Data: A Survey', Springer International Publishing, pp. 167-178.
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Alhosaini, H, Wang, X, Yao, L, Chen, Y & Xu, G 1970, 'Caching Hierarchical Skylines for Efficient Service Composition on Service Graphs', 2022 IEEE International Conference on Services Computing (SCC), 2022 IEEE International Conference on Services Computing (SCC), IEEE, SPAIN, Barcelona, pp. 1-9.
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Service-oriented computing (SOC) is a paradigm for developing applications by reusing existing services. Through a standardized publishing, discovery, and composition process, SOC enables the orchestration of multiple (including third-party) services to constitute new applications. Hereby the quality of a composite service is fundamentally determined by its constituent services. To satisfy users’ non-functional requirements, it is important to identify the optimal set of constituent services to participate in the composition. Practical applications usually require the optimal set to be identified with high efficiency and accuracy. This poses challenges to existing service composition methods as they either provide no accuracy guarantee or are inapplicable to large-scale problems. The challenges become more evident when considering service graphs, which contain multiple execution paths that could multiply the computational overhead. In this paper, we propose a hierarchical skyline-based approach for highly efficient service composition, which maintains and reuses varying levels of service skylines to accelerate service composition. We discuss how the skylines can be selectively computed, lazily updated, and efficiently retrieved for reuse. Experiments demonstrate the effectiveness of our approach.
Alhosaini, H, Wang, X, Yao, L, Chen, Y & Xu, G 1970, 'Caching Hierarchical Skylines for Efficient Service Composition on Service Graphs', 2022 IEEE International Conference on Services Computing (SCC), 2022 IEEE International Conference on Services Computing (SCC), IEEE, Barcelona, Spain.
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Alhosaini, H, Wang, X, Yao, L, Yang, Z, Hussain, F & Lim, E-P 1970, 'Harnessing Confidence for Report Aggregation in Crowdsourcing Environments', 2022 IEEE International Conference on Services Computing (SCC), 2022 IEEE International Conference on Services Computing (SCC), IEEE, Barcelona, Spain, pp. 305-314.
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Crowdsourcing is an effective means of accomplishing human intelligence tasks by leveraging the collective wisdom of crowds. Given reports of various accuracy degrees from workers, it is important to make wise use of these reports to derive accurate task results. Intuitively, a task result derived from a sufficient number of reports bears lower uncertainty, and higher uncertainty otherwise. Existing report aggregation research, however, has largely neglected the above uncertainty issue. In this regard, we propose a novel report aggregation framework that defines and incorporates a new confidence measure to quantify the uncertainty associated with tasks and workers, thereby enhancing result accuracy. In particular, we employ a link analysis approach to propagate confidence information, subgraph extraction techniques to prioritize workers, and a progressive approach to gradually explore and consolidate workers’ reports associated with less confident workers and tasks. The framework is generic enough to be combined with existing report aggregation methods. Experiments on four real-world datasets show it improves the accuracy of several competitive state-of-the-art methods.
Ali, A & Hussian, W 1970, 'Challenges and Issues of the Internet of Things: Factoring Elements from the Social, Political and Information Systems', Springer International Publishing, pp. 73-83.
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Ali, H, Afzal, MU, Mukhopadhyay, S & Esselle, KP 1970, 'Polarization Diversity via Aperture Sharing Between Orthogonal Sub-arrays', 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/USNC-URSI), IEEE, pp. 1054-1055.
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Ali, H, Afzal, MU, Shrestha, S, Mukhopadhyay, S & Esselle, KP 1970, 'Enhancement of Near-Field Beam Steering with a Flanched-Cross Cell', 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/USNC-URSI), IEEE, pp. 1634-1635.
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Altaf, T & Braun, R 1970, 'A Roadmap to Smart Homes Security Aided SDN and ML', 2022 5th Conference on Cloud and Internet of Things (CIoT), 2022 5th Conference on Cloud and Internet of Things (CIoT), IEEE, pp. 129-136.
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The smart home is one of those significant technology trends which enhances comfort and allows the integration of environment-friendly smart applications in daily life. It contains a large pool of internet-enabled devices some of which have a limited capability and operate in a specific manner. Due to heterogeneity and operational complexity, home networks pose extremely challenging vulnerabilities concerning privacy and security. This paper is a review of recent attempts to provide privacy and security in IoT smart homes. We summarize the research efforts in the past few years, discuss and classify them based on technologies deployed i.e. SDN, ML. We then propose an approach for the integration of SDN with ML in the home network for an automatic and reconfigurable network security mechanism. We emphasize that the proposed approach addresses the heterogeneity and scalability issues (by implementing SDN) as well as pave ways to prevent harmful attacks efficiently and in a timely manner.
Altulyan, MS, Huang, C, Yao, L, Wang, X & Kanhere, S 1970, 'Deep Reinforcement Learning for Dynamic Things of Interest Recommendation in Intelligent Ambient Environment', Australasian Joint Conference on Artificial Intelligence, Australasian Joint Conference on Artificial Intelligence, Springer International Publishing, Hybrid (Sydney, online), pp. 393-404.
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An, Y, Han, SH & Ling, SH 1970, 'Multi-classification for EEG Motor Imagery Signals using Auto-selected Filter Bank Regularized Common Spatial Pattern.', ISMICT, IEEE International Symposium on Medical Information and Communication Technology, IEEE, Lincoln, NE, USA, pp. 1-6.
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Motor Imagery MI is a critical topic in Brain Computer Interface BCI Due to the low signal to noise ratio it is not easy to accurately classify motor imagery signals especially for multiple classification tasks Common Spatial Pattern CSP is a spatial transformation method that can effectively extract spatial features of EEG signals However the covariance matrix is inaccurate due to the small training data size Thus in this paper a regularization parameter auto selection algorithm is proposed to automatically adjust the ratio of the covariance matrix calculated by other subjects data based on the mutual information It can be used to tackle the problem of an inaccurate mixed covariance matrix caused by fixed regularization parameters To illustrate the merits of the proposed Auto selected Filter Bank Regularized Common Spatial Pattern AFBRCSP we used the ten folds cross validation accuracy and Kappa as the evaluation metrics to evaluate two data sets BCI4 2a and BCI3a data set Both data set include four mental classes By using BCI4 2a data set we found that the mean accuracy of AFBRSP is 77 31 and the Kappa is 0 6975 which is higher than Filter Bank Regularized Common Spatial Pattern FBRCSP by 5 67 and 0 0756 respectively By using BCI3a data set the proposed AFBRCSP improved the accuracy by 8 34 and the Kappa by 0 1111 compared with FBRCSP where the mean accuracy of AFBRCSP is 80 56 and the kappa is 0 7407 The overall Kappa obtained by the proposed method is also higher than some state of the art methods implying that the proposed method is more reliable
Anbarasi, LJ, Jawahar, M, Mukherjee, B, Narendra, M, Rahimi, M & Gandomi, AH 1970, 'Brazilian Air Traffic Network Analysis Using Social Network Metrics', 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), IEEE, pp. 264-269.
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Ang, JD & Zhu, X 1970, 'Recent Advances in On-Chip Silicon-based Passive Components for RF and Millimeter-Wave Applications', 2022 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP), 2022 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP), IEEE, pp. 1-3.
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Angerschmid, A, Theuermann, K, Holzinger, A, Chen, F & Zhou, J 1970, 'Effects of Fairness and Explanation on Trust in Ethical AI', Machine Learning and Knowledge Extraction, Springer International Publishing, pp. 51-67.
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AI ethics has been a much discussed topic in recent years. Fairness and explainability are two important ethical principles for trustworthy AI. In this paper, the impact of AI explainability and fairness on user trust in AI-assisted decisions is investigated. For this purpose, a user study was conducted simulating AI-assisted decision making in a health insurance scenario. The study results demonstrated that fairness only affects user trust when the fairness level is low, with a low fairness level reducing user trust. However, adding explanations helped users increase their trust in AI-assisted decision making. The results show that the use of AI explanations and fairness statements in AI applications is complex: we need to consider not only the type of explanations, but also the level of fairness introduced. This is a strong motivation for further work.
Ansari, M, Jones, B & Jay Guo, Y 1970, 'A Wide Angle Scanning Spherical Luneburg Lens Antenna Employing Metamaterial', 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/USNC-URSI), IEEE, pp. 1632-1633.
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Anwar, MJ, Gill, AQ & Proper, HA 1970, 'A Conceptual Model to Assess the Maturity Of Information Security Audit Process.', PoEM Workshops, Practice of Enterprise Modelling 2022 Workshops and Models at Work, CEUR-WS.org, London, UK.
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One of the critical aspects of information security management is the security audit, both internal and external audits. The fundamental challenge for organisations is the effective design and implementation of the information security audits to better understand their information security capability. In this paper, we present insights from an action design research (ADR) project and propose a conceptual model to assess the maturity of security audit processes. The results of this research can be used to create an improvement plan, which will guide organisations to reach their target process maturity level. The maturity model proposed in this paper was evaluated by way of feedback workshops in the target organization. The model forms the basis for future work for generalising the research into a formal reference architecture (involving models and principles) for audit process maturity.
Ao, S, Zhou, T, Jiang, J, Long, G, Song, X & Zhang, C 1970, 'EAT-C: Environment-Adversarial sub-Task Curriculum for RL', Proceedings of Machine Learning Research, pp. 822-843.
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Reinforcement learning (RL) is inefficient on long-horizon tasks due to sparse rewards and its policy can be fragile to slightly perturbed environments. We address these challenges via a curriculum of tasks with coupled environments, generated by two policies trained jointly with RL: (1) a co-operative planning policy recursively decomposing a hard task into a coarse-to-fine sub-task tree; and (2) an adversarial policy modifying the environment in each sub-task. They are complementary to acquire more informative feedback for RL: (1) provides dense reward of easier sub-tasks while (2) modifies sub-tasks' environments to be more challenging and diverse. Conversely, they are trained by RL's dense feedback on sub-tasks so their generated curriculum keeps adaptive to RL's progress. The sub-task tree enables an easy-to-hard curriculum for every policy: its top-down construction gradually increases sub-tasks the planner needs to generate, while the adversarial training between the environment and RL follows a bottom-up traversal that starts from a dense sequence of easier sub-tasks allowing more frequent environment changes. We compare EAT-C with RL/planning targeting similar problems and methods with environment generators or adversarial agents. Extensive experiments on diverse tasks demonstrate the advantages of our method on improving RL's efficiency and generalization.
Apers, S, Efron, Y, Gawrychowski, P, Lee, T, Mukhopadhyay, S & Nanongkai, D 1970, 'Cut Query Algorithms with Star Contraction', 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS), 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS), IEEE, pp. 507-518.
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Arefin, MF, Farhan Ahmed, C, Rizvee, RA, Leung, CK & Cao, L 1970, 'Mining Contextual Item Similarity without Concept Hierarchy', 2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM), 2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM), IEEE, pp. 1-8.
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Atmakuru, A & Chakraborty, S 1970, 'Do Deep Learning Models Mimic Human Personality Traits? – An Empirical Study', Acis 2022 Australasian Conference on Information Systems Proceedings.
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Two key aspects of artificial intelligence are its ability to make decisions and attempt to mimic humans. Decision-making in humans is, however not straightforward and depends significantly on the person’s mental state, personal biases, and personality. In this study, we attempt to empirically understand if deep learning image classifiers also exhibit such inherent biases or if they act neutrally in any given situation. To this end, we perform three experiments – left-brain right-brain test, psychological images test, and Rorschach’s inkblot test on eight different stat-of-the-art deep learning classifiers. A detailed analysis of the SoftMax probability scores is done rather than an analysis on measures like accuracy and F1. The experimental results suggested that most models work similar to a left-brained person, do not always predict the same class when given images consisting of multiple object classes, and usually detect larger objects rather than smaller ones. We believe that understanding these inherent biases would help future researchers take necessary actions while building image classification models.
Bakhanova, E, Anjum, M, Garcia, JA, Raffe, WL & Voinov, A 1970, 'GAMIFICATION OF DISCUSSOO: AN ONLINE AI-BASED FORUM FOR SERIOUS DISCUSSIONS', 16th International Conference on Interfaces and Human Computer Interaction Ihci 2022 and 15th International Conference on Game and Entertainment Technologies 2022 Get 2022 Held at the 16th Multi Conference on Computer Science and Information Systems Mccsis 2022, pp. 157-164.
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Engagement in the discussion process is one of the common challenges of asynchronous online forums. It becomes especially crucial if the discussion is organized over a serious topic about a complex problem with a group of diverse stakeholders. Gamification gives much promise in addressing this challenge. In this paper, we propose possible game design solutions to the engagement challenge for an existing online AI-based platform Discussoo and reflect on the results from the expert interviews and an experiment with students.
Bakhanova, E, Garcia Marin, J, Raffe, W & Voinov, A 1970, 'Gamified process of conceptual model developmentwith stakeholders', Proceedings of the International Environmental Modelling and Software Society Conference 2022, Proceedings of the International Environmental Modelling and Software Society Conference 2022, Brussels, Belgium.
Balakrishnan, HK, Dumée, LF, Merenda, A, Aubry, C, Yuan, D, Doeven, EH & Guijt, RM 1970, 'SIMULTANEOUS FABRICATION OF DENSE AND MACRO POROUS DOMAINS BY GRAYSCALE 3D PRINTING FOR THE MANUFACTURE OF FUNCTIONALLY INTEGRATED FLUIDIC DEVICES', Microtas 2022 26th International Conference on Miniaturized Systems for Chemistry and Life Sciences, pp. 71-72.
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Grayscale digital light projection (G-DLP) 3D printing is introduced to address the challenge in 3D printing and the integration of structures with controlled porosity. Structural properties of the printed materials were controlled from effectively dense to porous with interconnected pores up to 250 nm, realized within an individual print layer using a single ink. Using grayscale masks, heterostructures with physically dense areas were formed contiguous to intrinsically porous domains (porosity 23%). This single-step fabrication of functionally graded porous materials within a single layer was used to create a membrane-integrated device for the chemical analysis of soil samples.
Ball, JE 1970, 'Urban Flood Modelling-What is Accurate?', Proceedings of the IAHR World Congress, pp. 6490-6499.
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Management of flood risk remains a major problem in many urban environments. To generate the data needed for estimation of the flood risk, catchment models have been used with the reliability of the predicted catchment response for design flood estimation dependent upon the model calibration. However, the level of calibration required to achieve reliable design flood estimation remains unspecified. The purpose of this paper is to assess the event modelling accuracy needed if data from the calibrated model are to be used for continuous simulation of data for flood frequency analysis. For this purpose, a SWMM based catchment model was investigated using 25 monitored events while assessment of the calibration was based on a normalised peak flow error. Alternative sets of parameter values were used to obtain estimates of the peak flow for each of the selected events. The best performing sets of These sets of parameter values were used with SWMM in a continuous simulation mode to predict flow sequences for extraction of Annual Maxima Series for an At-Site Flood Frequency Analysis. From analysis of these At-Site Flood Frequency Analyses, it was concluded that the normalised peak flow error needed to be less than 10% if reliable design flood quantile estimates were to be obtained.
Bandara, KAMN, Fahmideh, M, Beydoun, G, Ahmad, A, Khan, A & Shrestha, A 1970, 'Role of ontologies in beach safety management analytics systems', PACIS 2022 Proceedings, Pacific Asia Conference on Information Systems, AIS, Taipei-Sydney, pp. 1-16.
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Events at public beaches are one of the most popular recreational activities of local communities and international visitors in all places around the world. Amongst others, the beach safety management in protected areas needs support towards continuous analysis and decision making on incidents at the beach areas. There is a lack of available standard models to assist data scientists to represent analytics models including different spheres of interplay domain variables related to beach safety management. Using the Design Science Research Methodology (DSRM), we developed ontological models that facilitate a unified representation and maintenance analytics models. We contribute to the ontology design theory for analytics models underlying analytics systems for beach safety management domain. Our research findings can be used in the general class of ontology design problem for analytics systems in practice.
Barzegarkhoo, R, Farhangi, M, Lee, SS, Aguilera, RP & Siwakoti, YP 1970, 'A Novel Seven-Level Switched-Boost Common-Ground Inverter With Single-Stage Dynamic Voltage Boosting Gain', 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia), 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia), IEEE, Himeji, Japan, pp. 873-877.
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Single-stage grid-connected PV inverters with a transformerless (TL) concept have been a hot spot research topic in both the academia and industry in the latest years. Dynamic voltage boosting feature through the adjustment of de duty cycle, reduced value of the current and voltage stress across the switches, common-ground (CG)-based circuit architecture and capability of larger number of output voltage levels generation can make these types of inverters an attractive option for an efficient and compact design. In this paper, a novel seven-level (7L)-CG-based TL inverter is proposed, which possesses all the above-mentioned features for a compact design. The working principle and modulation strategy are discussed. Some simulation results are presented to attest a proper performance of the converter.
Barzegarkhoo, R, Farhangi, M, Lee, SS, Aguilera, RP, Siwakoti, YP & Liserre, M 1970, 'Active Neutral Point-Clamped Five-Level Inverter With Single-Stage Dynamic Voltage Boosting Capability', 2022 IEEE 13th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 2022 IEEE 13th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), IEEE, pp. 1-6.
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The circuit performance of conventional active neutral point-clamped (ANPC) inverter is widely accepted in many renewable energy-based applications like photovoltaic (PV) or electric vehicle grid-connected systems. This is mainly because of its excellent characteristics in terms of voltage/current stress profile of the switches, bidirectional power flow capability, and efficient operation. Nonetheless, due to its half-dc link voltage utilization in the ac output voltage, another power processing stage with additional active and passive elements is required to make its output voltage compatible with the grid when low and wide varying input dc source is available. In this paper, a novel ANPC-based five-level (ANPC5L) inverter with a single-stage boost-integrated circuit design is presented. The proposed topology is able to make the peak output voltage of the conventional ANPC5L inverter followed by a front-end bidirectional boost converter double using the same number of power switches but with less total standing voltage across semiconductors. The working principles of the proposed topology is discussed. Experimental results obtained from 1.3 kW laboratory-built prototype under the grid-connected condition are also given to support the discussion.
Bau', M, Zini, M, Nastro, A, Ferrari, M, Ferrari, V & Lee, JE-Y 1970, 'Electronic technique and system for non-contact reading of temperature sensors based on piezoelectric MEMS resonators', 2022 IEEE International Symposium on Circuits and Systems (ISCAS), 2022 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, Austin, TX, USA, pp. 2409-2413.
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This work investigates an electronic technique and system for non contact reading of the temperature dependent resonant frequency of piezoelectric MEMS resonators The proposed approach exploits magnetic coupling between an interrogation unit and a sensor unit to achieve non contact operation A dedicated electronic circuit in the interrogation unit alternatively switches the system between the excitation and detection phases thus implementing a time gated technique The MEMS resonator in the sensor unit is driven into resonance during the excitation phase while its damped response is sensed in the detection phase An electronic circuit down mixes the damped response of the resonator and the frequency of the resulting signal is measured through a post processing technique based on autocorrelation The system has been applied to the reading of a temperature sensor based on a MEMS aluminum nitride thin film piezoelectric on silicon disk resonator vibrating in radial contour mode The experimental characterization of the non contact system determined the temperature coefficient of frequency of the MEMS resonator to be 47 4 ppm C in good agreement with the measurements taken by directly probing the resonator
Bauer, D, Patten, T & Vincze, M 1970, 'SporeAgent: Reinforced Scene-level Plausibility for Object Pose Refinement', 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE, pp. 196-204.
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Beck, BRG, Tipper, J & Su, S 1970, 'Comparison of Constant PID Controller and Adaptive PID Controller via Reinforcement Learning for a Rehabilitation Robot', 2022 Australian & New Zealand Control Conference (ANZCC), 2022 Australian & New Zealand Control Conference (ANZCC), IEEE, pp. 218-223.
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Benedict, G, Gill, A & Sullivan, C 1970, 'Governance Challenges of AI-enabled Decentralized Autonomous Organizations: Toward a Research Agenda', International Conference on Information Systems Icis 2022 Digitization for the Next Generation, International Conference on Information Systems, AIS, Copenhagen, Denmark, pp. 1-9.
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The emergence of novel applications using distributed ledger technologies (DLTs) has gathered pace since the introduction of Bitcoin and the subsequent release of the Ethereum platform for decentralized applications (dApps). Such decentrally governed DLT systems are accelerating the displacement of intermediaries in regulated contexts such as the financial system and challenging the efficacy of governance regimes that have conventionally levered governance controls on identifiable, accountable decision-makers. The governance challenges of DLT systems are exacerbated by the arrival of digital autonomous organizations (DAOs) that use on-ledger decision-making mechanisms to further displace or eliminate human decision-makers. When DAOs are augmented with artificial intelligence (AI), their potent combination of computational power and access to large on-platform data sets and resources, signals a significant disruption to conventional institutional, regulatory, and legal governance regimes. This paper discusses the governance challenges of AI-enabled DAOs and presents a research agenda to address these challenges.
Berta, M & Tomamichel, M 1970, 'Chain rules for quantum channels', 2022 IEEE International Symposium on Information Theory (ISIT), 2022 IEEE International Symposium on Information Theory (ISIT), IEEE, pp. 2427-2432.
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Best, G, Garg, R, Keller, J, Hollinger, GA & Scherer, S 1970, 'Resilient Multi-Sensor Exploration of Multifarious Environments with a Team of Aerial Robots', Robotics: Science and Systems XVIII, Robotics: Science and Systems 2022, Robotics: Science and Systems Foundation.
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Bhandari, S, Fatahi, B, Khabbaz, H, Lee, J, Xu, Z & Zhong, J 1970, 'Evaluating the Influence of Soil Plasticity on the Vibratory Roller—Soil Interaction for Intelligent Compaction', Lecture Notes in Civil Engineering, 4th International Conference on Transportation Geotechnics (ICTG), Springer International Publishing, ELECTR NETWORK, pp. 247-260.
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Use of intelligent compaction (IC) is a growing technique for compaction in the field of construction. It provides an efficient way of evaluating the soil compaction level with a higher degree of certainty than traditional quality control methods. IC involves the interpretation of measured values received through the accelerometer and other sensors attached to the roller. The key objective of this paper is to analyse the dynamic roller–soil interaction via a three-dimensional nonlinear finite element model, capturing soil nonlinear response and damping in both small and large strain ranges as a result of dynamic load applied via the vibratory roller. In particular, the impact of soil plasticity index (PI) on the response of a typical vibratory roller is assessed. Indeed, the soil plasticity impacts stiffness degradation with shear strain influencing the soil stiffness during compaction and the roller response. The numerical predictions exhibit that the soil plasticity can significantly influence the response of the roller and the ground settlement level; hence, practising engineers can consider the soil plasticity index as an influencing factor to interpret the intelligent compaction results and optimize the compaction process.
Bhatawdekar, RM, Raina, AK & Jahed Armaghani, D 1970, 'A Comprehensive Review of Rockmass Classification Systems for Assessing Blastability', Springer Nature Singapore, pp. 563-578.
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Bhatawdekar, RM, Roy, B, Changtham, S, Khandelwal, M, Armaghani, DJ, Mohamad, ET, Pathak, P, Mondal, S, Kumar, R & Md Dan, MF 1970, 'Intelligent Techniques for Prediction of Drilling Rate for Percussive Drills in Topically Weathered Limestone', Springer Nature Singapore, pp. 457-471.
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Borhani, A, Borhani, A, Dossick, CS & Jupp, J 1970, 'Smart Building Conceptualization: A Comparative Analysis of Literature and Standards', Construction Research Congress 2022, Construction Research Congress 2022, American Society of Civil Engineers, pp. 310-318.
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Bourahmoune, K, Ishac, K, Carmichael, M & Amagasa, T 1970, 'Owro: A Novel Robot For Sitting Posture Training Based On Adaptive Human Robot Interaction', 2022 IEEE International Conference on Big Data (Big Data), 2022 IEEE International Conference on Big Data (Big Data), IEEE.
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Bourke, MA & Wijayaratna, KDS 1970, 'THE WHITE TRIANGLE: A PATH TOWARDS EFFICIENT AND INTEGRATED LIGHT RAIL SYSTEMS', Proceedings of the 26th International Conference of Hong Kong Society for Transportation Studies Hksts 2022, International Conference of Hong Kong Society for Transportation Studies, Hong Kong, pp. 382-391.
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Australian light rail networks include significant lengths of on-street running, where frequent intersections increase passenger journey times. This paper reviews the White Triangle signals used on Sydney's light rail network. These signals allow drivers to continue up to a red signal at speed, knowing that it will change in their favour. Whilst similar systems are used elsewhere around the world, there exists limited guidance to aid signal designers and support the use of these signals. This paper presents a framework to optimise White Triangle display times to reduce intersection delays. This was found to provide a theoretical time saving of 3 to 30 seconds per intersection. Whilst this is only partially achievable in practice, the paper demonstrates that White Triangles can be used to reduce LRV phase lengths and maximise Transit Signal Priority effectiveness. The signals thus offer potential reductions in passenger journey times and cost savings to network operators.
Bown, O, Mikolajczyk, K, Ferguson, S & Carey, B 1970, 'ISEA2022, International Symposium on Electronic Art, Barcelona. Proceedings', International Symposium on Electronic Art 2022, ISEA2022, International Symposium on Electronic Art, Barcelona. Proceedings, ISEA & UOC, Barcelona, Spain.
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Boyd-Weetman, B, Thomas, P, De Silva, P & Sirivivatnanon, V 1970, 'Alkali Immersion effects on alkali silica reaction progress in an Australian aggregate concrete', 76th RILEM Annual Week 2022 and International Conference on Regeneration and Conservation of Structures, Kyoto, Japan.
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The Alkali Silica Reaction is a deleterious reaction between alkalis and reactive aggregate materials in concrete structures. ASR has caused significant damage and reduces service life of structures. The mechanism for ASR developed deterioration has been a subject of study for almost a century continuing to the modern day. To prevent ASR from affecting the service life of current and future structures, characterisation of concrete material’s propensity to form ASR is vital to inform material choice for durable concrete. Accelerated testing methods for identifying aggregate propensity towards deleterious ASR have been developed and improved upon since the reaction’s discovery. Minimising alkali is important in protecting the long-term durability of structures however boosting alkali of concrete in controlled experimental conditions is required to asses an aggregates propensity for ASR reactivity. Accelerating ASR reactive conditions via alkaline solution immersion techniques are emerging as a viable ASR investigative tool. This study investigates the effect of boosted alkali binders and immersion solutions on the ASR mechanism. Concrete prism specimens of a range of alkali loadings with a specific aggregate are immersed in alkaline solution approximating their internal pore solution. The effect on deleterious expansion, aggregate damage and ASR mechanisms are assessed.
Boye, T 1970, 'Accessibility of Work-Integrated Learning in Engineering, IT, and Computer Science for Students with Disabilities', 2022 Conference on Research in Equitable and Sustained Participation in Engineering, Computing, and Technology (RESPECT), 2022 Conference on Research in Equitable and Sustained Participation in Engineering, Computing, and Technology (RESPECT), IEEE, Philadelphia, USA, pp. 109-113.
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Universities put significant resources into supporting students with disabilities on campus. However, this support from universities can be limited off-campus. With universities and governments strongly advocating for the inclusion of Work-Integrated Learning (WIL) placements in all programs, particularly in technical areas such as engineering, IT, and computer science, students with disabilities are increasingly expected to leave campus to attain their degree. In these arrangements the expectations are unclear and greatly vary, with most employers taking full responsibility for students on a day-to-day basis. Although engineering, IT, and computer science industries have become more open to and inclusive of diverse workforces in recent years, student WIL experiences can vary dramatically between employers and this can leave students vulnerable to the culture and accessibility of the workplace they undertake WIL activities in. Additionally, many programs require students to find their own placements, which is challenging given the reportedly low employment opportunities available to people with disabilities. In this paper, we recommend that students with disabilities need to be considered more than they currently are in the design of WIL placement programs and that programs could provide greater support to these students to ensure the positive outcomes associated with WIL are equitable for all students. It is also suggested that work needs to be done to bring students with disabilities into the conversation through co-design and participatory research in order to understand what their experiences are like in WIL and how universities and employers can better support them to reach their goals.
Boye, T 1970, 'Reflecting on Experience of Investigating the Accessibility of Work-Integrated Learning using a Participatory Research Methodology', ACEN 2022 Conference, ACEN Conference, ACEN, Melbourne, Australia, pp. 6-11.
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People with disabilities face significant barriers in work and study and are thus underrepresented in both, withonly 48% of people in any form of employment and only 6% of people in higher education identifying as having adisability. Work-Integrated Learning (WIL) is becoming increasingly popular in Higher Education to improveemployability, however, there are concerns WIL is not accessible to all. This project seeks to understand theexperience of students with disabilities in WIL using a participatory research methodology. This paper reflects on participatory workshops conducted for this research and the methods used. While researchers need to keep some key caveats in mind (time, protocols, etc), it is recommended that participatory research be considered by WIL researchers as an approach for further work in the WIL equity space.
Boye, T, Cheng, E & Gan, W 1970, 'IT Industry Service Management Tools for Managing Large Classes', 2022 IEEE Frontiers in Education Conference (FIE), 2022 IEEE Frontiers in Education Conference (FIE), IEEE, Uppsala, Sweden, pp. 1-6.
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Innovation Paper: With ever-increasing numbers of students entering university, large classes are becoming the norm rather than the exception for universities in many parts of the world such as Australia and some areas of the USA. This is particularly true in technical degrees such as engineering and IT. Cohorts of around 700 or even 1000 students are no longer uncommon. With so many students it can be difficult for academics to manage the administration of these classes, taking time away from curriculum development and teaching duties. Hundreds of email enquiries grace the modern academics’ inbox, and in addition, increasingly there are multiple communication channels to monitor such as Microsoft Teams, Slack, LMS discussion forums, LMS instant messaging, and many more.The IT industry has had a long history of dealing with large volumes of enquiries or in the case of software development, ‘bug’ reports. Further, tools developed by the IT industry have increasingly been seen outside the IT sector to help with non-IT service delivery. This project sought to use IT industry knowledge and tools to manage the administration of our large first-year engineering class \emph{Introduction to Engineering Projects}, a project subject that sees approximately 1000 students per year asymmetrically split between approximately 700 students in the first semester and approximately 300 students in the second semester. To that end, in the second semester of 2021 (July – November) we implemented a service management tool, Atlassian’s Jira Service Management Cloud, to manage our large volume of student enquiries, replacing traditional emails and most other communication channels.While we discovered these tools can be complex to set up, once set up they significantly improved academic-student communication and communication management. The tool allowed for emails to also feed into the enquiry portal, simplifying where students submit enquiries to coordination staff. Staff...
Boye, T, Machet, T & Narai, R 1970, 'Co-designing an engineering professional practice program with students', 2022 IEEE Frontiers in Education Conference (FIE), 2022 IEEE Frontiers in Education Conference (FIE), IEEE, Uppsala, Sweden, pp. 1-6.
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To most students the internal machinations of the university are a black box, very rarely are they permitted to see behind the curtain. While in many areas academia has started to move away from the sage-on-the-stage mentality, much of what is done still does not involve the students' voice. While they have the opportunity to provide feedback on individual subjects, the structure of students' whole degrees are still the domain of the sage.
At the University of Technology Sydney (UTS) we are reviewing our professional practice program for engineering. This program sees students complete professional experience activities such as internships, reflections and professional skill development in order to give students the opportunity to develop as professionals. While the program is well received by most stakeholders, it has remained largely the same for some time. Changes in the Higher Education sector, changing student needs and learning from the COVID-19 disruption have resulted in a review looking to redevelop the program.
Typically a program review would be an opaque process for students if they were aware of it at all. However, UTS sought to bring students into the program development from an early stage. Engineering and IT students from any year of study were invited to apply to join a seven-week co-design studio over their Summer semester to reimagine professional practice at UTS. They were taken through the design thinking process to imagine a future program that meets the needs of all stakeholders. Students worked through empathising with past and current students, program academics, Work-Integrated Learning (WIL) experts, industry professionals and others they identified as important stakeholders. Additionally, the students completed independent research on context topics they identified as critical to understanding the space.
The results of the project were that students identified three key foci for their program:
\begin{itemize}
\item Supporting the ...
Boyle, F, Hadgraft, R, Lindsay, E & Ulseth, R 1970, 'Innovating Engineering Education at Greenfield Sites: Transferable Insights from Doblin’s Model of Innovation', 2022 IEEE Frontiers in Education Conference (FIE), 2022 IEEE Frontiers in Education Conference (FIE), IEEE, pp. 1-8.
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Bozov, H, Bozova, G, Sotirova, E & Shannon, A 1970, 'A Generalized Net Model with Intuitionistic Fuzzy Assessments of the Process of Cardiopulmonary Resuscitation', Springer International Publishing, pp. 100-112.
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Brandt, L, Wambsganss, A, Sick, N & Broering, S 1970, 'Renewal strategies in corporate venturingThe case of the chemical industry', Australian and New Zealand Academy of Management, Gold Coast.
Briozzo, P, Fiford, R, Willey, K, Gardner, A & Lowe, D 1970, 'Creativity in Mechanical Design: Establishing Student Perceptions of Creative Designs and Impediments to Creative Solutions', 9th Research in Engineering Education Symposium (REES 2021) and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021), 9th Research in Engineering Education Symposium & 32nd Australasian Association for Engineering Education Conference, Research in Enineering Education Network (REEN), Perth, Australia, pp. 344-355.
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Brown, N, Cheng, E & Whelan, K 1970, 'Developing intersectional inclusion capability in engineering students', 9th Research in Engineering Education Symposium (REES 2021) and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021), 9th Research in Engineering Education Symposium & 32nd Australasian Association for Engineering Education Conference, Research in Enineering Education Network (REEN), pp. 1126-1126.
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Brown, N, Smith, J, Daniel, S & Birzer, C 1970, 'Exploring the Position of Humanitarian Engineering in Australia', Proceedings of the AAEE 2022 33rd Annual Conference, Annual Conference of the Australasian Association for Engineering Education, Western Sydney University, Western Sydney University, pp. 1-10.
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CONTEXTThe position of an engineering field, such as a discipline, specialisation, or area of practice, reflectsthe body of knowledge, skills and techniques required to practice. Humanitarian Engineering,which was recognised with a 6-digital Field of Research code in 2020, works within a range ofcontexts and communities where there are inherent power imbalances, and decisions and actionsthat affect immediate livelihoods and wellbeing. This can be considered similar to fields withinEngineers Australia such as Amusement Rides and Devices (an area of practice) and Fire Safety(both an area of practice and technical society). These fields do not have large memberships, butare sufficiently specialised and high risk to require dedicated scrutiny. Humanitarian Engineeringcan be considered to warrant similar levels of enhanced scrutiny of its practice and education.PURPOSEThe Engineers Australia Humanitarian Engineering Community of Practice have devised a six-itemagenda for the professionalisation of Humanitarian Engineering in Australia. This seeks to bring thesame level of rigour, review, and recognition to Humanitarian Engineering as for any field, area ofpractice or discipline of engineering. This study set out to determine a broad consensus on theposition of Humanitarian Engineering within existing frameworks in response to the agenda.APPROACHThe study adopted a modified Delphi method in which key stakeholders and representatives ofHumanitarian Engineering education and practice in Australia were invited to a workshop to openlydiscuss and debate the position of Humanitarian Engineering in Australia. A pre-workshop surveyestablished a starting point for discussion at a 2-hour workshop while a post-workshop surveyidentified and tested key insights and findings.OUTCOMESA specific position on Humanitarian Engineering in Australia was not reached during the workshop.Rather, underlying assumptions were challenged and tested. Humanitarian ...
Brown, N, Smith, J, Daniel, S, Rosenqvist, T & Birzer, C 1970, 'Teaching engineering for complex contexts', 9th Research in Engineering Education Symposium (REES 2021) and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021), 9th Research in Engineering Education Symposium & 32nd Australasian Association for Engineering Education Conference, Research in Enineering Education Network (REEN), pp. 1144-1144.
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Bu, Y, Liu, M, Zhai, Y, Ding, Y, Xia, F, Acuña, DE & Zhang, Y 1970, 'International Workshop on Data-driven Science of Science', Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 4856-4857.
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Citation data, along with other bibliographic datasets, have long been adopted by the knowledge and data discovery community as an important direction for presenting the validity and effectiveness of proposed algorithms and strategies. Many top computer scientists are also excellent researchers in the science of science. The purpose of this workshop is to bridge the two communities (i.e., the knowledge discovery community and the science of science community) together as the scholarly activities become salient web and social activities that start to generate a ripple effect on broader knowledge discovery communities. This workshop will showcase the current data-driven science of science research by highlighting several studies and constructing a community of researchers to explore questions critical to the future of data-driven science of science, especially a community of data-driven science of science in Data Science so as to facilitate collaboration and inspire innovation. Through discussion on emerging and critical topics in the science of science, this workshop aims to help generate effective solutions for addressing environmental, societal, and technological problems in the scientific community.
Bui, H, Hussain, OK, Saberi, M & Hussain, FK 1970, 'Proof of Earnestness- Subjective information’s Trustworthiness in Blockchains as a Service', 2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), IEEE, pp. 321-327.
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Byun, H, Zhao, L, Kim, J & Huang, S 1970, 'Comparison Between MATLAB Bundle Adjustment Function and Parallax Bundle Adjustment', 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV), IEEE, pp. 60-65.
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Bundle Adjustment (BA) takes a crucial part in Structure from Motion (SfM) which refines a visual reconstruction by optimizing the camera poses and feature positions. The performance of BA can differ depending on the parametrization methods. This paper evaluates two bundle adjustment techniques using standard BA function from MATLAB and Parallax BA. The two BA techniques are compared using data from the 'Starry Night' and 'MALAGA Parking-6L' with different initial inputs. The accuracy and convergence properties of the two BA methods have been evaluated. The effect of the different parameterization techniques and initial information was also analyzed. In most cases, the results of Parallax BA show better accuracy with lower final reprojection error and are less sensitive to the initialization values. It is evaluated that the parallax angle avoids the singularity issue commonly found in Standard BA, which shows that Parallax BA outperforms Standard BA. Furthermore, visual-inertial SLAM (VI-SLAM), based on Parallax BA, has been presented. It is much more reliable than a pure-vision system, showing further improved performance in terms of robustness and accuracy, even with less feature observation. The open-source code can be found in: https://github.com/uts-hb/ParallaxBA.git
Cai, L, Ferguson, S, Lu, H & Fang, G 1970, 'Feature Selection Approaches for Optimising Music Emotion Recognition Methods', Artificial Intelligence, Soft Computing and Applications, 12th International Conference on Artificial Intelligence, Soft Computing and Applications, Academy and Industry Research Collaboration Center (AIRCC), pp. 09-27.
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The high feature dimensionality is a challenge in music emotion recognition. There is no common consensus on a relation between audio features and emotion. The MER system uses all available features to recognize emotion; however, this is not an optimal solution since it contains irrelevant data acting as noise. In this paper, we introduce a feature selection approach to eliminate redundant features for MER. We created a Selected Feature Set (SFS) based on the feature selection algorithm (FSA) and benchmarked it by training with two models, Support Vector Regression (SVR) and Random Forest (RF) and comparing them against with using the Complete Feature Set (CFS). The result indicates that the performance of MER has improved for both Random Forest (RF) and Support Vector Regression (SVR) models by using SFS. We found using FSA can improve performance in all scenarios, and it has potential benefits for model efficiency and stability for MER task.
Cao, J, Liu, B, Wen, Y, Zhu, Y, Xie, R, Song, L, Li, L & Yin, Y 1970, 'Hiding Among Your Neighbors: Face Image Privacy Protection with Differential Private k-anonymity', 2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE, pp. 1-6.
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The development of modern social media allows millions of private photos to be uploaded and shared, which provides a wide range of image acquisition but extremely threatens personal image privacy. Face de-identification is treated as an important privacy protection tool in multimedia data processing by modifying image identity information. Although there exist many traditional methods widely used to hide sensitive private information, they all fail to balance the trade-off between privacy and utility in qualitative and quantitative manners and cannot generate de-identified results with satisfactory visual perception. In this paper, we propose a novel face image privacy protection method with differential private k-anonymity, which can not only generate de-identified results with good image quality but also control the balance between privacy protection and image utility according to different application scenarios. The framework consists of the following three steps: facial attributes prediction, privacy-preserving attributes obfuscation, and naturally realistic de-identificated image generation. Our extensive experiments demonstrate the stability and effectiveness of the proposed model.
Cao, MX, Ramakrishnan, N, Berta, M & Tomamichel, M 1970, 'One-Shot Point-to-Point Channel Simulation', 2022 IEEE International Symposium on Information Theory (ISIT), 2022 IEEE International Symposium on Information Theory (ISIT), IEEE.
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Caro, F & Carmichael, MG 1970, 'Laminar Jamming with Trapezoidal Pin Mechanism for Variable Stiffness Robotic Arms', 2022 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2022 IEEE International Conference on Robotics and Biomimetics (ROBIO), IEEE, pp. 1061-1066.
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Caruana, A, Bandara, M, Catchpoole, D & Kennedy, PJ 1970, 'Beyond Topics: Discovering Latent Healthcare Objectives from Event Sequences', AI 2021: Advances in Artificial Intelligence, Springer International Publishing, pp. 368-380.
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A meaningful understanding of clinical protocols and patient pathways helps improve healthcare outcomes. Electronic health records (EHR) reflect real-world treatment behaviours that are used to enhance healthcare management but present challenges; protocols and pathways are often loosely defined and with elements frequently not recorded in EHRs, complicating the enhancement. To solve this challenge, healthcare objectives associated with healthcare management activities can be indirectly observed in EHRs as latent topics. Topic models, such as Latent Dirichlet Allocation (LDA), are used to identify latent patterns in EHR data. However, they do not examine the ordered nature of EHR sequences, nor do they appraise individual events in isolation. Our novel approach, the Categorical Sequence Encoder (CaSE) addresses these shortcomings. The sequential nature of EHRs is captured by CaSE’s event-level representations, revealing latent healthcare objectives. In synthetic EHR sequences, CaSE outperforms LDA by up to 37% at identifying healthcare objectives. In the real-world MIMIC-III dataset, CaSE identifies meaningful representations that could critically enhance protocol and pathway development.
Casalino, F, Carry, J, Marchi, R, Durual, S, Gay, V, Neerman-Arbez, M & Casini, A 1970, 'Fibrinogen replacement in hereditary dysfibrinogenemia: how much is enough?', SWISS MEDICAL WEEKLY, E M H SWISS MEDICAL PUBLISHERS LTD, pp. 56S-56S.
Cedieu, S, Grigoletto, FB, Lee, SS, Barzegarkhoo, R & Siwakoti, YP 1970, 'Four-Switch Five-Level Common-Ground Transformerless Inverter', 2022 14th Seminar on Power Electronics and Control (SEPOC), 2022 14th Seminar on Power Electronics and Control (SEPOC), IEEE, pp. 1-6.
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Chattopadhyay, S, Chanda, R, Kumar, S & Adak, C 1970, 'OffDQ: An Offline Deep Learning Framework for QoS Prediction', Proceedings of the ACM Web Conference 2022, WWW '22: The ACM Web Conference 2022, ACM, pp. 1987-1996.
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With the increasing trend of web services over the Internet, developing a robust Quality of Service (QoS) prediction algorithm for recommending services in real-time is becoming a challenge today. Designing an efficient QoS prediction algorithm achieving high accuracy, while supporting faster prediction to enable the algorithm to be integrated into a real-time system, is one of the primary focuses in the domain of Services Computing. The major state-of-the-art QoS prediction methods are yet to efficiently meet both criteria simultaneously, possibly due to the lack of analysis of challenges involved in designing the prediction algorithm. In this paper, we systematically analyze the various challenges associated with the QoS prediction algorithm and propose solution strategies to overcome the challenges, and thereby propose a novel offline framework using deep neural architectures for QoS prediction to achieve our goals. Our framework, on the one hand, handles the sparsity of the dataset, captures the non-linear relationship among data, figures out the correlation between users and services to achieve desirable prediction accuracy. On the other hand, our framework being an offline prediction strategy enables faster responsiveness. We performed extensive experiments on the publicly available WS-DREAM dataset to show the trade-off between prediction performance and prediction time. Furthermore, we observed our framework significantly improved one of the parameters (prediction accuracy or responsiveness) without considerably compromising the other as compared to the state-of-the-art methods.
Chauhan, R, Sengupta, A & Yafi, E 1970, 'Artificial Intelligence an Influential Review: Pandemic Scenario', 2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM), 2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM), IEEE, pp. 1-7.
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Chemalamarri, VD, Abolhasan, M & Braun, R 1970, 'An agent-based approach to disintegrate and modularise Software Defined Networks controller', 2022 IEEE 47th Conference on Local Computer Networks (LCN), 2022 IEEE 47th Conference on Local Computer Networks (LCN), IEEE, Edmonton, CANADA, pp. 407-413.
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The Software Defined Network paradigm deviates from traditional networks by logically centralising and physically separating the control plane from the data plane. In this work, we present the idea of a modular, agent-based SDN controller. We first highlight issues with current SDN controller designs, followed by a description of the proposed framework. We present a prototype for our design to demonstrate the controller in action using a few common use-cases. We continue the discussion by highlighting areas that require further research.
Chen, C, Chen, Z, Zhao, J, Guo, Y & Liao, X 1970, 'An Impulse Modulation Strategy for the M-Phase Permanent Magnet Synchronous Motor with the Current Source Inverter', 2022 International Conference on Power Energy Systems and Applications (ICoPESA), 2022 International Conference on Power Energy Systems and Applications (ICoPESA), IEEE, pp. 381-389.
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Chen, D, Li, M, Guo, P & Liu, Y 1970, 'A Novel Multi-Linear Polarization Reconfigurable Antenna Array', 2022 IEEE 10th Asia-Pacific Conference on Antennas and Propagation (APCAP), 2022 IEEE 10th Asia-Pacific Conference on Antennas and Propagation (APCAP), IEEE, pp. 1-2.
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Chen, F, Long, G, Wu, Z, Zhou, T & Jiang, J 1970, 'Personalized Federated Learning With a Graph', Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}, International Joint Conferences on Artificial Intelligence Organization, pp. 2575-2582.
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Knowledge sharing and model personalization are two key components in the conceptual framework of personalized federated learning (PFL). Existing PFL methods focus on proposing new model personalization mechanisms while simply implementing knowledge sharing by aggregating models from all clients, regardless of their relation graph. This paper aims to enhance the knowledge-sharing process in PFL by leveraging the graph-based structural information among clients. We propose a novel structured federated learning (SFL) framework to learn both the global and personalized models simultaneously using client-wise relation graphs and clients' private data. We cast SFL with graph into a novel optimization problem that can model the client-wise complex relations and graph-based structural topology by a unified framework. Moreover, in addition to using an existing relation graph, SFL could be expanded to learn the hidden relations among clients. Experiments on traffic and image benchmark datasets can demonstrate the effectiveness of the proposed method.
Chen, H, Zhang, Y, Jin, Q & Wang, X 1970, 'Exploring Patterns of Academic-Industrial Collaboration for Digital Transformation Research: A Bibliometric-Enhanced Topic Modeling Method', 2022 Portland International Conference on Management of Engineering and Technology (PICMET), 2022 Portland International Conference on Management of Engineering and Technology (PICMET), IEEE, pp. 1-9.
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Interactions between industry and academia provide inspiration for knowledge fusion, and more importantly serve as a stimulus for both basic and applied research, creating impact and potential opportunities. From the perspective of text mining and co-authorship network analysis, this paper aims to explore the patterns of academic-industrial collaboration using publication data. We propose a bibliometric-enhanced topic modeling method to profile the core constituents of industry and academia collaborative hotspots in digital transformation research, using a 10-year publication dataset from 2009 to 2018 extracted from Web of Science. We then examine interactions and distinctions between topics authored by only academic researchers and having industrial collaboration to further develop a comprehensive understanding of the content and driving force of industrial engagement. The empirical insights of this paper provide a detailed picture of academic-industrial linkages, which potentially can be used to lead academics to engage with industry, and assist innovation management and problem-solving in digital transformation research and practice.
Chen, S, Zhao, L, Huang, S & Hao, Q 1970, 'Multi-robot Active SLAM based on Submap-joining for Feature-based Representation Environments', Australasian Conference on Robotics and Automation Acra, Australasian Conference on Robotics and Automation, Brisbane.
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The ability to acquire knowledge of the environment actively is essential for autonomous system. In this paper, we propose a multi-robot active simultaneous localization and mapping (SLAM) algorithm based on mutual information for feature-based representation environments that do not depend on the grid map. A multi-layer motion planner and virtual landmarks are introduced to improve exploration efficiency and reduce planning time. To improve the system's accuracy and scalability, we also developed a decentralized version of the active SLAM based on the submap-joining approach. Both simulations and real-world experiments are performed to validate the effectiveness of the proposed methods.
Chen, S-L & Li, Z 1970, 'Recent Advances in Beam Steering/Scanning Leaky-Wave Antennas', 2022 IEEE Conference on Antenna Measurements and Applications (CAMA), 2022 IEEE Conference on Antenna Measurements and Applications (CAMA), IEEE, pp. 1-3.
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Leaky-wave antennas (LWAs) is a promising antenna technology for future wireless systems. In this paper, we present a number of our recent innovative designs to overcome challenging issues existed in LWAs. Our innovations include realizing fixed-frequency beam scanning, radiating wideband fixed beam, and achieving two-dimensional (2D) high gain directive beams. These developed techniques advance the LWA properties remarkably.
Chen, S-L, Liu, Y, Chen, D & Guo, YJ 1970, 'High-Gain Multi-Linear Polarization Reconfigurable Antenna in the Millimeter-Wave Band', 2022 16th European Conference on Antennas and Propagation (EuCAP), 2022 16th European Conference on Antennas and Propagation (EuCAP), IEEE, pp. 1-4.
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Chen, S-L, Ziolkowski, RW, Jones, B & Guo, YJ 1970, 'Closed-Path Toroidal-Waveguide Leaky-Wave Antenna with Directive Beam', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 15-16.
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Chen, X, Dai, W, Ni, W, Wang, X, Zhang, S, Xu, S & Sun, Y 1970, 'New Two-Stage Deep Reinforcement Learning for Task Admission and Channel Allocation of Wireless-Powered Mobile Edge Computing', ICC 2022 - IEEE International Conference on Communications, ICC 2022 - IEEE International Conference on Communications, IEEE, pp. 1208-1213.
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Chen, X, Khan, MIW, Yi, X, Li, X, Chen, W, Zhu, J, Yang, Y, Kolodziej, KE, Monroe, NM & Han, R 1970, 'A 140GHz Transceiver with Integrated Antenna, Inherent-Low-Loss Duplexing and Adaptive Self-Interference Cancellation for FMCW Monostatic Radar', 2022 IEEE International Solid- State Circuits Conference (ISSCC), 2022 IEEE International Solid- State Circuits Conference (ISSCC), IEEE, pp. 80-82.
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Sub-THz radars in CMOS are attractive in vital-sign and security-sensing applications, due to their low cost, small size, and high resolution. The commonly used bistatic configuration, however, leads to serious beam misalignment between TX and RX, when large-aperture lenses/mirrors are used for longer range and higher spatial precision. As shown in [1], a 4mm physical separation between TRX antennas at 122GHz can cause 6° TRX beam misalignment, exceeding the 3dB beamwidth of the 29dBi-directivity beam. Monostatic radars are, therefore, preferred in those applications, when sufficient TRX isolation is achieved to avoid saturating the RX. Prior monostatic radars [2]-[6] adopt hybrid/directional couplers for passive TRX duplexing, but at the cost of 3dB+3dB insertion loss inherent to couplers. In [3], such extra loss is mitigated through two sets of hybrid couplers and a quad-feed circularly polarized antenna. Note that in all full-duplex systems, antenna interface mismatch degrades the TRX isolation; in [3], the achieved 26dB isolation relies on excellent antenna matching enabled by backside radiation through a silicon lens. In comparison, frontside radiation allows for low-cost packaging and pairing with compact, large-aperture planar lens, but it causes much degraded antenna matching, hence is challenging for monostatic operation. In this paper, we present a 140GHz monostatic radar in CMOS, which not only circumvents the 6dB inherent insertion loss of couplers, but also facilitates the highly-desired frontside radiation through an adaptive self-interference cancellation (SIC), achieving 33.3dB of total TRX isolation.
Chen, X, Yao, L, Chang, X & Wang, S 1970, 'Empowerment-driven Policy Gradient Learning with Counterfactual Augmentation in Recommender Systems', 2022 IEEE International Conference on Data Mining (ICDM), 2022 IEEE International Conference on Data Mining (ICDM), IEEE, pp. 885-890.
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Deep reinforcement learning (DRL) has been proven its efficiency in capturing users' dynamic interests in recent literature. However, training a DRL agent is challenging, because of the sparse environment in recommender systems (RS), DRL agents could spend times either exploring informative user-item interaction trajectories or using existing trajectories for policy learning. It is also known as the exploration and exploitation trade-off which affects the recommendation performance significantly when the environment is sparse. It is more challenging to balance the exploration and exploitation in DRL RS where RS agent need to deeply explore the informative trajectories and exploit them efficiently in the context of recommender systems. As a step to address this issue, We design a novel empowerment-driven exploration method to increase the capability of exploring informative interaction trajectories in the sparse environment, which are further enriched via a counterfactual augmentation strategy for more efficient exploitation. The extensive experiments on four offline datasets and an online simulation platform demonstrate the superiority of our model to a set of existing state-of-the-art methods.
Chen, X, Yao, L, McAuley, J, Guan, W, Chang, X & Wang, X 1970, 'Locality-Sensitive State-Guided Experience Replay Optimization for Sparse Rewards in Online Recommendation', Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, Madrid, Spain, pp. 1316-1325.
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Online recommendation requires handling rapidly changing user preferences. Deep reinforcement learning (DRL) is an effective means of capturing users' dynamic interest during interactions with recommender systems. Generally, it is challenging to train a DRL agent in online recommender systems because of the sparse rewards caused by the large action space (e.g., candidate item space) and comparatively fewer user interactions. Leveraging experience replay (ER) has been extensively studied to conquer the issue of sparse rewards. However, they adapt poorly to the complex environment of online recommender systems and are inefficient in learning an optimal strategy from past experience. As a step to filling this gap, we propose a novel state-aware experience replay model, in which the agent selectively discovers the most relevant and salient experiences and is guided to find the optimal policy for online recommendations. In particular, a locality-sensitive hashing method is proposed to selectively retain the most meaningful experience at scale and a prioritized reward-driven strategy is designed to replay more valuable experiences with higher chance. We formally show that the proposed method guarantees the upper and lower bound on experience replay and optimizes the space complexity, as well as empirically demonstrate our model's superiority to several existing experience replay methods over three benchmark simulation platforms.
Chen, Y & Jupp, JR 1970, 'Exploring the Nexus between Digital Engineering and Systems Engineering and the Role of Information Management Standards', The 45th Australasian Universities Building Education Association Conference: Global Challenges in a Disrupted World: Smart, Sustainable and Resilient Approaches in the Built Environment, Australasian Universities Building Education Association Conference, Western Sydney University, Sydney, Australia.
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Chen, Y, Ding, C, Zhu, H & Guo, YJ 1970, 'A Dual-Slant-Polarized Differentially-Fed In-band Full-duplex (IBFD) Antenna', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 221-222.
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In this paper, a dual-polarized antenna is developed with high isolation between its transmitting (TX) and receiving (RX) ports for in-band full-duplex (IBFD) applications. A square patch antenna with horizontal and vertical polarizations is adopted as the antenna element. A new self-interference cancellation (SIC) feed network is proposed to differentially feed the antenna and combine the horizontal/vertical polarizations into ±45° polarizations. By making use of the symmetry of the antenna configuration and differential feeding, the proposed network can cancel out the coupled and reflected signals, leading to high isolation between the TX and RX ports. A high isolation of 46 dB is realized within the working band from 3.31 to 4 GHz (18.5%) and the gain is above 7.5 dBi. In addition, across the operation band, the radiation patterns show a good stability with the frequency variation.
Cheng, Q & Long, G 1970, 'Federated Learning Operations (FLOps): Challenges, Lifecycle and Approaches', 2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), 2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), IEEE, pp. 12-17.
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Cheng, X, Zhang, G, Wang, H & Sui, Y 1970, 'Path-sensitive code embedding via contrastive learning for software vulnerability detection', Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA '22: 31st ACM SIGSOFT International Symposium on Software Testing and Analysis, ACM, pp. 519-531.
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Machine learning and its promising branch deep learning have shown success in a wide range of application domains. Recently, much effort has been expended on applying deep learning techniques (e.g., graph neural networks) to static vulnerability detection as an alternative to conventional bug detection methods. To obtain the structural information of code, current learning approaches typically abstract a program in the form of graphs (e.g., data-flow graphs, abstract syntax trees), and then train an underlying classification model based on the (sub)graphs of safe and vulnerable code fragments for vulnerability prediction. However, these models are still insufficient for precise bug detection, because the objective of these models is to produce classification results rather than comprehending the semantics of vulnerabilities, e.g., pinpoint bug triggering paths, which are essential for static bug detection. This paper presents ContraFlow, a selective yet precise contrastive value-flow embedding approach to statically detect software vulnerabilities. The novelty of ContraFlow lies in selecting and preserving feasible value-flow (aka program dependence) paths through a pretrained path embedding model using self-supervised contrastive learning, thus significantly reducing the amount of labeled data required for training expensive downstream models for path-based vulnerability detection. We evaluated ContraFlow using 288 real-world projects by comparing eight recent learning-based approaches. ContraFlow outperforms these eight baselines by up to 334.1%, 317.9%, 58.3% for informedness, markedness and F1 Score, and achieves up to 450.0%, 192.3%, 450.0% improvement for mean statement recall, mean statement precision and mean IoU respectively in terms of locating buggy statements.
Chi, H, Liu, F, Han, B, Yang, W, Lan, L, Liu, T, Niu, G, Zhou, M & Sugiyama, M 1970, 'META DISCOVERY: LEARNING TO DISCOVER NOVEL CLASSES GIVEN VERY LIMITED DATA', Iclr 2022 10th International Conference on Learning Representations.
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In novel class discovery (NCD), we are given labeled data from seen classes and unlabeled data from unseen classes, and we train clustering models for the unseen classes. However, the implicit assumptions behind NCD are still unclear. In this paper, we demystify assumptions behind NCD and find that high-level semantic features should be shared among the seen and unseen classes. Based on this finding, NCD is theoretically solvable under certain assumptions and can be naturally linked to meta-learning that has exactly the same assumption as NCD. Thus, we can empirically solve the NCD problem by meta-learning algorithms after slight modifications. This meta-learning-based methodology significantly reduces the amount of unlabeled data needed for training and makes it more practical, as demonstrated in experiments. The use of very limited data is also justified by the application scenario of NCD: since it is unnatural to label only seen-class data, NCD is sampling instead of labeling in causality. Therefore, unseen-class data should be collected on the way of collecting seen-class data, which is why they are novel and first need to be clustered.
Chi, Z, Wang, S, Li, X, Chang, C-T, Islam, M, Holkar, A, Pronger, S, Liu, T, Lam, K-M & He, X 1970, 'Multi-level unsupervised domain adaption for privacy-protected in-bed pose estimation', International Workshop on Advanced Imaging Technology (IWAIT) 2022, International Workshop on Advanced Imaging Technology (IWAIT 2022), SPIE, pp. 118-118.
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Chin Derix, E, Wah Leong, T & Prior, J 1970, '“It's A Drag”: Exploring How to Improve Parents’ Experiences of Managing Mobile Device Use During Family Time', CHI Conference on Human Factors in Computing Systems, CHI '22: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-20.
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Chinnakannu Murthy, K & Machel, T 1970, 'Systematic Literature Review of Students’ perception of Employability Skills', 9th Research in Engineering Education Symposium (REES 2021) and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021), 9th Research in Engineering Education Symposium & 32nd Australasian Association for Engineering Education Conference, Research in Enineering Education Network (REEN), pp. 169-177.
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Chockalingam, M & Singh, A 1970, 'Assessing Virtual Reality's potential to influence emotional states from negative to provide an instant positive effect', 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), IEEE, pp. 1-9.
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Choong, DSW, Goh, DJ, Liu, J, Merugu, S, Zhang, QX, Lee, HK, Chang, P, Leotti, A, Tan, H-S, Magbujos, V, Hur, YJ, Lin, H, Chadnra Rao, BSS, Ghosh, S, Ramegowda, PC, Chen, DS-H, Giusti, D, Quaglia, F, Ng, EJ & Lee, JE-Y 1970, 'Correlation of Wafer-scale Film Stress Effects on ScAlN pMUT Parameters', 2022 IEEE International Ultrasonics Symposium (IUS), 2022 IEEE International Ultrasonics Symposium (IUS), IEEE, pp. 1-4.
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Chowdhury, T, Cheraghian, A, Ramasinghe, S, Ahmadi, S, Saberi, M & Rahman, S 1970, 'Few-Shot Class-Incremental Learning for 3D Point Cloud Objects', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature Switzerland, pp. 204-220.
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Few-shot class-incremental learning (FSCIL) aims to incrementally fine-tune a model (trained on base classes) for a novel set of classes using a few examples without forgetting the previous training. Recent efforts address this problem primarily on 2D images. However, due to the advancement of camera technology, 3D point cloud data has become more available than ever, which warrants considering FSCIL on 3D data. This paper addresses FSCIL in the 3D domain. In addition to well-known issues of catastrophic forgetting of past knowledge and overfitting of few-shot data, 3D FSCIL can bring newer challenges. For example, base classes may contain many synthetic instances in a realistic scenario. In contrast, only a few real-scanned samples (from RGBD sensors) of novel classes are available in incremental steps. Due to the data variation from synthetic to real, FSCIL endures additional challenges, degrading performance in later incremental steps. We attempt to solve this problem using Microshapes (orthogonal basis vectors) by describing any 3D objects using a pre-defined set of rules. It supports incremental training with few-shot examples minimizing synthetic to real data variation. We propose new test protocols for 3D FSCIL using popular synthetic datasets (ModelNet and ShapeNet) and 3D real-scanned datasets (ScanObjectNN and CO3D). By comparing state-of-the-art methods, we establish the effectiveness of our approach in the 3D domain. Code is available at: https://github.com/townim-faisal/FSCIL-3D.
Chu, D, Zhang, F, Zhang, W, Lin, X & Zhang, Y 1970, 'Hierarchical Core Decomposition in Parallel: From Construction to Subgraph Search', 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, pp. 1138-1151.
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Chuahan, R, Gola, N, Yafi, E, Farez, M & Prasad, M 1970, 'Smart Cities with Recognizance in Air Quality', 2022 International Visualization, Informatics and Technology Conference (IVIT), 2022 International Visualization, Informatics and Technology Conference (IVIT), IEEE, pp. 130-135.
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The worldwide populace keeps on developing at a consistent speed, and more individuals are moving to urban communities. This led to the generation of idea of smart cities which eventually builds the sustainable environment around the world by advancing technologies which can implacably applied to understand and control various processes of the city which are subjected on water, air and energy. In current study of approach, the focus relies specifically on atmospheric pollutants which arise due to industries, factories, mining, and the combustion of fossil fuels. These activities release air pollutants that are harmful to all living things including Sulphur dioxide, nitrogen dioxide, carbon monoxide, ozone, and various others air pollutants. Additionally, it is a major risk factor for several health conditions, including bronchitis, lung cancer, heart problems, throat and eye disorders, asthma, skin infections, and respiratory system ailments. The aim of the current study was to conduct discrete factor analysis to analyze the factors which are responsible for degradation of the air quality. The proposed study is carried out in two phases, with the first phase measured the variation in the AQI (Air Quality Index) value of different smart cities of India for years 2015-2020, whereas in second phase we analyze the contribution of different gases such as NO2, NO, benzene, toluene, xylene, O3, CO, SO2, NOx towards the AQI value.
Coluccia, A, Fascista, A, Schumann, A, Sommer, L, Dimou, A, Zarpalas, D, Sharma, N, Nalamati, M, Eryuksel, O, Ozfuttu, KA, Akyon, FC, Sahin, K, Buyukborekci, E, Cavusoglu, D, Altinuc, S, Xing, D, Unlu, HU, Evangeliou, N, Tzes, A, Nayak, A, Bouazizi, M, Ahmad, T, Gonçalves, A, Rigault, B, Jain, R, Matsuo, Y, Prendinger, H, Jajaga, E, Rushiti, V, Ramadani, B & Pavleski, D 1970, 'Drone-vs-Bird Detection Challenge at ICIAP 2021', Springer International Publishing, pp. 410-421.
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Cui, L, Long, Y, Hoang, DT & Gong, S 1970, 'Hierarchical Learning Approach for Age-of-Information Minimization in Wireless Sensor Networks', 2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), IEEE, pp. 130-136.
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In this paper, we focus on a multi-user wireless network coordinated by a multi-antenna access point (AP). Each user can generate the sensing information randomly and report it to the AP. The freshness of information is measured by the age of information (AoI). We formulate the AoI minimization problem by jointly optimizing the users' scheduling and transmission control strategies. Moreover, we employ the intelligent reflecting surface (IRS) to enhance the channel conditions and thus reduce the transmission delay by controlling the AP's beamforming vector and the IRS's phase shifting matrices. The resulting AoI minimization becomes a mixed-integer program and difficult to solve due to uncertain information of the sensing data arrivals at individual users. By exploiting the problem structure, we devised a hierarchical deep reinforcement learning (DRL) framework to search for optimal solution in two iterative steps. Specifically, the users' scheduling strategy is firstly determined by the outer-loop DRL approach, and then the inner-loop optimization adapts either the uplink information transmission or downlink energy transfer to all users. Our numerical results verify that the proposed algorithm can outperform typical baselines in terms of the average AoI performance.
Cullen, M, Ji, J & Zhao, S 1970, 'Acoustic based GMAW penetration depth identification using droplet transfer monitoring', 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), IEEE, pp. 2369-2374.
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Process monitoring and quality control for industrial robotic Gas Metal Arc Welding (GMAW) systems are key components in ensuring the reliability of the produced products. While being a widely used process, there is still a lack of a robust, plug and play monitoring solution. In particular, weld bead penetration depth is a crucial factor in many fabrication applications, where substantial bonding strength is crucial. This paper introduces a new penetration depth estimation method using the emitted acoustic signal to monitor the droplet transfer process. By monitoring the droplet transfer, an estimation of the welding energy transferred to the base material can be obtained while accounting for variations in the welding process. Using this method, the penetration depth is able to be measured within an error of +15%, proving to be a promising solution for online monitoring in robotic welding applications.
Dagdanov, R, Eksen, F, Durmus, H, Yurdakul, F & Ure, NK 1970, 'DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Driving', 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), IEEE, pp. 4215-4220.
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Dai, B, Gentil, CL & Vidal-Calleja, T 1970, 'A Tightly-Coupled Event-Inertial Odometry using Exponential Decay and Linear Preintegrated Measurements', 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 9475-9482.
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In this paper, we introduce an event-based visual odometry and mapping framework that relies on decaying event-based corners. Event cameras, unlike conventional cam-eras, can provide sensor data during high-speed motions or in scenes with high dynamic ranges. Rather than providing intensity information at a global shutter rate, events are trig-gered asynchronously depending on whether there is a change in brightness at the pixel location. This novel sensing paradigm calls for unconventional ego-motion estimation techniques to address these new challenges. The key aspect of our framework is the use of a continuous representation of inertial measurements to characterise the system's motion which accommodates the asynchronous nature of the event data while estimating a discrete state in an optimisation-based approach. The proposed method relies on corners extracted from events-only data and associates them with a spatio-temporal locality scheme based on exponential decay. Event tracks are then tightly coupled with temporally accurate preintegrated inertial measurements, allowing for the estimation of ego-motion and a sparse map. The proposed method is evaluated on the Event Camera Dataset showing performance against the state-of-art in event-based visual-inertial odometry.
Dang, CC, Dang, LC & Khabbaz, H 1970, 'Predicting the Stability of Riverbank Slope Reinforced with Columns Under Various River Water Conditions', Lecture Notes in Civil Engineering, 4th International Conference on Transportation Geotechnics (ICTG), Springer International Publishing, ELECTR NETWORK, pp. 513-523.
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A numerical analysis on the stability of soil–cement column-reinforced riverbank along a river delta region in Viet Nam is presented in this paper. The numerical analyses based on the limit equilibrium method (LEM) were performed to assess the safety factor of the column-reinforced riverbank system under various river water level (RWL) conditions. Several factors influencing the riverbank slope stability including the position, length, quantity of soil–cement columns, and RWL changes were investigated. The simulated results showed that the riverbank stability is improved with an increase in the column quantity and the column length when subjected to a constant RWL. Moreover, the predicted results by LEM indicated that the column location and the RWL change significantly influence the stability of riverbank with column reinforcement. The column location between the middle and the slope toe had a significant improvement of the riverbank slope stability, where an initial drawdown of RWL resulted in a notable reduction of the riverbank slope safety factor. These factors should be taken into consideration in the design of a riverbank slope, reinforced with columns, under variable RWL. It is worth mentioning that the use of soil–cement column-reinforced riverbank could be a practical and possible engineering countermeasure to prevent a steep riverbank slope under RWL variations from sliding failure.
Dang, L & Khabbaz, H 1970, 'Numerical Investigation on the Boiling Stability of Sheet Piles Supported Excavations in Cohesionless Soil', Lecture Notes in Civil Engineering, 6th International Conference on Geotechnics, Civil Engineering and Structures (CIGOS), Springer Nature Singapore, Hanoi, VIETNAM, pp. 401-410.
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This paper presents the findings of numerical investigations on the boiling stability against seepage failure of a sheet piled cofferdam supported excavation in cohesionless soil. A numerical analysis based on the finite element method using both plane strain and three-dimensional model was conducted to investigate the influence of seepage force on the stability of supported excavation. The results of this numerical analysis were validated with the report data of a case study on seepage force induced boiling failure inside a sheet pile cofferdam in support of deep excavations. Subsequently, a parametric study was undertaken to evaluate further the influence of different design parameters, including size of excavations against excavated level and penetration depths, on the boiling stability by seepage force of sheet piled-cofferdam supported excavations in sand. The numerical results demonstrated that the cofferdam stability against seepage failure significantly improved with an increase in the cofferdam size. Meanwhile, shallower sheet-pile penetration and deeper excavation level in the cofferdam base were found to have a substantial influence on the excavation base stability when the size effect of cofferdam was taken into consideration. Consequently, possible and practical solutions to improve the boiling stability of sheet pile-supported excavations are also proposed in this investigation.
Dang, LC & Khabbaz, H 1970, 'A Practical Application Using Industrial Waste for Enhancing the Mechanical Properties of Expansive Soil', Lecture Notes in Civil Engineering, Springer Singapore, pp. 80-88.
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In this study, a series of laboratory tests was conducted to investigate the possibility of enhancing the mechanical properties of expansive soil using bagasse fibre (BF, a waste by-product of sugar industry) integrated without or with lime stabilisation as a novel, practical application of reuse of industrial waste materials for sustainability. Soil samples reinforced with three different contents of bagasse fibre ranging from 0% to 2% without or with lime combination in a range of 0–6%, were systematically prepared to assess their effect on improved engineering mechanism of expansive soil. The results revealed that BF reinforcement produced the shear strength development of reinforced soils. Moreover, a lime-BF combination provided better improvement in the shrink-swell behaviour and the compressibility of reinforced soils as compared to soils treated with lime or bagasse fibre alone. The findings also indicated that adding BF into lime-soil mixtures reduced the compressible properties of lime-treated soils. Meanwhile, excessively increasing bagasse fibre content greater than 1% caused a minor decrease in the compressibility improvement of reinforced soils. Hence, an appropriate combination of lime and BF should be determined and used as an environmental-friendly, cost-effective and green solution for stabilisation of expansive soil to facilitate sustainable civil infrastructure development.
Dang-Ngoc, H, Nguyen, DN, Hoang, DT, Ho-Van, K & Dutkiewicz, E 1970, 'Cooperative Friendly Jamming in Swarm UAV-assisted Communications with Wireless Energy Harvesting', 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring), IEEE, Helsinki, Finland, pp. 1-6.
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This article proposes a cooperative friendly jamming framework for swarm unmanned aerial vehicle (UAV)-assisted amplify-and-forward (AF) relaying networks with wireless energy harvesting. We consider a swarm of hovering UAVs that relays information from a terrestrial source to a distant mobile user and simultaneously generates jamming signals to obfuscate an eavesdropper. Due to the limited energy of the UAVs, we develop a collaborative time-switching relaying protocol that allows the UAVs to collaborate to harvest wireless energy, relay information, and jam the eavesdropper. To evaluate the secrecy rate, we derive the expressions of the secrecy outage probability (SOP) in the integral form for two popular detection techniques used by the eavesdropper, i.e., selection combining and maximum-ratio combining in high signal-to-noise ratio regime. Monte Carlo simulations validate the derived SOP and show that the proposed framework outperforms the conventional AF relaying system, in terms of SOP. The insights from SOP and analysis in this work sheds light on optimizing the energy harvesting time, the number of UAVs in the swarm as well as their placements, to achieve the required secrecy protection level.
Delhomme, F, Castel, A, Almeida, A, Jiang, C, Moreau, D, Gan, Y, Wang, X & Wilkinson, S 1970, 'Mechanical, Acoustic and Thermal Performances of Australian Hempcretes', Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 753-761.
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This paper is investigating the performance of Australian hemp in hempcrete including unretted and retted hurd and fines for wall and render applications, respectively. The mechanical, thermal and acoustic characteristics of hempcrete are assessed including the effect of retting process. Although the retting process caused about 12% decrease in shiv bulk density, which was attributed to the degradation of hemp and a lower solid volume fraction, hempcrete bulk density, mechanical characteristics, thermal conductivity and acoustic performance are not significantly influenced by the retting process. The thermal conductivity appears to be proportional to the bulk density which is proportional to the hemp content of hempcrete. Acoustic performance of wall mix specimens was outstanding with a maximum sound absorption coefficient around 0.90 for a frequency around 700 Hz. However, the acoustic performance of render mix specimens was extremely poor compared to that of wall mix specimens with a sound absorption coefficient less or equal to 0.13. The combined effects of fine particle size and high binder content is responsible for this drastic drop in sound absorption coefficient. Acoustic performance was much more impacted than thermal conductivity by the hemp fine particle size and high binder content of the render mix.
Deuse, J, Hoffmann, F, Sick, N, Bennett, N, Lammers, T & Hernandez Moreno, V 1970, 'Digitization of the work environment for sustainable production – How could algae based sink technologies enable a neutral Product Carbon Footprint?', Digitization of the work environment for sustainable production, GITO Verlag, pp. 193-205.
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The alarming progress of climate change requires immediate and rigorous action from our society. This also affects the manufacturing industry to a large extent. Many companies have recognised the situation and set themselves ambitious goals with regard to achieving CO2 neutrality. An important step is the detailed analysis of CO2 emissions across the entire supply chain, differentiated by Scopes 1, 2 and 3. Initiated by Siemens, industry and university partners worldwide are currently in the process of founding a non-profit, non-government organisation, called Estainium. Remarkable about the goals of the organisation is that not only CO2 sources, but in particular certified CO2 sinks are to be integrated into the emerging platform. Terrestrial and aquatic sinks without the desired CO2 neutrality could not be achieved must be connected in a clearly traceable and fraud-proof manner. The paper describes the need to create transparency with regard to a product-related carbon footprint and the development of a closed sink system for the accurate carbon capture of emissions generated by value-added processes.
Deuse, J, Sick, N, Guertler, M, Hoffmann, F, Lammers, T & Hernandez Moreno, V 1970, 'Digitization of the work environment for sustainable production How could Digital Technologies enable a neutral Product Carbon Footprint?', Luxemburg.
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Ding, Y, Wang, L, Liang, B, Liang, S, Wang, Y & Chen, F 1970, 'Domain Generalization by Learning and Removing Domain-specific Features', Advances in Neural Information Processing Systems.
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Deep Neural Networks (DNNs) suffer from domain shift when the test dataset follows a distribution different from the training dataset. Domain generalization aims to tackle this issue by learning a model that can generalize to unseen domains. In this paper, we propose a new approach that aims to explicitly remove domain-specific features for domain generalization. Following this approach, we propose a novel framework called Learning and Removing Domain-specific features for Generalization (LRDG) that learns a domain-invariant model by tactically removing domain-specific features from the input images. Specifically, we design a classifier to effectively learn the domain-specific features for each source domain, respectively. We then develop an encoder-decoder network to map each input image into a new image space where the learned domain-specific features are removed. With the images output by the encoder-decoder network, another classifier is designed to learn the domain-invariant features to conduct image classification. Extensive experiments demonstrate that our framework achieves superior performance compared with state-of-the-art methods. Code is available at https://github.com/yulearningg/LRDG.
Dinh, PV, Nguyen, DN, Hoang, DT, Uy, NQ, Bao, SP & Dutkiewicz, E 1970, 'Balanced Twin Auto-Encoder for IoT Intrusion Detection', GLOBECOM 2022 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, IEEE, pp. 3387-3392.
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Intrusion detection systems (IDSs) provide an ef-fective solution for protecting loT systems. However, due to the massive number of loT devices (in billions) and their heterogeneity, IDSs face challenges posed by the complexity of loT data such as correlation-based features, high dimensions, and imbalance. To address these problems, this paper proposes a novel neural network architecture, called Balanced Twin Auto-Encoder (BTAE) which consists of three components, i.e., an encoder, a hermaphrodite, and a decoder. The encoder of BTAE first aims to transfer the input data into the latent space before data samples (pre-images) are translated into this space by different translation vectors. In addition, the data of the skewed labels are also generated in the latent space to address the problem of imbalanced data in which the number of attack samples is often significantly lower than those of the benign samples. Second, the hermaphrodite component serves as a bridge to move the data from the encoder to the decoder. Third, the decoder tries to copy the distribution of the samples in the latent space. BTAE is trained by a supervised learning technique, and its data representation extracted from the decoder can well distinguish the attack from the normal data. The experiments on five loT botnet datasets show that BTAE outperforms three existing groups of methods, e.g., the typical supervised learning, the well-known sampling, and the state-of-the-art representation learning. In addition, the false alarm rate (FAR) of BTAE applied for loT intrusion detection is less than equal to 1.2%.
Dinh, PV, Quang Uy, N, Nguyen, DN, Thai Hoang, D, Bao, SP & Dutkiewicz, E 1970, 'Twin Variational Auto-Encoder for Representation Learning in IoT Intrusion Detection', 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 848-853.
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Intrusion detection systems (IDSs) play a pivotal role in defending IoT systems. However, developing a robust and efficient IDS is challenging due to the rapid and continuing evolving of various forms of cyber-attacks as well as a massive number of low-end IoT devices. In this paper, we introduce a novel deep learning architecture based on auto-encoders that allows to develop a robust intrusion detection system. Specifically, we propose a novel neural network architecture called Twin Variational Auto-Encoder (TVAE) for representation learning. TVAE includes a variational Auto-Encoder (VAE) and an Auto-Encoder (AE) that share a common stage where the decoder of the VAE is used as the encoder of the AE. The TVAE is trained in an unsupervised manner to effectively transform the original representation of data at the input of the VAE into a new representation at the output of the AE. In the new representation space, the difference between normal and attack data is more distinguishable. A variant of TVAE, namely Twin Sparse Variational Auto-Encoder (TSVAE) is also introduced by imposing a sparsity constraint on the representation units. The effectiveness of TVAE and TSVAE is evaluated using popular IDS and IoT botnet datasets. The simulation results show that the accuracy of TVAE and TSVAE can achieve the best results on six datasets, which is higher than those of state-of-the-art AE and VAE variants. We also investigate various characteristics of TVAE in the latent space as well as in the data extraction process. Besides applications on the IoT IDS, TVAE can also be applicable to all conventional network IDSs.
Dinh, TH, Doan, QM, Trung, NL, Nguyen, DN & Lin, C-T 1970, 'Masked Face Detection with Illumination Awareness', 2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT), 2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT), IEEE, pp. 1-6.
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Mask mandate has been applied in many countries in the last two years as a simple but effective way to limit the Covid-19 transmission. Besides the guidance from authorities regarding mask use in public, numerous vision-based approaches have been developed to aid with the monitoring of face mask wearing. Despite promising results have been obtained, several challenges in vision-based masked face detection still remain, primarily due to the insufficient of a quality dataset covering adequate variations in lighting conditions, object scales, mask types, or occlusion levels. In this paper, we investigate the effectiveness of a lightweight masked face detection system under different lighting conditions and the possibility of enhancing its performance with the employment of an image enhancement algorithm and an illumination awareness classifier. A dataset of human subjects with and without face masks in different lighting conditions is first introduced. An illumination awareness classifier is then trained on the collected dataset, the labeling of which is processed automatically based on the difference in detection accuracy when an image enhancement algorithm is taken into account. Experimental results have shown that the combination of the masked face detection system with the illumination awareness and an image enhancement algorithm can boost the system performance to up to 8.6%, 7.4%, and 8.5% in terms of Accuracy, F1-score, and AP-M, respectively.
Doan, S, Fatahi, B, Khabbaz, H & Rasekh, H 1970, 'Analytical Solution for Plane Strain Consolidation of Soft Soil Stabilised by Stone Columns', Lecture Notes in Civil Engineering, Springer International Publishing, pp. 753-767.
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This paper presents an analytical solution for free strain consolidation of a stone column-stabilised soft soil under instantly applied loading and two-dimensional plane strain conditions. Both horizontal and vertical flows of water were integrated into the mathematical model of the problem, while the total vertical stresses induced by the external load were assumed to distribute uniformly within each column and soil region. By utilising the separation of variables method, an exact series solution was obtained to predict the variation of excess pore water pressure and settlement with time for any point in the model. The achieved solution can capture the drain resistance effect due to the inclusion of permeability and size of the stone column in the mathematical model. A worked example investigating the dissipation of excess pore water pressure was conducted to exhibit the capabilities of the obtained analytical solution. The correctness of the solution was verified against a finite element modelling with good agreements. Besides, a parametric study to inspect the influence of consolidation parameters of soil on performance objectives (e.g. average degree of consolidation and average differential settlement) was also reported in this study. The results from the parametric analysis show that an increase in permeability of soil sped up considerably the consolidation and differential settlement. Furthermore, an increase in soil stiffness accelerated the consolidation and reduced the average differential settlement between stone column and soft soil significantly. Eventually, the proposed analytical solution is also feasible to predict the consolidation of soft soil with the inclusion of prefabricated vertical drains or pervious columns by adopting appropriate consolidation parameters and stress concentration ratio.
Dohuee, M, Khosravi, V, Shirazi, A, Shirazy, A, Nazerian, H, Pour, AB, Hezarkhani, A & Pradhan, B 1970, 'Alteration Detections Using ASTER Remote Sensing data and Fractal Geometry for Mineral prospecting in Hemich Area, NE Iran', IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IEEE, pp. 5520-5523.
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Dong, J, Wang, L, Fang, Z, Sun, G, Xu, S, Wang, X & Zhu, Q 1970, 'Federated Class-Incremental Learning', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 10154-10163.
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Federated learning (FL) has attracted growing attentions via data-private collaborative training on decentralized clients. However, most existing methods unrealistically assume object classes of the overall framework are fixed over time. It makes the global model suffer from significant catastrophic forgetting on old classes in real-world scenarios, where local clients often collect new classes continuously and have very limited storage memory to store old classes. Moreover, new clients with unseen new classes may participate in the FL training, further aggravating the catastrophic forgetting of global model. To address these challenges, we develop a novel Global-Local Forgetting Compensation (GLFC) model, to learn a global class-incremental model for alleviating the catastrophic forgetting from both local and global perspectives. Specifically, to address local forgetting caused by class imbalance at the local clients, we design a class-aware gradient compensation loss and a class-semantic relation distillation loss to balance the forgetting of old classes and distill consistent inter-class relations across tasks. To tackle the global forgetting brought by the non-i.i.d class imbalance across clients, we propose a proxy server that selects the best old global model to assist the local relation distillation. Moreover, a prototype gradient-based communication mechanism is developed to protect the privacy. Our model outperforms state-of-the-art methods by 4.4%15.1% in terms of average accuracy on representative benchmark datasets. The code is available at https://github.com/conditionWang/FCIL.
Du, J, Yao, L, Wang, X, Guo, B & Yu, Z 1970, 'Hierarchical Task-aware Multi-Head Attention Network', Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, Madrid, pp. 1933-1937.
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Neural Multi-task Learning is gaining popularity as a way to learn multiple tasks jointly within a single model. While related research continues to break new ground, two major limitations still remain, including (i) poor generalization to scenarios where tasks are loosely correlated; and (ii) under-investigation on global commonality and local characteristics of tasks. Our aim is to bridge these gaps by presenting a neural multi-task learning model coined Hierarchical Task-aware Multi-headed Attention Network (HTMN). HTMN explicitly distinguishes task-specific features from task-shared features to reduce the impact caused by weak correlation between tasks. The proposed method highlights two parts: Multi-level Task-aware Experts Network that identifies task-shared global features and task-specific local features, and Hierarchical Multi-Head Attention Network that hybridizes global and local features to profile more robust and adaptive representations for each task. Afterwards, each task tower receives its hybrid task-adaptive representation to perform task-specific predictions. Extensive experiments on two real datasets show that HTMN consistently outperforms the compared methods on a variety of prediction tasks.
Duan, W, Xuan, J, Qiao, M & Lu, J 1970, 'Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples', Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), pp. 6550-6558.
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Graph Convolutional Neural Networks (GCNs) have been generally accepted to be an effective tool for node representations learning. An interesting way to understand GCNs is to think of them as a message passing mechanism where each node updates its representation by accepting information from its neighbours (also known as positive samples). However, beyond these neighbouring nodes, graphs have a large, dark, all-but forgotten world in which we find the non-neighbouring nodes (negative samples). In this paper, we show that this great dark world holds a substantial amount of information that might be useful for representation learning. Most specifically, it can provide negative information about the node representations. Our overall idea is to select appropriate negative samples for each node and incorporate the negative information contained in these samples into the representation updates. Moreover, we show that the process of selecting the negative samples is not trivial. Our theme therefore begins by describing the criteria for a good negative sample, followed by a determinantal point process algorithm for efficiently obtaining such samples. A GCN, boosted by diverse negative samples, then jointly considers the positive and negative information when passing messages. Experimental evaluations show that this idea not only improves the overall performance of standard representation learning but also significantly alleviates over-smoothing problems.
Dutkiewicz, E & Nguyen, D 1970, 'Keynote Speaker 2: Enabling Metaverse with Secure and Smart Network Resource Slicing', 2022 9th NAFOSTED Conference on Information and Computer Science (NICS), 2022 9th NAFOSTED Conference on Information and Computer Science (NICS), IEEE, pp. xxiii-xxiv.
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Eslahi, H, Hamilton, TJ & Khandelwal, S 1970, 'Ultra Compact and Linear 4-bit Digital-to-Analog Converter in 22nm FDSOI Technology', 2022 IEEE International Symposium on Circuits and Systems (ISCAS), 2022 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, pp. 2778-2781.
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Fahmideh, M, Beydoun, G, Bandara, M, Ahmad, A, Khan, AA & Shrestha, A 1970, 'Role of ontologies in beach safety management analytics systems.', PACIS, pp. 101-101.
Fan, H, Chang, X, Zhang, W, Cheng, Y, Sun, Y & Kankanhalli, M 1970, 'Self-Supervised Global-Local Structure Modeling for Point Cloud Domain Adaptation with Reliable Voted Pseudo Labels', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, New Orleans, LA, pp. 6367-6376.
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In this paper, we propose an unsupervised domain adaptation method for deep point cloud representation learning. To model the internal structures in target point clouds, we first propose to learn the global representations of unla-beled data by scaling up or down point clouds and then predicting the scales. Second, to capture the local structure in a self-supervised manner, we propose to project a 3D local area onto a 2D plane and then learn to reconstruct the squeezed region. Moreover, to effectively transfer the knowledge from source domain, we propose to vote pseudo labels for target samples based on the labels of their nearest source neighbors in the shared feature space. To avoid the noise caused by incorrect pseudo labels, we only select re-liable target samples, whose voting consistencies are high enough, for enhancing adaptation. The voting method is able to adaptively select more and more target samples during training, which in return facilitates adaptation because the amount of labeled target data increases. Experiments on PointDA (ModelNet-10, ShapeNet-10 and ScanNet-10) and Sim-to-Real (ModelNet-11, ScanObjectNN-11, ShapeNet-9 and ScanObjectNN-9) demonstrate the effectiveness of our method.
Fang, Z, Li, Y, Lu, J, Dong, J, Han, B & Liu, F 1970, 'Is Out-of-Distribution Detection Learnable?', Advances in Neural Information Processing Systems, Advances in Neural Information Processing Systems (NeurIPS 2022),, New Orleans.
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Supervised learning aims to train a classifier under the assumption that training and test data are from the same distribution. To ease the above assumption, researchers have studied a more realistic setting: out-of-distribution (OOD) detection, where test data may come from classes that are unknown during training (i.e., OOD data). Due to the unavailability and diversity of OOD data, good generalization ability is crucial for effective OOD detection algorithms. To study the generalization of OOD detection, in this paper, we investigate the probably approximately correct (PAC) learning theory of OOD detection, which is proposed by researchers as an open problem. First, we find a necessary condition for the learnability of OOD detection. Then, using this condition, we prove several impossibility theorems for the learnability of OOD detection under some scenarios. Although the impossibility theorems are frustrating, we find that some conditions of these impossibility theorems may not hold in some practical scenarios. Based on this observation, we next give several necessary and sufficient conditions to characterize the learnability of OOD detection in some practical scenarios. Lastly, we also offer theoretical supports for several representative OOD detection works based on our OOD theory.
Farhangi, M, Brazegarkhoo, R, Lee, SS, Lu, D & Siwakoti, Y 1970, 'An Interleaved Switched-Boost Common-Ground Five-Level Inverter', 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia), 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia), IEEE, pp. 867-872.
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Transfomerless grid-connected inverters with common-grounded circuit architectures and single-stage dy-namic voltage boosting gain are promising candidates for PV energy conversion applications. In this work, a new five-level (5L) variant of this category of inverters is introduced. Key features of the presented inverter are the reduced current stress profile, modularity, uniform peak voltage stress across the switches, higher power handling capability, and bi-directional power flow operation. The proposed topology is comprised of two input inductors, two capacitors, and ten power switches. Through a modular design with a phase-shifted SPWM technique, the injected grid current can be shared among the modules, while the size of the grid-interface filters can be reduced. The working principle of the converter is discussed, and some simulation and experimental results are presented to validate its feasibility.
Farhood, H, Saberi, M & Najafi, M 1970, 'Human-in-the-Loop Optimization for Artificial Intelligence Algorithms', Springer International Publishing, pp. 92-102.
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Faris, FB, Ball, JE & Bharathy, GK 1970, 'Dynamically Linked Stormwater and Traffic Models', Hydrology and Water Resources Symposium Hwrs 2022, pp. 743-749.
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Within urban environments, stormwater drainage systems and transport networks are two areas where modelling is a common approach to generation of data relevant to their management. There is a long history in the usage of both stormwater and transport models. However, a common theme of this history is the operation of stormwater and transport models independently with the generated data from one being unrelated to data generated by the other. In other words, the time-dependent hydrologic failure of a stormwater system is not incorporated into the constraints experienced by a transport network. At the same time, the direct intangible transport costs associated with the hydrologic failure of a stormwater system are not included in the estimates of the average annual damages. When the real world is considered, however, there are linkages between the transport network and the stormwater drainage system with the conditions in one influencing the other. To mitigate the disconnect, a project aimed at linking stormwater and transport models has commenced at UTS. Using the Alexandria Canal catchment as a case study, a stormwater model based on SWMM is being developed for the prediction of the time and magnitude of hydrologic failures of the stormwater system. An agent-based transport model (ABM), with agents representing both vehicles and pedestrians, is being developed to predict delay times induced by ponding of stormwater runoff. Presented herein will be a discussion of current development. This development has focussed on the dynamic linkage of SWMM with the ABM-based transport model for individual events; in other words, the instantaneous flows predicted by SWMM are part of the data used by the agents in deciding upon their actions and hence their movements. Also shown will be some preliminary results.
Farrugia, E, Machet, T, Boye, T, Hadgraft, R & Tomc, E 1970, 'Student Reflections on Experiences in Curriculum Design', 33rd Australasian Association for Engineering Education Conference (AAEE 2022): Future of Engineering Education, Annual Conference of the Australasian Association for Engineering Education, AAEE, Paramatta, NSW.
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CONTEXT The value of the student perspective in developing and evaluating engineering course content and curricula is increasingly being recognised (Lubicz-Nawrocka and Bovil, 2021; Bovil, Bulley and Morss, 2011). However, there is little written on students' own perspectives of the process. As a final-year engineering student, the first author became interested in working on projects that could have a direct impact on their education, so they undertook three co-design projects under the supervision of academic staff and engineering education designers. PURPOSE OR GOAL This paper explores a student's experiences of their involvement in educational design and development projects in engineering education. The paper draws on the experience of three different models used in projects for eliciting student input into educational design. The perspective adds to our understanding on how to engage students in this process, the potential value to learning and how we can use the student perspective in education design. APPROACH OR METHODOLOGY/METHODS The first author elected to complete three curriculum development projects: one took a design thinking approach in a team work context to redesign an engineering professional practice curriculum; the second was an honours thesis project to investigate a new undergraduate engineering offering and the third was a self-directed study elective to design a sub-major. The projects differed in terms of learning outcomes, project structure and assessment design, supervision, and my motivations for taking the projects. This paper reports on reflections of these aspects and situates the experience in the context of student contributions to educational design. ACTUAL OR ANTICIPATED OUTCOMES The reflections presents the differences in experience given the different project approaches with a particular focus on the benefits and challenges between the methods used to implement the activities. The motivations for taking subjects, t...
Fathalla, A, Salah, A, Mohamed, MA, Lestari, NI & Bekhit, M 1970, 'A Novel Dual Prediction Scheme for Data Communication Reduction in IoT-Based Monitoring Systems', Springer International Publishing, pp. 208-220.
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Fattoruso, V, Sepehrirahnama, S, Tofigh, F, Lai, JCS, Nowotny, M & Oberst, S 1970, 'CONSIDERATION ON HOW TO IMPROVE GROUND REACTION FORCE MEASUREMENTS IN SMALL WALKING INSECTS', Proceedings of the International Congress on Sound and Vibration, 28th International Congress on Sound and Vibration, Singapore.
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Micro-vibrations caused by the motion of insects, provide a content-rich signal that may be perceived by nestmates, competitors or predators. Knowing the ground reaction forces of a single leg impacting the surface can provide quantitative information about the interaction with the substrate, the substrate itself, physiological and behavioural state of an individual, through mechanistic constraints and the diversity of the gait. Micro-force plates have been used for measuring the ground reaction forces in the order of micro-Newton, using highly sensitive strain gauges attached to compliant load-bearing parts of an underlying mechanical structure. However, their calibration and signal-to-noise-ratio are some of the main challenges of designing these highly sensitive systems. For fine movement analysis, the micro-force plates need to be coupled to high speed video recording systems; the synchronisation of the camera and force plate represents another challenge. For an existing micro-force plate designed for ant measurements, which showed linear signal response in the calibrated force with a lower limit of 120 μN, the linearity of force measurement and sensitivity of the device are investigated in a lower force range, extending the opportunity to study also insects with a lighter footfall. We take into account the difficulties of adapting such devices to the insects' needs related to the environment (i.e. temperature, light...) and morphology (i.e. dimension, weight...). Based on the experiments of the force plate, we consider how to design an experimental setup that overcomes many of the behavioural and technical challenges, to enable more efficient and accurate measurements for insects with body weights less than 5 mg.
Feng, Q, Peng, Y, Zhang, W, Zhang, Y & Lin, X 1970, 'Towards Real-Time Counting Shortest Cycles on Dynamic Graphs: A Hub Labeling Approach', 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, pp. 512-524.
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With the ever-increasing prevalence of graph data in a wide spectrum of applications, it becomes essential to analyze structural trends in dynamic graphs on a continual basis. The shortest cycle is a fundamental pattern in graph analytics. In this paper, we investigate the problem of shortest cycle counting for a given vertex in dynamic graphs in light of its applicability to problems such as fraud detection. To address such queries efficiently, we propose a 2-hop labeling based algorithm called Counting Shortest Cycle (CSC for short). Additionally, techniques for dynamically updating the CSC index are explored. Comprehensive experiments are conducted to demonstrate the efficiency and effectiveness of our method. In particular, CSC enables query evaluation in a few hundreds of microseconds for graphs with millions of edges, and improves query efficiency by two orders of magnitude when compared to the baseline solutions. Also, the update algorithm could efficiently cope with edge insertions (deletions).
Feng, S, Tan, Z, Chen, Z, Wang, N, Yu, P, Zheng, Q, Chang, X & Luo, M 1970, 'PAR: Political Actor Representation Learning with Social Context and Expert Knowledge', Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp. 12022-12036.
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Modeling the ideological perspectives of political actors is an essential task in computational political science with applications in many downstream tasks. Existing approaches are generally limited to textual data and voting records, while they neglect the rich social context and valuable expert knowledge for holistic ideological analysis. In this paper, we propose PAR, a Political Actor Representation learning framework that jointly leverages social context and expert knowledge. Specifically, we retrieve and extract factual statements about legislators to leverage social context information. We then construct a heterogeneous information network to incorporate social context and use relational graph neural networks to learn legislator representations. Finally, we train PAR with three objectives to align representation learning with expert knowledge, model ideological stance consistency, and simulate the echo chamber phenomenon. Extensive experiments demonstrate that PAR is better at augmenting political text understanding and successfully advances the state-of-the-art in political perspective detection and roll call vote prediction. Further analysis proves that PAR learns representations that reflect the political reality and provide new insights into political behavior.
Fernandez, L & Carmichael, MG 1970, 'Preliminary Analysis of a Redundancy Resolution Method for Mobile Manipulators used in Physical Human Robot Interaction', Australasian Conference on Robotics and Automation Acra, Australasian Conference on Robotics and Automation, Brisbane.
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Adding mobility to a manipulator significantly increases its reachable workspace, allowing it to perform a wider variety of tasks. However, this advantage introduces the problem of expanding the system's solution space, resulting in an increase of different possible joint configurations for one specific end effector pose. Existing frameworks and algorithms have been developed that resolve this redundancy for a specific task. However, special care must be taken when mobile manipulators are used for Physical Human Robot Interaction (pHRI) applications. This paper proposes a basic framework utilising the Projected Gradient (PG) method that can be implemented on mobile manipulators used in pHRI. It illustrates how this redundancy resolution method has characteristics that makes it favourable for pHRI applications. The framework effectively makes use of the manipulator's high degree of precision when the task being executed is within its immediate workspace, while making use of the mobile platform's mobility when the task to be performed is outside of the manipulator's immediate workspace. This framework is validated firstly in simulation and then in a physical experiment. Both experiments successfully demonstrate the desired behaviour of a mobile manipulator system when used in the context of pHRI.
Firman, A, Muktiyanto, A, Inan, DI, Juita, R, Beydoun, G & Daryono 1970, 'UT Metaverse: Beyond Universitas Terbuka Governance Transformation and Open Challenges', 2022 Seventh International Conference on Informatics and Computing (ICIC), 2022 Seventh International Conference on Informatics and Computing (ICIC), IEEE, pp. 1-6.
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Frijat, L, Tomidei, L, Guertler, M & Sick, N 1970, 'Collaborative Robotics: A new work health and safety risk assessment for a novel technology', Asia Pacific Occupational Safety & Health Organization, Melbourne.
Gan, W, Gardner, A & Daniel, S 1970, 'Female International Students in Engineering: A Qualitative Review', 9th Research in Engineering Education Symposium (REES 2021) and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021), 9th Research in Engineering Education Symposium & 32nd Australasian Association for Engineering Education Conference, Research in Enineering Education Network (REEN), pp. 942-950.
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CONTEXT When international students relocate overseas to pursue higher education, they undergo transitions in social culture, educational approaches and professional practice. These transitions shape various aspects of their identity (e.g., personal, professional), engineering identity being one of them. Engineering identity is a complex, contested construct that informs how engineering is perceived, how education curricula are developed, and which student it attracts. Due to stereotypes about engineering, white middle-class males continue to dominate the profession. However, there is a need for a more diverse engineering workforce that better represents the society. With female international students' varied journeys and intersectional identities, a closer look at this population will shed light on ways to attract and retain diverse individuals within engineering. PURPOSE OR GOAL As a first step in a larger study about understanding the identities and experiences of female international students, in this paper we ask the following research question: What research has been conducted on female international students in engineering? APPROACH OR METHODOLOGY/METHODS As a starting point, the following keywords (and their synonyms) are searched on Scopus and targeted journals: 'international student', 'wom*n', 'engineer*'. After the abstracts are screened based on their relevance to the research question, the remaining abstracts are analysed to determine an appropriate scope for this review, and the inclusion and exclusion criteria are refined. References from the included papers are screened and analysed using the same process. ACTUAL OR ANTICIPATED OUTCOMES Based on the search strategy as well as inclusion and exclusion criteria, 6 papers were identified as relevant to the research question, and the findings were qualitatively analysed based on two categories: university and family/society. Discussion on university focussed on female international students'...
Gan, W, Gardner, A & Daniel, S 1970, 'What can we learn from narratives? Unpacking engineering stories from the periphery', Proceedings of the AAEE 2022 33rd Annual Conference, Annual Conference of the Australasian Association for Engineering Education, Western Sydney University, Western Sydney University, pp. 1-9.
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CONTEXTQualitative research is commonly used in conjunction with quantitative studies in engineeringeducation research. However, they are also increasingly used to illuminate aspects that do not fitthe dominant discourse, for example, the experiences of marginalised populations. Many of thesestudies focus on learning from small numbers, and often do so by collecting participant narratives(or stories). Narrative studies can take on many different forms – for instance, the researcher mayuse narratives as data or method, or conduct analysis paradigmatically or narratively.PURPOSE OR GOALThis paper aims to address the following research question: “How might we use narratives tounderstand the experiences of marginalised populations?” It will do so by providing a landscape ofnarrative studies within engineering education research and discuss narrative features in relation toeach study.APPROACH OR METHODOLOGY/METHODSStudies from selected journals that mention ‘narrative’, ‘story’, or ‘journey’ in the title or abstractwere identified and reviewed. Five papers were selected to guide understanding on somedistinctions within narrative research. For each paper, an overview of how narratives werecollected, analysed and presented was discussed.ACTUAL OR ANTICIPATED OUTCOMESNarratives can be used as data (where it is collected) or method (where it is constructed). Twotypes of cognition (modes of thought) can be used when analysing narratives: paradigmaticcognition, which focuses on identifying common themes or concepts from stories; and narrativecognition, which focuses on making sense of stories. Findings from narrative studies can beorganised by participant or by topic, and participant narratives can be presented in first person orthird person with direct or indirect quotes.CONCLUSIONS/RECOMMENDATIONS/SUMMARYNarratives can be collected, analysed and presented using a wide range of approaches. Thispaper highlighted five distinct approaches a...
Gandomi, AH 1970, 'GECCO 2022#', Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '22: Genetic and Evolutionary Computation Conference, ACM, pp. 922-936.
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Gandy, J & Ball, JE 1970, 'Long short-term memory networks for vehicle sensor fusion', Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022, Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022, SPIE, pp. 21-21.
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Gao, S, Hwang, JD, Kanno, S, Wakaki, H, Mitsufuji, Y & Bosselut, A 1970, 'ComFact: A Benchmark for Linking Contextual Commonsense Knowledge', Findings of the Association for Computational Linguistics: EMNLP 2022, Findings of the Association for Computational Linguistics: EMNLP 2022, Association for Computational Linguistics, pp. 1656-1675.
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Recent progress regarding the use of language models (LMs) as knowledge bases (KBs) has shown that language models can act as structured knowledge bases for storing relational facts. However, most existing works only considered the LM-as-KB paradigm in a static setting, which ignores the analysis of temporal dynamics of world knowledge. Furthermore, a basic function of KBs, i.e., the ability to store conflicting information (i.e., 1-N, N-1 and N-M relations), is underexplored. In this paper, we formulate two practical requirements for treating LMs as temporal KBs: (i) the capacity to store temporally-scoped knowledge that contains conflicting information and (ii) the ability to use stored knowledge for temporally-scoped knowledge queries. We introduce a new dataset called LAMA-TK which is aimed at probing temporally-scoped knowledge, and investigate the two above requirements to explore the LM-as-KB paradigm in the temporal domain. On the one hand, experiments show that LMs can memorize millions of temporally-scoped facts with relatively high accuracy and transfer stored knowledge to temporal knowledge queries, thereby expanding the LM-as-KB paradigm to the temporal domain. On the other hand, we show that memorizing conflicting information, which has been neglected by previous works, is still challenging for LMs and hinders the memorization of other unrelated one-to-one relationships.
Gao, Y, Xu, G, Li, L, Luo, X, Wang, C & Sui, Y 1970, 'Demystifying the underground ecosystem of account registration bots', Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE '22: 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ACM, pp. 897-909.
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Garcia, JA & Tenorio, JF 1970, 'Assessing the capabilities of the HTC Vive as a tool to assess the risk of falling in older people', 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), IEEE, pp. 1-6.
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Gavan, R, Parker, L, Mammucari, R & Miao, G 1970, 'Where are we at with combined engineering degrees?', 9th Research in Engineering Education Symposium (REES 2021) and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021), 9th Research in Engineering Education Symposium & 32nd Australasian Association for Engineering Education Conference, Research in Enineering Education Network (REEN), pp. 273-281.
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Gentile, C, Polonchuk, L, Roche, CD & Sharma, P 1970, 'VASCULAR NETWORK FORMATION WITHIN 3D BIOPRINTED HEART TISSUES USING VASCULARIZED CARDIAC SPHEROIDS AS BUILDING BLOCKS', TISSUE ENGINEERING PART A, MARY ANN LIEBERT, INC, pp. S356-S356.
Gentile, C, Roche, C, Beck, D & Xue, M 1970, '3D bioprinted cardiac tissues for heart repair', TISSUE ENGINEERING PART A, Tissue-Engineering-and-Regenerative-Medicine-International-Society-Asia-Pacific-Chapter Conference (TERMIS-AP), MARY ANN LIEBERT, INC, SOUTH KOREA, Jeju, pp. 169-169.
Gentile, C, Sharma, P, Ming, CLC, Beck, D, Figtree, G & Boyle, A 1970, 'Advanced pathophysiological in vitro 3D models of the human heart using cardiac spheroids', TISSUE ENGINEERING PART A, Tissue-Engineering-and-Regenerative-Medicine-International-Society-Asia-Pacific-Chapter Conference (TERMIS-AP), MARY ANN LIEBERT, INC, SOUTH KOREA, Jeju, pp. 207-207.
Gerrard, L, Peng, X, Clarke, A, Schlegel, C & Jiang, J 1970, 'Predicting Outcomes for Cancer Patients with Transformer-Based Multi-task Learning', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 381-392.
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Cancer patients often experience numerous hospital admissions as a result of their cancer and treatment, which can negatively impact treatment progress and quality of life. Accurately predicting outcomes for cancer patients is therefore crucial in providing personalised care and improving patient outcomes. Existing models leveraging deep learning with Electronic Health Record (EHR) data to predict outcomes for cancer patients are limited, despite the demonstrated success of these approaches with cancer imaging data and non-cancer EHR applications. Additionally, current methods focus on single-task predictions, and increasing evidence suggests jointly training a model on two related tasks can improve predictive performance. To address these limitations, we propose a Transformer-based Multi-Task (TransMT) model that captures relationships between diagnosis codes and sequential hospital visits to simultaneously predict related outcomes for hospitalised cancer patients. Experiments conducted on two public datasets show the proposed model outperforms both single-task and recurrent neural network approaches in predicting future diagnosis and hospital readmission, and demonstrates the benefits of using deep learning with EHR data for cancer-related research.
Gholami, K, Azizivahed, A, Arefi, A, Moktadir Rahman, M & Rabiul Islam, M 1970, 'Risk-Based Operation of Soft Open Points in Distribution Grids Integrated with Photovoltaic Resources and Energy Storage Systems', 2022 IEEE IAS Global Conference on Emerging Technologies (GlobConET), 2022 IEEE IAS Global Conference on Emerging Technologies (GlobConET), IEEE, pp. 1005-1010.
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Ghosh, S, Ramegowda, P, Ng, E, Ali, Z, Goh, DJ, Sharma, J, Wong, HX & Lee, J 1970, 'Parameter Extraction of Thin-Film Scandium-Doped Aluminum Nitride in Piezoelectric Over Silicon-On-Nothing Platform', 2022 IEEE International Ultrasonics Symposium (IUS), 2022 IEEE International Ultrasonics Symposium (IUS), IEEE, Venice, Italy, pp. 1-4.
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We present a method to extract transverse elastic properties Young s modulus shear modulus and Poisson s ratio and relative permittivity of 15 scandium Sc doped aluminum nitride AIN film from electrical measurements of resonators and parallel plate capacitors The resonators comprise a vertical stack of boldsymbol 0 3 mu mathbf m thick mathbf Sc boldsymbol 0 15 mathbf Al boldsymbol 0 85 mathbf N being sandwiched between boldsymbol 0 2 mu mathbf m thick molybdenum Mo and boldsymbol 2 mu mathbf m thick degenerately doped pre released silicon Si membrane on cavity as fabricated using our piezoelectric over silicon on nothing platform Parallel plate capacitors followed the same vertical stack except that these were fabricated on unreleased but degenerately doped Si layers Despite the large thickness ratio between pre released degenerately doped Si membrane to mathbf Sc boldsymbol 0 15 mathbf Al boldsymbol 0 85 mathbf N film we have successfully extracted 2 elements mathbf S 11 and mathbf S 12 from the compliance matrix using an iterative gradient descent method and relative permittivity of mathbf Sc boldsymbol 0 15 mathbf Al boldsymbol 0 85 mathbf N film
Giubilato, R, Vayugundla, M, Le Gentil, C, Schuster, MJ, McDonald, W, Vidal-Calleja, T, Wedler, A & Triebel, R 1970, 'Robust place recognition with Gaussian Process Gradient Maps for teams of robotic explorers in challenging lunar environments', Proceedings of the International Astronautical Congress Iac.
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Teams of mobile robots will play a key role towards future planetary exploration missions. In fact, plans for upcoming lunar exploration, and other extraterrestrial bodies, foresee an extensive usage of robots for the purposes of in-situ analysis, building infrastructure and realizing maps of the environment for its exploitation. To enable prolonged robotic autonomy, however, it is critical for the robotic agents to be able to robustly localize themselves during their motion and, concurrently, to produce maps of the environment. To this end, visual SLAM (Simultaneous Localization and Mapping) techniques have been developed during the years and found successful application in several terrestrial fields, such as autonomous driving, automated construction and agricultural robotics. To this day, autonomous navigation has been demonstrated in various robotic missions to Mars, e.g., from NASA's Mars Exploration Rover (MER) Missions, to NASA's Mars Science Laboratory (Curiosity) and the current Mars2020 Perseverance, thanks to the implementation of Visual Odometry, using cameras to robustly estimate the rover's ego-motion. While VO techniques enable the traversal of large distances from one scientific target to the other, future operations, e.g., for building or maintenance of infrastructure, will require robotic agents to repeatedly visit the same environment. In this case, the ability to re-localize themselves with respect to previously visited places, and therefore the ability to create consistent maps of the environment, is paramount to achieve localization accuracies, that are far above what is achievable from global localization approaches. The planetary environment, however, poses significant challenges to this goal, due to extreme lighting conditions, severe visual aliasing and a lack of uniquely identifiable natural “features”. For this reason, we developed an approach for re-localization and place recognition, that relies on Gaussian Processes, to ef...
Good, A, Lin, J, Yu, X, Sieg, H, Ferguson, M, Zhe, S, Wieczorek, J & Serra, T 1970, 'Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm', Advances in Neural Information Processing Systems.
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Pruning techniques have been successfully used in neural networks to trade accuracy for sparsity. However, the impact of network pruning is not uniform: prior work has shown that the recall for underrepresented classes in a dataset may be more negatively affected. In this work, we study such relative distortions in recall by hypothesizing an intensification effect that is inherent to the model. Namely, that pruning makes recall relatively worse for a class with recall below accuracy and, conversely, that it makes recall relatively better for a class with recall above accuracy. In addition, we propose a new pruning algorithm aimed at attenuating such effect. Through statistical analysis, we have observed that intensification is less severe with our algorithm but nevertheless more pronounced with relatively more difficult tasks, less complex models, and higher pruning ratios. More surprisingly, we conversely observe a de-intensification effect with lower pruning ratios, which indicates that moderate pruning may have a corrective effect to such distortions.
Grace, K, Finch, E, Gulbransen-Diaz, N & Henderson, H 1970, 'Q-Chef: The impact of surprise-eliciting systems on food-related decision-making', CHI Conference on Human Factors in Computing Systems, CHI '22: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-14.
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Grigorev, A, Mihaita, A-S, Saleh, K & Piccardi, M 1970, 'Traffic incident duration prediction via a deep learning framework for text description encoding', 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), IEEE, pp. 1770-1777.
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Predicting the traffic incident duration is a hard problem to solve due to the stochastic nature of incident occurrence in space and time, a lack of information at the beginning of a reported traffic disruption, and lack of advanced methods in transport engineering to derive insights from past accidents. This paper proposes a new fusion framework for predicting the incident duration from limited information by using an integration of machine learning with traffic flow/speed and incident description as features, encoded via several Deep Learning methods (ANN autoencoder and character-level LSTM-ANN sentiment classifier). The paper constructs a cross-disciplinary modelling approach in transport and data science. The approach improves the incident duration prediction accuracy over the top-performing ML models applied to baseline incident reports. Results show that our proposed method can improve the accuracy by 60% when compared to standard linear or support vector regression models, and a further 7% improvement with respect to the hybrid deep learning auto-encoded GBDT model which seems to outperform all other models. The application area is the city of San Francisco, rich in both traffic incident logs (Countrywide Traffic Accident Data set) and past historical traffic congestion information (5-minute precision measurements from Caltrans Performance Measurement System).
Grundy, S, Miao, G, Brown, N, Belkina, M & Goldfinch, T 1970, 'Current best practice, support mechanisms and experiences of project-based learning', 9th Research in Engineering Education Symposium (REES 2021) and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021), 9th Research in Engineering Education Symposium & 32nd Australasian Association for Engineering Education Conference, Research in Enineering Education Network (REEN), pp. 1131-1131.
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Gu, Z, Yang, X, Jia, W, Xu, C, Yu, P, He, X, Chen, H & Lin, Y 1970, 'StrokePEO: Construction of a Clinical Ontology for Physical Examination of Stroke', 2022 9th International Conference on Digital Home (ICDH), 2022 9th International Conference on Digital Home (ICDH), IEEE, pp. 218-223.
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Clinical ontology is a standardized medical knowledge representation model that facilitates the integration and analysis of a large amount of heterogeneous electronic health record (EHR) data. Using ontologies to represent clinical terms can improve data integration to build robust and interoperable medical information systems. To date, there is no ontology existing to represent the medical knowledge for physical examination of stroke, which has inhibited the stroke physicians to make full use of clinical information captured in EHR data to understand stroke patient's health status and plan effective medication and rehabilitation treatment. In this research, we co-design with two stroke clinical specialists a stroke clinical ontology 'StrokePEO'using advanced natural language processing and deep learning techniques to extract terms and their relationships from real clinical case records provided by a tertiary hospital in China. We apply the W3C Resource Description Framework (RDF) data model to represent these clinical terms and relationships, and successfully store all case data in a graph database with StrokePEO. Our experiment results suggest that our methods and the output of StrokePEO can be applied in various medical contexts that require extraction of medical knowledge from free text for decision making. These include, but not limited to, physical assessment, drug and rehabilitation treatment outcome evaluation, medication effect analysis, and patient risk prediction.
Guan, J & Yu, N 1970, 'A Probabilistic Logic for Verifying Continuous-time Markov Chains', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 3-21.
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AbstractA continuous-time Markov chain (CTMC) execution is a continuous class of probability distributions over states. This paper proposes a probabilistic linear-time temporal logic, namely continuous-time linear logic (CLL), to reason about the probability distribution execution of CTMCs. We define the syntax of CLL on the space of probability distributions. The syntax of CLL includes multiphase timed until formulas, and the semantics of CLL allows time reset to study relatively temporal properties. We derive a corresponding model-checking algorithm for CLL formulas. The correctness of the model-checking algorithm depends on Schanuel’s conjecture, a central open problem in transcendental number theory. Furthermore, we provide a running example of CTMCs to illustrate our method.
Guan, J, Fang, W & Ying, M 1970, 'Verifying Fairness in Quantum Machine Learning.', CAV (2), 34th International Conference on Computer-Aided Verification (CAV) held as part of the Federated Logic Conference (FLoC), Springer, Haifa, ISRAEL, pp. 408-429.
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Due to the beyond-classical capability of quantum computing, quantum machine learning is applied independently or embedded in classical models for decision making, especially in the field of finance. Fairness and other ethical issues are often one of the main concerns in decision making. In this work, we define a formal framework for the fairness verification and analysis of quantum machine learning decision models, where we adopt one of the most popular notions of fairness in the literature based on the intuition—any two similar individuals must be treated similarly and are thus unbiased. We show that quantum noise can improve fairness and develop an algorithm to check whether a (noisy) quantum machine learning model is fair. In particular, this algorithm can find bias kernels of quantum data (encoding individuals) during checking. These bias kernels generate infinitely many bias pairs for investigating the unfairness of the model. Our algorithm is designed based on a highly efficient data structure—Tensor Networks—and implemented on Google’s TensorFlow Quantum. The utility and effectiveness of our algorithm are confirmed by the experimental results, including income prediction and credit scoring on real-world data, for a class of random (noisy) quantum decision models with 27 qubits (2 27 -dimensional state space) tripling (2 18 times more than) that of the state-of-the-art algorithms for verifying quantum machine learning models.
Guan, W, Jiao, F, Song, X, Wen, H, Yeh, C-H & Chang, X 1970, 'Personalized Fashion Compatibility Modeling via Metapath-guided Heterogeneous Graph Learning', Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, Madrid, SPAIN, pp. 482-491.
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Fashion Compatibility Modeling (FCM) is a new yet challenging task, which aims to automatically access the matching degree among a set of complementary items. Most of existing methods evaluate the fashion compatibility from the common perspective, but overlook the user's personal preference. Inspired by this, a few pioneers study the Personalized Fashion Compatibility Modeling (PFCM). Despite their significance, these PFCM methods mainly concentrate on the user and item entities, as well as their interactions, but ignore the attribute entities, which contain rich semantics. To address this problem, we propose to fully explore the related entities and their relations involved in PFCM to boost the PFCM performance. This is, however, non-trivial due to the heterogeneous contents of different entities, embeddings for new users, and various high-order relations. Towards these ends, we present a novel metapath-guided personalized fashion compatibility modeling, dubbed as MG-PFCM. In particular, we creatively build a heterogeneous graph to unify the three types of entities (i.e., users, items, and attributes) and their relations (i.e., user-item interactions, item-item matching relations, and item-attribute association relations). Thereafter, we design a multi-modal content-oriented user embedding module to learn user representations by inheriting the contents of their interacted items. Meanwhile, we define the user-oriented and item-oriented metapaths, and perform the metapath-guided heterogeneous graph learning to enhance the user and item embeddings. In addition, we introduce the contrastive regularization to improve the model performance. We conduct extensive experiments on the real-world benchmark dataset, which verifies the superiority of our proposed scheme over several cutting-edge baselines. As a byproduct, we have released our source codes to benefit other researchers.
Guan, W, Song, X, Zhang, H, Liu, M, Yeh, C-H & Chang, X 1970, 'Bi-directional Heterogeneous Graph Hashing towards Efficient Outfit Recommendation', Proceedings of the 30th ACM International Conference on Multimedia, MM '22: The 30th ACM International Conference on Multimedia, ACM, pp. 268-276.
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Guertler, MR, Adams, N, Caldwell, G, Donovan, J, Hopf, A & Roberts, J 1970, 'A Life-Cycle Framework to Manage Collaboration and Knowledge Exchange in Open Organisations', Proceedings of the Design Society, Cambridge University Press (CUP), pp. 181-190.
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AbstractSuccessful research and development requires interdisciplinary collaboration, often across organisational boundaries and for extended timeframes, such as in innovation networks or ecosystems. Open Organisation (OO) research can support collaboration and knowledge exchange in such situations. It builds on established concepts of Open Innovation through enhancing the exchange of knowledge by the exchange of humans. This paper contributes to OO research by presenting an OO lifecycle framework, which analyses evolving organisational and collaboration characteristics and resulting management needs.
Guertler, MR, Clemon, LM, Bennett, NS & Deuse, J 1970, 'Design for Additive Manufacturing (DfAM): Analysing and Mapping Research Trends and Industry Needs', 2022 Portland International Conference on Management of Engineering and Technology (PICMET), 2022 Portland International Conference on Management of Engineering and Technology (PICMET), IEEE, Portland, Oregon, USA, pp. 1-10.
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Since its early days as a rapid prototyping technology, additive manufacturing has significantly evolved and become an important enabling technology for advanced manufacturing. Despite the benefits, its application in industry is not trivial as, for example, products need to be re-designed and processes changed, and it is not always the optimal manufacturing technology. Design for Additive Manufacturing (DfAM) is a key approach to support the successful use of additive manufacturing in industry and to bridge the gap between research and practice. Aside from design process and technology related methods and tools, DfAM also considers organisational and procedural aspects. To support the success of DfAM and as a result additive manufacturing, it is important to understand how industry needs are addressed by current DfAM methods/tools and related research activities. In this respect, a comprehensive analysis is missing. Therefore, this paper systematically analyses current research topics, fields and trends as well as industry needs and DfAM requirements from an engineering management perspective. Mapping them allows for a systematic discussion between academia and industry to identify the most pressing research needs.
Gui, Y, Wan, Y, Zhang, H, Huang, H, Sui, Y, Xu, G, Shao, Z & Jin, H 1970, 'Cross-Language Binary-Source Code Matching with Intermediate Representations', 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), IEEE, pp. 601-612.
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Gul, M, Kalam, MA, Zulkifli, NWM, Masjuki, HH & Mujtaba, MA 1970, 'The Comparison of Tribological Characteristics of TMP Based Cotton-Bio Lubricant and Commercial Lubricant for Cylinder Liner-Piston Ring Combination', Springer Singapore, pp. 22-28.
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Gunasinghe, D, Suddrey, G, Lamont, R, Mount, J, Sukkar, F, Vidal-Calleja, T & Roberts, J 1970, 'A Novel Passive Grasping Robot Control Framework Towards Vision-Based Industrial Steel Bar Conveyor Removal', Australasian Conference on Robotics and Automation Acra, Australasian Conference on Robotics and Automation, Brisbane, QLD Australia.
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Material handling using robotic automation is critical for enabling efficient and safe environments for numerous industries. In the steel bar manufacturing industry bars of shorter length are occasionally produced that do not match the required batch length. Currently human operators visually classify and manually remove the short bar from a batch of rods moving on a conveyor. This can present a manual handling health and safety risk. This paper demonstrates the output of a feasibility study investigating this problem; resulting in a novel, passive grasping robotic control framework that: (a) emulates the human operator's technique; and (b) successfully removes multiple bar types from a moving conveyor using closed-loop visual control.
Guo, P, Ren, Q, Chen, D, Chen, S & Liu, Y 1970, 'A low-profile polarization reconfigurable antenna', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 409-410.
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Guo, S, Su, Z, Tian, Z & Yu, S 1970, 'Utility-Aware Privacy-Preserving Federated Learning through Information Bottleneck', 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), IEEE, pp. 680-686.
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Guo, X, Ma, B, Zeng, Y, Liu, Z & Ma, J 1970, 'Fine-Grained Defense Methods in Encrypted Traffic Inspection', The 15th China Computer Networks and Information Security Conference & 2022 International Cyberspace Security Academic Summit Forum, Zhenjiang, Jiangsu, China.
Guo, X, Ma, B, Zeng, Y, Liu, Z & Ma, J 1970, 'Fine-Grained Defense Methods in Encrypted Traffic Inspection', The 15th China Computer Networks and Information Security Conference & 2022 International Cyberspace Security Academic Summit Forum, Zhenjiang, Jiangsu, China.
Guo, Y, Ba, X, Liu, L, Hou, L, Lei, G & Zhu, J 1970, 'Performance Enhancement of Permanent Magnet Synchronous Motors Based on Improved Circuit Models', 2022 25th International Conference on Electrical Machines and Systems (ICEMS), 2022 25th International Conference on Electrical Machines and Systems (ICEMS), IEEE, pp. 1-6.
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With the merits of low power loss, high torque density and high power density, permanent magnet synchronous motors (PMSMs) have been widely applied in various areas. In many applications, the PMSMs are requested to operate with high efficiency over wide speed and load ranges, so the study on proper core loss prediction has attracted much attention for the modeling, design, optimization and control of PMSMs. However, the equivalent circuit model, which is usually applied for motor performance analysis with fast calculation, rarely considers the core loss. This paper presents improved performance analysis of a permanent magnet transverse flux motor with soft magnetic composite stator, based on a modified equivalent circuit model considering core loss. Compared with the experimental measurements on the motor prototype, the performance prediction accuracy based on this new circuit model is much higher than that based on conventional circuit model without core loss.
Guo, Z, Halkon, B & Clemon, L 1970, 'Effects of infill parameters on the vibration characteristics of additively manufactured specimens', Proceedings of ISMA 2022 International Conference on Noise and Vibration Engineering and Usd 2022 International Conference on Uncertainty in Structural Dynamics, International Conference on Noise and Vibration Engineering, Leuven, Belgium, pp. 1908-1917.
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The mechanical properties of fused filament fabricated 3D printed parts are highly dependent on combinations of different process parameters. In this context, this study investigates the effects of infill parameters on the dynamic properties of carbon fiber reinforced 3D printed cuboids with varying infill percentages. Natural frequencies predicted for the first and second mode based on the volume average stiffness method and Euler-Bernoulli beam theory have been compared with those obtained from the experimental impact test, and the differences are within 7%. Subsequently, the effect of clamping force on the natural frequencies of different specimens is also investigated for fixed-free boundary condition. The theoretical and experimental results indicate that a wider bandwidth without resonance can be created with certain combinations of process parameters on the 3D printed specimens. This capability enables the fabrication of 3D printed parts with tuned natural frequencies while saving time and resources.
Hadgraft, R, Trede, F & Rummler, M 1970, 'How do Teachers Respond to Sustained Change?', 9th Research in Engineering Education Symposium (REES 2021) and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021), 9th Research in Engineering Education Symposium & 32nd Australasian Association for Engineering Education Conference, Research in Enineering Education Network (REEN), pp. 1132-1133.
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Hakami, M, Pradhan, S & Mastio, E 1970, 'Learning from Intermediaries to Overcome Cognitive-Related Barriers in the University-Industry Collaboration', Acis 2022 Australasian Conference on Information Systems Proceedings.
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Previous studies on university-industry collaboration have shown a number of different barriers that affect transferring knowledge through such collaborations. From the cognitive dimension of the social capital theory perspective, this paper explores barriers to knowledge transfer activities through the collaboration between university and industry and how intermediaries contribute to mitigating these barriers. By applying the qualitative research method, a total of 40 semi-structured interviews were conducted, targeting academics and practitioners across the various universities, industries, and intermediary organisations in Saudi Arabia. A thematic analysis of the data was then employed using MAXQDA 2022 software. Based on the findings, this paper contributes to the extant university-industry collaboration literature by providing insights into critical challenges that can be addressed to improve collaborative inter-organisation relationships. Additionally, these insights can also guide related partners in maintaining a successful collaboration.
Hakim, G & Braun, R 1970, 'Wireless Sensor Network Routing for Energy Efficiency', Springer Proceedings in Mathematics and Statistics, International Conference On Systems Engineering, Springer International Publishing, Wrocław, Poland, pp. 329-343.
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In this paper we present a new ABM model of Directed Diffusion using NetLogo that allows to study the impact of energy usage for various transmission activities. Furthermore we developed two new derivatives of this model of Directed Diffusion that could lead to energy saving in practical applications. We call these Lazy Diffusion and Gradient Diffusion. We exercise the models to produce results that show significant reduction in energy consumption over Directed Diffusion. We conclude that Wireless Sensor Networks with their routing protocols are complex systems, which can yield to Agent Based Modelling.
Halkon, B, Perrin, R & Guo, Z 1970, 'INVESTIGATING THE VIBROACOUSTICS OF INDIAN ELEPHANT BELLS', Proceedings of the International Congress on Sound and Vibration, Singapore.
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The geometry of a handmade, 13-tine Indian elephant bell replica, captured with manual measurements and updated using a contemporary and accessible, smartphone-based approach, has been used to generate a simple finite element model. Mode shapes, presented using a novel approach, are compared with those derived from group theory predictions for this bell's symmetries and show excellent agreement for the first two singlets and five of the first six doublets. Natural frequencies are compared with those obtained from an experimental modal analysis campaign using a scanning laser Doppler vibrometer and an automatic modal impact hammer. Again, reasonable agreement is observed for the mode shapes of interest. Results are also qualitatively similar, with appropriate adjustments, to those previously reported in the literature for a 16-tine bell of different design obtained using Electronic Speckle Pattern Interferometry. The results allow us to compare and contrast the effectiveness of these two non-contact optical vibration measurement methods in work of this type on 3D structures.
Han, M, Zhang, DJ, Wang, Y, Yan, R, Yao, L, Chang, X & Qiao, Y 1970, 'Dual-AI: Dual-path Actor Interaction Learning for Group Activity Recognition', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, New Orleans, LA, pp. 2980-2989.
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Learning spatial-temporal relation among multiple actors is crucial for group activity recognition. Different group activities often show the diversified interactions between actors in the video. Hence, it is often difficult to model complex group activities from a single view of spatial-temporal actor evolution. To tackle this problem, we propose a distinct Dual-path Actor Interaction (Dual-AI) framework, which flexibly arranges spatial and temporal transformers in two complementary orders, enhancing actor relations by integrating merits from different spatio-temporal paths. Moreover, we introduce a novel Multi-scale Actor Contrastive Loss (MAC-Loss) between two interactive paths of Dual-AI. Via self-supervised actor consistency in both frame and video levels, MAC-Loss can effectively distinguish individual actor representations to reduce action confusion among different actors. Consequently, our Dual-AI can boost group activity recognition by fusing such discriminative features of different actors. To evaluate the proposed approach, we conduct extensive experiments on the widely used benchmarks, including Volleyball [21], Collective Activity [II], and NBA datasets [49]. The proposed Dual-AI achieves state-of-the-art performance on all these datasets. It is worth noting the proposed Dual-AI with 50% training data outperforms a number of recent approaches with 100% training data. This confirms the generalization power of Dual-AI for group activity recognition, even under the challenging scenarios of limited supervision.
Han, Y, Miao, Y, Lu, J, Guo, M & Xiao, Y 1970, 'Exploring Intervention Strategies for Distracted Students in VR Classrooms', CHI Conference on Human Factors in Computing Systems Extended Abstracts, CHI '22: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-7.
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Hanna, B, Xu, G, Wang, X & Hossain, J 1970, 'Blockchain-Based Solutions for Humanitarian Supply Chain Management', 28th Americas Conference on Information Systems Amcis 2022, Americas Conference on Information Systems, AMCIS, Minneapolis, USA, pp. 195-218.
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The outbreak of the novel COVID-19 demonstrates how pandemics disturb supply chains (SC) all across the world. Policymakers and private-sector partners are increasingly acknowledging that we cannot tackle today's issues without leveraging the promise of new technology. Blockchain technology is increasingly being adopted to help humanitarian efforts in various fields. This paper presents conceptual research designed to assess how Blockchain distributed ledger technology can be leveraged to enhance humanitarian supply chain management (HSCM). This paper fills the present research gap on the Blockchain's potential implications for HSCM by proposing a framework built on the foundations of five prominent institutional economic theories: social exchange theory, principal-agent theory, transaction cost theory, resource-based view, and network theory. These theories could be utilized to generate research topics that are theory-based and industry-relevant. This conceptual framework assists institutions in making decisions about how to recover and rebuild their SC during disasters.
Hanna, B, Xu, G, Wang, X & Hossain, J 1970, 'Data-Driven Computational Algorithms for Predicting Electricity Consumption Missing Values: A Comparative Study', 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), IEEE, Adelaide, Australia, pp. 1-6.
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Hanna, P, Carmichael, M & Clemon, L 1970, 'Benefit of Optimal Actuator Selection – A Comparative Study', Volume 4: Biomedical and Biotechnology; Design, Systems, and Complexity, ASME 2022 International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers.
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Abstract Actuators are a vital component, and often the limiting factor in robotics and robotic-related applications like humanoids, exoskeletons, prosthetics and orthosis. Actuator selection is critical due to system design flow-on effects including weight, energy consumption and form factor. A designer’s challenge is often to optimize the actuator to minimize size or weight and meet the performance specifications usually with trade-offs. This paper investigates the design impacts of selecting more suited actuators on the system through a representative humanoid configuration performing a task. It also looks at variations based on the scale of the humanoid using human anthropometric data for variations in limb lengths. The torques and speeds required at each joint to complete the task is simulated and the system design is updated to keep a constant member stress across all designs. The total energy and weight are calculated and used to compare actuator selection impacts. By knowing the extent of the flow-on effects actuator selection has on a configuration, and how this effect scales, designers are able to determine what investment should be allocated to locating the ideal actuator for their task.
Hasan, ASMM & Trianni, A 1970, 'Towards a Framework to Assess the Impact of Industry 4.0 Technologies & Services on Production Resources', 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), IEEE, pp. 0752-0756.
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In the realm of manufacturing, Industry 4.0 is gaining increasing importance. Academic literature has highlighted the technologies part of Industry 4.0 mainly. However, there are very few studies that have discussed the Industry 4.0 technologies, services, and production resources at the industrial context. In particular, a research gap exists to an academic discussion encompassing the impact of Industry 4.0 technologies on its' services and production resources. This paper aims to present a preliminary framework showing the impact of Industry 4.0 technologies & services on production resources. The framework is applied at two different types of industries. This study can be used to best support and select suitable Industry 4.0 technologies for industrial decision-making purposes.
Hasan, S, Bennett, N & Hamarshi, M 1970, 'ASSESSING THE ROLE OF THE BIOFIRE PNEUMONIA PCR PANEL IN ANTIMICROBIAL STEWARDSHIP IN THE ICU', CRITICAL CARE MEDICINE, LIPPINCOTT WILLIAMS & WILKINS, pp. 601-601.
Hasan, SU, Siwakoti, YP & Lu, D 1970, 'Electromagnetic Compatibility Issues with Off-the-Shelf Power Converters: A Case Study', 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), IEEE, pp. 1-4.
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Hashmi, SS, Waheed, N, Tangari, G, Ikram, M & Smith, S 1970, 'Longitudinal Compliance Analysis of Android Applications with Privacy Policies', Springer International Publishing, pp. 280-305.
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Hason Rudd, D, Huo, H & Xu, G 1970, 'Causal Analysis of Customer Churn Using Deep Learning', 2021 International Conference on Digital Society and Intelligent Systems (DSInS), 2021 International Conference on Digital Society and Intelligent Systems, IEEE, Chengdu, China, pp. 319-324.
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Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Two main business marketing strategies play vital roles to increase market share dollar- value: gaining new and preserving existing customers. Customer acquisition cost can be five to six times that for customer retention, hence investing in customers with churn risk is smart. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and assist enterprises to identify effects and possible causes for churn and subsequently use that knowledge to apply tailored incentives. This paper proposes a framework using a deep feedforward neural network for classification accompanied by a sequential pattern mining method on high-dimensional sparse data. We also propose a causal Bayesian network to predict cause probabilities that lead to customer churn. Evaluation metrics on test data confirm the XGBoost and our deep learning model outperformed previous techniques. Experimental analysis confirms that some independent causal variables representing the level of super guarantee contribution, account growth, and customer tenure were identified as confounding factors for customer churn with a high degree of belief. This paper provides a real-world customer churn analysis from current status inference to future directions in local superannuation funds.
Hason Rudd, D, Huo, H & Xu, G 1970, 'Leveraged Mel Spectrograms Using Harmonic and Percussive Components in Speech Emotion Recognition', Advances in Knowledge Discovery and Data Mining, Springer International Publishing, pp. 392-404.
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Hason Rudd, D, Huo, H & Xu, G 1970, 'Predicting Financial Literacy Via Semi-Supervised Learning', AI 2021: Advances in Artificial Intelligence, Springer International Publishing, pp. 304-319.
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HASSANI, S, MOUSAVI, M & GANDOMI, AH 1970, 'MINIMIZING NOISE EFFECTS IN STRUCTURAL HEALTH MONITORING USING HILBERT TRANSFORM OF THE CONDENSED FRF', Proceedings of the 13th International Workshop on Structural Health Monitoring, Structural Health Monitoring 2021, Destech Publications, Inc..
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A novel model updating-based damage detection method is proposed that uses the Unwrapped Instantaneous Hilbert Phase (UIHP) of the condensed frequency response function (CFRF) as input to the objective function of an optimisation problem. The novelty of the proposed method lies in two items: (1) using the CFRF instead of the FRF itself, and (2) using the UIHP associated with the columns of the CFRF as input. The proposed modifications bring about the following improvements in the damage detection practice as follows: (1) CFRF will reduce the number of required degrees of freedom (DOFs) to be measured, and (2) the UIHP mitigates the effect of the measurement noise on damage detection. The problem of damage detection in a laminated composite plate with different number of layers and ply orientation has been solved where the results demonstrate the effectiveness of the proposed method.
Hayat, T, Afzal, MU, Ahmed, F & Esselle, KP 1970, 'Design and Performance Comparison of Compact Resonant Cavity Antennas Using Customized 3D Printing Techniques', 2022 16th European Conference on Antennas and Propagation (EuCAP), 2022 16th European Conference on Antennas and Propagation (EuCAP), IEEE, pp. 1-5.
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Hayati, H, Eager, D & Oberst, S 1970, 'Recurrence Plot Qualification Analysis of the Greyhound Rotary Gallop Gait', Springer International Publishing, Sapienza University of Rome, Italy (online), pp. 331-341.
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He, B, Armaghani, DJ, Bhatawdekar, RM & Lai, SH 1970, 'A Review of Soft Computing Techniques in Predicting Overbreak Induced by Tunnel Blasting', Springer Nature Singapore, pp. 3-13.
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He, Y, Ding, C, Wei, G & Guo, YJ 1970, 'An Embedded Dual-Band Base Station Antenna Array Employing Choked Bowl-Shaped Antenna for Cross-Band Scattering Mitigation', 2022 16th European Conference on Antennas and Propagation (EuCAP), 2022 16th European Conference on Antennas and Propagation (EuCAP), IEEE, Madrid, SPAIN, pp. 1-5.
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An embedded dual-band dual-polarized base station antenna (BSA) array is proposed in this paper. The array consists of two low-scattering bowl-shaped antenna elements working at the lower band (LB) and five cross-dipoles operating at the higher band (HB). Such an array configuration is intended to mitigate the negative effect on the HB antennas' radiation pattern caused by the presence of adjacent LB antennas. In this paper, a new LB antenna loaded with metal chokes is proposed to further reduce its scattering to the HB radiation. The results obtained with conventional bowl-shaped LB antenna and with choked LB antenna are compared to demonstrate the superiority of this de-scattering method. The simulation results show that the HB performance is significantly improved with the help of metal chokes while the LB performance remains nearly unchanged.
He, Y, Wang, K, Zhang, W, Lin, X & Zhang, Y 1970, 'Efficient Reinforcement of Bipartite Networks at Billion Scale', 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, pp. 446-458.
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He, Z, Han, Y, Ouyang, Z, Gao, W, Chen, H, Xu, G & Wu, J 1970, 'DIALMED: A Dataset for Dialogue-based Medication Recommendation', Proceedings International Conference on Computational Linguistics Coling, pp. 721-733.
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Medication recommendation is a crucial task for intelligent healthcare systems. Previous studies mainly recommend medications with electronic health records (EHRs). However, some details of interactions between doctors and patients may be ignored or omitted in EHRs, which are essential for automatic medication recommendation. Therefore, we make the first attempt to recommend medications with the conversations between doctors and patients. In this work, we construct DIALMED, the first high-quality dataset for medical dialogue-based medication recommendation task. It contains 11, 996 medical dialogues related to 16 common diseases from 3 departments and 70 corresponding common medications. Furthermore, we propose a Dialogue structure and Disease knowledge aware Network (DDN), where a QA Dialogue Graph mechanism is designed to model the dialogue structure and the knowledge graph is used to introduce external disease knowledge. The extensive experimental results demonstrate that the proposed method is a promising solution to recommend medications with medical dialogues. The dataset and code are available at https://github.com/f-window/DialMed.
Herrera, IT, Nguyen, LV, Le, T, Aguilera, RP & Ha, Q 1970, 'UAV Target Tracking using Nonlinear Model Predictive Control', 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), IEEE, Prague, Czech Republic, pp. 1-7.
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This paper presents a Nonlinear Model Predictive Control (NMPC) formulation for the attitude control of a fixed-wing Unmanned Aerial Vehicle (UAV) tracking a ground target. The vehicle is required to orbit around the target and as such, the tracking system can be modeled in two dimensions, namely the range and bearing angle. The system constraints are considered to account for real-world limitations. Subject to these constraints, the optimal input is obtained from solving a quadratic cost function. Extensive simulation was conducted for several case studies with various trajectories of the target, given position measurements of the UAV. The control development is then applied to track an estimated path taken from a mining truck during operation. The proposed control formulation is compared with a standard linear Model Predictive Control (MPC). The numerical results show that NMPC can cope with both constraints and nonlinearities, resulting in highly accurate tracking even when the UAV initial position is far away from the target, and overcoming poor tracking performance when using linear MPC. Using the current hardware standards, a quantitative analysis is also provided based on the required execution time for solving the constrained quadratic optimization problem at each sampling instant.
Hoang, LM, Nguyen, D, Zhang, JA & Thai Hoang, D 1970, 'Multiple Correlated Jammers Suppression: A Deep Dueling Q-Learning Approach', 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 998-1003.
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For wireless networks under jamming attacks, suppressing the jammer is essential to guarantee a rehable communication link. However, it can be problematic to nullify the jamming signal when the correlations between transmitted jamming signals are deliberately varied over tone. Specifically recent studies reveal that the time-varying correlations create a 'virtual change'm the jamming channel and thus their nullspace, even when the physical channels remain unchanged Unlike existing studies that only consider unchanged correlations or merely propose a heuristic solution to the 'virtual change'problem by continuously monitoring the residual jamming signal then updating the beam-forming matrix, we develop a deep dueling Q-learning technique to minimize the magnitude of the 'virtual change'by choosing a suitable allocated time for different phases of each communication frame. Extensive simulations show that the proposed techniques can suppress the jamming signal, even when the correlations vary over time, and the correlations' trajectory is unrevealed. Moreover, our techniques do not require monitoring the residual jamming signals then updating the beam-forming matrix. Therefore, our technique can improve the system's spectral efficiency and reduce the outage probability.
Hoang, VT & Phung, MD 1970, 'Enhanced Teaching-Learning-Based Optimization for 3D Path Planning of Multicopter UAVs', Springer International Publishing, pp. 743-753.
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Holmewood, R, Halkon, B & Darwish, A 1970, 'Towards real-time vibroacoustic classification, verification and tracking of in-flight UAVs', Proceedings of ISMA 2022 International Conference on Noise and Vibration Engineering and Usd 2022 International Conference on Uncertainty in Structural Dynamics, International Conference on Noise and Vibration Engineering, Leuven, Belgium, pp. 3564-3576.
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Over the past decade, the development of counter unmanned aerial systems (C-UAS) has accelerated due to the influx of public drone use. Proposed in this paper is a novel drone classification and verification solution that utilises a convolutional neural network (CNN) trained through a transfer learning approach to use inflight laser Doppler vibrometer (LDV) captured vibroacoustic data processed into images of frequency spectrograms. An initial CNN network performance comparison was conducted between SqueezeNet and AlexNet CNNs across a dataset with six classes and 4,453 spectrogram samples. SqueezeNet was selected, achieving a 0.39% lower mean accuracy but with a network size of 0.5 Mb (480 times less than AlexNet). A network performance characterization was performed on SqueezeNet to characterise the effects on accuracy when altering spectrogram visualization parameters prior to training. A series of k-fold cross validation runs were conducted, where an optimised mean classification accuracy of 97.37% was achieved.
Homaira, M, Atif, A & Lim, L-A 1970, 'Understanding the learning of disabled students: An exploration of machine learning approaches', 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), IEEE, Gold Coast, pp. 1-6.
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Research does not demonstrate how learning
analytics can support students with learning disability. It is vital to scrutinise the demographics of students with and without disabilities and their effect on performance. Machine learning algorithms can provide valuable insights by mining this expanding education data. This study aims to analyse the relationship between disabled and nondisabled
students' demographic with their scores and the number of
attempts to complete a module to develop prediction models. Three models were used: Adaptive Boosting, Random Forest, and K nearest neighbour. The results found that the Adaptive boosting algorithm delivered the highest prediction accuracy.
Hong, X, Feng, Y, Li, S & Ying, M 1970, 'Equivalence Checking of Dynamic Quantum Circuits', Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design, ACM, pp. 1-8.
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Hong, X, Feng, Y, Li, S & Ying, M 1970, 'Equivalence Checking of Dynamic Quantum Circuits.', ICCAD, ACM, pp. 127:1-127:1.
Hossain, M & Eager, D 1970, 'Simulating Theoretical Jerk by Numerical Modelling for Greyhound Racing', Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SCITEPRESS - Science and Technology Publications, Lisbon, PORTUGAL, pp. 379-385.
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Hu, S, Chen, C & Dong, D 1970, 'Deep Reinforcement Learning for Control Design of Quantum Gates', 2022 13th Asian Control Conference (ASCC), 2022 13th Asian Control Conference (ASCC), IEEE, pp. 2367-2372.
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Hu, S, Xie, C, Liang, X & Chang, X 1970, 'Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL', Proceedings of Machine Learning Research, pp. 9041-9071.
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Cooperative multi-agent reinforcement learning (MARL) is making rapid progress for solving tasks in a grid world and real-world scenarios, in which agents are given different attributes and goals, resulting in different behavior through the whole multi-agent task. In this study, we quantify the agent's behavior difference and build its relationship with the policy performance via Role Diversity, a metric to measure the characteristics of MARL tasks. We define role diversity from three perspectives: action-based, trajectory-based, and contribution-based to fully measure a multi-agent task. Through theoretical analysis, we find that the error bound in MARL can be decomposed into three parts that have a strong relation to the role diversity. The decomposed factors can significantly impact policy optimization on three popular directions including parameter sharing, communication mechanism, and credit assignment. The main experimental platforms are based on Multiagent Particle Environment (MPE) and The StarCraft Multi-Agent Challenge (SMAC). Extensive experiments clearly show that role diversity can serve as a robust measurement for the characteristics of a multi-agent cooperation task and help diagnose whether the policy fits the current multi-agent system for a better policy performance.
Huang, H, Geng, X, Long, G & Jiang, D 1970, 'Understand before Answer: Improve Temporal Reading Comprehension via Precise Question Understanding', Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, pp. 375-384.
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This work studies temporal reading comprehension (TRC), which reads a free-text passage and answers temporal ordering questions. Precise question understanding is critical for temporal reading comprehension. For example, the question “What happened before the victory” and “What happened after the victory” share almost all words except one, while their answers are totally different. Moreover, even if two questions query about similar temporal relations, different varieties might also lead to various answers. For example, although both the question “What usually happened during the press release?” and “What might happen during the press release” query events which happen after the press release, they convey divergent semantics. To this end, we propose a novel reading comprehension approach with precise question understanding. Specifically, a temporal ordering question is embedded into two vectors to capture the referred event and the temporal relation. Then we evaluate the temporal relation between candidate events and the referred event based on that. Such fine-grained representations offer two benefits. First, it enables a better understanding of the question by focusing on different elements of a question. Second, it provides good interpretability when evaluating temporal relations. Furthermore, we also harness an auxiliary contrastive loss for representation learning of temporal relations, which aims to distinguish relations with subtle but critical changes. The proposed approach outperforms strong baselines and achieves state-of-the-art performance on the TORQUE dataset. It also increases the accuracy of four pre-trained language models (BERT base, BERT large, RoBERTa base, and RoBETRa large), demonstrating its generic effectiveness on divergent models.
Huang, J, Zhang, L, Gong, Y, Zhang, J, Nie, X & Yin, Y 1970, 'Series Photo Selection via Multi-View Graph Learning', 2022 IEEE International Conference on Multimedia and Expo (ICME), 2022 IEEE International Conference on Multimedia and Expo (ICME), IEEE, pp. 01-06.
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Huang, S, Li, Y, Ma, B, Feng, Y, Lei, G & Zhu, J 1970, 'A Mortar Method Based Domain Decomposition Approach for Winding Loss Computation of Electrical Machines', 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC), 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC), IEEE, pp. 1-2.
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Huang, S, Liu, Y, Ren, Y, Tsang, IW, Xu, Z & Lv, J 1970, 'Learning Smooth Representation for Multi-view Subspace Clustering', Proceedings of the 30th ACM International Conference on Multimedia, MM '22: The 30th ACM International Conference on Multimedia, ACM, pp. 3421-3429.
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Huang, S, Tsang, I, Xu, Z, Lv, J & Liu, Q-H 1970, 'Multi-View Clustering on Topological Manifold', Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), pp. 6944-6951.
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Multi-view clustering has received a lot of attentions in data mining recently. Though plenty of works have been investigated on this topic, it is still a severe challenge due to the complex nature of the multiple heterogeneous features. Particularly, existing multi-view clustering algorithms fail to consider the topological structure in the data, which is essential for clustering data on manifold. In this paper, we propose to exploit the implied data manifold by learning the topological relationship between data points. Our method coalesces multiple view-wise graphs with the topological relevance considered, and learns the weights as well as the consensus graph interactively in a unified framework. Furthermore, we manipulate the consensus graph by a connectivity constraint such that the data points from the same cluster are precisely connected into the same component. Substantial experiments on both toy data and real datasets are conducted to validate the effectiveness of the proposed method, compared to the state-of-the-art algorithms over the clustering performance.
Huang, W, Li, Y, Du, W, Yin, J, Xu, RYD, Chen, L & Zhang, M 1970, 'TOWARDS DEEPENING GRAPH NEURAL NETWORKS: A GNTK-BASED OPTIMIZATION PERSPECTIVE', Iclr 2022 10th International Conference on Learning Representations.
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Graph convolutional networks (GCNs) and their variants have achieved great success in dealing with graph-structured data. Nevertheless, it is well known that deep GCNs suffer from the over-smoothing problem, where node representations tend to be indistinguishable as more layers are stacked up. The theoretical research to date on deep GCNs has focused primarily on expressive power rather than trainability, an optimization perspective. Compared to expressivity, trainability attempts to address a more fundamental question: Given a sufficiently expressive space of models, can we successfully find a good solution via gradient descent-based optimizers? This work fills this gap by exploiting the Graph Neural Tangent Kernel (GNTK), which governs the optimization trajectory under gradient descent for wide GCNs. We formulate the asymptotic behaviors of GNTK in the large depth, which enables us to reveal the dropping trainability of wide and deep GCNs at an exponential rate in the optimization process. Additionally, we extend our theoretical framework to analyze residual connection-based techniques, which are found to be merely able to mitigate the exponential decay of trainability mildly. Inspired by our theoretical insights on trainability, we propose Critical DropEdge, a connectivity-aware and graph-adaptive sampling method, to alleviate the exponential decay problem more fundamentally. Experimental evaluation consistently confirms using our proposed method can achieve better results compared to relevant counterparts with both infinite-width and finite-width.
Huang, W, Wang, H, Xia, J, Wang, C & Zhang, J 1970, 'Density-driven Regularization for Out-of-distribution Detection', Advances in Neural Information Processing Systems.
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Detecting out-of-distribution (OOD) samples is essential for reliably deploying deep learning classifiers in open-world applications. However, existing detectors relying on discriminative probability suffer from the overconfident posterior estimate for OOD data. Other reported approaches either impose strong unproven parametric assumptions to estimate OOD sample density or develop empirical detectors lacking clear theoretical motivations. To address these issues, we propose a theoretical probabilistic framework for OOD detection in deep classification networks, in which two regularization constraints are constructed to reliably calibrate and estimate sample density to identify OOD. Specifically, the density consistency regularization enforces the agreement between analytical and empirical densities of observable low-dimensional categorical labels. The contrastive distribution regularization separates the densities between in distribution (ID) and distribution-deviated samples. A simple and robust implementation algorithm is also provided, which can be used for any pre-trained neural network classifiers. To the best of our knowledge, we have conducted the most extensive evaluations and comparisons on computer vision benchmarks. The results show that our method significantly outperforms state-of-the-art detectors, and even achieves comparable or better performance than methods utilizing additional large-scale outlier exposure datasets.
Huang, Y, Feng, B, Dong, P, Tian, A & Yu, S 1970, 'A Multi-objective based Inter-Layer Link Allocation Scheme for MEO/LEO Satellite Networks', 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Austin, TX, USA, pp. 1301-1306.
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Recently, there is a growing interest in Double-Layered Satellite Networks (DLSN) which integrate Medium-Earth-Orbit (MEO) and Low-Earth-Orbit (LEO) satellites for provision of mobile and personal services. However, it is still in the early stage with several challenges unaddressed, and one of the key problems is the inter-layer link allocations between MEO and LEO satellites, as DLSN topology is dynamically changed over the time and satellites are with the limited number of connections onboard. To this end, we propose a corresponding Inter-layer Link Allocation (ILA) scheme in this paper, taking the visible duration between satellites, transmitting power consumed onboard and geographical distributions of user load into account, aiming to maximize the utilization efficiency of DLSN inter-layer links. Then, we formulate it as a constrained multi-objective linear programming problem and evaluate its performance with other three benchmarks. Numerical results have demonstrated that the proposed ILA scheme can decrease the number of ILL handovers and average inter-satellite distance, with load balanced between LEO and MEO satellites.
Huang, Y, Kang, D, Chen, L, Zhe, X, Jia, W, Bao, L & He, X 1970, 'CAR: Class-Aware Regularizations for Semantic Segmentation', Computer Vision – ECCV 2022, Springer Nature Switzerland, pp. 518-534.
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Huang, Y, Kang, D, Jia, W, Liu, L & He, X 1970, 'Channelized Axial Attention - Considering Channel Relation within Spatial Attention for Semantic Segmentation', THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / THE TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 36th AAAI Conference on Artificial Intelligence / 34th Conference on Innovative Applications of Artificial Intelligence / 12th Symposium on Educational Advances in Artificial Intelligence, ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE, ELECTR NETWORK, pp. 1016-1025.
Huang, Y, Kang, D, Jia, W, Liu, L & He, X 1970, 'Channelized Axial Attention – considering Channel Relation within Spatial Attention for Semantic Segmentation', Proceedings of the AAAI Conference on Artificial Intelligence, The Thirty-Sixth AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Online virtual conference, pp. 1016-1025.
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Spatial and channel attentions, modelling the semantic interdependencies in spatial and channel dimensions respectively, have recently been widely used for semantic segmentation. However, computing spatial and channel attentions separately sometimes causes errors, especially for those difficult cases. In this paper, we propose Channelized Axial Attention (CAA) to seamlessly integrate channel attention and spatial attention into a single operation with negligible computation overhead. Specifically, we break down the dot-product operation of the spatial attention into two parts and insert channel relation in between, allowing for independently optimized channel attention on each spatial location. We further develop grouped vectorization, which allows our model to run with very little memory consumption without slowing down the running speed. Comparative experiments conducted on multiple benchmark datasets, including Cityscapes, PASCAL Context, and COCO-Stuff, demonstrate that our CAA outperforms many state-of-the-art segmentation models (including dual attention) on all tested datasets.
Hurkmans, EGE, Groothuismink, JMG, Vos, HI, van der Graaf, WTA, Flucke, U, Versleijen-Jonkers, YMH, Roeffen, MHS, Koenderink, JB, van den Heuvel, JJMW, Schreuder, BWB, Hagleitner, MM, Gelderblom, H, Cleton-Jansen, A-M, Bovee, JVMG, de Bont, ESJM, Kremer, LCM, Bras, J, Caron, H, Windsor, R, Whelan, J, Patino-Garcia, A, Gonzalez-Neira, A, McCowage, G, Nagabushan, S, Saletta, F, Catchpoole, D, Guchelaar, H-J, Brunner, HG, te Loo, DMWM & Coenen, MJH 1970, 'Pharmacogenetics of chemotherapy response in osteosarcoma: a genetic variant in SLC7A8 is associated with progressive disease', EUROPEAN JOURNAL OF HUMAN GENETICS, SPRINGERNATURE, pp. 515-515.
Hussain, I, Yaqub, M, Mortazavi, M, Ehsan, MA & Uzair, M 1970, 'Finite Element Modeling and Statistical Analysis of Fire-Damaged Reinforced Concrete Columns Repaired Using Smart Materials and FRP Confinement', Springer International Publishing, pp. 101-110.
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Indraratna, B, Ngo, T, Qi, Y & Rujikiatkamjorn, C 1970, 'Track Geomechanics for Future Railways: Use of Artificial Inclusions', Advances in Transportation Geotechnics IV Proceedings of the 4th International Conference on Transportation Geotechnics Volume 2, 4th International Conference on Transportation Geotechnics (ICGT2020), Springer International Publishing, Chicago, Illinois, USA, pp. 139-154.
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This volume presents selected papers presented during the 4th International Conference on Transportation Geotechnics.
Indraratna, B, Qi, Y, Tawk, M & Rujikiatkamjorn, C 1970, 'Mining Waste Materials and Recycled Rubber Matrix for Rail Tracks under Cyclic Loading', Geo-Congress 2022, Geo-Congress 2022, American Society of Civil Engineers, pp. 239-248.
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Inwumoh, J, Baguley, C & Gunawardane, K 1970, 'A Novel Single-Clamped Hybrid-Arm MMC with DC-FRT and STATCOM Capability', 2022 IEEE 7th Southern Power Electronics Conference (SPEC), 2022 IEEE 7th Southern Power Electronics Conference (SPEC), IEEE, pp. 1-5.
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Iqbal, H, Zheng, J, Chai, R & Chandrasekaran, S 1970, 'Regression Based Real Time Hand Gesture Recognition and Control for Electric Powered Wheelchair', Australasian Conference on Robotics and Automation Acra.
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Steering an electric-powered wheelchair is an onerous task for a paralyzed person. Hence, there is a need for either designing a new one or modifying the existing electric-powered wheelchair that is intelligent enough and provides easy daily use for a person who is not capable of handling the manual steering process. Our proposed system is designed to receive, process and classify the surface electromyography (sEMG) signals and gesture recognition techniques before controlling the wheelchair. This paper is based on an analysis of sEMG signals and gesture recognition techniques of a user's dominant limb, and its deployment through Artificial Intelligence based machine learning algorithms. In myoelectric control, classification has been showing promising results with high accuracy but is well known for non-intuitive control. The regression model, on the other hand, allows human-like natural movements, producing proportional and simultaneous control. We are using hand gesture control of an unidirectional wheelchair using sEMG as wearable sensors. Five basic gestures are recognized and classified using feature extraction and a machine learning algorithm. These gestures are mapped to the unidirectional motion commands to steer the wheelchair. The classified algorithm and realtime navigation of the smart wheelchair using the proposed algorithm have been tested by 6 healthy subjects. The results demonstrate performance improvement and gesture recognition accuracy of 95.50% and reduced training time (< 2 mins), compared to state-of-art regression models. In addition, this algorithm has been applied to proportional and simultaneous myoelectric control in real-time.
Irga, P, Torpy, F, Fleck, R, Duani, G, Matheson, S, Douglas, A & Lyu, L 1970, 'The Benefits of Plants at Work', Workplace Wellness Festival, Workplace Wellness Festival, International Conference Centre, Sydney Australia.
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The human environment is the indoor environment.86% of your time every day is spent indoors. As we spend more time indoors, we have an extended exposure time to the pollutants encountered in indoor environments. As far as workplace environments are concerned, more and more people are staying back doing overtime (paid or not), which puts the onus on the employer to provide an indoor environment that does not pose an ongoing risk for the occupants.In Australia, ambient air pollution standards are set by The National Environment Protection (Ambient Air quality) Measure (Air-NEPM). The standards used are similar to those from most western countries. The Air-NEPM standards cover most of the common air pollutants, including particulates less than 10 mm in size (PM10), particulates less than 2.5 mm (PM2.5), SO2, NO2, and CO. Australian threshold limits for indoor workspace CO2 and volatile organic compounds (VOC) are set by Work Safe Australia.Regarding the procedure of air purification by potted plants, many researchers and decades of work have confirmed that the plants remove CO2 through photosynthesis, degrade VOCs through the metabolic action of rhizospheric microbes, and can sequester particulate matter through a range of physical mechanisms.Apart from the occasional report documenting trivial VOC removal capacity by plant tissues , or bacteria on the leaf surface or phyllosphere, the quantitative bioremediation of VOCs is now generally accepted to primarily occur through microbial activity, where the VOCs are used directly as carbon sources by microorganisms. Most research suggests that all potted plant / substrate combinations can remove VOCs at similar rates, with slightly reduced rates for plants grown in hydroculture, probably due to the lower microbial density in the substrate. Two recent experiments have challenged this observation, finding significant variation between plant species, although in common with earlier research, found no patte...
Islam, F, Ball, J & Goodin, C 1970, 'Dynamic path planning for traversing autonomous vehicle in off-road environment using MAVS', Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022, Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022, SPIE.
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Iyer, S, Blair, A, White, C, Dawes, L, Moses, D & Sowmya, A 1970, 'Vertebral Compression Fracture detection using Multiple Instance Learning and Majority Voting', 2022 26th International Conference on Pattern Recognition (ICPR), 2022 26th International Conference on Pattern Recognition (ICPR), IEEE, pp. 4630-4636.
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Jayan Chirayath Kurian, J 1970, 'Digital workplaces: Generating value for community-based emergency services', Queensland Government Disaster Management Forum, Brisbane.
Jayan Chirayath Kurian, J, John, BM & Lang, A 1970, 'Redesigning Learning Spaces During a Pandemic', Copenhagen.
Ji, Z, Natarajan, A, Vidick, T, Wright, J & Yuen, H 1970, 'Quantum soundness of testing tensor codes', 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS), 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS), IEEE, pp. 586-597.
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A locally testable code is an error-correcting code that admits very efficient probabilistic tests of membership. Tensor codes provide a simple family of combinatorial constructions of locally testable codes that generalize the family of Reed-Muller codes. The natural test for tensor codes, the axis-parallel line vs. point test, plays an essential role in constructions of probabilistically checkable proofs. We analyze the axis-parallel line vs. point test as a two-prover game and show that the test is sound against quantum provers sharing entanglement. Our result implies the quantum-soundness of the low individual degree test, which is an essential component of the MIP∗ = RE theorem. Our proof also generalizes to the infinite-dimensional commuting-operator model of quantum provers.
Jia, M, Alboom, MV, Goubert, L, Bracke, P, Gabrys, B & Musial, K 1970, 'Analysing Egocentric Networks via Local Structure and Centrality Measures: A Study on Chronic Pain Patients', 2022 International Conference on Information Networking (ICOIN), 2022 International Conference on Information Networking (ICOIN), IEEE, SOUTH KOREA, pp. 152-157.
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Typical centrality measures assess the importance of a node based on the distances to other nodes, shortest paths passing through it, or the eigen-structure of the adjacency matrix. Local structure measures, on the other hand, capture network topological features by measuring how a motif is constructed from a substructure. In this paper, we discuss the suitability of several centrality measures and local structure measures in egocentric networks and investigate the relationships among them. Through experiments on 303 ego social networks of chronic pain patients, we find that patients of lower pain grade indeed have better connections in their networks than those of higher pain grade, and that including centrality measures and local structure measures as additional features leads to significant improvement in a machine learning task that predicts the patients' pain grades.
Jia, M, Van Alboom, M, Goubert, L, Bracke, P, Gabrys, B & Musial, K 1970, 'Analysing Ego-Networks via Typed-Edge Graphlets: A Case Study of Chronic Pain Patients', Complex Networks & Their Applications X, Springer International Publishing, pp. 514-526.
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Graphlets, being the fundamental building blocks, are essential for understanding and analysing complex networks. The original notion of graphlets, however, is unable to encode edge attributes in many types of networks, especially in egocentric social networks. In this paper, we introduce a framework to embed edge type information in graphlets and generate a Typed-Edge Graphlets Degree Vector (TyE-GDV). Through applying the proposed method to a case study of chronic pain patients, we find that not only a patient’s social network structure could inform his/her perceived pain grade, but also particular types of social relationships, such as friends, colleagues and healthcare workers, are more important in understanding the effect of chronic pain. Further, we demonstrate that including TyE-GDV as additional features leads to significant improvement in a typical machine learning task.
Jiang, G, Ansari, A, Sivakumar, M & McCarthy, T 1970, 'Evaluation of H5P interactive videos in enhanced elearning of an environmental engineering course during COVID-19 pandemic', 9th Research in Engineering Education Symposium (REES 2021) and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021), 9th Research in Engineering Education Symposium & 32nd Australasian Association for Engineering Education Conference, Research in Enineering Education Network (REEN), pp. 500-508.
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Jiao, S, Zhang, G, Navasardyan, S, Chen, L, Zhao, Y, Wei, Y & Shi, H 1970, 'Mask Matching Transformer for Few-Shot Segmentation', Advances in Neural Information Processing Systems.
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In this paper, we aim to tackle the challenging few-shot segmentation task from a new perspective. Typical methods follow the paradigm to firstly learn prototypical features from support images and then match query features in pixel-level to obtain segmentation results. However, to obtain satisfactory segments, such a paradigm needs to couple the learning of the matching operations with heavy segmentation modules, limiting the flexibility of design and increasing the learning complexity. To alleviate this issue, we propose Mask Matching Transformer (MM-Former), a new paradigm for the few-shot segmentation task. Specifically, MM-Former first uses a class-agnostic segmenter to decompose the query image into multiple segment proposals. Then, a simple matching mechanism is applied to merge the related segment proposals into the final mask guided by the support images. The advantages of our MM-Former are two-fold. First, the MM-Former follows the paradigm of decompose first and then blend, allowing our method to benefit from the advanced potential objects segmenter to produce high-quality mask proposals for query images. Second, the mission of prototypical features is relaxed to learn coefficients to fuse correct ones within a proposal pool, making the MM-Former be well generalized to complex scenarios or cases. We conduct extensive experiments on the popular COCO-20i and Pascal-5i benchmarks. Competitive results well demonstrate the effectiveness and the generalization ability of our MM-Former. Code is available at github.com/Picsart-AI-Research/Mask-Matching-Transformer.
Jin, C, Bell, JA, Deverell, L, Gates, F, Gorodo, I, Hossain, S, Lin, CT, Melencio, M, Nguyen, M, Nguyen, V, Singh, A & Zhu, H 1970, 'Acoustic touch: An auditory sensing paradigm to support close reaching for people who are blind', Proceedings of the International Congress on Acoustics, 24th International Congress on Acoustics, Korea.
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This work explores an auditory sensory augmentation paradigm we call acoustic touch, to assist people who are blind with reaching for close objects. The sensory augmentation system is constructed based on the Nreal augmented-reality glasses using a custom application running on an android phone. The system recognizes and localizes objects visually using cameras in the glasses, then renders objects as sound within a limited field-of-view, so we shall refer to the glasses as a foveated audio device. The repetition of the sound varies depending on the location of the object within the field of view of the foveated audio device. Psychophysical tests of the spatial perception of multiple objects are conducted comparing the acoustic touch paradigm with two other conditions: (1) a verbal clock face description of object locations and (2) a sequential audio presentation of the objects using Bluetooth speakers located with the objects. We report on the results of the psychophysical study with blind and blindfolded sighted participants.
Jin, X & Kelly, A 1970, 'Creating Graduates with SDG Attributes through Engineering Ethics Education', Sydney, New South Wales, AUSTRALIA.
Jin, Y, Zhu, L & Mu, Y 1970, 'Complex Video Action Reasoning via Learnable Markov Logic Network', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 3232-3241.
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Johansen, S, Senaratne, H, Burden, A, Howard, D, Caldwell, GA, Donovan, J, Duenser, A, Guertler, M, Mcgrath, M, Paris, C, Rittenbruch, M & Roberts, J 1970, 'Empowering People in Human-Robot Collaboration: Bringing Together and Synthesising Perspectives', Proceedings of the 34th Australian Conference on Human-Computer Interaction, OzCHI '22: 34th Australian Conference on Human-Computer Interaction, ACM, pp. 352-355.
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Kan, B, Lu, W, Peng, X, Wang, S, Zhang, G, Zhang, W & Qiao, X 1970, 'Word Sense Disambiguation Based on Memory Enhancement Mechanism', Knowledge Science, Engineering and Management, Springer International Publishing, pp. 249-260.
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Word sense disambiguation (WSD) is a very critical yet challenging task in natural language processing (NLP), which aims at identifying the most suitable meaning of ambiguous words in the given contexts according to a predefined sense inventory. Existing WSD methods usually focus on learning the semantic interactions between a special ambiguous word and the glosses of its candidate senses and thus ignore complicated relations between the neighboring ambiguous words and their glosses, leading to insufficient learning of the interactions between words in context. As a result, they are difficult to leverage the knowledge from the other ambiguous words which might provide some explicit clues to identify the meaning of current ambiguous word. To mitigate this challenge, this paper proposes a novel neural model based on memory enhancement mechanism for WSD task, which stores the gloss knowledge of previously identified words into a memory, and further utilizes it to assist the disambiguation of the next target word. Extensive experiments, which are conducted on a unified evaluation framework of the WSD task, demonstrate that our model achieves better disambiguation performance than the state-of-the-art approaches (Code: https://github.com/baoshuo/WSD ).
Kapellos, T, Bassler, K, Fujii, W, Pecht, T, Bonaguro, L, Galvao, I, Saglam, A, Dudkin, E, Frishberg, A, De Domenico, E, Horne, A, Donovan, C, Kim, RY, Gallego-Ortega, D, Becker, M, Händler, K, Ulas, T, Hasenauer, J, Pizarro, C, Hansbro, PM, Skowasch, D & Schultze, JL 1970, 'Inflammatory blood neutrophils in COPD stem from activated bone marrow progenitors', Airway cell biology and immunopathology, ERS Lung Science Conference 2022 abstracts, European Respiratory Society, pp. 210-210.
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Kapellos, T, Bassler, K, Fujii, W, Pecht, T, Bonaguro, L, Galvao, I, Saglam, A, Dudkin, E, Frishberg, A, De Domenico, E, Horne, A, Donovan, C, Kim, RY, Gallego-Ortega, D, Becker, M, Händler, K, Ulas, T, Hasenauer, J, Pizarro, C, Hansbro, PM, Skowasch, D & Schultze, JL 1970, 'Inflammatory blood neutrophils in COPD stem from activated bone marrow progenitors', 03.02 - Airway cell biology and immunopathology, ERS International Congress 2022 abstracts, European Respiratory Society, pp. LSC-0210.
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Katuwandeniya, K, Kiss, SH, Shi, L & Miro, JV 1970, 'Exact-likelihood User Intention Estimation for Scene-compliant Shared-control Navigation', 2022 International Conference on Robotics and Automation (ICRA), 2022 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 6437-6443.
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Kavadi, DP, Sanaboina, CS, Patan, R & Gandomi, A 1970, 'N-Gram-Based Machine Learning Approach for Bot or Human Detection from Text Messages', Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, ISMSI 2022: 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, ACM, pp. 80-85.
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Social bots are computer programs created for automating general human activities like the generation of messages. The rise of bots in social network platforms has led to malicious activities such as content pollution like spammers or malware dissemination of misinformation. Most of the researchers focused on detecting bot accounts in social media platforms to avoid the damages done to the opinions of users. In this work, n-gram based approach is proposed for a bot or human detection. The content-based features of character n-grams and word n-grams are used. The character and word n-grams are successfully proved in various authorship analysis tasks to improve accuracy. A huge number of n-grams is identified after applying different pre-processing techniques. The high dimensionality of features is reduced by using a feature selection technique of the Relevant Discrimination Criterion. The text is represented as vectors by using a reduced set of features. Different term weight measures are used in the experiment to compute the weight of n-grams features in the document vector representation. Two classification algorithms, Support Vector Machine, and Random Forest are used to train the model using document vectors. The proposed approach was applied to the dataset provided in PAN 2019 competition bot detection task. The Random Forest classifier obtained the best accuracy of 0.9456 for bot/human detection.
Kelly, R, Indraratna, B, Powrie, W, Zapata, C, Kikuchi, Y, Tutumluer, E & Correia, AG 1970, 'State of the Art on Transport Geotechnics', Proceedings of 20th International Conference on Soil Mechanics and Geotechnical Engineering, Australian Geomechanics Society, Sydney, pp. 41-41.
Khan, AF & Nanda, P 1970, 'Hybrid blockchain-based Authentication Handover and Flow Rule Validation for Secure Software Defined 5G HetNets', 2022 International Wireless Communications and Mobile Computing (IWCMC), 2022 International Wireless Communications and Mobile Computing (IWCMC), IEEE, pp. 223-230.
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Khan, IA & Hussain, FK 1970, 'Regression Analysis Using Machine Learning Approaches for Predicting Container Shipping Rates', Springer International Publishing, pp. 269-280.
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Khan, S & Hussain, FK 1970, 'Software-Defined Overlay Network Implementation and Its Use for Interoperable Mission Network in Military Communications', Springer International Publishing, pp. 554-565.
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Khatkar, J, Clemon, L & Mettu, R 1970, 'Toolpath Planning With Thermal Stress Awareness for Material Extrusion Additive Manufacturing', Volume 2A: Advanced Manufacturing, ASME 2022 International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers.
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Abstract Additive manufacturing has emerged as a next-generation technology for advanced fabrication. Fused Filament Fabrication (FFF) is the most widespread form of material extrusion additive manufacturing and has growing applications in large scale construction. Despite its advantages, FFF is limited by structural weaknesses introduced by cooling of the material between layers. This paper presents an approach to reduce the probability of failure for a given object under known loading conditions through improved toolpath planning which considers temperature decay. Our approach reorders the fabrication sequence to vary the time to print between layers such that the thermal stress induced in fabrication is reduced in regions most likely to fail at the expense of increasing thermally induced stress in less critical areas. In our simulation experiments, we found that our approach offers the greatest improvement when the rate of cooling is large enough for significant temperature decay to occur, but not so large that cooling occurs too quickly for the print order to have any effect. Our approach offers the potential to improve the performance of 3D printed components under known loading conditions by considering the temperature of the print in the planning of the toolpath.
Khatkar, J, Clemon, LM, Fitch, R & Mettu, R 1970, 'A Reeb Graph Approach for Faster 3D Printing.', CASE, 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), IEEE, pp. 277-282.
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Material extrusion additive manufacturing is an essential technology for rapid prototyping. The standard approach to planning the deposition toolpath for this technology builds each layer sequentially. Unfortunately this approach typically results in significant wasted motion, which is a barrier for use in industrial production. In this paper, we give a new method for toolpath planning that improves on the layer-based approach as well as our own previous methods that build toolpaths across layers. Our approach utilizes a Reeb decomposition on the input model, which is a geometric decomposition that allows toolpath planning over subcomponents of the model rather than over individual extrusion segments. This allows a top-down construction of toolpaths, and is highly effective. We test our new approach, which we call Reeb planning, over a benchmark of 50 models and achieve a reduction of 49.7% in wasted motion over standard layer-based methods. Our decomposition scheme also provides insight into model classification, which can be used for improved production planning.
Khatkar, J, Yoo, C, Fitch, R, Clemon, LM & Mettu, R 1970, 'Coordinated Toolpath Planning for Multi-Extruder Additive Manufacturing.', IROS, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 10230-10237.
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We present a new algorithm for coordinating the motion of multiple extruders to increase throughput in fused filament fabrication (FFF)/fused deposition modeling (FDM) additive manufacturing. Platforms based on FFF are commonly available and advantageous to several industries, but are limited by slow fabrication time and could be could be significantly improved through efficient use of multiple extruders. We propose the coordinated toolpath planning problem for systems of extruders mounted as end-effectors on robot arms with the objective of maximizing utilization and avoiding collisions. Building on the idea of dependency graphs introduced in our earlier work, we develop a planning and control framework that precomputes a set of multi-layer toolpath segments from the input model and efficiently assigns them to individual extruders such that executed toolpaths are collision-free. Our method overcomes key limitations of existing methods, including utilization loss from workspace partitioning, precomputed toolpaths subject to collisions with the partially fabricated object, and wasted motion resulting from strict layer-by-layer fabrication. We report simulation results that show a major increase in utilization compared to single and multi-extruder methods, and favorable fabrication results using commodity hardware that demonstrate the feasibility of our method in practice.
Khawaldeh, HA, Al-soeidat, M, Lu, DD-C & Li, L 1970, 'Power Loss Reduction for PV Emulator Using Transistor-based PV Model', 2022 IEEE Energy Conversion Congress and Exposition (ECCE), 2022 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, pp. 1-8.
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Recently, a photovoltaic (PV) emulator based on a combination of a constant current source and a semiconductor string, i.e., transistors or diodes, has demonstrated much faster dynamics than switching mode power supply (SMPS) based solution and shown also compatible performance with that of a real PV system. While it has high power efficiency at the maximum power point (MPP), the power loss of the emulator increases beyond the MPP and is at the highest at the open-circuit voltage (OCV) operation condition. This paper presents a hybrid solution where the semiconductor string works in the current-source region of the I-V curve and a new switching circuit, which sits in parallel with the semiconductor string, activates in the voltage-source region. Experimental results show that the efficiency and temperature of the PV emulator based on transistor string alone configuration reach 4.8% and 93.5°C, respectively, in the worst-case scenario, i.e., OCV condition, compared to 88.3% and 26.3°C, respectively, for the proposed solution. The switching circuit handles only a fraction of the rated emulator power and has much narrower control bandwidth requirement than pure switching converter based solution. A new control algorithm is proposed to manage the transition between the two regions seamlessly.
Khoi Tran, N, Sabir, B, Babar, MA, Cui, N, Abolhasan, M & Lipman, J 1970, 'ProML: A Decentralised Platform for Provenance Management of Machine Learning Software Systems', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 16th European Conference on Software Architecture (ECSA), Springer International Publishing, Prague, CZECH REPUBLIC, pp. 49-65.
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Large-scale Machine Learning (ML) based Software Systems are increasingly developed by distributed teams situated in different trust domains. Insider threats can launch attacks from any domain to compromise ML assets (models and datasets). Therefore, practitioners require information about how and by whom ML assets were developed to assess their quality attributes such as security, safety, and fairness. Unfortunately, it is challenging for ML teams to access and reconstruct such historical information of ML assets (ML provenance) because it is generally fragmented across distributed ML teams and threatened by the same adversaries that attack ML assets. This paper proposes ProML, a decentralised platform that leverages blockchain and smart contracts to empower distributed ML teams to jointly manage a single source of truth about circulated ML assets’ provenance without relying on a third party, which is vulnerable to insider threats and presents a single point of failure. We propose a novel architectural approach called Artefact-as-a-State-Machine to leverage blockchain transactions and smart contracts for managing ML provenance information and introduce a user-driven provenance capturing mechanism to integrate existing scripts and tools to ProML without compromising participants’ control over their assets and toolchains. We evaluate the performance and overheads of ProML by benchmarking a proof-of-concept system on a global blockchain. Furthermore, we assessed ProML’s security against a threat model of a distributed ML workflow.
Khwaji, A, Alsahafi, Y & Hussain, FK 1970, 'Conceptual Framework of Blockchain Technology Adoption in Saudi Public Hospitals Using TOE Framework', Springer International Publishing, pp. 78-89.
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Kiyani, A, Nasimuddin, N, Abbas, SM, Asadnia, M & Esselle, KP 1970, 'A Hybrid Design Technique for Realizing Metasurface based Wideband and Wide Dual-Band Circularly Polarized Dielectric Resonator Antennas', 2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC), 2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC), IEEE, pp. 1-4.
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Klettner, A, Sainty, R & Cetindamar Kozanoglu, D 1970, 'Corporate purpose as a signalling mechanism to facilitate and guide stakeholder governance', European Academy of Management, Winterthur, Switzerland, pp. 1-30.
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There are strong indications that acceptance of the shareholder primacy view of the corporation is on the decline and a stakeholder theory approach to corporate governance is becoming more mainstream. Yet we have very little idea on how stakeholder governance can be achieved in practice, nor how it might be understood theoretically. Certified B Corps are at the front of this movement with their commitment to achieving both profit and a positive impact on society and the environment. Through interviews with 18 B Corp leaders in Australia and New Zealand we explore emerging theories of stakeholder governance and how it interacts with corporate purpose. We use signalling theory to understand stakeholder governance as a proactive process of communication of priorities rather than a reactive process of stakeholder management. We find that an organisation-specific corporate purpose acts as a signal to pre-empt and prevent stakeholder conflicts. A unique corporate purpose makes conflicts less likely but also provides an ethical compass for decision-making in situations where conflict is unavoidable. Together, corporate purpose and a commitment to stakeholder governance raise the legitimacy of non-shareholder stakeholders and increase their relative salience.
Klettner, A, Sainty, R & Cetindamar Kozanoglu, D 1970, 'Corporate purpose as a signalling mechanism to facilitate and guide stakeholder governance', European Academy of Management Conference, EURAM, Zurich, Switzerland, pp. 1-30.
Knight, J, Johnston, A & Berry, A 1970, 'Machine Art: Exploring Abstract Human Animation Through Machine Learning Methods', Proceedings of the 8th International Conference on Movement and Computing, MOCO '22: 8th International Conference on Movement and Computing, ACM, pp. 1-7.
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Visual media and performance art have a symbiotic relationship. They support one another and engage the audience by providing an experience or telling a story. This comparative study explores the accuracy, efficiency, and cost factors of using machine learning based motion capture methods in performance art. There is extensive research in the field of machine learning methods for human pose estimation, but the outputs of such work are rarely used as inputs for performance art. In this paper we present a practice-based research project that involves producing animations that match a performer's movements using machine learning based motion capture methods. We use human poses derived from low-cost video capture as an input into high-resolution abstract forms that accompany and synchronise with dance performances. A single-camera approach is examined and compared to existing methods. We find that compared with existing motion capture methods the machine learning based methods require less setup time, and less equipment is required resulting in considerably lower cost. This research suggests that machine learning has considerable potential to improve the quality of human pose estimation in performance art, visual effects and motion capture, and make it more accessible for arts companies with limited resources.
Koh, Y, Goh, DJ, Choong, DSW, Chen, W, Chen, DS-H, Ng, EJ & Lee, JE-Y 1970, 'Trapping of Microbead Spheroids by pMUTs in Microfluidic Channels Embedded with an Acoustic Reflector', 2022 IEEE International Ultrasonics Symposium (IUS), 2022 IEEE International Ultrasonics Symposium (IUS), IEEE, pp. 1-4.
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Koli, MNY, Esselle, KP, Thalakotuna, DN, Afzal, MU & Islam, MZ 1970, 'Highly Efficient and Wideband Millimeter-Wave Slotted-Array Antenna Technology for 5G Communications', 2022 16th European Conference on Antennas and Propagation (EuCAP), 2022 16th European Conference on Antennas and Propagation (EuCAP), IEEE, pp. 1-4.
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Koli, NY, Esselle, KP, Mukhopadhyay, S & Islam, MZ 1970, 'Design and Performance Evaluation of a Compact Beam-Tilted Circularly Polarized Slotted Waveguide Antenna', 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/USNC-URSI), IEEE, pp. 1226-1227.
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Kong, Q, Calderon, P, Ram, R, Boichak, O & Rizoiu, M-A 1970, 'Interval-censored Transformer Hawkes: Detecting Information Operations using the Reaction of Social Systems', Proceedings of the ACM Web Conference 2023 (WWW '23), May 1--5, 2023, Austin, TX, USA.
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Social media is being increasingly weaponized by state-backed actors toelicit reactions, push narratives and sway public opinion. These are known asInformation Operations (IO). The covert nature of IO makes their detectiondifficult. This is further amplified by missing data due to the user andcontent removal and privacy requirements. This work advances the hypothesisthat the very reactions that Information Operations seek to elicit within thetarget social systems can be used to detect them. We propose anInterval-censored Transformer Hawkes (IC-TH) architecture and a novel dataencoding scheme to account for both observed and missing data. We derive anovel log-likelihood function that we deploy together with a contrastivelearning procedure. We showcase the performance of IC-TH on three real-worldTwitter datasets and two learning tasks: future popularity prediction and itemcategory prediction. The latter is particularly significant. Using theretweeting timing and patterns solely, we can predict the category of YouTubevideos, guess whether news publishers are reputable or controversial and, mostimportantly, identify state-backed IO agent accounts. Additional qualitativeinvestigations uncover that the automatically discovered clusters ofRussian-backed agents appear to coordinate their behavior, activatingsimultaneously to push specific narratives.
Kozanoglu, D, Chakraborty, S & Murad, MAU 1970, 'Public Procurement, Big Data Analytics Capabilities, and Healthcare Supply Chain Sustainability.', HICSS, ScholarSpace, pp. 1-8.
Krzywda, M, Lukasik, S & Gandomi, AH 1970, 'Graph Neural Networks in Computer Vision - Architectures, Datasets and Common Approaches', 2022 International Joint Conference on Neural Networks (IJCNN), 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, Padua, Italy, pp. 1-10.
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Graph Neural Networks (GNNs) are a family of graph networks inspired by mechanisms existing between nodes on a graph. In recent years there has been an increased interest in GNN and their derivatives, i.e., Graph Attention Networks (GAT), Graph Convolutional Networks (GCN), and Graph Recurrent Networks (GRN). An increase in their usability in computer vision is also observed. The number of GNN applications in this field continues to expand; it includes video analysis and understanding, action and behavior recognition, computational photography, image and video synthesis from zero or few shots, and many more. This contribution aims to collect papers published about GNN-based approaches towards computer vision. They are described and summarized from three perspectives. Firstly, we investigate the architectures of Graph Neural Networks and their derivatives used in this area to provide accurate and explainable recommendations for the ensuing investigations. As for the other aspect, we also present datasets used in these works. Finally, using graph analysis, we also examine relations between GNN-based studies in computer vision and potential sources of inspiration identified outside of this field.
Kuloğlu, B, Özkan, E & Shannon, AG 1970, 'The Narayana Sequence in Finite Groups', The Fibonacci Quarterly, Informa UK Limited, pp. 212-221.
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Kumar, A, Esmaili, N & Piccardi, M 1970, 'A Temperature-Modified Dynamic Embedded Topic Model', Communications in Computer and Information Science, Springer Nature Singapore, pp. 15-27.
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Topic models are natural language processing models that can parse large collections of documents and automatically discover their main topics. However, conventional topic models fail to capture how such topics change as the collections evolve. To amend this, various researchers have proposed dynamic versions which are able to extract sequences of topics from timestamped document collections. Moreover, a recently-proposed model, the dynamic embedded topic model (DETM), joins such a dynamic analysis with the representational power of word and topic embeddings. In this paper, we propose modifying its word probabilities with a temperature parameter that controls the smoothness/sharpness trade-off of the distributions in an attempt to increase the coherence of the extracted topics. Experimental results over a selection of the COVID-19 Open Research Dataset (CORD-19), the United Nations General Debate Corpus, and the ACL Title and Abstract dataset show that the proposed model – nicknamed DETM-tau after the temperature parameter – has been able to improve the model’s perplexity and topic coherence for all datasets.
Lai, J & Yang, Y 1970, 'A 3D Printing 120 GHz Lens Antenna for Terahertz 1D Beam-Scanning Applications', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 75-76.
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Lai, J, Sun, Y, Luo, Z & Yang, Y 1970, '3D Printed Lens Antenna for Contactless Heartbeat and Respiration Detection Using mm-Wave Radar Sensing', 2022 IEEE MTT-S International Microwave Biomedical Conference (IMBioC), 2022 IEEE MTT-S International Microwave Biomedical Conference (IMBioC), IEEE, ELECTR NETWORK, pp. 180-182.
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This paper proposes a 3D printed lens antenna for contactless heartbeat and respiration detection using the millimeter-wave (mm-wave) radar to improve the signal-To-noise ratio (SNR). The proposed device consists of an mm-wave radar and a 3D printed lens antenna. A 60 GHz pulsed coherent radar is integrated with the sensor module to receive reflected signals. A customized 3D printed lens is utilized to improve the directivity of radar radiation. In experiments, the proposed device can improve the SNR of the heartbeat and respiration and improve the accuracy of detected heartbeat and respiratory signals. The results prove the feasibility of the proposed solution in improving SNR for contactless heartbeat and respiration detection.
Lama, S & Pradhan, S 1970, 'Community Homestay Platform to enhance Sustainable Tourism in Developing Countries', Acis 2022 Australasian Conference on Information Systems Proceedings.
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Developing countries are increasingly embracing community homestays to provide an authentic cultural experience to tourists. While these engagements enable local communities' economic and social development, homestays face various challenges. Digital innovation and transformations can aid homestays in promoting, managing and creating a resilient post-pandemic business model. This study aims to co-design and co-develop a community homestay management information system for greater scalability and sustainability in assisting homestay management committees in maintaining, monitoring and sustaining an equitable economy. Initially, a conceptual diagram of the system has been proposed based on the information extracted from existing literature and field interviews. The elicitations of requirements help ascertain the scope of ICT use in homestay. Design science research methodology will be applied to co-develop an interactive prototype after iterative evaluation. This study advances the discourse of ICT use in community homestays by identifying the opportunities and challenges and conceptualising a community homestay management system.
Larpruenrudee, P, Bennett, NS, Hossain, J, Fitch, R & Islam, MS 1970, 'Hydrogen Energy Storage System: How does the semi-cylindrical helical coil heat exchanger affect metal hydride beds' thermal conductivity?', Australasian Fluid Mechanics Conference, Australasian Fluid Mechanics Conference, Sydney, Australia.
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Metal hydride (MH) is classified as one of the solid material storage technologies for hydrogen storage. This material has been recently used worldwide because of its ability to provide a large hydrogen storage capacity, low operating pressure and high safety. However, the disadvantage of this material is having low thermal conductivity, which leads to it having a slow hydrogen absorption time. For the absorption process, faster heat removal from the MH storage will result in faster absorption. Therefore, enhancing heat transfer performance is one of the most effective ways to improve storage performance. This paper aims to improve the heat transfer performance by employing a semi-cylindrical coil as a heat exchanger embedded inside the storage material. Air is used as the heat transfer fluid (HTF). A comparison of the hydrogen absorption duration and the bed temperature between the semi-cylindrical coil heat exchanger (SCHE) and the traditional helical coil heat exchanger (HCHE) has been made to investigate the effect of heat exchanger configuration designs. These two configurations are designed based on the constant volume of the heat exchanger tube and metal hydride. The numerical simulations are performed by using ANSYS Fluent 2020 R2. The results from this study indicate that the average bed temperature inside the storage by using SCHE is reduced faster than using HCHE, which leads to having a faster hydrogen absorption, approximately 59% time reduction. The key finding from this study could be an important enabler for industrial applications.
Le, VA, Nimbalkar, S, Zobeiry, N & Malek, S 1970, 'MULTI-SCALE VISCOELASTIC BENDING ANALYSIS OF LAMINATED COMPOSITES WITH SOFT INTERFACES', Eccm 2022 Proceedings of the 20th European Conference on Composite Materials Composites Meet Sustainability, pp. 755-762.
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This study investigates the bending behaviour of the orthotropic elastic and viscoelastic multi-layered plates with resin rich inter-plies to improve our understanding of the effects of shear deformation and ply slippage on wrinkle formation. This is accomplished by employing a three-dimensional (3D) multi-scale modelling framework that incorporates analysis at different scales (micro-, meso-, and macro-scale). The variation of resin viscoelastic characteristics at the early stage of cure and its effect on the bending properties of the composite is investigated numerically. The results highlight the importance of considering the material's rate dependency in describing the bending behaviour of composite prepregs accurately. Moreover, the bending response of the thin uncured prepregs is found to be dominated by their ply bending stiffness rather than inter-ply friction.
Lee, S & Chen, F 1970, 'Identifying the Effective Restriction and Vaccination Policies During the COVID-19 Crisis in Sydney: A Machine Learning Approach', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 34th Australasian Joint Conference on Artificial Intelligence (AI), Springer International Publishing, Univ Technol Sydney, ELECTR NETWORK, pp. 356-367.
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This study identified effective COVID-19 restriction policies and the best times to deploy them to minimise locally acquired COVID-19 cases in Sydney. We normalised stringency levels of individual COVID-19 policies, usage levels of urban mobility, and vaccination rates to establish unbiased multivariate time-series features. We introduced the time-lag from 1 day to 15 d before when the governments have officially announced the number of locally acquired COVID-19 cases to the multivariate features. This time-lag dimension allows us to decide critical timings for announcing various COVID-19 related policies and vaccinations to control rapidly increasing infections. We used principal component analysis (PCA) to reduce the dimensions of the multivariate features. A Gaussian process regression (GPR) estimated the daily number of locally acquired COVID-19 cases based on the reduced dimensional features. The model outperformed diverse parametric and non-parametric models in estimating the daily number of infections. We successfully identified effective restriction policies and the best times to implement them to minimise the rate of confirmed COVID-19 cases by analysing PCA coefficients and kernel functions in GPR.
Lee, S, Ngoduy, D & Chen, F 1970, 'Real-Time Prediction of the Lane-Based Delay for Group-Based Adaptive Traffic Operations Using Long Short-Term Memory', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 34th Australasian Joint Conference on Artificial Intelligence (AI), Springer International Publishing, Univ Technol Sydney, ELECTR NETWORK, pp. 417-427.
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This study proposes a deterministic real-time lane-based control delay model for traffic operations based on Long Short-Term Memory (LSTM). Our proposed framework includes a model-based approach to compute the control delay in an individual lane for a single cycle and a data-driven approach to predict the queueing profiles and adjustment factors used in the future control delay formula. This framework not only secures an excellent performance of the proposed model under a wide range of data availability but also guarantees a lower computational burden for a real-time non-linear optimisation process in adaptive control logic. The modified deep learning method has three primary components in the proposed architecture of the lane-based control delay model cycle-by-cycle. First, the data-driven and model-based approaches are integrated to improve the reliability and the accuracy of the control delay predictive formula. Second, the novel LSTM network is constructed to predict a cycle-based control delay in an individual lane while minimising inherent errors in the algorithm. Third, the predicted queue lengths at inflection points and adjustment factors are used to construct the delay polygons in the future cycle. Numerical simulations are set up using both synthetic and real-world data to give insights into the proposed model's performance compared to the existing models.
Lee, SS, Siwakoti, YP, Barzegarkhoo, R & Lee, K-B 1970, 'A Five-Level Unity-Gain Active Neutral-Point-Clamped Inverter Designed Using Half-Bridges', 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia), 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia), IEEE, pp. 859-866.
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This paper proposes a novel 5-level active neutral-point-clamped (ANPC) inverter that doubles the voltage gain of the conventional topology from half to unity. Each phase of the proposed topology is constituted by three half-bridges that control a flying capacitor to generate 5 symmetrical ac voltage levels. Natural voltage balancing of dc-link and flying capacitors in the proposed topology implies that the sensors and voltage balancing controller commonly used in the conventional ANPC inverter is no longer necessary. In addition to switch count reduction, the most noteworthy merit of the proposed topology is its ease of implementation with commercial half-bridge modules, where the design of dedicated circuit is not needed. The operation of the proposed 5-level unity-gain ANPC (5L-VG-ANPC) inverter is analyzed and validated through simulation and experimental tests.
Lestari, NI, Bekhit, M, Mohamed, MA, Fathalla, A & Salah, A 1970, 'Machine Learning and Deep Learning for Predicting Indoor and Outdoor IoT Temperature Monitoring Systems', Springer International Publishing, pp. 185-197.
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Li, A, Yang, B, Huo, H & Hussain, F 1970, 'Hypercomplex Graph Collaborative Filtering', Proceedings of the ACM Web Conference 2022, WWW '22: The ACM Web Conference 2022, ACM, pp. 1914-1922.
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Li, B, Guo, T, Li, R, Wang, Y, Gandomi, AH & Chen, F 1970, 'A Two-Stage Self-adaptive Model for Passenger Flow Prediction on Schedule-Based Railway System', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Springer International Publishing, SW Jiaotong Univ, Chengdu, PEOPLES R CHINA, pp. 147-160.
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Platform-level passenger flow prediction is crucial for addressing the overcrowding problem on platforms that endangered the passengers’ safety and experience in railway systems. Although some studies exist on this topic, it remains difficult to apply these methods in the real world e.g., the data deficiency in older railway systems, potential impacts of dynamic interchange passenger flows, real-time predictive ability. Thus, we propose a two-stage self-adaptive model for accurately and timely predicting platform-level flow. In the first stage, a self-attention-based prediction model is introduced to predict the next-day passenger flow based on the historical boarding record. The proposed decomposing components transferring the discrete boarding records into continuous patterns make the module able to deliver a robust minute-level prediction. In the second stage, a real-time fine-tuning model is developed to adjust the predicted flow based on the real-time emergencies in passenger flows. The combination of offline deep learning mechanism and real-time reallocation algorithm ensures the real-time response without loss of accuracy. The experiments show that our model can offer accurate predictions to trip planners for timetable design and provide timely decision support for controllers when emergencies happen, and our end-to-end framework has been applied to the railway system in Sydney, Australia.
Li, C, Liu, D, Li, H, Zhang, Z, Lu, G, Chang, X & Cai, W 1970, 'Domain Adaptive Nuclei Instance Segmentation and Classification via Category-Aware Feature Alignment and Pseudo-Labelling', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Springer Nature Switzerland, Singapore, SINGAPORE, pp. 715-724.
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Unsupervised domain adaptation (UDA) methods have been broadly utilized to improve the models’ adaptation ability in general computer vision. However, different from the natural images, there exist huge semantic gaps for the nuclei from different categories in histopathology images. It is still under-explored how could we build generalized UDA models for precise segmentation or classification of nuclei instances across different datasets. In this work, we propose a novel deep neural network, namely Category-Aware feature alignment and Pseudo-Labelling Network (CAPL-Net) for UDA nuclei instance segmentation and classification. Specifically, we first propose a category-level feature alignment module with dynamic learnable trade-off weights. Second, we propose to facilitate the model performance on the target data via self-supervised training with pseudo labels based on nuclei-level prototype features. Comprehensive experiments on cross-domain nuclei instance segmentation and classification tasks demonstrate that our approach outperforms state-of-the-art UDA methods with a remarkable margin.
Li, C, Yang, L, Yu, S, Qin, W & Ma, J 1970, 'SEMMI: Multi-party Security Decision-making Scheme Under the Internet of Medical Things', ICC 2022 - IEEE International Conference on Communications, ICC 2022 - IEEE International Conference on Communications, IEEE, pp. 2792-2797.
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In the Internet of Medical Things, the intelligent auxiliary decision-making system uses machine learning algorithms to analyze medical data for disease diagnosis, auxiliary intervention, and analysis and early warning. However, in the process of medical data transmission, processing, and storage, a large amount of private information is also at risk of leakage. Therefore, this article proposes a smart classification and decision-making program in the Internet of Medical Things scenario-SEMMI, which can effectively deal with the risk of data leakage in the process of medical data processing. At the same time, it reduces the huge computing and storage pressure caused by encryption and decryption operations in medical institutions. In the scheme, data collection, processing, transmission, storage and calculation are completed by ciphertext. In addition, in view of the relatively weak computing and storage capabilities of sensor nodes, we use chaos theory to construct a stream cipher algorithm to ensure the security of transmission from sensor to user; the homomorphic encryption algorithm is used to ensure the computability of the ciphertext and the security of storage. Through security analysis, it can be concluded that this scheme can resist attacks from adversaries; at the same time, the experimental results show that the scheme has good performance in terms of calculation, storage overhead, accuracy, and so on.
Li, C, Zhuang, B, Wang, G, Liang, X, Chang, X & Yang, Y 1970, 'Automated Progressive Learning for Efficient Training of Vision Transformers', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, New Orleans, LA, pp. 12476-12486.
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Recent advances in vision Transformers (ViTs) have come with a voracious appetite for computing power, highlighting the urgent need to develop efficient training methods for ViTs. Progressive learning, a training scheme where the model capacity grows progressively during training, has started showing its ability in efficient training. In this paper, we take a practical step towards efficient training of ViTs by customizing and automating progressive learning. First, we develop a strong manual baseline for progressive learning of ViTs, by introducing momentum growth (MoGrow) to bridge the gap brought by model growth. Then, we propose automated progressive learning (AutoProg), an efficient training scheme that aims to achieve lossless acceleration by automatically increasing the training overload on-the-fly; this is achieved by adaptively deciding whether, where and how much should the model grow during progressive learning. Specifically, we first relax the optimization of the growth schedule to sub-network architecture optimization problem, then propose one-shot estimation of the sub-network performance via an elastic supernet. The searching overhead is reduced to minimal by recycling the parameters of the supernet. Extensive experiments of efficient training on ImageNet with two representative ViT models, DeiT and VOLO, demonstrate that AutoProg can accelerate ViTs training by up to 85.1% with no performance drop.11Code:https://github.com/changlin31/AutoProg.
Li, J, Xie, J, Qian, L, Zhu, L, Tang, S, Wu, F, Yang, Y, Zhuang, Y & Wang, XE 1970, 'Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence Learning', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, New Orleans, LA, pp. 3022-3031.
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Temporal grounding in videos aims to localize one target video segment that semantically corresponds to a given query sentence. Thanks to the semantic diversity of natural language descriptions, temporal grounding allows activity grounding beyond pre-defined classes and has received increasing attention in recent years. The semantic diversity is rooted in the principle of compositionality in linguistics, where novel semantics can be systematically described by combining known words in novel ways (compositional generalization). However, current temporal grounding datasets do not specifically test for the compositional generalizability. To systematically measure the compositional generalizability of temporal grounding models, we introduce a new Compositional Temporal Grounding task and construct two new dataset splits, i.e., Charades-CG and ActivityNet-CG. Evaluating the state-of-the-art methods on our new dataset splits, we empirically find that they fail to generalize to queries with novel combinations of seen words. To tackle this challenge, we propose a variational cross-graph reasoning framework that explicitly decomposes video and language into multiple structured hierarchies and learns fine-grained semantic correspondence among them. Experiments illustrate the superior compositional generalizability of our approach. The repository of this work is at ht tps: / / gi thub. com/YYJMJC/ Composi tional- Temporal-Grounding.
Li, J, Xie, J, Zhu, L, Qian, L, Tang, S, Zhang, W, Shi, H, Zhang, S, Wei, L, Tian, Q & Zhuang, Y 1970, 'Dilated Context Integrated Network with Cross-Modal Consensus for Temporal Emotion Localization in Videos', Proceedings of the 30th ACM International Conference on Multimedia, MM '22: The 30th ACM International Conference on Multimedia, ACM, pp. 5083-5092.
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Li, J, Yao, L, Li, B, Wang, X & Sammut, C 1970, 'Multi-agent Transformer Networks for Multimodal Human Activity Recognition', Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM '22: The 31st ACM International Conference on Information and Knowledge Management, ACM, pp. 1135-1145.
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Human activity recognition has become an important challenge yet to resolve while also having promising benefits in various applications for years. Existing approaches have made great progress by applying deep-learning and attention-based methods. However, the deep learning-based approaches may not fully exploit the features to resolve multimodal human activity recognition tasks. Also, the potential of attention-based methods still has not been fully explored to better extract the multimodal spatial-temporal relationship and produce robust results. In this work, we propose Multi-agent Transformer Network (MATN), a multi-agent attention-based deep learning algorithm, to address the above issues in multimodal human activity recognition. We first design a unified representation learning layer to encode the multimodal data, which preprocesses the data in a generalized and efficient way. Then we develop a multimodal spatial-temporal transformer module that applies the attention mechanism to extract the salient spatial-temporal features. Finally, we use a multi-agent training module to collaboratively select the informative modalities and predict the activity labels. We have extensively conducted experiments to evaluate MATN's performance on two public multimodal human activity recognition datasets. The results show that our model has achieved competitive performance compared to the state-of-the-art approaches, which also demonstrates scalability, effectiveness, and robustness.
Li, JJ & Little, C 1970, 'WORKING TOWARDS A REGENERATIVE THERAPY FOR OSTEOARTHRITIS: INFLUENCE OF STEM CELLS', TISSUE ENGINEERING PART A, MARY ANN LIEBERT, INC, pp. S89-S90.
Li, JJ, Yang, Y & Xing, D 1970, 'The role of microRNA-1 in cartilage repair and osteoarthritis', TISSUE ENGINEERING PART A, Tissue-Engineering-and-Regenerative-Medicine-International-Society-Asia-Pacific-Chapter Conference (TERMIS-AP), MARY ANN LIEBERT, INC, SOUTH KOREA, Jeju, pp. 270-270.
Li, K, Cui, Q, Zhu, Z, Ni, W & Tao, X 1970, 'Lightweight, Privacy-Preserving Handover Authentication for Integrated Terrestrial-Satellite Networks', ICC 2022 - IEEE International Conference on Communications, ICC 2022 - IEEE International Conference on Communications, IEEE, pp. 25-31.
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Li, K, Lu, J, Zuo, H & Zhang, G 1970, 'Source-Free Multi-Domain Adaptation with Generally Auxiliary Model Training', 2022 International Joint Conference on Neural Networks (IJCNN), 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, Padua, Italy, pp. 1-8.
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Unsupervised domain adaptation transfers gained knowledge from labeled source domain(s) to a similar unlabeled target domain by eliminating the domain shifts. Most existing domain adaptation methods require the access to source data to match the source and target distributions. However, data privacy concerns make it difficult or impossible to share source data, leading to failures in existing domain adaptation methods. Admittedly, a few previous studies deal with domain adaptation without source data, but they rarely pay heed to data free domain adaptation with multiple source domains containing richer knowledge. In this paper, we propose a new multi-source data-free domain adaptation method- generally auxiliary model training (GAM)- which fits the source models to the target domain under the supervision of pseudo target labels rather than matching data distributions. To collect high-quality initial pseudo target labels, our approach learns both specific and general source models to improve the generality of source models based on auxiliary learning. Going further, we introduce a class balanced coefficient of each category based on the number of samples to reduce the misclassification often caused by data imbalance. Experiments on real-world classification datasets show that the propsosed generally auxiliary training has a superiority over the baselines.
Li, K, Ni, W, Kurunathan, H & Dressler, F 1970, 'Data-driven Deep Reinforcement Learning for Online Flight Resource Allocation in UAV-aided Wireless Powered Sensor Networks', ICC 2022 - IEEE International Conference on Communications, ICC 2022 - IEEE International Conference on Communications, IEEE, pp. 1-6.
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Li, M, Cai, W, Verspoor, K, Pan, S, Liang, X & Chang, X 1970, 'Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, New Orleans, LA, pp. 20624-20633.
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Automatic generation of ophthalmic reports using datadriven neural networks has great potential in clinical practice. When writing a report, ophthalmologists make inferences with prior clinical knowledge. This knowledge has been neglected in prior medical report generation methods. To endow models with the capability of incorporating expert knowledge, we propose a Cross-modal clinical Graph Transformer (CGT) for ophthalmic report generation (ORG), in which clinical relation triples are injected into the visual features as prior knowledge to drive the decoding procedure. However, two major common Knowledge Noise (KN) issues may affect models' effectiveness. 1) Existing general biomedical knowledge bases such as the UMLS may not align meaningfully to the specific context and language of the report, limiting their utility for knowledge injection. 2) Incorporating too much knowledge may divert the visual features from their correct meaning. To overcome these limitations, we design an automatic information extraction scheme based on natural language processing to obtain clinical entities and relations directly from in-domain training reports. Given a set of ophthalmic images, our CGT first restores a sub-graph from the clinical graph and injects the restored triples into visual features. Then visible matrix is employed during the encoding procedure to limit the impact of knowledge. Finally, reports are predicted by the encoded cross-modal features via a Transformer decoder. Extensive experiments on the large-scale FFA-IR benchmark demonstrate that the proposed CGT is able to outperform previous benchmark methods and achieve state-of-the-art performances.
Li, M, Liu, J, Hu, Z & Yang, Y 1970, '3D Broadband FSS with Through Holes and Low Profile for UHF and SHF Applications', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 443-444.
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Modern communication systems need low-frequency devices with high gain and wide operational bands. This paper proposes a frequency selective surface (FSS) with a wide passband and low profile, which can be additively manufactured. Each unit of the FSS consists of a centre cube and four surrounding walls with two metal layers covering the top and bottom sides. Through drills are introduced in the design to improve the return loss and the insertion loss in the operational band. The proposed FSS prototype is designed and can be fabricated in a single substrate with a multi-material additively manufacturing technology, and its performance is verified in simulation. It resonates at 3.75 GHz with a fractional bandwidth of 29.3%. Good out-of-band suppression is obtained as well
Li, Q, Tian, P, Shi, Y, Shi, Y & Tuan, HD 1970, 'Distributionally Robust Optimization for Vehicle-to-grid with Uncertain Renewable Energy', 2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS), 2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS), IEEE, pp. 462-467.
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Li, S, Phillips, JM, Yu, X, Kirby, RM & Zhe, S 1970, 'Batch Multi-Fidelity Active Learning with Budget Constraints', Advances in Neural Information Processing Systems.
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Learning functions with high-dimensional outputs is critical in many applications, such as physical simulation and engineering design. However, collecting training examples for these applications is often costly, e.g., by running numerical solvers. The recent work (Li et al., 2022) proposes the first multi-fidelity active learning approach for high-dimensional outputs, which can acquire examples at different fidelities to reduce the cost while improving the learning performance. However, this method only queries at one pair of fidelity and input at a time, and hence has a risk to bring in strongly correlated examples to reduce the learning efficiency. In this paper, we propose Batch Multi-Fidelity Active Learning with Budget Constraints (BMFAL-BC), which can promote the diversity of training examples to improve the benefit-cost ratio, while respecting a given budget constraint for batch queries. Hence, our method can be more practically useful. Specifically, we propose a novel batch acquisition function that measures the mutual information between a batch of multi-fidelity queries and the target function, so as to penalize highly correlated queries and encourages diversity. The optimization of the batch acquisition function is challenging in that it involves a combinatorial search over many fidelities while subject to the budget constraint. To address this challenge, we develop a weighted greedy algorithm that can sequentially identify each (fidelity, input) pair, while achieving a near (1-1/e)-approximation of the optimum. We show the advantage of our method in several computational physics and engineering applications.
Li, T, Bai, J-J, Sui, Y & Hu, S-M 1970, 'Path-sensitive and alias-aware typestate analysis for detecting OS bugs', Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS '22: 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ACM, pp. 859-872.
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Operating system (OS) is the cornerstone for modern computer systems. It manages devices and provides fundamental service for user-level applications. Thus, detecting bugs in OSes is important to improve reliability and security of computer systems. Static typestate analysis is a common technique for detecting different types of bugs, but it is often inaccurate or unscalable for large-size OS code, due to imprecision of identifying alias relationships as well as high costs of typestate tracking and path-feasibility validation. In this paper, we present PATA, a novel path-sensitive and aliasaware typestate analysis framework to detect OS bugs. To improve the precision of identifying alias relationships in OS code, PATA performs a path-based alias analysis based on control-flow paths and access paths. With these alias relationships, PATA reduces the costs of typestate tracking and path-feasibility validation, to boost the efficiency of path-sensitive typestate analysis for bug detection. We have evaluated PATA on the Linux kernel and three popular IoT OSes (Zephyr, RIOT and TencentOS-Tiny) to detect three common types of bugs (null-pointer dereferences, uninitialized variable accesses and memory leaks). PATA finds 574 real bugs with a false positive rate of 28%. 206 of these bugs have been confirmed by the developers of the four OSes.We also compare PATA to seven state-of-The-Art static approaches (Cppcheck, Coccinelle, Smatch,CSA, Infer, Saber and SVF). PATA finds many real bugs missed by them, with a lower false positive rate.
Li, W, Dong, W & Shah, SP 1970, 'Piezoresistivity of Carbon Black/Cement-Based Sensor Enhanced with Polypropylene Fibre', Springer International Publishing, pp. 889-899.
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In this study, polypropylene (PP) was added to develop carbon black (CB)/cementitious composites as cement-based sensors. The mechanical properties and piezoresistivity were been experimentally investigated. The compressive strength slightly decreased, while the flexural strength was significantly increased with the increased amount of PP fibres. The improvement is mainly achieved by the reduced CB concentration in cement matrix and the excellent tensile strength of PP fibres. Under the cyclic compression, the piezoresistivity increased by three times for 0.4 wt% PP fibres filled CB/cementitious composite, regardless of the loading rates. The flexural stress sensing efficiency was considerably lower than that of compressive stress sensing, but it increased with the amount of PP fibres. Electrical conductivity increased with the amount of PP fibres, due to the enclosed CB nanoparticles and more conductive passages. Moreover, fitting formulas were proposed and used to evaluate the self-sensing capacity, with the attempts to apply cement-based sensors for structural health monitoring.
Li, W, Gao, M, Wen, D, Zhou, H, Ke, C & Qin, L 1970, 'Manipulating Structural Graph Clustering.', ICDE, 38th IEEE International Conference on Data Engineering (ICDE), IEEE, ELECTR NETWORK, pp. 2749-2761.
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Structural graph clustering (SCAN) is a popular clustering technique. Using the concept of ?-neighborhood, SCAN defines the core vertices that uniquely determine the clusters of a graph. Most existing studies assume that the graph processed by SCAN contains no controlled edges. Few studies, however, have focused on manipulating SCAN by injecting edges. Manipulation of SCAN can be used to assess its robustness and lay the groundwork for developing robust clustering algorithms. To fill this gap and considering the importance of the ?-neighborhood for SCAN, we propose a problem, denoted as MN, for manipulating SCAN. The MN problem aims to maximize the ?-neighborhood of the target vertex by inserting some edges. On the theoretical side, we prove that the MN problem is both NP-hard and APX-hard, and also is non-submodular and non-monotonic. On the algorithmic side, we design an algorithm by focusing on how to select vertices to join ?-neighborhood and thus avoid enumerating edges to report a solution. As a result, our algorithm bypasses the non-monotonicity nature of the MN problem. Extensive experiments on real-world graphs show that our algorithm can effectively solve the proposed MN problem.
Li, W, Qiao, M, Qin, L, Chang, L, Zhang, Y & Lin, X 1970, 'On Scalable Computation of Graph Eccentricities.', SIGMOD Conference, International Conference on Management of Data (SIGMOD), ACM, Philadelphia, PA, pp. 904-916.
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Given a graph, eccentricity measures the distance from each node to its farthest node. Eccentricity indicates the centrality of each node and collectively encodes fundamental graph properties: the radius and the diameter-the minimum and maximum eccentricity, respectively, over all the nodes in the graph. Computing the eccentricities for all the graph nodes, however, is challenging in theory: any approach shall either complete in quadratic time or introduce a 1/3 relative error under certain hypotheses. In practice, the state-of-the-art approach PLLECC in computing exact eccentricities relies heavily on a precomputed all-pair-shortest-distance index whose expensive construction refrains PLLECC from scaling up. This paper provides insights to enable scalable exact eccentricity computation that does not rely on any index. The proposed algorithm IFECC handles billion-scale graphs that no existing approach can process and achieves up to two orders of magnitude speedup over PLLECC. As a by-product, IFECC can be terminated at any time during execution to produce approximate eccentricities, which is empirically more stable and reliable than KBFS, the state-of-the-art algorithm for approximately computing eccentricities.
Li, X, Cui, Q, Xue, Q, Ni, W, Guo, J & Tao, X 1970, 'A New Batch Access Scheme with Global QoS Optimization for Satellite-Terrestrial Networks', GLOBECOM 2022 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, IEEE, pp. 3929-3934.
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Li, X, Liu, H, Li, C, Chen, G & Wen, S 1970, 'PPO-based Pricing Method for Shared Energy Storage System', 2022 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia), 2022 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia), IEEE, pp. 424-428.
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The shared energy storage system has the potential to promote the popularity of the battery energy storage system (BESS). In a shared energy storage system, prosumers could rent capacity and optimize its operation, whereas the operator also seeks to maximize the revenue of the BESS from both rental service and the virtual power plant (VPP) market. To optimize the pricing policy of the BESS, a novel pricing method based on deep reinforcement learning (DRL) is proposed for this energy storage rental service. The interaction between the BESS operator and prosumers is formulated as a bi-level optimization problem, which is further reformulated as a Markov decision process (MDP) and solved through the proximal policy optimization (PPO)-based DRL method. The case study shows that the proposed method could further increase the revenues of the BESS operator.
Li, Y, Aghvami, AH & Dong, D 1970, 'Path Planning for Cellular-Connected UAV: A DRL Solution With Quantum-Inspired Experience Replay', IEEE Transactions on Wireless Communications, Institute of Electrical and Electronics Engineers (IEEE), pp. 7897-7912.
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Li, Y, Bei, X, Qiao, Y, Tao, D & Chen, Z 1970, 'Heterogeneous Multi-commodity Network Flows over Time', Computer Science – Theory and Applications, Springer International Publishing, pp. 238-255.
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In the 1950’s, Ford and Fulkerson introduced dynamic flows by incorporating the notion of time into the network flow model (Oper. Res., 1958). In this paper, motivated by real-world applications including route planning and evacuations, we extend the framework of multi-commodity dynamic flows to the heterogeneous commodity setting by allowing different transit times for different commodities along the same edge. We first show how to construct the time-expanded networks, a classical technique in dynamic flows, in the heterogeneous setting. Based on this construction, we give a pseudopolynomial-time algorithm for the quickest flow problem when there are two heterogeneous commodities. We then present a fully polynomial-time approximation scheme when the nodes have storage for any number of heterogeneous commodities. The algorithm is based on the condensed time-expanded network technique introduced by Fleischer and Skutella (SIAM J. Comput., 2007).
Li, Y, Hirche, C & Tomamichel, M 1970, 'Sequential Quantum Channel Discrimination', 2022 IEEE International Symposium on Information Theory (ISIT), 2022 IEEE International Symposium on Information Theory (ISIT), IEEE, pp. 270-275.
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Li, Y, Long, G, Shen, T & Jiang, J 1970, 'Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision', Findings of the Association for Computational Linguistics: NAACL 2022, Findings of the Association for Computational Linguistics: NAACL 2022, Association for Computational Linguistics, pp. 316-326.
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Distant supervision uses triple facts in knowledge graphs to label a corpus for relation extraction, leading to wrong labeling and longtail problems. Some works use the hierarchy of relations for knowledge transfer to longtail relations. However, a coarse-grained relation often implies only an attribute (e.g., domain or topic) of the distant fact, making it hard to discriminate relations based solely on sentence semantics. One solution is resorting to entity types, but open questions remain about how to fully leverage the information of entity types and how to align multi-granular entity types with sentences. In this work, we propose a novel model to enrich distantlysupervised sentences with entity types. It consists of (1) a pairwise type-enriched sentence encoding module injecting both context-free and -related backgrounds to alleviate sentencelevel wrong labeling, and (2) a hierarchical type-sentence alignment module enriching a sentence with the triple fact's basic attributes to support long-tail relations. Our model achieves new state-of-the-art results in overall and long-tail performance on benchmarks.
Li, Y, Verma, S, Yang, S, Zhou, J & Chen, F 1970, 'Are Graph Neural Network Explainers Robust to Graph Noises?', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 161-174.
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With the rapid deployment of graph neural networks (GNNs) based techniques in a wide range of applications such as link prediction, community detection, and node classification, the explainability of GNNs become an indispensable component for predictive and trustworthy decision making. To achieve this goal, some recent works focus on designing explainable GNN models such as GNNExplainer, PGExplainer, and Gem. These GNN explainers have shown remarkable performance in explaining the predictive results from GNNs. Despite their success, the robustness of these explainers is less explored in terms of vulnerabilities of GNN explainers. Graph perturbations such as adversarial attacks can lead to inaccurate explanations and consequently cause catastrophes. Thus, in this paper, we take the first step and strive to explore the robustness of GNN explainers. To be specific, we first define two adversarial attack scenarios—aggressive adversary and conservative adversary to contaminate graph structures. We then investigate the impacts of the poisoned graphs on the explainability of three prevalent GNN explainers with three standard evaluation metrics: Fidelity+, Fidelity-, and Sparsity. We conduct experiments on synthetic and real-world datasets and focus on two popular graph mining tasks: node classification and graph classification. Our empirical results suggest that GNN explainers are generally not robust to the adversarial attacks caused by graph structural noises.
Li, Z & Luo, Z 1970, 'Topology Optimization for Pentamode Lattice Metamaterial', 15th World Congress on Computational Mechanics, 15th World Congress on Computational Mechanics, Yokohama.
Li, Z, Wang, X, Yao, L, Chen, Y, Xu, G & Lim, E-P 1970, 'Graph Neural Network with Self-attention and Multi-task Learning for Credit Default Risk Prediction', International Conference on Web Information Systems Engineering, International Conference on Web Information Systems Engineering, Springer International Publishing, Biarritz, France, pp. 616-629.
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Li, Z, Zeng, J, Zhang, W, Zhou, S & Liu, RP 1970, '6G mURLLC over Cell-Free Massive MIMO Systems in the Finite Blocklength Regime', Springer International Publishing, pp. 425-437.
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Liang, J, Zeng, H & Zhang, L 1970, 'Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 5647-5656.
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Liang, L, Lin, X, Ma, B, Wang, X, He, Y, Liu, RP & Ni, W 1970, 'Leveraging Byte-Level Features for LSTM-based Anomaly Detection in Controller Area Networks', GLOBECOM 2022 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, IEEE, Rio de Janeiro, Brazil, pp. 4903-4908.
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Liang, Y, Feng, Q, Zhu, L, Hu, L, Pan, P & Yang, Y 1970, 'SEEG: Semantic Energized Co-speech Gesture Generation', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, New Orleans, LA, pp. 10463-10472.
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Talking gesture generation is a practical yet challenging task that aims to synthesize gestures in line with speech. Gestures with meaningful signs can better convey useful information and arouse sympathy in the audience. Current works focus on aligning gestures with the speech rhythms, which are difficult to mine the semantics and model semantic gestures explicitly. This paper proposes a novel semantic Energized Generation (SEEG) method for semantic-aware gesture generation. Our method contains two parts: DEcoupled Mining module (DEM) and Semantic Energizing Module (SEM). DEM decouples the semantic-irrelevant information from inputs and separately mines information for the beat and semantic gestures. SEM conducts semantic learning and produces semantic gestures. Apart from representational similarity, SEM requires the predictions to express the same semantics as the ground truth. Besides, a semantic prompter is designed in SEM to leverage the semantic-aware supervision to predictions. This promotes the networks to learn and generate semantic gestures. Experimental results reported in three metrics on different benchmarks prove that SEEG efficiently mines semantic cues and generates semantic gestures. SEEG outperforms other methods in all semantic-aware evaluations on different datasets. Qualitative evaluations also indicate the superiority of SEEG in semantic expressiveness. Code is available via https://github.com/akira-l/SEEG.
Liang, Y, Zhu, L, Wang, X & Yang, Y 1970, 'A Simple Episodic Linear Probe Improves Visual Recognition in the Wild', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, New Orleans, LA, pp. 9549-9559.
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Understanding network generalization and feature discrimination is an open research problem in visual recognition. Many studies have been conducted to assess the quality of feature representations. One of the simple strategies is to utilize a linear probing classifier to quantitatively evaluate the class accuracy under the obtained features. The typical linear probe is only applied as a proxy at the inference time, but its efficacy in measuring features' suitability for linear classification is largely neglected in training. In this paper, we propose an episodic linear probing (ELP) classifier to reflect the generalization of visual rep-resentations in an online manner. ELP is trained with detached features from the network and re-initialized episodically. It demonstrates the discriminability of the visual representations in training. Then, an ELP-suitable Regularization term (ELP-SR) is introduced to reflect the distances of probability distributions between the ELP classifier and the main classifier. ELP-SR leverages are-scaling factor to regularize each sample in training, which modulates the loss function adaptively and encourages the features to be discriminative and generalized. We observe significant improvements in three real-world visual recognition tasks: fine-grained visual classification, long-tailed visual recognition, and generic object recognition. The performance gains show the effectiveness of our method in im-proving network generalization and feature discrimination.
Lin, L-X, Tu, Z-H & Zhu, H 1970, 'Isolation Enhancement in Millimeter-wave MIMO Array Base on Array-Antenna Decoupling Surface', 2022 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP), 2022 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP), IEEE, pp. 1-3.
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Lin, S, Xie, H, Wang, B, Yu, K, Chang, X, Liang, X & Wang, G 1970, 'Knowledge Distillation via the Target-aware Transformer', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, New Orleans, LA, pp. 10905-10914.
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Knowledge distillation becomes a de facto standard to improve the performance of small neural networks. Most of the previous works propose to regress the representational features from the teacher to the student in a one-to-one spatial matching fashion. However, people tend to overlook the fact that, due to the architecture differences, the semantic information on the same spatial location usually vary. This greatly undermines the underlying assumption of the one-to-one distillation approach. To this end, we propose a novel one-to-all spatial matching knowledge distillation approach. Specifically, we allow each pixel of the teacher feature to be distilled to all spatial locations of the student features given its similarity, which is generated from a target-aware transformer. Our approach surpasses the state-of-the-art methods by a significant margin on various computer vision benchmarks, such as ImageNet, Pascal VOC and COCOStuff10k. Code is available at https://github.com/sihaoevery/TaT.
Lin, X, Ma, B, Wang, X, He, Y, Liu, RP & Ni, W 1970, 'Multi-layer Reverse Engineering System for Vehicular Controller Area Network Messages', 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), IEEE, pp. 1185-1190.
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The undisclosed Controller Area Network (CAN) decoding specification is important to the in-vehicle network (IVN) research for both industry and academia. Researchers have developed several CAN reverse engineering systems to predict signal boundaries and labels in order to map out CAN signal decoding specifications. Existing works mainly use one parameter (i.e., bit flip rate) to determine CAN signals boundary, which results in biased slicing and labelling of CAN signals. In this paper, we propose a multi-layer CAN reverse engineering system to cluster signal boundary at byte-level and label sliced CAN signal blocks at bit-level. The proposed system avoids biased signal slicing and labelling by introducing multiple parameters in signal classification, while existing works only use the bit flip rate and the number of unique value. The feasibility and adaptability of the proposed system is assessed by deploying it into a web application as a functionality module. We evaluate the proposed system with CAN messages from real cars. Compared with existing reverse engineering models, the proposed system introduces multi-layer signal processing to avoid over-slicing and over-labelling problem.
Lin, Y, Li, L, Zhang, J & Wang, J 1970, 'A model predictive control approach for cotton farm microgrid operation under uncertainties', 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), IEEE, pp. 1-6.
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Lindeck, J, Boye, T, Marjanovic, O & Dimasi, T 1970, 'Evaluating In-house Work Integrated Learning Experiences Using the Business Model Canvas', Annual Conference of the Australasian Association for Engineering Education, Australasian Association for Engineering Education, Paramatta, NSW, pp. 1-9.
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CONTEXTThe school of Professional Practice and Leadership at UTS set up Optik Consultancy to provide students unable to access internships, with engineering projects set up by industry partners in a simulated workplace. In 2021, in the midst of the COVID-19 crisis, 120 students (85 international and 35 domestic) completed Work Integrated Learning (WIL) in this manner. This was the 5th iteration of the project with the number of students increasing each year. This model has the potential to be extended to other groups such as refugees needing existing qualifications validated, or engineers returning to the workplace after an extended absence. To do this successfully, it is necessary to ensure the program meets participants’ requirements. This requires recognition of the complexity of the program and the development of a framework to ensure all elements that make a successful program are in place.PURPOSE OR GOALThis paper analyses the Optik Consultancy through the lens of the ‘Business Model Canvas’ (Osterwalder & Pigneur (2010). As illustrated by Kline et al (2017), this framework can be adapted to design a template to meet the specific needs of educational projects. We aim to analyse the main activities and processes of the Optik Consultancy and redesign the Business Model Canvas for WIL engineering projects to identify the elements necessary for designing a similar project in other settings.APPROACHFirstly, we will investigate the Optik Consultancy through the lens of the ‘Business Model Canvas. This will enable us to identify key areas relevant to a simulated internship program in order to form an engineering WIL canvas. This canvas will explain what we do, how we do it and why. We will then apply our new canvas to the Optik Consultancy to see how far it conforms to our template. Finally, we will conceptualise a new canvas that can be replicated as a template for setting up similar programs in other disciplines.ACTUAL OR ANTICIPATED OUTCOMESBy ...
Lindeck, J, Daniel, S, Whelan, K, Rhodes-Dicker, L, Machet, T, Cheng, E, Brown, N, Boye, T & Bhatia, T 1970, 'Adopting an integrated inclusion practice: Preliminaryfindings and reflections', AAEE 2022 Online Proceedngs, Annual Conference of the Australasian Association for Engineering Education, AAEE, Paramatta, NSW, pp. 1-9.
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CONTEXTFor the engineering profession to tackle global challenges, it needs engineering teams with diversebackgrounds and life experiences. However, the engineering profession in Australia lacks inclusion,and does not reflect Australian society. This paper reviews the adoption of an integrated inclusionpractice developed at three Australian universities and presents preliminary findings of fostering inclusion and belonging in engineering students in first-year classrooms. The paper reports researchprogress from an AAEE grant awarded in 2021.PURPOSEThis project aims to cultivate an inclusive learning experience for engineering students, and to enable the development of students’ inclusion competencies. This will involve an iterative cycle ofcontextualising, delivering, reflecting, and improving a combined approach to an integrated inclusion practice. This paper reports reflections and findings from the first iteration of this approach.APPROACHPrevious approaches to fostering inclusivity focused on activities or directions unrelated to the content or context of the unit of study. These activities frequently addressed inclusion as a single eventrather than an ongoing process. The onus of change was placed on the underrepresented minorities. We believe inclusion needs to involve everyone through a whole-of-program approach, with ashared direction throughout the teaching team. The inclusion program involves the development ofinclusive teaching environments and teaching activities.OUTCOMESSurveys show that students see inclusive work environments as an important part of the engineering profession. However, they may not feel confident in creating an inclusive environment. It is interesting to note that many students do not identify as part of the engineering or IT professions.SUMMARYInclusion involves the creation of an atmosphere where diversity is expected, rather than one offactivities aimed at promoting inclusion. Approaches to inclusion ...
Lindeck, J, Machet, T, Boye, T, Cheng, E, Daniel, S & Bhatia, T 1970, 'Identifying and developing the factors necessary for the creation of functional groups', 9th Research in Engineering Education Symposium (REES 2021) and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021), 9th Research in Engineering Education Symposium & 32nd Australasian Association for Engineering Education Conference, Research in Enineering Education Network (REEN), Perth, WA, pp. 651-660.
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In 2020, research was carried out into three, group-work based engineering and IT
undergraduate subjects each with approximately 600 students. The research was focused on
students’ experience of online group work, and what emerged were several factors that
contributed to developing a capacity for successful group work online. These factors included
common expectations amongst group members, students’ confidence in themselves and
fellow group members, and a strategic approach to task completion.
PURPOSE OR GOAL
Students frequently find group work assessments challenging and unenjoyable due to
reasons unconnected to the assessment itself. Tensions within the group may result in
students not participating in the task, disengaging from the group work, and in extreme cases
dropping out of the subject. Meanwhile, other students have to pick up the slack and
complete the remaining work. Factors such as group trust, individual attitude and aligned
motivation have been identified as indicators of successful group work. We aim to further
understand the conditions necessary to creating functional groups and to use this knowledge
to develop tools and activities to help create functional groups.
APPROACH OR METHODOLOGY/METHODS
Over three semesters, focus groups of first- and second-year students in subjects requiring
group work discussed factors contributing to their group’s success or failure. Focus groups
were also run with tutors to determine features they considered important in creating
successful groups. The data was analysed for themes that indicated the factors that support
and inhibit the development of functional groups. These results have been used to adopt
tools and develop activities to improve group dynamics which will be used in future classes.
ACTUAL OR ANTICIPATED OUTCOMES
This research provides further indications of the elements contributing to group achievement.
It has given insight into conditions that need to be avoided for groups to succeed. The
literat...
Lister, R 1970, 'Some thoughts on designing eye movement studies for novice programmers', Proceedings of the Tenth International Workshop on Eye Movements in Programming, ICSE '22: 44th International Conference on Software Engineering, ACM, pp. 15-22.
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Litvinov, A, Gardner, A, Pradhan, S & Childers, J 1970, 'Beyond planned learning objectives: Entrepreneurial education as the source of accidental competencies for engineering students', 9th Research in Engineering Education Symposium (REES 2021) and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021), 9th Research in Engineering Education Symposium & 32nd Australasian Association for Engineering Education Conference, Research in Enineering Education Network (REEN), pp. 472-480.
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Litvinov, A, Gardner, A, Pradhan, S & Childers, J 1970, 'The role of empathic experiences of entrepreneurial engineers within accelerators: a phenomenological study', 2022 IEEE Frontiers in Education Conference (FIE), 2022 IEEE Frontiers in Education Conference (FIE), IEEE, pp. 1-7.
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Liu, A, Lin, S, Wang, J & Kong, X 1970, 'A Method for Non-line of Sight Identification and Delay Correction for UWB Indoor Positioning', 2022 IEEE 17th Conference on Industrial Electronics and Applications (ICIEA), 2022 IEEE 17th Conference on Industrial Electronics and Applications (ICIEA), IEEE, pp. 9-14.
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Liu, C, Chao, Z, Wang, S, Wang, Y, Lei, G, Guo, Y & Zhu, J 1970, 'Design and Performance Analysis of a Permanent Magnet Claw Pole Machine with Hybrid Cores', 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC), 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC), IEEE, pp. 1-2.
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Permanent magnet claw pole machine (PMCPM) is a new type of transverse flux permanent magnet machine (TFPMM). Due to its special structure, its stator core is usually made by soft magnetic composite (SMC) materials. Based on PMCPM with SMC cores, a new PMCPM with hybrid cores is proposed in this paper. The hybrid core is produced by the combination of silicon sheet cores and SMC cores. Compared with the PMCPM with SMC cores, the PMCPM with hybrid cores has higher average torque and efficiency. However, as the permeability of silicon sheets is higher than that of SMC material and it replaced the SMC in part of stator yoke, the power factor of PMCPM with hybrid cores is lower than that with SMC cores. For performance evaluation, 3-D finite element method (FEM) is adopted, and the PMCPM with SMC cores is used as the benchmark comparison.
Liu, J, Choong, DSW, Goh, DJ, Merugu, S, Zhang, QX, Chang, P, Leotti, A, Tan, H-S, Hidayat, A, Ghosh, S, Ramegowda, PC, Chen, DS-H, Giusti, D, Quaglia, F, Pedrini, C, Barabani, L, Castoldi, L, Ng, EJ & Lee, JE-Y 1970, 'Sputtered PZT pMUT with Bias-Tunable Electromechanical Coupling Coefficient for Air-coupled Ranging Applications', 2022 IEEE International Ultrasonics Symposium (IUS), 2022 IEEE International Ultrasonics Symposium (IUS), IEEE, Venice, Italy, pp. 1-4.
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We report preliminary wafer level measurement results of air coupled phase vapor deposition PVD PZT pMUTs for ranging applications recently fabricated in our Lab in Fab 8 inch line Dual port PVD PZT pMUTs designed to resonate in the 100kHz range were fabricated based on a boldsymbol 2 mu mathbf m thick PZT film on a boldsymbol 4 mu mathbf m thick silicon diaphragm The standard deviation of the PZT stack capacitance was 2 across the wafer Despite the sensitivity of compliant diaphragms to film residual stress the standard deviation of frequency was 5 Out of 16 sites across wafer probed all devices were verified to be in good working condition Given the strong piezoelectric constant of the PZT film mathbf e boldsymbol 31 mathbf f boldsymbol sim 16 mathbf C mathbf m boldsymbol 2 and compliance of the diaphragm designed for ranging applications we demonstrate large tunability in frequency 120 210kHz mathbf K mathbf t boldsymbol 2 1 7 3 and quality factor 50 100 for a bias voltage range of0 40V Results have been obtained without poling of the PVD PZT film No unresponsive devices were found
Liu, J, Zhou, L, Barthe, G & Ying, M 1970, 'Quantum Weakest Preconditions for Reasoning about Expected Runtimes of Quantum Programs', Proceedings of the 37th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS '22: 37th Annual ACM/IEEE Symposium on Logic in Computer Science, ACM, pp. 1-13.
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We study expected runtimes for quantum programs. Inspired by recent work on probabilistic programs, we frst defne expected runtime as a generalisation of quantum weakest precondition. Then, we show that the expected runtime of a quantum program can be represented as the expectation of an observable (in physics). A method for computing the expected runtimes of quantum programs in fnite-dimensional state spaces is developed. Several examples are provided as applications of this method, including computing the expected runtime of quantum Bernoulli Factory a quantum algorithm for generating random numbers. In particular, using our new method, an open problem of computing the expected runtime of quantum random walks introduced by Ambainis et al. (STOC 2001) is solved.
Liu, J, Zhou, L, Barthe, G & Ying, M 1970, 'Quantum Weakest Preconditions for Reasoning about Expected Runtimes of Quantum Programs.', LICS, ACM, pp. 4:1-4:1.
Liu, K, Zhao, F, Xu, G, Wang, X & Jin, H 1970, 'Temporal Knowledge Graph Reasoning via Time-Distributed Representation Learning', 2022 IEEE International Conference on Data Mining (ICDM), 2022 IEEE International Conference on Data Mining (ICDM), IEEE, Orlando, Florida, USA, pp. 279-288.
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Liu, L, Guo, Y, Lei, G & Zhu, J 1970, 'Multi-level Design Optimization of an IPMSM Drive System Considering an Improved Loss Model', 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), IEEE, pp. 1-6.
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Liu, L, Guo, Y, Lei, G, Yin, W, Ba, X & Zhu, J 1970, 'Construction Method and Application Prospect of Electrical Machine Digital Twin', 2022 25th International Conference on Electrical Machines and Systems (ICEMS), 2022 25th International Conference on Electrical Machines and Systems (ICEMS), IEEE, pp. 1-6.
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The digital twin (DT) technique, defined as a dynamic mapping for the physical system to virtual replica, has attracted worldwide attention, which can realize the near real-time interaction among human, machine and environment. This paper aims to present an overview on the research status and technologies of the DT technique and propose an overall DT modeling framework for electrical machines. Among them, property modeling for advanced electrical machine design and analysis is one of the most important research topics which will then be fully investigated. Finally, the key problems that need to be broken through are summarized and the application prospect is also discussed. All the above-mentioned works may trigger in-depth thinking and bring research directions for the application of the DT technique in the field of electrical machines adhering to Industry 4.0 'informatization, digitalization and interaction' concepts.
Liu, W, Xie, K, Pang, L, Bailey, J, Cao, L & Zhang, Y 1970, 'Deep Learning for Search and Recommendation', Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM '22: The 31st ACM International Conference on Information and Knowledge Management, ACM, pp. 5171-5172.
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Liu, Y, Guan, J, Zhu, Q & Wang, W 1970, 'Anomalous Sound Detection Using Spectral-Temporal Information Fusion', ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Singapore, Singapore, pp. 816-820.
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Unsupervised anomalous sound detection aims to detect unknown abnormal sounds of machines from normal sounds. However, the state-of-the-art approaches are not always stable and perform dramatically differently even for machines of the same type, making it impractical for general applications. This paper proposes a spectral-temporal fusion based self-supervised method to model the feature of the normal sound, which improves the stability and performance consistency in detection of anomalous sounds from individual machines, even of the same type. Experiments on the DCASE 2020 Challenge Task 2 dataset show that the proposed method achieved 81.39%, 83.48%, 98.22% and 98.83% in terms of the minimum AUC (worst-case detection performance amongst individuals) in four types of real machines (fan, pump, slider and valve), respectively, giving 31.79%, 17.78%, 10.42% and 21.13% improvement compared to the state-of-the-art method, i.e., Glow_Aff. Moreover, the proposed method has improved AUC (average performance of individuals) for all the types of machines in the dataset. The source codes are available at https://github.com/liuyoude/STgram_MFN
Liu, Y, Liu, J, Ni, W & Song, L 1970, 'Abnormal Event Detection with Self-guiding Multi-instance Ranking Framework', 2022 International Joint Conference on Neural Networks (IJCNN), 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 01-07.
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Liu, Y, Lu, Q, Yu, G, Paik, H-Y, Perera, H & Zhu, L 1970, 'A Pattern Language for Blockchain Governance', Proceedings of the 27th European Conference on Pattern Languages of Programs, EuroPLop '22: 27th European Conference on Pattern Languages of Programs, ACM, pp. 1-16.
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Liu, Y, Yao, L, Li, B, Wang, X & Sammut, C 1970, 'Social Graph Transformer Networks for Pedestrian Trajectory Prediction in Complex Social Scenarios', Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM '22: The 31st ACM International Conference on Information and Knowledge Management, ACM, pp. 1339-1349.
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Liu, Z, Liu, A, Zhang, G & Lu, J 1970, 'An Empirical Study of Fuzzy Decision Tree for Gradient Boosting Ensemble', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 34th Australasian Joint Conference on Artificial Intelligence (AI), Springer International Publishing, Univ Technol Sydney, ELECTR NETWORK, pp. 716-727.
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Gradient boosting has been proved to be an effective ensemble learning paradigm to combine multiple weak learners into a strong one. However, its improved performance is still limited by decision errors caused by uncertainty. Fuzzy decision trees are designed to solve the uncertainty problems caused by the collected information’s limitation and incompleteness. This paper investigates whether the robustness of gradient boosting can be improved by using fuzzy decision trees even when the decision conditions and objectives are fuzzy. We first propose and implement a fuzzy decision tree (FDT) by referring to two widely cited fuzzy decision trees. Then we propose and implement a fuzzy gradient boosting decision tree (FGBDT), which integrates a set of FDTs as weak learners. Both the algorithms can be set as non-fuzzy algorithms by parameters. To study whether fuzzification can improve the proposed algorithms in classification tasks, we pair the algorithms with their non-fuzzy algorithms and run comparison experiments on UCI Repository datasets in the same settings. The experiments show that the fuzzy algorithms perform better than their non-fuzzy algorithms in many classical classification tasks. The code is available at github.com/ZhaoqingLiu/FuzzyTrees.
Low, Y-Y, Tanvy, A, Phan, RC-W & Chang, X 1970, 'AdverFacial: Privacy-Preserving Universal Adversarial Perturbation Against Facial Micro-Expression Leakages', ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 2754-2758.
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Lu, D & Aljarajreh, H 1970, 'Non-isolated Multiport DC/DC Converters: Applications, Challenges, and Solutions', 2022 IEEE 9th International Conference on Power Electronics Systems and Applications (PESA), 2022 9th International Conference on Power Electronics Systems and Applications (PESA), IEEE, pp. 1-5.
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This paper presents an overview of different aspects of non-isolated Multiport Converters (MPCs). A systematic converter circuit derivation tool based on power flow graphs is presented. It assists power electronic engineers and researchers in creating and identifying multiport converter topologies that are more reliable and less complex. Then, major challenges with MPCs and some solutions are presented. These challenges are power-sharing and cross-regulation issues, limited or range-reduced operation modes, heavy computational burden and sensor requirement, and multi-parametric optimization and compromises. Reliability of MPCs and how component stress translates into failure calculations are discussed, followed by the Fault Tolerance (FT) feature to increase the reliability of MPCs.
Lu, DD-C, Aljarajreh, H & Hassan, W 1970, 'Reliability Assessment of Selected DC/DC Boost-Converter-Based Multiport Converter Topologies', 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), IEEE, pp. 1-6.
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Multiport power converters (MPCs) have the unique feature of interfacing with multiple sources and loads, which are nearby, simultaneously and effectively. They have found potential newer applications in such as hybrid energy systems, electric transportation and portable electronic devices. However, there are concerns about their reliability as compared with conventional single-input, single-output (SISO) and cascaded or paralleled converter structure due to sharing of the same components for multiple power flow paths within the converter circuit and associated higher component stresses. In this paper, six different boost-converter-derived topologies are selected and studied, which include conventional and newer designs. The converters are configured to operate for a standalone solar PV-battery application which demonstrates five distinctive operation modes. The reliability assessment is based on the MIL-HBDK-217F Standard information and LTSpice simulation. The analytical results have shown that while conventional cascaded design generally offers lower failure rates across the board, some alternative designs may offer better MPC reliability.
Lu, L, Xiao, J, Ni, W, Du, H & Zhang, D 1970, 'Deep-Reinforcement-Learning-based User-Preference-Aware Rate Adaptation for Video Streaming', 2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), IEEE, pp. 416-424.
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Lu, Y, Niu, K, Peng, X, Zeng, J & Pei, S 1970, 'Multi-modal Intermediate Fusion Model for diagnosis prediction', 2022 the 6th International Conference on Innovation in Artificial Intelligence (ICIAI), ICIAI 2022: 2022 the 6th International Conference on Innovation in Artificial Intelligence, ACM, pp. 38-43.
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The goal of the diagnostic prediction task is to predict what disease patients are likely to have at their next visit, based on their historical electronic medical records. Existing studies mainly conduct the prediction task by separately using discrete medical codes or clinical notes. However, few existing studies fuse multi-modal features from medical codes and clinical notes together for diagnostic prediction. Practically, using multiple modes of EHRs data can obtain more complete patient representation to improve the predictive performance of the model. Therefore, we proposed a Multi-modal intermediate Fusion Model (MFM) to predict patient diagnosis based on diagnostic codes and clinical notes. Specifically, MFM is mainly based on recurrent neural network to model data in different modes to extract effective features. Then, an intermediate fusion module is used to not only extract the global context information of data in each mode, but also capture the correlation between data in different modes. Finally, a multi-modal fusion matrix is generated for diagnosis prediction. Experimental results on a real dataset show that the proposed method improves the prediction performance compared with the baseline methods.
Lubich, M, Shannon, A, Slavov, C, Pencheva, T, Ribagin, S & Atanassov, K 1970, 'A Generalized Net Model of the Pattern of Behavior in Patients with eGFR < 20 mL/min (CKD Stage IV-V)', Springer International Publishing, pp. 113-120.
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Lukmanjaya, WK, Islam, MR & Xu, G 1970, 'Evidence-Based Process in Research Involving Machine Learning Algorithms', 2022 9th International Conference on Behavioural and Social Computing (BESC), 2022 9th International Conference on Behavioural and Social Computing (BESC), IEEE, pp. 1-7.
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The current state of research involving the application of machine learning (ML) algorithms on various topics that directly impact human beings does not sufficiently focus on identifying and filling in research gaps. Evidence is lacking that the maximum accuracy showcased in multiple research papers is the best accuracy possible. However, it is vital to fill in research gaps to be cost-effective and actionable in the big picture. This study proposes a guideline that can be useful for future work involving ML algorithms on high-risk topics to fill in research gaps as much as possible for a particular problem. In this study, 10 different models were conducted with 12.7 million different parameter combinations to create this guideline. The results from the experiments demonstrated experimentation with different algorithms and parameters is crucial in research to deduct which algorithm performs well. Exposure to accurate and inaccurate models can assist researchers and relevant professionals in highlighting evidence-based methods that might contribute to improved findings in various areas. It suggests what works and does not and which task is the most appropriate to fill research gaps. It is also important to consider the guideline produced in this study as inspiration. More research is necessary to improve the guideline.
Lyu, B & Wen, S 1970, 'TND-NAS: Towards Non-Differentiable Objectives in Differentiable Neural Architecture Search', Proceedings of the 3rd International Symposium on Automation, Information and Computing, International Symposium on Automation, Information and Computing, SCITEPRESS - Science and Technology Publications, pp. 177-181.
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Ma, B, Lin, X, Wang, X, Liu, B, He, Y, Ni, W & Liu, RP 1970, 'New Cloaking Region Obfuscation for Road Network-Indistinguishability and Location Privacy', Proceedings of the 25th International Symposium on Research in Attacks, Intrusions and Defenses, RAID 2022: 25th International Symposium on Research in Attacks, Intrusions and Defenses, ACM, Cyprus, pp. 160-170.
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Ma, F, Shou, MZ, Zhu, L, Fan, H, Xu, Y, Yang, Y & Yan, Z 1970, 'Unified Transformer Tracker for Object Tracking', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, New Orleans, LA, pp. 8771-8780.
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As an important area in computer vision, object tracking has formed two separate communities that respectively study Single Object Tracking (SOT) and Multiple Object Tracking (MOT). However, current methods in one tracking scenario are not easily adapted to the other due to the divergent training datasets and tracking objects of both tasks. Although UniTrack [45] demonstrates that a shared appearance model with multiple heads can be used to tackle individual tracking tasks, it fails to exploit the large-scale tracking datasets for training and performs poorly on the single object tracking. In this work, we present the Unified Transformer Tracker (UTT) to address tracking problems in different scenarios with one paradigm. A track transformer is developed in our UTT to track the target in both SOT and MOT where the correlation between the target feature and the tracking frame feature is exploited to localize the target. We demonstrate that both SOT and MOT tasks can be solved within this framework, and the model can be simultaneously end-to-end trained by alternatively optimizing the SOT and MOT objectives on the datasets of individual tasks. Extensive experiments are conducted on several benchmarks with a unified model trained on both SOT and MOT datasets.
Ma, J, Xie, M & Long, G 1970, 'Personalized Federated Learning with Robust Clustering Against Model Poisoning', Springer Nature Switzerland, pp. 238-252.
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Ma, R, Pang, G, Chen, L & van den Hengel, A 1970, 'Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation', Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, ACM, pp. 704-714.
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Graph-level anomaly detection (GAD) describes the problem of detecting graphs that are abnormal in their structure and/or the features of their nodes, as compared to other graphs. One of the challenges in GAD is to devise graph representations that enable the detection of both locally- and globally-anomalous graphs, i.e., graphs that are abnormal in their fine-grained (node-level) or holistic (graph-level) properties, respectively. To tackle this challenge we introduce a novel deep anomaly detection approach for GAD that learns rich global and local normal pattern information by joint random distillation of graph and node representations. The random distillation is achieved by training one GNN to predict another GNN with randomly initialized network weights. Extensive experiments on 16 real-world graph datasets from diverse domains show that our model significantly outperforms seven state-of-the-art models. Code and datasets are available at https://git.io/GLocalKD.
Ma, W, Chang, Y-C, Wang, Y-K & Lin, C-T 1970, 'Human-Autonomous Teaming Framework Based on Trust Modelling', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 707-718.
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With the development of intelligent technology, autonomous agents are no longer just simple tools; they have gradually become our partners. This paper presents a trust-based human-autonomous teaming (HAT) framework to realize tactical coordination between human and autonomous agents. The proposed trust-based HAT framework consists of human and autonomous trust models, which leverage a fusion mechanism to fuse multiple performance metrics to generate trust values in real-time. To obtain adaptive trust models for a particular task, a reinforcement learning algorithm is used to learn the fusion weights of each performance metric from human and autonomous agents. The adaptive trust models enable the proposed trust-based HAT framework to coordinate actions or decisions of human and autonomous agents based on their trust values. We used a ball-collection task to demonstrate the coordination ability of the proposed framework. Our experimental results show that the proposed framework can improve work efficiency.
Machet, T, Boye, T, Lindeck, J, Daniel, S, Brown, N, Rhodes-Dicker, L, Bhatia, T, Cheng, E & Whelan, K 1970, 'Inclusive teaching practices: a comparative case study of integrated inclusion in different contexts', 2022 IEEE IFEES World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), 2022 IEEE IFEES World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), IEEE, Cape Town, South Africa, pp. 1-5.
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Machet, T, Lindeck, J, Boye, T, Cheng, E, Daniel, S & Bhatia, T 1970, 'Fostering a capacity for relational agency in undergraduate engineering and IT', 9th Research in Engineering Education Symposium (REES 2021) and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021), 9th Research in Engineering Education Symposium & 32nd Australasian Association for Engineering Education Conference, Research in Enineering Education Network (REEN), pp. 1005-1012.
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Machet, T, Willey, K & Leigh, E 1970, 'What Do Students Say About Complexity?', 9th Research in Engineering Education Symposium (REES 2021) and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021), 9th Research in Engineering Education Symposium & 32nd Australasian Association for Engineering Education Conference, Research in Enineering Education Network (REEN), Perth, Australia, pp. 613-621.
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Madhisetty, S 1970, 'Understanding Risks of Sharing Images in the Context of Deepfakes Technology', Springer International Publishing, pp. 132-140.
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Magnani, A, Liu, F, Chaidaroon, S, Yadav, S, Reddy Suram, P, Puthenputhussery, A, Chen, S, Xie, M, Kashi, A, Lee, T & Liao, C 1970, 'Semantic Retrieval at Walmart', Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 3495-3503.
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Male, S, Daniel, S, Beddoes, K, Eaton, R, Goldsmith, R, Lamborn, J & Nikolic, S 1970, 'Publishing in the Australasian Journal of Engineering Education', 9th Research in Engineering Education Symposium (REES 2021) and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021), 9th Research in Engineering Education Symposium & 32nd Australasian Association for Engineering Education Conference, Research in Enineering Education Network (REEN), pp. 1150-1150.
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Malisetty, RS, Indraratna, B, Ngo, T & Tucho, A 1970, 'Dynamic stress response of track layers under high speed trains', Proceedings of the 20th International Conference on Soil Mechanics and Geotechnical Engineering, 20th Internation Conference on Soil Mechanics and Geotechnical Engineering, Australian Geomechanics Society, Sydney, pp. 1673-1678.
Mao, Z, Zhao, L, Huang, S, Fan, Y & Lee, APW 1970, 'DSR: Direct Simultaneous Registration for Multiple 3D Images', Springer Nature Switzerland, pp. 98-107.
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Marjanovic, O, Ariyachandra, T & Dinter, B 1970, 'Looking Ahead: Business Intelligence & Analytics Research in the Post-Pandemic New Normal', Proceedings of the Annual Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Virtual Conference.
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Markham, G, Seiler, KM, Balamurali, M & Hill, AJ 1970, 'Load-Haul Cycle Segmentation with Hidden Semi-Markov Models', 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), IEEE, pp. 447-454.
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Martin, L, Thomas, P, De Silva, P & Sirivivatnanon, V 1970, 'Susceptibility of Heat-Cured Concrete to Deleterious DEF; the Role of Alkali, Sulfate, and Temperature', 76th RILEM Annual Week 2022 and International Conference on Regeneration and Conservation of Structures, Kyoto, Japan.
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Durability of concrete materials is important for their global use in infrastructure. One potential cause of durability loss is delayed ettringite formation (DEF), a form of internal sulfate attack. DEF can lead to deleterious expansion, microcracking, and strength loss in affected elements and is of most concern in precast concrete, with major factors including pore solution alkalinity, elevated curing temperatures, and sulfate and aluminate binder contents. The role of chemical factors and heat in deleterious DEF has been investigated in this study with concrete prisms and hydrated cement paste samples via linear expansion and phase development. Results show that a combination of sustained heat and elevated alkali and sulfate contents are necessary for deleterious DEF to occur.
Martin, L, Thomas, P, De Silva, P & Sirivivatnanon, V 1970, 'The combined role of alkali silica reaction and delayed ettringite formation in durability loss of concrete structures.', Proceedings of the 16th International Conference on Alkali-Aggregate Reaction in Concrete, International Conference on Alkali-Aggregate Reaction in Concrete, LABORATÓRIO NACIONAL DE ENGENHARIA CIVIL, I. P., Lisbon, Portugal, pp. 469-479.
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Durability of concrete as a structural material is key in its worldwide use for buildings and infrastructure,with the lifetime service of concrete greatly impacting its economic, environmental, and social costs.Causes of durability loss in some concrete structures can be attributed to the alkali-silica reaction (ASR)and delayed ettringite formation (DEF). Both are chemical reactions with the potential of deleteriouscracking, expansion, and strength loss in affected elements. Significant overlap exists in the factorscontributing to ASR and DEF in concrete structures, with widely reported evidence of deleterious DEFfrequently occurring in conjunction with mild or moderate ASR. For precast concrete, binder andtemperature limits based on mortar experiments are used during curing to minimise DEF risk. The roleof other constituents in concrete specimens, notably the aggregate, have been overlooked. This paperinvestigates the role of ASR in the susceptibility of concrete to DEF. Concrete specimens weremanufactured under conditions that promoted deleterious ASR and DEF, in including aggregatereactivity, binder composition, and curing temperature. Linear expansion and compressive strengthwere measured over 1-year. Only concrete systems subject to heat-curing and elevated alkali andsulfate contents showed deleterious effects.
Martínez, AT, Gil-Lafuente, AM, Keropyan, A & MerigóLindahl, JM 1970, 'Application of the Forgotten Effects Theory to the Qualitative Analysis of the Operational Risk Events', Springer International Publishing, pp. 261-270.
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Mathur, S, Sankaran, S, MacAulay, S & Tsang, I 1970, 'MINIMUM VIABLE GOVERNANCE FOR DATA SCIENCE INITIATIVES A TRANSPORT FOR NSW CASE STUDY', Value co-creation in the project society, 10th IPMA Research conference: Value co-creation in the project society, International Project Management Association, Serbian Project Management Association, pp. 65-76.
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Too much governance can stifle innovation in organizations. Too little governance can waste precious organizational resources. Business agility demands empowerment of people to take decisions on initiatives designed to deliver innovative products and services. Traditional monthly and quarterly governance forums such as steering committees and program boards for decision-making potentially impede the flow of work when the delivery of a program or project is done using agile methods in two-weekly sprints and decisions are required at a different and more frequent cadence. Data Science Initiatives (DSIs) which are exploratory and innovative in nature follow agile delivery methods. This paper is an exploratory study of implementing governance for DSIs based on a single case study. It investigates agile governance at project, program, and portfolio level for DSIs and suggests eight guiding principles focusing on product and portfolio governance. It is targeted at practitioners to guide them in setting minimum viable governance to ensure value is realized from their DSIs and for academics to advance research in governance of DSIs.
Mayer, J, Hilligan, K, Chandler, J, Eccles, D, Old, S, Domingues, R, Yang, J, Webb, G, Munoz-Erazo, L, Hyde, E, Wakelin, K, Tang, S-C, Chappell, S, von Daake, S, Brombacher, F, Mackay, C, Sher, A, Tussiwand, R, Connor, L, Gallego-Ortega, D, Jankovic, D, Le Gros, G, Hepworth, M, Lamiable, O & Ronchese, F 1970, 'Homeostatic IL-13 in healthy skin directs dendritic cell differentiation to promote TH2 and inhibit TH17 cell polarization', EUROPEAN JOURNAL OF IMMUNOLOGY, WILEY, pp. 34-35.
McGregor, C & Inibhunu, C 1970, 'A Framework for the Design, Development, Testing and Deployment of Reliable Big Data Platforms', 2022 IEEE International Conference on Big Data (Big Data), 2022 IEEE International Conference on Big Data (Big Data), IEEE, pp. 2660-2666.
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We consider the problem of reliability in big data science projects that are comprised of multiple computing platforms and complex architectures that harness data. Specifically on their ability to capture, process and analyze streaming high frequency data from vast complex systems reliably with effective scalability for deployment in vast domains such as clinical care, smart cities or within extreme climatic work environments. This paper introduces a framework to enable reliable data science projects by integrating multiple computing principles of autonomy, local responsibility, fault tolerance, symmetry, decentralization, well-understood building blocks, and simplicity. The designed framework is applied in the development of a decoupled data pipeline demonstrated through a case study on pre-deployment acclimation strategies that is continuously monitored to ensure reliability and availability is effectively quantified.
Medawela, S, Indraratna, B & Athuraliya, S 1970, 'Acidic Flow-Induced Clogging of Permeable Reactive Barriers in Pyritic Terrain', Geo-Congress 2022, Geo-Congress 2022, American Society of Civil Engineers, pp. 39-49.
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Mei, G, Huang, X, Liu, J, Zhang, J & Wu, Q 1970, 'Unsupervised Point Cloud Pre-Training Via Contrasting and Clustering', 2022 IEEE International Conference on Image Processing (ICIP), 2022 IEEE International Conference on Image Processing (ICIP), IEEE, pp. 66-70.
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The annotation for large-scale point clouds is still time-consuming and unavailable for many complex real-world tasks. Point cloud pre-training is a promising direction to auto-extract features without labeled data. Therefore, this paper proposes a general unsupervised approach, named ConClu for point cloud pre-training by jointly performing contrasting and clustering. Specifically, the contrasting is formulated by maximizing the similarity feature vectors produced by encoders fed with two augmentations of the same point cloud. The clustering simultaneously clusters the data while enforcing consistency between cluster assignments produced different augmentations. Experimental evaluations on downstream applications outperform state-of-the-art techniques, which demonstrates the effectiveness of our framework.
Mei, G, Huang, X, Zhang, J & Wu, Q 1970, 'Overlap-Guided Coarse-to-Fine Correspondence Prediction for Point Cloud Registration', 2022 IEEE International Conference on Multimedia and Expo (ICME), 2022 IEEE International Conference on Multimedia and Expo (ICME), IEEE, Taipei, Taiwan, pp. 1-6.
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Establishing reliable correspondences between a pair of point clouds is essential for registration with partial overlaps. However, existing correspondence estimation works usually struggle to distinguish the points in overlap and non-overlap regions. This paper thus proposes an Overlap-guided Coarse-to-Fine Network, named OCFNet, which first establishes correspondences at a coarse level and then refines them at a point level. Specifically, at the coarse level, our model first aggregates two point clouds into smaller sets of super-points with associated features and overlap scores, followed by establishing coarse-level correspondences between the two sets of super-points under the guidance of overlap scores. On the fine stage, a decoder recovers the raw points while jointly learning the associated features and overlap scores. Coarse-level proposals are then expanded to patches, and point-level correspondences are sequentially refined from the corresponding patches. We conducted comprehensive experiments on 3DMatch, 3DLoMatch, and KITTI benchmarks to show the effectiveness of the proposed method. [code]
Mei, G, Huang, X, Zhang, J & Wu, Q 1970, 'Partial Point Cloud Registration Via Soft Segmentation', 2022 IEEE International Conference on Image Processing (ICIP), 2022 IEEE International Conference on Image Processing (ICIP), IEEE, pp. 681-685.
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Most existing correspondence-free registration methods suffer from performance degradation in partial overlapped point clouds. To solve the partial overlapped point cloud registration, this paper proposes, SegReg, a soft Segmentationbased correspondence-free Registration approach. Specifically, we first softly segment both source and target point clouds into a discrete number of geometric partitions, respectively. Then registration is achieved through iteratively using the IC-LK algorithm to minimize the distance between the feature descriptors of the corresponded partitions. Extensive experiments on synthetic synthetic dataset ModelNet40 and real dataset 7Scene show that the proposed method achieves state-of-the-art performance.
Mei, G, Saltori, C, Poiesi, F, Zhang, J, Ricci, E, Sebe, N & Wu, Q 1970, 'Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding', Bmvc 2022 33rd British Machine Vision Conference Proceedings, British Machine Vision Conference, London, UK.
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Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods. However, data augmentation is not ideal as it requires a careful selection of the type of augmentations to perform, which in turn can affect the geometric and semantic information learned by the network during self-training. To overcome this issue, we propose an augmentation-free unsupervised approach for point clouds to learn transferable point-level features via soft clustering, named SoftClu. SoftClu assumes that the points belonging to a cluster should be close to each other in both geometric and feature spaces. This differs from typical contrastive learning, which builds similar representations for a whole point cloud and its augmented versions. We exploit the affiliation of points to their clusters as a proxy to enable self-training through a pseudo-label prediction task. Under the constraint that these pseudo-labels induce the equipartition of the point cloud, we cast SoftClu as an optimal transport problem. We formulate an unsupervised loss to minimize the standard cross-entropy between pseudo-labels and predicted labels. Experiments on downstream applications, such as 3D object classification, part segmentation, and semantic segmentation, show the effectiveness of our framework in outperforming state-of-the-art techniques [code].
Mendez, J, Aplin, R & Wang, Y-K 1970, 'Remote Guided Robotics via Leap Motion and Mixed Reality', 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), IEEE, pp. 1-7.
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The global teleoperation and telerobotics market is projected to reach 98.3billion (USD) by 2027. With the benefits of 5G coverage almost anywhere in the world will allow teleoperated robots to be implemented globally via the cloud. Remote Guided Robotics has excellent potential in existing markets, attempting to remove operators from dangerous tasks and providing access to labour in remote areas, where labour is scarce. This research introduces a configuration of technology that reduces the knowledge gap of what is required to successfully achieve high accuracy and realistic control of a robot in real-time. The system implemented in this paper utilises an infrared camera from Leap Motion to capture and track hand gestures and movements, a Kuka 6 DOF robot and software packages to follow the hand movements from the UltraLeap Leap Motion Controller (LMC) in real-time, and an HTC VR headset for a mixed reality experience where two cameras are placed in the robot work-cell to send back a 3D stereoscopic image to allow the operator to have an immersed experience when remotely controlling the robot. The system proved to have a repeatability of approx. ± 10 mm, and an accuracy of 0.1mm. The gripper was able to successfully pick up 86% of the time, with a final test of gripping an item and guiding it through a bandsaw at an average rate of 24.2 seconds. Testings and simulations completed in this paper outline the level of accuracy and repeatability of the remote guide robotic system. This teleoperated robotic system has the ability to protect operators' safety and meet the explosive growth of needs in the various industries.
Migalin, M, Keshavarz, R & Shariati, N 1970, 'mm-Wave Polarization Insensitive Spiral Antenna for 5G Energy Harvesting Applications', 2022 Wireless Power Week (WPW), 2022 Wireless Power Week (WPW), IEEE, pp. 417-421.
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Mihaita, A-S, Marche, B, Camargo, M, Rahimi, I & Bachmann, C 1970, 'Multi-objective modelling of a roadside mowing problem: a case study in France', 2022 IEEE 28th International Conference on Engineering, Technology and Innovation (ICE/ITMC) & 31st International Association For Management of Technology (IAMOT) Joint Conference, 2022 IEEE 28th International Conference on Engineering, Technology and Innovation (ICE/ITMC) & 31st International Association For Management of Technology (IAMOT) Joint Conference, IEEE, pp. 1-8.
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Milton, J, Halkon, B, Oberst, S, Chiang, YK & Powell, D 1970, 'SONAR-BASED BURIED OBJECT DETECTION VIA STATISTICS OF RECURRENCE PLOT QUANTIFICATION MEASURES', Proceedings of the International Congress on Sound and Vibration, International Congress on Sound and Vibration, Society of Acoustics, Singapore, Singapore, pp. 1-8.
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Active sonar has been successfully deployed for naval mine countermeasures (MCM) to detect, localise, and classify mines and mine-like objects (MLOs). One of the most challenging problems in MCM operations is the detection and classification of (partially) covered objects; traditional image-based sonar processing techniques cannot readily detect objects within the seabed. In this paper, a processing technique that utilises recurrence plot quantification analysis, a class of nonlinear time series analysis, is proposed for improved covered MLO detection in raw sonar signals. Recurrence plots are binary, graphical visualisations of the recurrence matrix generated from time series data. Following an embedding process to reconstruct a copy of the dynamics in phase space, recurrence plot quantification analysis measures can be extracted and further statistically analysed. Using computationally generated sonar signals extracted from simplified representations of real-world relevant scenarios, this study explores the application of such an approach and its sensitivity to the user-defined parameters for detecting the presence of an MLO, irrespective of the level of burial.
Mirdad, A & Hussain, FK 1970, 'Blockchain-Based Pharmaceutical Supply Chain: A Literature Review', Springer International Publishing, pp. 106-115.
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Recently, blockchain technology was introduced to the public in order to provide a secure environment that is immutable, consensus-based and transparent in the finance technology world. However, there have been many efforts to apply blockchain to other fields where trust and transparency are a requirement. The ability to reliably share pharmaceutical information between various stakeholders is essential. The use of blockchain technology adds traceability and visibility to supply chains such as pharmaceuticals to provide all the information from end to end. Currently, the data is stored and managed by large manufacturers and pharmacy retailers using their own centralized systems. Several existing approaches and methods that allow pharmaceutical information to be stored and shared between the healthcare provider and other stakeholders in a centralized manner have been discussed in the literature. Due to the lack of comprehensive literature review studies that focus on pharmaceutical supply chain using blockchain technology, thus this paper highlights and addresses this gap. This paper reviews several studies which have applied blockchain technology in pharmaceutical supply chains. This paper overviews the knowledge on blockchain technology, discusses and explores the most recent and relevant studies that adopt blockchain technology in the field of pharmaceutical supply chains, describes the challenges associated with blockchain technology, and presents some ideas for future work.
Moradian, M & Gunawardane, K 1970, 'A Review: Supercapacitors’ Suitability as an Alternative to Li-Ion Batteries in Ultra-Low Power Devices', 2022 IEEE Industrial Electronics and Applications Conference (IEACon), 2022 IEEE Industrial Electronics and Applications Conference (IEACon), IEEE, pp. 31-36.
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Moreira, C, Nobre, IB, Sousa, SC, Pereira, JM & Jorge, J 1970, 'Improving X-ray Diagnostics through Eye-Tracking and XR', 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE, pp. 450-453.
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MOUSAVI, M & GANDOMI, AH 1970, 'UNSUPERVISED CONDITION MONITORING OF STRUCTURES USING VMD AND ISOLATION FOREST', Proceedings of the 13th International Workshop on Structural Health Monitoring, Structural Health Monitoring 2021, Destech Publications, Inc..
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In this paper, an unsupervised condition monitoring of civil infrastructures under environmental and operational variations is proposed. To this end, a couple of the lowest natural frequency signals of the structure recorded over a long period of time are studied for damage. Most of the existing techniques are supervised and require baseline information from the healthy state of the structure. In this paper, however, we explore the possibility of performing unsupervised condition monitoring using Isolation Forest (IF) as a well-known unsupervised machine learning approach. We show that a preprocessing of the frequency signals using the Variational Mode Decomposition (VMD) algorithm plays a key role in the success of the IF algorithm in the unsupervised condition monitoring of structures. To investigate the performance of the proposed method, the benchmark problem of the Z24 bridge is studied in this paper. The results show that the proposed methodology stays successful in most of the cases but at a period of very cold temperature where a nonlinear relationship between the frequencies and temperature presents.
Mousavi, M & Gandomi, AHH 1970, 'Deep learning-based unsupervised methods for real-time condition monitoring of structures: a state-of-the-art survey', Health Monitoring of Structural and Biological Systems XVI, Health Monitoring of Structural and Biological Systems XVI, SPIE, pp. 91-91.
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Mousavirad, SJ, Gandomi, AH & Homayoun, H 1970, 'A Clustering-based Differential Evolution Boosted by a Regularisation-based Objective Function and a Local Refinement for Neural Network Training', 2022 IEEE Congress on Evolutionary Computation (CEC), 2022 IEEE Congress on Evolutionary Computation (CEC), IEEE, Padua, Italy, pp. 1-8.
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The performance of feed-forward neural networks (FFNN) is directly dependant on the training algorithm. Conventional training algorithms such as gradient-based approaches are so popular for FFNN training, but they are susceptible to get stuck in local optimum. To overcome this, population-based metaheuristic algorithms such as differential evolution (DE) are a reliable alternative. In this paper, we propose a novel training algorithm, Reg-IDE, based on an improved DE algorithm. Weight regularisation in conventional algorithms is an approach to reduce the likelihood of over-fitting and enhance generalisation. However, to the best of our knowledge, the current DE-based trainers do not employ regularisation. This paper, first, proposes a regularisation-based objective function to improve the generalisation of the algorithm by adding a new term to the objective function. Then, a region-based strategy determines some regions in search space using a clustering algorithm and updates the population based on the information available in each region. In addition, quasi opposition-based learning enhances the exploration of the algorithm. The best candidate solution found by improved DE is then used as the initial network weights for the Levenberg-Marquardt (LM) algorithm, as a local refinement. Experimental results on different benchmarks and in comparison with 26 conventional and population-based approaches apparently demonstrate the excellent performance of Reg-IDE.
Mughal, F, Raffe, W, Stubbs, P & Garcia, J 1970, 'Towards depression monitoring and prevention in older populations using smart wearables: Quantitative Findings', 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), IEEE, pp. 1-8.
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Depression has become a growing concern over the recent years. Since the start of the COVID-19 pandemic, depression among all age groups has increased significantly. As mental health is often stigmatized among older aged people, it is less openly discussed or treated. We propose a mental health monitoring approach that limits explicit user interaction, using Fitbit smartwatch data to determine depressive tendencies in older-aged people. We analysed physiological user data extracted from a Fitbit Alta HR device and use this data to train a machine learning model to detect depressive tendencies. While this is not a diagnostic tool, the aim is to identify physiological signs early on and direct the user toward professional medical guidance and treatment. We trained 19 predictive models on our dataset, the gradient boosting regressor outperformed all other models. The best performing model achieved at R-square of 0.32 although most models were poorly performing. Due to the limited sample size, there is a risk of model overfitting. Although these preliminary results are promising for one model, they would need to be replicated in a larger sample of older people, who exhibit a wider range of depressive tendencies.
Muhammad, K, Hussain, T, Del Ser, J, Ding, W, Gandomi, AH & De Albuquerque, VHC 1970, 'Efficient Video Summarization for Smart Surveillance Systems', 2022 IEEE Symposium Series on Computational Intelligence (SSCI), 2022 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 672-677.
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Munot, S, Redfern, J, Bray, J, Angell, B, Bauman, A, Coggins, A, Denniss, AR, Ferry, C, Jennings, G, Kovoor, P, Kumar, S, Lai, K, Khanlari, S, Marschner, S, Middleton, P, Nelson, M, Oppermann, I, Semsarian, C, Taylor, L, Vukasovic, M, Vukasovic, M, Ware, S & Chow, CK 1970, 'The Relation of Country-of-Birth With Willingness to Respond to Out-of-Hospital Cardiac Arrest in Multiethnic Communities of New South Wales (NSW), Australia', CIRCULATION, Scientific Sessions of the American-Heart-Association / Resuscitation Science Symposium, LIPPINCOTT WILLIAMS & WILKINS, IL, Chicago.
Murad, MAU, Kozanoglu, DC & Chakraborty, S 1970, 'Public Procurement, Big Data Analytics Capabilities, and Healthcare Supply Chain Sustainability', PROCEEDINGS OF THE 55TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, 55th Annual Hawaii International Conference on System Sciences (HICSS), HICSS, ELECTR NETWORK, pp. 296-303.
Nabeel, MI, Ahmed, F, Afzal, MU, Thalakotuna, DN & Esselle, KP 1970, 'A Dual Band Resonant-Cavity Antenna for Satellite Communication', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 201-202.
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Naji, M, Alyassine, W, Nizamani, QUA, Zhang, L, Wei, X, Xu, Z, Braytee, A & Anaissi, A 1970, 'Deep Learning Algorithm Based Support Vector Machines', Springer International Publishing, pp. 281-289.
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Nalamati, M, Saqib, M, Sharma, N & Blumenstein, M 1970, 'Exploring Transformers for Intruder Detection in Complex Maritime Environment', Springer International Publishing, pp. 428-439.
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Nasiri, M, Cheraghian, A, Chowdhury, TF, Ahmadi, S, Saberi, M & Rahman, S 1970, 'Prompt-guided Scene Generation for 3D Zero-Shot Learning', 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, pp. 1-8.
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Nastiti, FE, Musa, S, Yafi, E & Chauhan, R 1970, 'Systematic Literature Review: Machine Learning Prediction Model for Covid-19 Spreading', 2022 4th International Conference on Cybernetics and Intelligent System (ICORIS), 2022 4th International Conference on Cybernetics and Intelligent System (ICORIS), IEEE, pp. 1-5.
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Nawaz, W, Elchalakani, M, Karrech, A & Yehia, S 1970, 'Shear strengthening of high strength lightweight SCC beams internally reinforced with GFRP bars and external CFRP strips', Materials Today: Proceedings, Elsevier BV, pp. 915-919.
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Nerse, C & Oberst, S 1970, 'NUMERICAL VIBRATION ANALYSIS OF HONEYBEE COMB STRUCTURES', Proceedings of the International Congress on Sound and Vibration, International Congress on Sound and Vibration, Singapore.
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Since ancient times much has been written about the geometrical perfection of honeybee comb structures. The hexagonal shape, trademark of the comb cell, has been credited for auxetic mechanical properties and efficient storage of honey. More recent studies on Apis mellifera ligustica have shown that bees have complex nest-building practices through ecological and behavioural evolution. Although mostly dominated by hexagonal cells, the comb structure is shown to feature imperfections due to uneven distribution of worker and drone cells, as well as tilting and merging of cluster of cells. The shape and conditions of the substrate in which the hive is built upon also affects the expansion of the comb structure. Experimental studies have shown that the honeybee comb may have unusual physical properties of vibration amplification and phase reversal. However, the confined nature of these studies poses challenges in understanding the physical mechanisms. In this study, we examine the sensitivity of geometrical and viscoelastic material properties of a honeybee comb on structural vibration transmission. For this purpose, a finite element model of a comb has been developed to obtain modal and frequency response characteristics. The results have shown that lateral deflection of the walls may contribute to efficient vibration transmission at certain resonant frequencies of the cells. Findings might elucidate on why certain frequencies have been observed in experiments, irrespective of the shape and the boundary conditions of the overall honeycomb, and how bees may use this feature to communicate within the colony.
Nerse, C, Oberst, S, Moore, S & MacGillivray, I 1970, 'ASSESSMENT OF FLANKING TRANSMISSIONS IN MEASUREMENTS OF SOUND TRANSMISSION LOSS OF MULTILAYER PANELS', Proceedings of the International Congress on Sound and Vibration, International Congress on Sound and Vibration, Singapore.
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The sound transmission loss measurements of small-sized panels ideally require perfect sealing of the panel frame and a rigid construction of the filler wall that encloses the panels. In practice, suppression of flanking transmission is achieved by having a sufficient isolation between both the source and the receiver rooms and blocking the indirect transmission by installing additional elements on the surfaces of both rooms. At the outer edges of the panel, the frame is supported by acoustically reflective materials and insulations to reduce the energy propagating into the wall. The sound transmission loss of the panels can be improved by installing layers that contribute to additional or more efficient dissipation. These layers are installed in such a way that they are tightly bolted into the frame with a niche being introduced on sides to further secure the panel within the opening. However, for panels with alternating layers of solid and porous materials, or with acoustic cavities, the structural rigidity of the supporting frame and joints are the primary factors that cause the flanking transmission. In this study, we investigate the extent of this transmission, and identify the vibration transmission paths and assess their negligibility in measurement of the sound transmission loss of the multilayer panels. A source-path-receiver approach has been proposed for ranking the critical transmission paths for different panel configurations. For this purpose, a numerical framework has been developed to measure the acoustic response of the room and vibration response of the structural elements at operating conditions. A finite element model in COMSOL is set to validate the results and is compared with an in-house analytical solution which shows good agreements. Assessment of the vibration and acoustic signals at sub-structures reveals transmission paths that are significant for the performance evaluation of multilayer panels.
Ngiam, ATC, Islam, MR & Xu, G 1970, 'ECTRS: A Personalised Early Career Trajectory Recommender System for Youths', 2022 9th International Conference on Behavioural and Social Computing (BESC), 2022 9th International Conference on Behavioural and Social Computing (BESC), IEEE, pp. 1-7.
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Choosing a career pathway to pursue can be daunting for many youths. However, it is challenging for them when they lack exposure to career knowledge and do not understand whether a particular career suits them. Without proper career guidance, youths are at risk of poor career choices, which lead to career dissatisfaction that can inevitably cause damage to one's health and quality of work. In this paper, we aim to design an Early Career Trajectory Recommender System (ECTRS) to solve youths' career pathway uncertainty and support them in kick-starting their Information Technology career journey. The ECTRS uses the Occupational Information Network (O∗NET) database to generate similar Information on Technology careers for youths based on their personality and interest. The efficacy of the ECTRS was evaluated on 22 university students, all pursuing Information Technology-related courses. The results show that participants agreed with the relevance of the generated career recommendations compared to their actual career goals. Participants also agreed that they were able to discover new types of Information Technology careers. It shows the capabilities of the ECTRS in guiding youths to the right Information Technology career path, thus, allowing them to make well-informed decisions. The demo of the proposed framework is publicly available at https://recsysv4.herokuapp.com/.
Ngo, QT, Phan, KT, Mahmood, A & Xiang, W 1970, 'DRL-Based Secure Beamforming for Hybrid-RIS Aided Satellite Downlink Communications', 2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), 2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), IEEE, pp. 432-437.
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Ngo, T, Indraratna, B & Rujikiatkamjorn, C 1970, 'DEM Modelling on the Interface Behavior of Geogrid-Stabilized Sub-Ballast', Geo-Congress 2022, Geo-Congress 2022, American Society of Civil Engineers, pp. 486-495.
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This paper presents a study on the interface behavior of geogrids and sub-ballast (capping) using a series of large-scale direct shear tests and discrete element modelling (DEM). Direct shear tests were carried out on sub-ballast with and without geogrid inclusions. The laboratory test data show that the interface shear strength is governed by normal stress and types of geogrid. The three-dimensional DEM was used to study the interface shear behavior of the sub-ballast subjected to direct shearing loads. Irregular-shaped particles of capping aggregates were modelled by clumping of many balls together in appropriate sizes and positions. Different types of geogrids were modelled by bonding small spheres together to form the desired grid geometry and apertures. The DEM model was then used to investigate the evolutions of contact force distributions and fabric anisotropy during the shear tests and the role of geogrid in micro-mechanical perspective.
Nguyen, CT, Hoang, DT, Nguyen, DN & Dutkiewicz, E 1970, 'MetaChain: A Novel Blockchain-based Framework for Metaverse Applications', 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring), IEEE, Helsinki, Finland, pp. 1-5.
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Metaverse has recently attracted paramount attention due to its potential for future Internet. However, to fully realize such potential, Metaverse applications have to overcome various challenges such as massive resource demands, interoperability among applications, and security and privacy concerns. In this paper, we propose MetaChain, a novel blockchain-based framework to address emerging challenges for the development of Metaverse applications. In particular, by utilizing the smart contract mechanism, MetaChain can effectively manage and automate complex interactions among the Metaverse Service Provider (MSP) and the Metaverse users (MUs). In addition, to allow the MSP to efficiently allocate its resources for Metaverse applications and MUs’ demands, we design a novel sharding scheme to improve the underlying blockchain’s scalability. Moreover, to leverage MUs’ resources as well as to attract more MUs to support Metaverse operations, we develop an incentive mechanism using the Stackelberg game theory that rewards MUs’ contributions to the Metaverse. Through numerical experiments, we clearly show the impacts of the MUs’ behaviors and how the incentive mechanism can attract more MUs and resources to the Metaverse.
Nguyen, CT, Nguyen, DN, Hoang, DT, Pham, H-A & Dutkiewicz, E 1970, 'Optimize Coding and Node Selection for Coded Distributed Computing over Wireless Edge Networks', 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Austin, TX, pp. 1248-1253.
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This paper aims to develop a highly-effective framework to significantly enhance the efficiency in using coded computing techniques for distributed computing tasks over heterogeneous wireless edge networks. In particular, we first formulate a joint coding and node selection optimization problem to minimize the expected total processing time for computing tasks, taking into account the heterogeneity in the nodes' computing resources and communication links. The problem is shown to be NP-hard. To circumvent it, we leverage the unique characteristic of the problem to develop a linearization approach and a hybrid algorithm based on binary search and branch-and-bound (BB) algorithms. This hybrid algorithm can not only guarantee to find the optimal solution, but also significantly reduce the computational complexity of the BB algorithm. Simulations based on real-world datasets show that the proposed approach can reduce the total processing time up to 2.4 times compared with that of state-of-the-art approach, even without perfect knowledge regarding the node's performance and their straggling parameters.
Nguyen, D-A, Tran, X-T & Iacopi, F 1970, 'GAQ-SNN: A Genetic Algorithm based Quantization Framework for Deep Spiking Neural Networks', 2022 International Conference on IC Design and Technology (ICICDT), 2022 International Conference on IC Design and Technology (ICICDT), IEEE, pp. 93-96.
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Nguyen, LV, Torres Herrera, I, Le, TH, Phung, DM, Aguilera, RP & Ha, QP 1970, 'Stag hunt game-based approach for cooperative UAVs', Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC), 39th International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC), pp. 367-374.
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Unmanned aerial vehicles (UAVs) are being employed in many areas such as photography, emergency, entertainment, defence, agriculture, forestry, mining and construction. Over the last decade, UAV technology hasfound applicationsin numerous construction project phases, ranging from site mapping, progress monitoring, building inspection, damage assessments, and material delivery. While extensive studies have been conducted on the advantages of UAVs for various construction-related processes, studies on UAV collaboration to improve the task capacity and efficiency are still scarce. This paper proposes a new cooperative path planning algorithm for multiple UAVs based on the stag hunt game and particle swarm optimization (PSO). First, a cost function for each UAV is defined, incorporating multiple objectives and constraints. The UAV game framework is then developed to formulate the multi-UAV path planning into the problem of finding payoff-dominant e quilibrium. Next, a PSO-based algorithm is proposed to obtain optimal paths for the UAVs. Simulation results for a large construction site inspected by three UAVs indicate the effectiveness of the proposed algorithm in generating feasible and efficient flight paths for UAV formation during the inspection task.
Nguyen, N-T, Yu, H, Tuan, HD, Nguyen, DN & Dutkiewicz, E 1970, 'Maximization of Geometric Mean of Secrecy Rates in RIS-aided Communications Networks', 2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS), 2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS), IEEE, pp. 310-315.
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Nguyen, QD, Afroz, S & Castel, A 1970, 'CHLORIDE DIFFUSION RESISTANCE OF LIMESTONE CALCINED CLAY CEMENT (LC3) CONCRETE BASED ON CALCINED CLAY REACTIVITY', Fib Symposium, pp. 233-239.
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This study investigated the chloride diffusion resistance of limestone calcined clay cement (LC3) concrete depending on the calcined clay reactivity. Two calcined clays with different reactivity were used to fabricate LC3 concretes. The replacement rate of calcined clay and limestone in binder was adjusted based on the reactivity in order to achieve similar compressive strength after 28 days of curing. The chloride diffusion resistance of LC3 concretes was evaluated using the bulk diffusion test protocol according to NT BUILD 443 (or ASTM C1556). Both LC3 concretes outperformed the reference general purpose cement concrete with significantly lower chloride diffusion coefficients. The chloride diffusion coefficients of the two LC3 concretes were similar despite the different replacement rates of calcined clay and limestone due to different calcined clay’s reactivity. This result indicates that LC3 concrete chloride diffusion resistance is only marginally dependent on the type of calcined clay used if similar compressive strength can be obtained within the LC3 concretes by using an optimum replacement rate.
Nguyen, T, Indraratna, B, Rujikiatkamjorn, C & Xu, B-H 1970, 'Evaluation on the performance of field embankment testing biodegradable drains based on spectral method analysis', 20th International Conference of Soil Mechanics and Geotechnical Engineering (ICSMGE), Sydney, pp. 3031-3036.
Nguyen, TT, Indraratna, B, Rujikiatkamjorn, C, Singh, M, Korkitsuntornsan, W & Novais Silva, I 1970, 'Effects of Plastic Properties on the Fluidization Behaviour of Subgrade Soil under Heavy Haul Rail Load', Geo-Congress 2022, Geo-Congress 2022, American Society of Civil Engineers, Charlotte, US, pp. 204-213.
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Nguyen, TV & Nguyen, DN 1970, 'Secondary Reflections Amongst Multiple IRSs: Friends or Foes?', 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 1347-1352.
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Ni, Z, Zhang, JA, Huang, X & Yang, K 1970, 'Asynchronous Uplink Sensors Fused in Perceptive Mobile Networks', 2022 IEEE International Conference on Communications Workshops (ICC Workshops), 2022 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE, Seoul, SOUTH KOREA, pp. 824-829.
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This paper proposes a scheme that solves two challenging problems in parameter estimation using communication signals: (1) asynchronous transmitter and receiver; and (2) sensing receiver with a small number of antennas. These problems exist in parameter estimation for perceptive mobile networks and WiFi. The geometrically-separated transmitter and receiver in communications are typically asynchronous at clock level. For a small base-station or WiFi, the number of antenna elements in an array is usually limited, which limits the resolution of estimating the angle-of-arrivals (AOAs) of multipath signals. In this paper, we employ cross-antenna cross-correlation (CACC) operation to resolve the asynchronous issue and use the CACC outputs to generate a multi-domain signal block that combines three-domain receive samples to efficiently increase the resolution of AOAs. The proposed scheme enables the direct use of uplink communication signals for radio sensing, without requiring any modifications on infrastructure or advanced hardware, such as a full-duplex transceiver. It also enables the estimation of more number of paths than the number of antennas, hence sensing in a small base-station or WiFi becomes possible.
Ni, Z, Zhang, JA, Yang, K & Liu, R 1970, 'Frequency-Hopping Based Joint Automotive Radar-Communication Systems Using A Single Device', 2022 IEEE International Conference on Communications Workshops (ICC Workshops), 2022 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE, Seoul, SOUTH KOREA, pp. 480-485.
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Dual-functional radar-communication (DFRC), integrating the two functions into one system and sharing one transmitted signal, shows its great potential in self-driving networks. In this paper, we develop a single-device based multi-input single-output (MISO) DFRC vehicular system. Modulations of un-slotted ALOHA frequency-hopping (UA-FH) and fast FH, commonly used in automotive radar, are adopted to transmit the DFRC waveforms and to address severe interferences caused by an interfering vehicle that serves as a communication transmitter. Due to the asynchrony between vehicles, the FH sequences of the interfering vehicle are chosen from a fixed codebook. All channel parameters are then extracted via FH decoding from radar backscattered channels and communication channels, respectively. To further increase the accuracy, we proceed to propose an iterative algorithm that divides the signals into short segments and jointly obtains all parameters with high resolution. Finally, simulation results are provided and validate the proposed DFRC vehicular system.
Nikkhah, N, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 1970, 'Efficient Dual-Band Single-Port Rectifier for RF Energy Harvesting at FM and GSM Bands', 2022 Wireless Power Week (WPW), 2022 Wireless Power Week (WPW), IEEE, Bordeaux, France, pp. 141-145.
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This paper presents an efficient dual-band rectifier for radiofrequency energy harvesting (RFEH) applications at FM and GSM bands. The single-port rectifier circuit, which comprises a 3-port network, optimized T-matching circuits and voltage doubler, is designed, simulated and fabricated to obtain a high RF-to-DC power conversion efficiency (PCE). Measurement results show PCE of26% and 22% at -20dBm, and also 58% and 51% at -10dBm with a maximum amount of 69% and 65% at -2.5dBm and -5dBm, with single tone at 95 and 925 MHz, respectively. Besides, the fractional bandwidth of 21% at FM and 11% at GSM band is achieved. The measurement and simulation results are in good agreement. Consequently, the proposed rectifier can be a potential candidate for ambient RF energy harvesting and wireless power transfer (WPT). It should be noted that a 3-port network as a duplexer is designed to be integrated with single-port antennas which cover both FM and GSM bands as a low-cost solution. Moreover, based on simulation results, PCE has small variations when the load resistor varies from 10 to 18 k$\Omega$. Therefore, this rectifier can be utilized for any desired resistance within the range, such as sensors and IoT devices.
Nikolic, S, Suesse, T, Haque, R, Hassan, G, Lyden, S, Grundy, S, Daniel, S, Lal, S & Belkina, M 1970, 'An Australian University Comparison of Engineering Laboratory Learning Objectives Rankings', 33rd Australasian Association for Engineering Education Conference (AAEE 2022): Future of Engineering Education, Annual Conference of the Australasian Association for Engineering Education, AAEE, Sydney.
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CONTEXT The laboratory plays an important role within engineering education. Systematic literature reviews suggest the major focus of laboratory research is on the cognitive domain or that learning objectives are not clearly articulated. Work is needed to better understand holistic learning. PURPOSE OR GOAL This study builds upon previous research to develop a holistic understanding of laboratory learning in engineering. This study scaffolds previous research by exploring the importance of a holistic list of learning objectives. It further develops an understanding of what factors may influence ranking decisions. APPROACH OR METHODOLOGY/METHODS Australian academics were requested to rank items from the Laboratory Learning Objectives Measurement (LLOM) instrument using a Qualtrics survey. The items are separated across the cognitive, psychomotor and affective learning domains. A total of 95 academics from Australian institutions completed the survey. ACTUAL OR ANTICIPATED OUTCOMES While a general structure of alignment was found across the learning objectives across the three domains, the alignment was strongest across the affective domain. Evidence suggests that engineering discipline based decisions influence ranking order in the cognitive and psychomotor domains. CONCLUSIONS/RECOMMENDATIONS/SUMMARY The most important and least important objectives for each domain were found and was mostly consistent across Australian institutions. For everything in between (cognitive and psychomotor domains), further research is required to understand the impacts of discipline influences on ranking order. While previous research shows that affective items differ across international borders, they appeared uniform within Australian borders. This suggests that local culture, accreditation or expectations influence the importance of non-technical items and requires further exploration.
Nisal, S, Patibanda, R, Saini, A, Van Den Hoven, E & Mueller, FF 1970, 'TouchMate: Understanding the Design of Body Actuating Games using Physical Touch', Extended Abstracts of the Annual Symposium on Computer-Human Interaction in Play, CHI PLAY '22: The Annual Symposium on Computer-Human Interaction in Play, ACM, pp. 153-158.
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Okour, M, Falque, R, Vidal-Calleja, T & Alempijevic, A 1970, 'Sim2real Cattle Pose Prediction in 3D pointclouds', Australasian Conference on Robotics and Automation Acra, Australasian Conference on Robotics and Automation, ARAA, Brisbane, Australia, pp. 1-8.
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Cattle's body shape and joint articulation carry significant information about their well-being. Building a large dataset of any animals' 3D scans is a challenging task. However, such a dataset is required for training deep learning algorithms for 3D body pose estimation. In this work, we investigate how such a dataset can be constructed for cattle from a single 3D model animated by a digital artist. Further, we reduce the sim2real gap between the virtual dataset and real scans of animals by augmenting the shape of the 3D model to cover the range of possible body shapes. The generated dataset is tested on semantic key points detection with an encoder-decoder architecture.
Oliveira, FT, Tong, BW, Garcia, JA & Gay, VC 1970, 'CogWorldTravel: Design of a Game-Based Cognitive Screening Instrument', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Joint Conference on Serious Games (JCSG), Springer International Publishing, Bauhaus Univ Weimar, Weimar, GERMANY, pp. 125-139.
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Cognitive Screening Instruments are helpful in the early detection of cognitive changes and possible underlying dementia. These instruments test all major cognitive domains of an individual. Serious games have been investigated as an alternative approach for cognitive assessment because of their ability to motivate. Previous work mostly focused on finding out whether it is feasible to use a serious game for such purpose. We decided to investigate further how a serious game can be engaging and fun while prioritizing the cognitive assessment. In this paper, we describe the design, development, and evaluation of CogWorldTravel, a serious game that has the potential to be used for cognitive screening as it measures at least one aspect of each cognitive domain. CogWorldTravel features six game tasks that involve recognition memory, attention, working memory, language, immediate memory span, processing speed, inhibition, recognition of emotions, visuoconstructional, perceptual-motor, and planning abilities. The serious game also accommodates age-related changes and considers the gameplay preferences of older adults.
Oliveira, FTV, Garcia, JA & Gay, VC 1970, 'Evaluation of CogWorldTravel: A Serious Game for Cognitive Screening', 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), IEEE, pp. 1-8.
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As the world population is growing older, there is an urge to develop new technologies to support older adults, who are at a greater risk for the onset of dementia. Cognitive Screening Instruments (CSIs) can be used to screen for dementia. While there are a significant number of available well-researched and accepted CSIs, they are associated with drawbacks. Serious games have been investigated as an alternative instrument to overcome the constraints of traditional methods. The use of serious games for cognitive screening is still a relatively new field of research, with previous works mostly focusing on finding out whether there is a correlation or not between games and cognitive performance. Serious games that engage older adults and meet the criteria of CSIs remain an open challenge. To address this challenge, we developed CogWorldTravel, a serious game for the cognitive screening of older adults. In this paper, we describe the results of the evaluation of CogWorldTravel, which consisted of conducting semi-structured interviews with five experts in dementia assessment. Results suggest that the game involves recognition memory, attention, working memory, language, immediate memory span, processing speed, inhibition, recognition of emotions, visuoconstructional, perceptual-motor, and planning abilities.
Olszak, CM, Kozanoglu, D & Zurada, JM 1970, 'Introduction to the Minitrack on Business Intelligence and Big Data for Innovative and Sustainable Development of Organizations.', HICSS, ScholarSpace, pp. 1-2.
Omar, A, Beydoun, G, Win, KT & Jelinek, HF 1970, 'The Incremental Development of a Diabetes 2 Knowledge Base System using Ripple Down Rules.', PACIS, pp. 18-18.
Ordibazar, AH, Hussain, O & Saberi, M 1970, 'A Recommender System and Risk Mitigation Strategy for Supply Chain Management Using the Counterfactual Explanation Algorithm', Springer International Publishing, pp. 103-116.
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Ouyang, Y & Tomamichel, M 1970, 'Learning quantum graph states with product measurements', 2022 IEEE International Symposium on Information Theory (ISIT), 2022 IEEE International Symposium on Information Theory (ISIT), IEEE, pp. 2963-2968.
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Pan, L, Yao, L, Zhang, W & Wang, X 1970, 'Mitigating Multi-class Unintended Demographic Bias in Text Classification with Adversarial Learning', Web Information Systems Engineering – WISE 2022, International Conference on Web Information Systems Engineering, Springer International Publishing, Biarritz, France, pp. 386-394.
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Text classification enables higher efficiency on text data queries in information retrieval. However, unintended demographic bias can impair text toxicity classification. Thus, we propose a novel debiasing framework utilizing Adversarial Learning on word embeddings of multi-class sensitive demographic words to alleviate this bias. Slight adjustment over word embeddings with flipped sensitive indices is achieved, and the modified word embeddings are used in the downstream classification task to realize Demographic Parity. The experimental results validate the effectiveness of our proposed method in mitigating multi-class unintended demographic bias without impairing the original classification accuracy.
Pang, G, Li, J, van den Hengel, A, Cao, L & Dietterich, TG 1970, 'ANDEA: Anomaly and Novelty Detection, Explanation, and Accommodation', Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 4892-4893.
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Pang, L, Liu, W, Wu, L, Xie, K, Guo, S, Chalapathy, R & Wen, M 1970, 'Applied Machine Learning Methods for Time Series Forecasting', Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM '22: The 31st ACM International Conference on Information and Knowledge Management, ACM, pp. 5175-5176.
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Parnell, J, Jauregi Unanue, I & Piccardi, M 1970, 'A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization', Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Dublin, IRELAND, pp. 5112-5128.
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Patan, R, Parizi, RM, Pouriyeh, S, Khan, MS & Gandomi, AH 1970, 'A Secured Certificateless Sign-encrypted Blockchain Communication for Intelligent Transport System', 2022 IEEE Conference on Communications and Network Security (CNS), 2022 IEEE Conference on Communications and Network Security (CNS), IEEE, pp. 1-7.
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Patibanda, R, Van Den Hoven, E & Mueller, FF 1970, 'Towards Understanding the Design of Body-Actuated Play', Extended Abstracts of the Annual Symposium on Computer-Human Interaction in Play, CHI PLAY '22: The Annual Symposium on Computer-Human Interaction in Play, ACM, pp. 388-391.
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Paul, G, Tomidei, L, Sick, N, Guertler, M, Carmichael, M & Wambsganss, A 1970, 'Guidelines for Safe Collaborative Robot Design and Implementation', Guidelines for Safe Collaborative Robot Design and Implementation, Guidelines for Safe Collaborative Robot Design and Implementation, Sydney.
Pearce, A, Zhang, JA & Xu, R 1970, 'Regional Trajectory Analysis through Multi-Person Tracking with mmWave Radar', 2022 IEEE Radar Conference (RadarConf22), 2022 IEEE Radar Conference (RadarConf22), IEEE, New York, NY.
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Pẽna, F, Mehami, J, Falque, R, Patten, T, Alempijevic, A & Vidal-Calleja, T 1970, 'Subcutaneous Fat Depth Regression Using Hyperspectral and Depth Imaging', Australasian Conference on Robotics and Automation Acra, Australasian Conference on Robotics and Automation, ARAA, Brisbane, Australia, pp. 1-10.
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Robotic perception is becoming an important component for automation in the meat processing industry. Whether for contaminant detection or automatic cutting, multimodal perception systems, in particular, based on hyperspectral imaging have the ability to provide information that goes beyond the texture and colour of a surface. In this paper, we present a learning-based method to estimate subcutaneous fat depth in meat cuts by leveraging hyperspectral data models that rely on the knowledge of modelled light sources and surface shape information. Data from a fully calibrated hyperspectral and colour depth (RGB-D) camera system is used as input. Fat depth ground truth is recovered via a novel systematic approach that ray casts a computed tomography (CT) mesh of the meat cuts, which is nonrigidly aligned with a depth reconstruction captured by the RGB-D camera. We thus evaluate machine learning methods that can handle small datasets, by employing dimensionality reduction and data augmentation to address the limited amount of imbalanced data that is acquired. Our results show that leveraging shape and light models, coupled with machine learning methods that capture nonlinearities and spatial correlations produces the most accurate results.
Peng, X, Liu, F, Zhang, J, Lan, L, Ye, J, Liu, T & Han, B 1970, 'Bilateral Dependency Optimization', Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 1358-1367.
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Peng, Y, Lin, X, Yu, M, Zhang, W & Qin, L 1970, 'TDB: Breaking All Hop-Constrained Cycles in Billion-Scale Directed Graphs.', CoRR.
Peng, Y, Song, A, Ciesielski, V, Fayek, HM & Chang, X 1970, 'PRE-NAS', Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '22: Genetic and Evolutionary Computation Conference, ACM, Boston, MA, pp. 1066-1074.
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Neural architecture search (NAS) aims to automate architecture engineering in neural networks. This often requires a high computational overhead to evaluate a number of candidate networks from the set of all possible networks in the search space during the search. Prediction of the networks' performance can alleviate this high computational overhead by mitigating the need for evaluating every candidate network. Developing such a predictor typically requires a large number of evaluated architectures which may be difficult to obtain. We address this challenge by proposing a novel evolutionary-based NAS strategy, Predictor-assisted E-NAS (PRE-NAS), which can perform well even with an extremely small number of evaluated architectures. PRE-NAS leverages new evolutionary search strategies and integrates high-fidelity weight inheritance over generations. Unlike one-shot strategies, which may suffer from bias in the evaluation due to weight sharing, offspring candidates in PRE-NAS are topologically homogeneous, which circumvents bias and leads to more accurate predictions. Extensive experiments on NAS-Bench-201 and DARTS search spaces show that PRE-NAS can outperform state-of-the-art NAS methods. With only a single GPU searching for 0.6 days, competitive architecture can be found by PRE-NAS which achieves 2.40% and 24% test error rates on CIFAR-10 and ImageNet respectively.
Peng, Y, Ying, M & Wu, X 1970, 'Algebraic reasoning of Quantum programs via non-idempotent Kleene algebra.', PLDI, 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation (PLDI), ACM, San Diego, CA, pp. 657-670.
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We investigate the algebraic reasoning of quantum programs inspired by the success of classical program analysis based on Kleene algebra. One prominent example of such is the famous Kleene Algebra with Tests (KAT), which has furnished both theoretical insights and practical tools. The succinctness of algebraic reasoning would be especially desirable for scalable analysis of quantum programs, given the involvement of exponential-size matrices in most of the existing methods. A few key features of KAT including the idempotent law and the nice properties of classical tests, however, fail to hold in the context of quantum programs due to their unique quantum features, especially in branching. We propose Non-idempotent Kleene Algebra (NKA) as a natural alternative and identify complete and sound semantic models for NKA as well as their quantum interpretations. In light of applications of KAT, we demonstrate algebraic proofs in NKA of quantum compiler optimization and the normal form of quantum while-programs. Moreover, we extend NKA with Tests (i.e., NKAT), where tests model quantum predicates following effect algebra, and illustrate how to encode propositional quantum Hoare logic as NKAT theorems.
Pereira, A, Kruzins, E, Sarawi, SA, Abbott, D, Menk, F, Yuversedyan, O, Schwitter, B & Fattorini, T 1970, 'Next Generation Phased Arrays for Deep Space Communications', 2022 IEEE Aerospace Conference (AERO), 2022 IEEE Aerospace Conference (AERO), IEEE, pp. 1-18.
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Perrin, R, Halkon, B & Guo, Z 1970, 'Sacred Geometry and Axial Symmetry in the Modern Hand Bell', Acoustical Society of New Zealand (ASNZ) Conference, Acoustical Society of New Zealand (ASNZ) Conference, Wellington, New Zealand.
Perry, S, Da Silva Cruz, LA, Prazeres, J, Pinheiro, A, Dumic, E, Lazzarotto, D & Ebrahimi, T 1970, 'Subjective and Objective Testing in Support of the JPEG Pleno Point Cloud Compression Activity', 2022 10th European Workshop on Visual Information Processing (EUVIP), 2022 10th European Workshop on Visual Information Processing (EUVIP), IEEE, pp. 1-6.
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Point clouds have many applications in today's society ranging from entertainment to autonomous driving. With these new applications comes the need to compress the growing volume of point cloud data in a manner that is both suitable for human visualization and machine processing applications. The JPEG Pleno Point Cloud activity has been working toward a learning-based coding standard for point clouds, offering a single-stream, compact compressed domain representation, supporting advanced flexible data access functionalities targeting both in-teractive human visualization, and effective performance for 3D processing and machine-related computer vision tasks. As part of this activity, the JPEG Committee has been performing a number of exploration studies to evaluate existing coding standards as well set up baseline anchors and examine objective metrics against which new learning-based solutions may be compared. This article provides an overview of the JPEG Pleno Point Cloud activity and discusses challenges and solutions to the problem of evaluating and comparing cloud coding solutions. Experimental results will be presented demonstrating methodologies used by the JPEG Committee for point cloud compression assessment as well as outlining the performance of current state of the art compression standards on point clouds as well as the sensitivity of the objective metrics used for this activity to various adjustable parameters.
Pham, T-M, Nguyen, T-M, Nguyen, X-T-T, Chu, H-N & Son, NH 1970, 'Fast Optimal Resource Allocation for Resilient Service Coordination in an NFV- Enabled Internet-of- Things System', 2022 International Conference on Advanced Technologies for Communications (ATC), 2022 International Conference on Advanced Technologies for Communications (ATC), IEEE, pp. 141-146.
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Pietroni, N, Campen, M, Sheffer, A, Cherchi, G, Bommes, D, Gao, X, Scateni, R, Ledoux, F, Remacle, J-F & Livesu, M 1970, 'A Course on Hex-Mesh Generation and Processing', ACM SIGGRAPH Asia 2022 Courses, SA '22: SIGGRAPH Asia 2022, ACM, pp. 1-78.
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Pileggi, SF 1970, 'Getting Formal Ontologies Closer to Final Users Through Knowledge Graph Visualization: Interpretation and Misinterpretation', Springer International Publishing, pp. 611-622.
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Pizarro, V, Merigó, JM, Valenzuela, L & Aciares, S 1970, 'A Bibliometric Study of Key Journals in Corporate Social Responsibility', Springer International Publishing, pp. 205-216.
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Poblete, P, Syasegov, YY, Farhangi, M, Aguilera, RP, Siwakoti, YP, Lu, D & Pereda, J 1970, 'Optimal Switching Sequence Direct Power Control for AC/DC Converters with Enhanced Converter Model for Lower Switching Frequencies', 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), IEEE, pp. 1-6.
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Polikarpov, M, Emelianov, G, Hubner, F, Farooq, A, Prasad, R, Deuse, J & Schiemann, J 1970, 'Automated Multi-sensory Data Collection System for Continuous Monitoring of Refrigerating Appliances Recycling Plants', 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, pp. 1-4.
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Pradhan, S & Lama, S 1970, 'Integrating Design Science Research with Sustainability: A Systematic Literature Review', Pacific Asia Conference on Information Systems, Taipei/Sydney.
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Sustainability is at the forefront in today’s business world. There is a growing trend of embedding design science research into sustainability projects as a research approach aimed at solving the real world problems. However, there are no clear guidelines and uniformity in terms of frameworks, methods and evaluations for using DSR for sustainability. Consequently, there is a need for systematic investigation of DSR for sustainability. In absence of such studies, we aim to conduct systematic literature review using PRISMA method to investigate how DSR is integrated in sustainability research. In addition to the descriptive findings from 42 selected articles, this review indicates that the use of DSR for sustainability is advancing and attracting many researchers, but, several studies have used DSR in their research without properly validating the need and relevance of methods to solve sustainability concerns.
Pradhan, S, Lama, S & Bunker, D 1970, 'ICT Adoption for Tourism Disaster Management: A Systematic Review', Proceedings of the International Iscram Conference, pp. 215-227.
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The tourism sector is not new to disruptions from natural disasters or human induced crises and has been recalibrating the way they operate and sustain. The scale and impact of the COVID-19 pandemic has highly impacted global tourism and the economies that rely on tourism. It has brought phenomenal challenges to humankind and many tourism organisations are on the brink of collapse and this will have a cascading effect on countries and their citizens for years to come. This paper presents the systematic literature review on the adoption of ICTs in tourism when preparing for and managing disasters. This review was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) Flow diagram. Out of 585 articles from four databases, 35 peer-reviewed journal and conference articles were included for analysis. Research on potential adoption of ICT and associated tools for tourism disaster management, remains scarce. With the world coming to terms with the “new normal” of social distancing and increased use of ICT tools such as virtual reality, virtual guides, chatbots, social media and contact tracing apps due to pandemic, the investigation of adoption of such tools is long overdue. Within limited empirical studies, this review shows some trends and opportunities for the development of a critical research agenda in this area. Other innovative tools such as AI, GIS, IoTs, and visual story telling have been adopted for managing disasters related to tourism. This research demonstrates the potential adoption of ICT tools for effective disaster management and the subsequent support of global tourism. To counter the catastrophic effect on the tourism industry from COVID-19 pandemic, it is paramount to recognise cultural sensitivities and study how advancement in technology can be harnessed in all contexts. In addition to this, further exploratory research should be conducted to better understand crisis as an opportunity to develop an...
Punetha, P & Nimbalkar, S 1970, 'Mathematical Modeling of the Short-Term Performance of Railway Track Under Train-Induced Loading', Lecture Notes in Civil Engineering, Springer International Publishing, pp. 15-24.
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The accurate prediction of the track deformation under train-induced repetitive loading is inevitable to assess the efficiency of a railway track. This paper presents an analytical technique to calculate the transient deformations in a railway track subjected to train-induced loading. The method considers the track substructure as multilayered media in which the behavior of an individual track layer is simulated using a mass-spring-dashpot model. Unlike existing approaches to model the track substructure as an equivalent single or double layer, the proposed analytical approach considers all the three layers of the ballasted track (i.e., ballast, capping or subballast and subgrade). The accuracy of the proposed technique is investigated by comparing the predicted values of track displacement with the published data available in the literature. The predicted results are found to be in good agreement with past studies. A parametric study on the substructure behavior revealed that the elastic modulus of track layers significantly influences the track response.
Qin, JC, Yan, Y, Jiang, R, Mo, H & Dong, D 1970, 'Alternating Direction Method of Multipliers for Solving Joint Chance Constrained Optimal Power Flow Under Uncertainties', IFAC-PapersOnLine, Elsevier BV, pp. 116-121.
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Qu, Y, Chen, S, Gao, L, Cui, L, Sood, K & Yu, S 1970, 'Personalized Privacy-Preserving Medical Data Sharing for Blockchain-based Smart Healthcare Networks', ICC 2022 - IEEE International Conference on Communications, ICC 2022 - IEEE International Conference on Communications, IEEE, pp. 4229-4234.
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With the growing proliferation of intelligent end devices and data analytics techniques, real momentum towards the development of smart healthcare networks (SHN) has already been evident. Multiple parties in SHNs continuously exchange medical data in order to achieve a precise diagnosis and process optimization. Privacy issue emerges since medical data are susceptible, while the combination of a series of medical data may lead to further privacy leakage. Adversaries launch unceasingly launch poisoning attacks, a dominant attack to maliciously manipulate data, severely impact the authenticity of the data transmitting over the SHNs, leading to misdiagnosing or even physical damage. In this paper, we propose a personalized differential privacy model built upon blockchain, in which the community density is exploited to customize the degree of privacy protection and inject corresponding noise data. Besides using blockchain as the underlying network architecture to defeat poisoning attacks. The proposed model can guarantee the authentication of the differentially private data, traceability of data, and single-point failure avoidance in SHN. Evaluation and extensive results using real-world data sets demonstrate the superiority of the proposed model.
Qu, Z, Tegegne, Y, Simoff, SJ, Kennedy, PJ, Catchpoole, DR & Nguyen, QV 1970, 'Enhancing Understandability of Omics Data with SHAP, Embedding Projections and Interactive Visualisations', Communications in Computer and Information Science, Springer Nature Singapore, pp. 58-72.
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Uniform Manifold Approximation and Projection (UMAP) is a new and effective non-linear dimensionality reduction (DR) method recently applied in biomedical informatics analysis. UMAP’s data transformation process is complicated and lacks transparency. Principal component analysis (PCA) is a conventional and essential DR method for analysing single-cell datasets. PCA projection is linear and easy to interpret. The UMAP is more scalable and accurate, but the complex algorithm makes it challenging to endorse the users’ trust. Another challenge is that some single-cell data have too many dimensions, making the computational process inefficient and lacking accuracy. This paper uses linkable and interactive visualisations to understand UMAP results by comparing PCA results. An explainable machine learning model, SHapley Additive exPlanations (SHAP) run on Random Forest (RF), is used to optimise the input single-cell data to make UMAP and PCA processes more efficient. We demonstrate that this approach can be applied to high-dimensional omics data exploration to visually validate informative molecule markers and cell populations identified from the UMAP-reduced dimensionality space.
Rafiei, A & Wang, Y-K 1970, 'Automated Major Depressive Disorder Classification using Deep Convolutional Neural Networks and Choquet Fuzzy Integral Fusion', 2022 IEEE Symposium Series on Computational Intelligence (SSCI), 2022 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 186-192.
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Major depressive disorder (MDD) is a common and severe ailment impacting functional frailty, while its concrete manifestations have been shrouded in mystery. Hence, manual diagnosis of MDD is an arduous and subjective task. Despite the aid of electroencephalogram (EEG) signals in the detection, developing intelligent systems are required to improve clinical utility, performance, and efficiency. In this study, we focus on the automated detection of MDD via raw EEG data using convolutional neural networks (CNN). For this objective, we first extracted the short-time Fourier transform (STFT) of EEG records for five distinct band powers and created an image representing the frequency oscillation of every channel during a resting state. Afterward, we applied three approaches to determine whether a subject is MDD or a Healthy individual. In the first approach, a 2D-CNN model was developed for each band power to detect MDD separately. Second, the outcomes of the developed models were used to establish a Choquet fuzzy integral fusion to classify subjects using all of the previous models. The third approach was dedicated to introducing a 3D-CNN architecture. This model received three-dimensional data by putting different band powers' images together. The two last approaches achieved a 95.65% accuracy and 100% sensitivity to detect MDD. The proposed approaches can help clinicians as straightforward, efficient, and intelligent diagnostic tools for detecting MDD.
Ragozin, K, Marky, K, Lu, J & Kunze, K 1970, 'EyeMove - Towards Mobile Authentication using EOG Glasses', Augmented Humans 2022, AHs 2022: Augmented Humans 2022, ACM, pp. 10-14.
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Rahimi, I, Picard, T, Morabito, A, Pampalis, K, Abignano, A & Gandomi, AH 1970, 'Comparison of Trajectory and Population-Based Algorithms for Optimizing Constrained Open-Pit Mining Problem', 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), IEEE, pp. 109-112.
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Ram, R & Rizoiu, M-A 1970, 'Data-driven ideology detection: a case study of far-right extremist', Defence Human Sciences Symposium, Defence Human Sciences Symposium, Sydney, Australia.
Raza, MR, Hussain, W & Varol, A 1970, 'Performance Analysis of Deep Approaches on Airbnb Sentiment Reviews', 2022 10th International Symposium on Digital Forensics and Security (ISDFS), 2022 10th International Symposium on Digital Forensics and Security (ISDFS), IEEE, Maltepe, TURKEY, pp. 1-5.
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Consumer reviews in the Airbnb marketplace are one of the key attributes to measure the quality of services and the main determinant of consumer rentals decisions. Such feedback can impact both a new and repeated consumer's choice decision. The way to manage poor reviews can help to save or damage the host's reputation. Sentiment analysis enables an Airbnb host to get an insight into the business, pinpoint degradation of the specific component of compound services and assist in managing it proactively. Multiple Deep Learning algorithms have been used for Natural Language Processing (NLP). For optimal sentiment management in the Airbnb marketplace, it is crucial to identify the right algorithm. The paper uses multiple Deep Learning algorithms to identify different aspects of guest reviews and analyze their accuracies. The paper uses four accuracy measurement benchmarks - Precision, Recall, F1-score and Support to analyze results. The analysis shows that the GRU method achieves the best results with the highest classification metrics values as compared to RNN and LSTM.
Reidsema, C, Hadgraft, R & Male, S 1970, 'Engineering Futures 2035: Implementing the Vision', 9th Research in Engineering Education Symposium (REES 2021) and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021), 9th Research in Engineering Education Symposium & 32nd Australasian Association for Engineering Education Conference, Research in Enineering Education Network (REEN), pp. 1140-1140.
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Reja, VK, Varghese, K & Ha, QP 1970, 'As-built data acquisition for vision-based construction progress monitoring: A qualitative evaluation of factors', Proceedings of 10th World Construction Symposium 2022, 10th World Construction Symposium, Building Economics and Management Research Unit (BEMRU), University of Moratuwa, pp. 138-149.
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The accuracy of computer vision-based progress monitoring of construction projects depends on the quality of data acquired. The data acquisition can be conducted through different vision-based sensors combined with several options for sensor mounting. Several factors affect this combination and considering these factors in selecting the acquisition technology and sensor mounting combination is critical for acquiring accurate vision-based data for the project. Currently, their definition and impact of these factors on the selection of these technologies are both subjective, and there are no formal studies to evaluate the impact. Hence, in this study, we first identify and define twelve key factors affecting data acquisition technology and eight factors affecting sensor mounting. Next, a questionnaire survey was designed, and responses from professionals were used to evaluate the Relative Importance Index (RII) for the individual factors for these technologies and methods. The obtained ratings were compared to the author's initial assessment, and the cause for a few variations obtained was justified. This study provides a clear assessment of these factors and forms a basis for selection based on the factors involved with the project requirements.
Ren, P, Li, C, Wang, G, Xiao, Y, Du, Q, Liang, X & Chang, X 1970, 'Beyond Fixation: Dynamic Window Visual Transformer', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, New Orleans, LA, pp. 11977-11987.
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Recently, a surge of interest in visual transformers is to reduce the computational cost by limiting the calculation of self-attention to a local window. Most current work uses a fixed single-scale window for modeling by default, ignoring the impact of window size on model performance. How-ever, this may limit the modeling potential of these window-based models for multi-scale information. In this paper, we propose a novel method, named Dynamic Window Vision Transformer (DW-ViT). The dynamic window strategy proposed by DW- ViT goes beyond the model that employs a fixed single window setting. To the best of our knowl-edge, we are the first to use dynamic multi-scale windows to explore the upper limit of the effect of window settings on model performance. In DW- ViT, multi-scale information is obtained by assigning windows of different sizes to different head groups of window multi-head self-attention. Then, the information is dynamically fused by assigning different weights to the multi-scale window branches. We con-ducted a detailed performance evaluation on three datasets, ImageNet-1K, ADE20K, and COCO. Compared with re-lated state-of-the-art (SoTA) methods, DW- ViT obtains the best performance. Specifically, compared with the current SoTA Swin Transformers [31], DW-ViT has achieved con-sistent and substantial improvements on all three datasets with similar parameters and computational costs. In addition, DW-ViT exhibits good scalability and can be easily inserted into any window-based visual transformers.11Code release: https://github.com/pzhren/DW-ViT. This work was done when the first author interned at Dark Matter AI.
Richmond, J & Halkon, B 1970, 'COVERT COLLECTION AND AUTOMATED ANALYSIS OF VIBROACOUSTIC INTELLIGENCE FROM DRONE MOUNTED LASER DOPPLER VIBROMETERS', Proceedings of the International Congress on Sound and Vibration, International Congress on Sound and Vibration, Society of Acoustics, Singapore, pp. 1-8.
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The synthesis of Laser Doppler Vibrometers (LDVs) with autonomous or remotely piloted vehicles such as drones has the potential to enable highly sensitive, non-invasive and discrete vibroacoustic intelligence gathering processes in hostile environments without risk to human life. This work builds upon a previously developed vibroacoustic noise reduction and speaker diarisation system by exploring the effect of feature extraction parameters on diarisation performance. By tuning the Mel Frequency Cepstral Coefficients (MFCC) and x-vector windowing parameters - how many samples are used to produce a single feature vector - the optimal combination was determined to be 0.305 and 0.5 seconds, respectively, resulting in an error of approximately 5%. This work also presents a live or'online' vibroacoustic intelligence processing and analysis system by utilising an open-set clustering algorithm - Real-Time Exponential Filter Clustering (RTEFC). Similarly, the effect of the similarity threshold D and the exponential filter parameter α on diarisation performance was explored. The most effective combination was 0.96 and 0.75, respectively, resulting in an error of approximately 10%. Furthermore, a live transcription stage has also been included using the Microsoft Azure Speech-to-Text API, automating another important intelligence analysis process.
Rifai, A & Williams, R 1970, '3D-PRINTED NANOMATERIAL-EMBEDDED HYDROGEL MATRICES FOR IMPROVED CLINICAL OUTCOMES IN SPINAL SURGERY', TISSUE ENGINEERING PART A, MARY ANN LIEBERT, INC, pp. S536-S537.
Rizoiu, M-A, Willingham, T & Kernot, D 1970, 'Grey Zone activity: measuring the resilience of social systems to influence operations', Australian Defence Science, Technology and Research Summit, Australian Defence Science, Technology and Research Summit, Sydney.
Rocha, CGD, Wijayaratna, K & Koskela, L 1970, 'Why Is Flow Not Flowing in the Construction Industry?', Annual Conference of the International Group for Lean Construction, 30th Annual Conference of the International Group for Lean Construction (IGLC), International Group for Lean Construction, Edmonton, pp. 283-294.
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Rogers, JT, Ball, JE & Gurbuz, AC 1970, 'Data-Driven Covariance Estimation', 2022 IEEE International Symposium on Phased Array Systems & Technology (PAST), 2022 IEEE International Symposium on Phased Array Systems & Technology (PAST), IEEE, pp. 1-5.
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Roshandel, E, Mahmoudi, A, Soong, WL, Kahourzade, S, Lei, G, Guo, Y & Kalisch, N 1970, 'Design of a 100 kW Axial Flux Permanent Magnet Direct Drive Machine for a Hybrid Electric Vehicle', 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), IEEE, pp. 1-6.
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Roy, P, Ghosh, S, Bhattacharya, S, Pal, U & Blumenstein, M 1970, 'Scene Aware Person Image Generation through Global Contextual Conditioning', 2022 26th International Conference on Pattern Recognition (ICPR), 2022 26th International Conference on Pattern Recognition (ICPR), IEEE, pp. 2764-2770.
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Roy, P, Ghosh, S, Bhattacharya, S, Pal, U & Blumenstein, M 1970, 'TIPS: Text-Induced Pose Synthesis', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature Switzerland, pp. 161-178.
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In computer vision, human pose synthesis and transfer deal with probabilistic image generation of a person in a previously unseen pose from an already available observation of that person. Though researchers have recently proposed several methods to achieve this task, most of these techniques derive the target pose directly from the desired target image on a specific dataset, making the underlying process challenging to apply in real-world scenarios as the generation of the target image is the actual aim. In this paper, we first present the shortcomings of current pose transfer algorithms and then propose a novel text-based pose transfer technique to address those issues. We divide the problem into three independent stages: (a) text to pose representation, (b) pose refinement, and (c) pose rendering. To the best of our knowledge, this is one of the first attempts to develop a text-based pose transfer framework where we also introduce a new dataset DF-PASS, by adding descriptive pose annotations for the images of the DeepFashion dataset. The proposed method generates promising results with significant qualitative and quantitative scores in our experiments.
Rubio, F, Pereda, J, Rojas, F & Poblete, P 1970, 'Hybrid Sorting Strategy for Modular Multilevel Converters With Partially Integrated 2nd Life Battery Energy Storage Systems for fast EV charging', 2022 IEEE 7th Southern Power Electronics Conference (SPEC), 2022 IEEE 7th Southern Power Electronics Conference (SPEC), IEEE, pp. 1-7.
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Rujikiatkamjorn, C, Indraratna, B, Arivalagan, J & Dzaklo, C 1970, 'Innovative ground improvement techniques to stabilize unstable subgrade', Proceedings of 16th International Conference on Geotechnical Engineering, Pakistan Geotechnical Society, Lahore, Pakistan, pp. 55-63.
Rujikiatkamjorn, C, Indraratna, B, Yin, JH, Baral, P & Leroueil, S 1970, 'Use of isotache for long term radial consolidation analysis', Proceedings of the 20th International Conference on Soil Mechanics and Geotechnical Engineering, Australian Geomechanics Society, Sydney, pp. 3079-3083.
Sabry, M, Gardner, A & Hadgraft, R 1970, 'Student Learning Outcomes from Work placement: A Systematic Literature Review', 9th Research in Engineering Education Symposium (REES 2021) and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021), 9th Research in Engineering Education Symposium & 32nd Australasian Association for Engineering Education Conference, Research in Enineering Education Network (REEN), pp. 452-462.
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Saini, A, Huang, H, Patibanda, R, Overdevest, N, Van Den Hoven, E & Mueller, FF 1970, 'SomaFlatables: Supporting Embodied Cognition through Pneumatic Bladders', Adjunct Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology, UIST '22: The 35th Annual ACM Symposium on User Interface Software and Technology, ACM, pp. 1-4.
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Applying the theory of Embodied Cognition through design allows us to create computational interactions that engage our bodies by modifying our body schema. However, in HCI, most of these interactive experiences have been stationed around creating sensing-based systems that leverage our body's position and movement to offer an experience, such as games using Nintendo Wii and Xbox Kinect. In this work, we created two pneumatic inflatables-based prototypes that actuate our body to support embodied cognition in two scenarios by altering the user's body schema. We call these 'SomaFlatables'and demonstrate the design and implementation of these inflatables based prototypes that can move and even extend our bodies, allowing for novel bodily experiences. Furthermore, we discuss the future work and limitations of the current implementation.
Saleh, K, Grigorev, A & Mihaita, A-S 1970, 'Traffic Accident Risk Forecasting using Contextual Vision Transformers', 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), IEEE, pp. 2086-2092.
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Saleh, K, Mihaita, A-S, Yu, K & Chen, F 1970, 'Real-time Attention-Augmented Spatio-Temporal Networks for Video-based Driver Activity Recognition', 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), IEEE, pp. 1579-1585.
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Saleh, K, Yu, K & Chen, F 1970, 'Video-Based Student Engagement Estimation via Time Convolution Neural Networks for Remote Learning', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 34th Australasian Joint Conference on Artificial Intelligence (AI), Springer International Publishing, Univ Technol Sydney, ELECTR NETWORK, pp. 658-667.
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Given the recent outbreak of COVID-19 pandemic globally, most of the schools and universities have adapted many of the learning materials and lectures to be delivered online. As a result, the necessity to have some quantifiable measures of how the students are perceiving and interacting with this ‘new normal’ way of education became inevitable. In this work, we are focusing on the engagement metric which was shown in the literature to be a strong indicator of how students are dealing with the information and the knowledge being presented to them. In this regard, we have proposed a novel data-driven approach based on a special variant of convolutional neural networks that can predict the students’ engagement levels from a video feed of students’ faces. Our proposed framework has achieved a promising mean-squared error (MSE) score of only 0.07 when evaluated on a real dataset of students taking an online course. Moreover, the proposed framework has achieved superior results when compared with two baseline models that are commonly utilised in the literature for tackling this problem.
Salgotra, R, Mirjalili, S & Gandomi, AH 1970, 'Enhancing Differential Evolution Algorithm: Adaptation for CEC 2017 and CEC 2021 Test Suites', 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), IEEE, pp. 235-240.
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Salgotra, R, Singh, S, Singh, U, Kundu, K & Gandomi, AH 1970, 'An Adaptive Version of Differential Evolution for Solving CEC2014, CEC 2017 and CEC 2022 Test Suites', 2022 IEEE Symposium Series on Computational Intelligence (SSCI), 2022 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 1644-1649.
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Salvadores, T, Pereda, J & Poblete, P 1970, 'Inter-Cluster Power Control of Modular Multilevel Converters with Integrated Battery Energy Storage for Electric Vehicle Low Voltage Operation', 2022 IEEE 7th Southern Power Electronics Conference (SPEC), 2022 IEEE 7th Southern Power Electronics Conference (SPEC), IEEE, pp. 1-6.
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Samal, PB, Chen, SJ & Fumeaux, C 1970, 'Miniaturized Wearable Antennas using Resonant Current Path Length Manipulation', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 169-170.
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Samal, PB, Chen, SJ, Zhang, Q & Fumeaux, C 1970, 'A PDMS-Based Low-Profile Monopole Antenna for Wearable Applications', 2022 IEEE MTT-S International Microwave Biomedical Conference (IMBioC), 2022 IEEE MTT-S International Microwave Biomedical Conference (IMBioC), IEEE, pp. 271-273.
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Samarakoon Jayasekara Mudiyans, SKM 1970, 'Acidic Flow-Induced Clogging of Permeable Reactive Barriers in Pyritic Terrain', Geo-Congress 2022, Charlotte, North Carolina.
Samarakoon Jayasekara Mudiyans, SKM 1970, 'PERMEABLE REACTIVE BARRIERS FOR REMEDIATING ACIDIC GROUNDWATER IN PYRITIC TERRAIN', Lahore, Pakistan.
Sangeetha, V, KrishanKumar, R, Ravichandran, KS & Gandomi, AH 1970, 'A Hybrid Gain-Ant Colony Algorithm for Green Vehicle Routing Problem', 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), IEEE, pp. 103-108.
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Sanni, MT, Pota, H, Dong, D & Mo, H 1970, 'A Hybrid Decoupled Method For Tertiary Voltage Control of Active Distribution Networks', 2022 IEEE Symposium Series on Computational Intelligence (SSCI), 2022 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 694-699.
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Sansom, T, Sepehrirahnama, S, Halkon, B, Lai, JCS & Oberst, S 1970, 'LASER INTENSITY-INDUCED DAMAGE EFFECTS ON DYNAMIC CHARACTERISATION OF WINGS OF THE EUROPEAN HONEYBEE (APIS MELLIFERA)', Proceedings of the International Congress on Sound and Vibration, International Congress on Sound and Vibration, Singapore.
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Micromechanical and mesoscopic structures including biological tissue, insect appendages or hearing organs can be dynamically characterised through laser Doppler vibrometry (LDV). LDV measures surface vibrations with high spatial resolution, and high dynamic and frequency ranges without causing obvious damage to the specimens. Generally speaking, higher laser intensities lead to higher signal-to-noise ratios, desirable for accurate vibration measurements. However, for certain wavelengths and too high intensity values, the LDV, though only 1 mW output, may damage organic tissue. We aim to illustrate LDV measurements by studying the vibration characteristics of forewings (N= 5) of the European honeybee (Apis mellifera, Hymenoptera). We qualify the level of damage caused by a laser vibrometer with a Helium-Neon laser (532 nm) of a microsystems analyser using a white light-microscope. We monitor the change in the first three eigenfrequencies and the non-damaging intensity level at which the forced-vibration response (FRF) of the wings can still be measured. The first three frequencies at 0.48±0.04 kHz, 1.05±0.06 kHz, and 1.55±0.12 kHz, and their mode shapes of damaged wings are compared against those reported in literature and show ca. 15% frequency deviation. Assuming the stiff element hypothesis, the wing's first bending mode is expected to be at higher frequencies (485±37 Hz) than the approximate wing-beat frequency (234±13.9 Hz). Implementing a finite element model of the wing using a reinforced membrane geometry approach, the measurement results of the undamaged wings are verified. Our results indicate that the intensity levels in LDV measurements on bee wings need to be carefully monitored. The established experimental methodology based on non-damaging laser intensity can also be used for studies of other insects' filigree structures such as their appendages and their vibration and acoustic sensing organs.
Saputra, YM, Nguyen, DN, Hoang, DT & Dutkiewicz, E 1970, 'In-Network Caching and Learning Optimization for Federated Learning in Mobile Edge Networks', ICC 2022 - IEEE International Conference on Communications, ICC 2022 - IEEE International Conference on Communications, IEEE, Seoul, Korea, Republic of, pp. 1653-1658.
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In this paper, we develop a novel privacy-aware framework to address straggling problem in a federated learning (FL)-based mobile edge network through maximizing profit for the mobile service provider (MSP). In particular, unlike the conventional FL process when participating mobile users (MUs) have to train their all data locally, we propose a highly-effective solution that allows MUs to encrypt parts of local data and upload/cache the encrypted data to nearby mobile edge nodes (MENs) and/or a cloud server (CS) to perform additional training processes. In this way, we can not only mitigate the straggling problem caused by limited computing/communications resources at MUs but also enhance the usage efficiency of learning data from all MUs in the FL process. To optimize portions of encrypted data cached and trained at MENs/CS given constraints from MUs and the MSP while considering data privacy and training costs, we first formulate the profit maximization problem for the MSP as an optimal in-network encrypted data caching and learning optimization. We then prove that the objective function is concave, and thus an interior-point method algorithm can be effectively adopted to quickly find the optimal solution. The numerical results demonstrate that our proposed framework can enhance the profit of the MSP up to 5.39 times compared with other FL methods.
Savery, R, Savery, A & Baird, J 1970, 'Robotic Arm Generative Painting Through Real-time Analysis of Music Performance', Proceedings of the 10th International Conference on Human-Agent Interaction, HAI '22: International Conference on Human-Agent Interaction, ACM, pp. 253-255.
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Sayem, ASM, Simorangkir, RBVB, Esselle, KP & Buckley, JL 1970, 'A Conformal and Transparent Frequency Reconfigurable Water Antenna', 2022 16th European Conference on Antennas and Propagation (EuCAP), 2022 16th European Conference on Antennas and Propagation (EuCAP), IEEE, pp. 1-4.
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Schreiberhuber, S, Weibel, J-B, Patten, T & Vincze, M 1970, 'GigaDepth: Learning Depth from Structured Light with Branching Neural Networks', Springer Nature Switzerland, pp. 214-229.
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Schuhmann, AH, Kleinfeller, N, Sepehrirahnama, S, Oberst, S, Adams, C & Melz, T 1970, 'Numerical analysis on defect detection using structural intensity in solid bodies', Proceedings of the International Congress on Acoustics.
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Analysing structural intensity (SI) offers the possibility to assess the transmission of wave energy within a structure. Measurement of SI has been mainly focused on thin shells and beams. In this work a measurement method is presented to evaluate SI within solid, homogeneous, and isotropic bodies. The method is based on the reciprocity principle, a fundamental assumption in linear vibroacoustics. It allows the reconstruction of the structural intensity field within the bulk of a solid body from the measured surface velocities on the exterior boundaries. From the preliminary results, we demonstrate the capability of this method in approximating the spatial variation of the reconstructed stress and velocity fields using finite element simulation results. Inspired by the reciprocity-based method, we also demonstrate a cavity detection technique using the structural intensity measured along a closed path on a surface of a solid block. Despite some discrepancies in the estimate of the magnitude, the method works well in principle for the benchmark problem of a rectangular cube and it can be verified using our recently set up experimental test. Our proposed method provides an alternative energy-based SI detection technique that may perform as well as those exploiting velocity/acceleration or strain/stress.
Sepehrirahnama, S, McManus, H & Oberst, S 1970, 'ACOUSTIC LEVITATOR-TWEEZER USING PRE-PROGRAMMED ACOUSTIC HOLOGRAMS', Proceedings of the International Congress on Sound and Vibration, 28th International Congress on Sound and Vibration 2022, Singapore.
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Objects in an acoustic field are subjected to acoustic radiation forces, which depend on the objects' scattering behaviour and becomes comparable to the objects' weight for sizes smaller than a few millimeters. This led to manipulation techniques with ultrasonic waves in fluids. In current acoustic levitators, naturally asymmetric objects undergo unwanted spin and rigid-body oscillations. We developed a design of an acoustic manipulator with the ability to levitate and tweeze in vertical and horizontal directions, respectively. This is realised, using three separate transducer arrays and a discretized, reflective floor, inspired by the MIT inForm machine. The floor is made of nine movable pins to change the surface topography and, consequently, manipulate the acoustic field. In this study, we implemented square, staircase, and flat surface configurations to apply pre-defined acoustic holograms for manipulating levitated objects. The two side arrays generate a strong horizontal trap for holding the objects stably at a point where the acoustic radiation force is near zero. The top array and the adjustable floor generate a radiation force as large as an object's weight at the point of levitation, indicated by its levitation height. The object responds to the change of pins by altering its original position in the chamber. Preliminary results obtained at a transducer driving frequency of 40 kHz indicate that an asymmetric object such as a Bee's wing can be levitated stably for more than half an hour with minimal response to external disturbances, and without using phased-array technique. Owing to acoustic radiation force, the measurements are contactless and potentially non-invasive or minimally invasive, dependent on the object. The suggested device design can be potentially employed in the study of delicate biological samples including insects' appendages, such as wings, legs or other filigree structures such as electronic components, wires or MEMS with d...
Shao, Z, Wang, S, Zhang, Q, Lu, W, Li, Z & Peng, X 1970, 'A Systematical Evaluation for Next-Basket Recommendation Algorithms', 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp. 1-10.
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Next basket recommender systems (NBRs) aim to recommend a user's next (shopping) basket of items via modeling the user's preferences towards items based on the user's purchase history, usually a sequence of historical baskets. Due to its wide applicability in the real-world E-commerce industry, the studies NBR have attracted increasing attention in recent years. NBRs have been widely studied and much progress has been achieved in this area with a variety of NBR approaches having been proposed. However, an important issue is that there is a lack of a systematic and unified evaluation over the various NBR approaches. Different studies often evaluate NBR approaches on different datasets, under different experimental settings, making it hard to fairly and effectively compare the performance of different NBR approaches. To bridge this gap, in this work, we conduct a systematical empirical study in NBR area. Specifically, we review the representative work in NBR and analyze their cons and pros. Then, we run the selected NBR algorithms on the same datasets, under the same experimental setting and evaluate their performances using the same measurements. This provides a unified framework to fairly compare different NBR approaches. We hope this study can provide a valuable reference for the future research in this vibrant area.
Sharari, N, Fatahi, B, Hokmabadi, A & Xu, R 1970, 'Impacts of Steel LNG Tank Aspect Ratio on Seismic Vulnerability Subjected to Near-Field Earthquakes', Springer Nature Singapore, pp. 941-956.
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Sharma, J, Koh, Y, Ghosh, S, Wong, HX & Joshua, LE-Y 1970, 'In-line test structures for yield improvement in MEMS/NEMS device', 2022 IEEE 24th Electronics Packaging Technology Conference (EPTC), 2022 IEEE 24th Electronics Packaging Technology Conference (EPTC), IEEE, pp. 242-245.
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Shaw, P, Alam, MM, Ul Hasan, S, Siwakoti, YP & Dah-Chuan Lu, D 1970, 'A New Dual-Input Single-Output Step-up DC-DC Converter for Grid-Connected Photovoltaic Applications', 2022 4th International Conference on Smart Power & Internet Energy Systems (SPIES), 2022 4th International Conference on Smart Power & Internet Energy Systems (SPIES), IEEE, pp. 846-851.
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Sheikh, MA, Khan, GZ & Hussain, FK 1970, 'Expression of Concern for: Systematic Analysis of DDoS Attacks in Blockchain', 2022 24th International Conference on Advanced Communication Technology (ICACT), 2022 24th International Conference on Advanced Communication Technology (ICACT), IEEE, pp. 1-1.
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Sheikh, MA, Khan, GZ & Hussain, FK 1970, 'Systematic Analysis of DDoS Attacks in Blockchain', 2022 24th International Conference on Advanced Communication Technology (ICACT), 2022 24th International Conference on Advanced Communication Technology (ICACT), IEEE, pp. 132-137.
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Sheikh, MA, Khattak, F, Khan, GZ & Hussain, FK 1970, 'Secured Land Title Transfer System in Australia using VPN based Blockchain Network', 2022 24th International Conference on Advanced Communication Technology (ICACT), 2022 24th International Conference on Advanced Communication Technology (ICACT), IEEE, pp. 125-131.
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Shen, L, Zhang, Y, Wang, J & Bai, G 1970, 'Better Together: Attaining the Triad of Byzantine-robust Federated Learning via Local Update Amplification', Proceedings of the 38th Annual Computer Security Applications Conference, ACSAC: Annual Computer Security Applications Conference, ACM, pp. 201-213.
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Shen, Y, Li, L, Xie, Q, Li, X & Xu, G 1970, 'A Two-Tower Spatial-Temporal Graph Neural Network for Traffic Speed Prediction', Springer International Publishing, pp. 406-418.
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Shi, K, Ma, B, Zeng, Y, Lin, X, Wang, Z & Wang, Z 1970, 'Layered classification method for darknet traffic based on Weighted K-NN', 2022 International Conference on Networking and Network Applications (NaNA), 2022 International Conference on Networking and Network Applications (NaNA), IEEE, Urumqi, Xinjiang, China., pp. 226-231.
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Shindi, O, Yu, Q, Girdhar, P & Dong, D 1970, 'A Modified Deep Q-Learning Algorithm for Optimal and Robust Quantum Gate Design of a Single Qubit System', 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, pp. 2116-2122.
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Shrestha, S, Abbas, SM, Asadnia, M & Esselle, KP 1970, 'Radiation Characteristics of Three Dimensional Printed Meta-surface in Broadside Condition', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 73-74.
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Shrestha, S, Zahra, H, Ali, H, Abbas, SM, Asadnia, M & Esselle, KP 1970, 'Conical Rotation of Beam using Three Dimensional Printable Prototype', 2022 International Workshop on Antenna Technology (iWAT), 2022 International Workshop on Antenna Technology (iWAT), IEEE, pp. 64-67.
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Singh, K, Afzal, MU & Esselle, KP 1970, 'Efficient Near-Field Meta-Steering Systems for Connectivity-On-The-Move Applications using Hybrid Metasurfaces', 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/USNC-URSI), IEEE, pp. 641-642.
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Singh, K, Ahmed, F, Esselle, KP & Thalakotuna, D 1970, 'Cross-Entropy Method for Combinatorial Mixed-Parameter Optimization of Waveguide Polarizers for Ku-Band', 2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA), 2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA), IEEE, pp. 1-7.
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Singh, K, Ahmed, F, Thalakotuna, D & Esselle, KP 1970, 'A Modified Approach to Optimize Phase-Gradient Metasurface-Based Beam-Steering Systems', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 31-32.
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Singh, M & Zhao, L 1970, 'Inerter-enhanced piezoelectric energy harvesting and vibration suppression', Active and Passive Smart Structures and Integrated Systems XVI, Active and Passive Smart Structures and Integrated Systems XVI, SPIE, pp. 49-49.
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Singh, M, Rathor, VS, Sagar Sahoo, K & Gandomi, AH 1970, 'Cooperative Geometric Scheme for Passive Localization of Target in an Indoor Environment', 2022 IEEE Symposium Series on Computational Intelligence (SSCI), 2022 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 238-245.
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Singh, R, Rattan, G, Singh, M, Manne, R, Oberoi, SK & Kaur, N 1970, 'Advanced Oxidation Processes for Wastewater Treatment: Perspective Through Nanomaterials', Springer International Publishing, pp. 57-68.
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Smith, J, Bhandari, A, Yuksel, B & Kocaballi, AB 1970, 'An Embodied Conversational Agent to Minimize the Effects of Social Isolation During Hospitalization', Acis 2022 Australasian Conference on Information Systems Proceedings, Australasian Conference on Information Systems, Melbourne.
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Social isolation and loneliness contribute to the development of depression and anxiety. Comorbidity of mental health issues in hospitalized patients increases the length of stay in hospital by up to 109% and costs the healthcare sector billions of dollars each year. This study aims to understand the potential suitability of embodied conversational agents (ECAs) to reduce feelings of social isolation and loneliness among hospital patients. To facilitate this, a video prototype of an ECA was developed for use in single-occupant hospital rooms. The ECA was designed to act as an intelligent assistant, a rehabilitation guide, and a conversational partner. A co-design workshop involving five healthcare professionals was conducted. The thematic analysis of the workshop transcripts identified some major themes including improving health literacy, reducing the time burden on healthcare professionals, preventing secondary mental health issues, and supporting higher acceptance of digital technologies by elderly patients.
Smith, W, Qin, Y, Furukawa, T & Dissanayake, G 1970, 'Autonomous Robotic Map Refinement for Targeted Resolution and Local Accuracy', 2022 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), 2022 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), IEEE, pp. 130-137.
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Song, C, Liu, Y, McManus, D & Dong, D 1970, 'Learning Control with Evolution Strategy for Inhomogeneous Open Quantum Ensembles', 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, pp. 2123-2128.
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Song, L-Z, Qin, P-Y & Du, J 1970, 'E-Band Multibeam Conformal Transmitarrays for Beyond 5G Wireless Networks', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 167-168.
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Sueza Raffa, L, Bennett, NS & Clemon, LM 1970, 'Opportunities for Energy Efficiency Improvements in Craft and Micro-Breweries', Volume 6: Energy, ASME 2022 International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers.
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Abstract Rising energy prices and increasing competitiveness in the brewing industry challenge beer producers to reduce costs. To address this issue — and the environmental concerns over climate change — more energy-efficient brewing processes are required. The brewhouse consumes around one-quarter of the total energy demand in a brewery, especially wort boiling, where heat energy in the form of vapour is often wasted and presents a large potential for recovering energy. Although the technology for heat recovery during wort boiling is commercially available for large breweries, the development of equipment technology for craft and micro-breweries still lags behind. Based on a survey of Australian local craft and micro-breweries and a nano-brewery case study, we compare the evaporation rates during wort boiling for different operational parameters and use the results to verify a proposed mathematical model of evaporation from a kettle. We also propose and analyse options for re-utilising the recovered energy, such as pre-heating water for use in a subsequent process or storage for a later brew. Our study shows that the vapour released during the production of one litre of beer has the potential to heat 0.6 to 1.6 litres of water from ambient to 65°C. As for the potential energy savings and environmental impact, the case study nano-brewery can save approximately 5% of the brewhouse’s energy consumption or 2% of the energy required by the entire brewing process, while each surveyed brewery can spare 16 to 133 tonnes of CO2-e from being released into the atmosphere each year. These results reinforce the potential of recovering waste energy from wort boiling vapours in assisting breweries to become more energy-efficient, competitive and environmentally responsible.
Sumaya, RJ, Mo, H, Dong, D & Pota, H 1970, 'High Impedance Fault Characterization on Single-Wire Earth Return Systems using Powerline Communication', 2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC), IEEE, pp. 1-6.
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Sumon, MKAH, Ashmafee, MH, Islam, MR & Mostofa Kamal, AR 1970, 'Explainable NLQ-based Visual Interactive System: Challenges and Objectives', Proceedings of the 2nd International Conference on Computing Advancements, ICCA 2022: 2nd International Conference on Computing Advancements, ACM, Dhaka, Bangladesh, pp. 420-425.
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Nowadays, visual interactive systems (Vis) are attracting more attention in research and industries because of their effectiveness in conveying information. Additionally, to make rational decisions based on extracted data, Vis is critical for identifying and comprehending trends, outliers, and patterns in data. Existing research has employed a broad range of methodologies to yield visualization insights into certain decision-making systems, allowing participants to perceive a specific problem from a wide range of viewpoints. However, there are still enough scopes to design a new Vis where some systematic techniques are required to visualize the data with proper explanations. In this regard, we analyze several existing works and observe a surge of research interest in the new realm of explainable and NLQ-based Vis. In this paper, our main goal is to present a novel idea for designing an explainable NLQ-based Vis named-ExNLQVis. Therefore, (i) we aim to discuss a proposed NLQ-based Vis that will follow a deep learning-based NLP approach to extract necessary information from user inputs, make visual-respective decisions, and generate appropriate visualizations based on the preceding decisions. (ii) we extend our prior model to an explainable visualization model that not only accurately visualizes data but also explains why it appears depending on the natural language query (NLQ). To accomplish this system, we consider several challenges and objectives and briefly discuss our proposed method accordingly. We also provide the implementation and evaluation guidelines to establish our system.
Sun, C, Xiong, X, Ni, W & Wang, X 1970, 'Three-Dimensional Trajectory Design for Energy-Efficient UAV-Assisted Data Collection', ICC 2022 - IEEE International Conference on Communications, ICC 2022 - IEEE International Conference on Communications, IEEE, pp. 3580-3585.
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Sun, T, Sun, J, Chen, Y, Tong, S & Zhao, S 1970, 'Development and Application of ACC20 Main Engine Remote-control Simulation System', 2022 7th International Conference on Automation, Control and Robotics Engineering (CACRE), 2022 7th International Conference on Automation, Control and Robotics Engineering (CACRE), IEEE, pp. 142-147.
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Sun, Y, Han, Y, Zhang, Y, Chen, M, Yu, S & Xu, Y 1970, 'DDoS Attack Detection Combining Time Series-based Multi-dimensional Sketch and Machine Learning', 2022 23RD ASIA-PACIFIC NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM (APNOMS 2022), 23rd Asia-Pacific Network Operations and Management Symposium (APNOMS), IEEE, JAPAN, Takamatsu, pp. 37-42.
Sun, Y, Han, Y, Zhang, Y, Chen, M, Yu, S & Xu, Y 1970, 'DDoS Attack Detection Combining Time Series-based Multi-dimensional Sketch and Machine Learning', 2022 23rd Asia-Pacific Network Operations and Management Symposium (APNOMS), 2022 23rd Asia-Pacific Network Operations and Management Symposium (APNOMS), IEEE, pp. 01-06.
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Machine learning-based DDoS attack detection methods are mostly implemented at the packet level with expensive computational time costs, and the space cost of those sketch-based detection methods is uncertain. This paper proposes a two-stage DDoS attack detection algorithm combining time series-based multi-dimensional sketch and machine learning technologies. Besides packet numbers, total lengths, and protocols, we construct the time series-based multi-dimensional sketch with limited space cost by storing elephant flow information with the Boyer-Moore voting algorithm and hash index. For the first stage of detection, we adopt CNN to generate sketch-level DDoS attack detection results from the time series-based multi-dimensional sketch. For the sketch with potential DDoS attacks, we use RNN with flow information extracted from the sketch to implement flow-level DDoS attack detection in the second stage. Experimental results show that not only is the detection accuracy of our proposed method much close to that of packet-level DDoS attack detection methods based on machine learning, but also the computational time cost of our method is much smaller with regard to the number of machine learning operations.
Sun, Z, Du, X, Song, F, Ni, M & Li, L 1970, 'CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning', Proceedings of the ACM Web Conference 2022, WWW '22: The ACM Web Conference 2022, ACM.
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Surawski, N, Awadallah, M, Walker, P & Zhou, S 1970, 'PROTOTYPING AND PERFORMANCE TESTING OF A HYDRAULIC HYBRID HEAVY COMMERCIAL VEHICLE', CASANZ22 26th International Clean Air and Environment Conference, CASANZ22 26th International Clean Air and Environment Conference, Adelaide.
Syasegov, YY, Barzegarkhoo, R, Hasan, S, Li, L & Siwakoti, YP 1970, 'A 5-Level Mid-Point Clamped HERIC Inverter', 2022 IEEE 13th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 2022 IEEE 13th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), IEEE, pp. 1-6.
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Multilevel inverter demonstrates advantages over conventional topologies to meet the grid codes and standards. Compared to 3-level inverters, multilevel inverter exhibits significantly better harmonic performance, better efficiency, lower filter size, and less dv/dt and di/dt in the switches. In this regard, a modified HERIC-based topology with a 5-level modulation technique is derived from the conventional 3-level passive midpoint clamped HERIC-based topology and introduced in this paper. A circuit description and working principles of the proposed converter with simulation results, and experimental results are given to demonstrate the feasibility of the concept.
Tabandeh, A, Hossain, MJ & Khalilpour, K 1970, 'A Planning Framework for Integration of Distribution Systems with Grid-Connected Hydrogen Refuelling Stations', 2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC), IEEE, pp. 1-6.
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Tahira, A, Hussain, W & Ali, A 1970, 'Review-Based Recommender System for Hedonic and Utilitarian Products in IoT Framework', Springer International Publishing, pp. 221-232.
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Talaei, S, Zhu, X & Li, J 1970, 'Transfer Learning based Condition assessment for Bridges', International Conference on Structural Engineering Research, Western Sydney University, Australia.
Tan, Y, Long, G, LIU, LU, Zhou, T, Lu, Q, Jiang, J & Zhang, C 1970, 'FedProto: Federated Prototype Learning across Heterogeneous Clients', Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), pp. 8432-8440.
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Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occurs in the gradient space. For example, clients may differ in terms of data distribution, network latency, input/output space, and/or model architecture, which can easily lead to the misalignment of their local gradients. To improve the tolerance to heterogeneity, we propose a novel federated prototype learning (FedProto) framework in which the clients and server communicate the abstract class prototypes instead of the gradients. FedProto aggregates the local prototypes collected from different clients, and then sends the global prototypes back to all clients to regularize the training of local models. The training on each client aims to minimize the classification error on the local data while keeping the resulting local prototypes sufficiently close to the corresponding global ones. Moreover, we provide a theoretical analysis to the convergence rate of FedProto under non-convex objectives. In experiments, we propose a benchmark setting tailored for heterogeneous FL, with FedProto outperforming several recent FL approaches on multiple datasets.
Tan, Y, Long, G, Ma, J, Liu, L, Zhou, T & Jiang, J 1970, 'Federated Learning from Pre-Trained Models: A Contrastive Learning Approach', Advances in Neural Information Processing Systems.
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Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to learn collaboratively without sharing their private data. However, excessive computation and communication demands pose challenges to current FL frameworks, especially when training large-scale models. To prevent these issues from hindering the deployment of FL systems, we propose a lightweight framework where clients jointly learn to fuse the representations generated by multiple fixed pre-trained models rather than training a large-scale model from scratch. This leads us to a more practical FL problem by considering how to capture more client-specific and class-relevant information from the pre-trained models and jointly improve each client* ability to exploit those off-the-shelf models. In this work, we design a Federated Prototype-wise Contrastive Learning (FedPCL) approach which shares knowledge across clients through their class prototypes and builds client-specific representations in a prototype-wise contrastive manner. Sharing prototypes rather than learnable model parameters allows each client to fuse the representations in a personalized way while keeping the shared knowledge in a compact form for efficient communication. We perform a thorough evaluation of the proposed FedPCL in the lightweight framework, measuring and visualizing its ability to fuse various pre-trained models on popular FL datasets.
Tan, Y, Long, Y, Zhao, S, Gong, S, Hoang, DT & Niyato, D 1970, 'Energy Minimization for Wireless Powered Data Offloading in IRS-assisted MEC for Vehicular Networks', 2022 International Wireless Communications and Mobile Computing (IWCMC), 2022 International Wireless Communications and Mobile Computing (IWCMC), IEEE, pp. 731-736.
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In this paper, we consider an IRS-assisted and wireless-powered mobile edge computing (MEC) system that allows both edge users and the IRS to harvest energy from the hybrid access point (HAP), co-located with the MEC server. Each edge user uses the harvested energy to offload its data to the MEC server. The IRS not only assists downlink energy transfer to the edge users, but also improves the users' uplink offloading rates. To minimize the overall energy consumption, we jointly optimize the users' offloading decisions, the HAP's active beamforming, as well as the IRS's energy harvesting and passive beamforming strategies. The energy minimization problem is intractable due to complicated couplings in both the objective function and constraints. We decompose this problem into the downlink energy transfer and the uplink data offloading phases. The uplink phase can be efficiently optimized by the conventional semi-definite relaxation (SDR) method, while the downlink phase depends on the alternating optimization between the users' offloading decisions and the joint active and passive beamforming strategies. Numerical results demonstrate that the proposed offloading scheme can significantly reduce the HAP's energy consumption compared with typical benchmarks.
Tang, G, Duong, DH, Joux, A, Plantard, T, Qiao, Y & Susilo, W 1970, 'Practical Post-Quantum Signature Schemes from Isomorphism Problems of Trilinear Forms', Advances in Cryptology – EUROCRYPT 2022, Springer International Publishing, pp. 582-612.
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Tang, T, Du, H, Yu, X & Yang, Y 1970, 'Monocular Camera-Based Point-Goal Navigation by Learning Depth Channel and Cross-Modality Pyramid Fusion', Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), pp. 5422-5430.
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For a monocular camera-based navigation system, if we could effectively explore scene geometric cues from RGB images, the geometry information will significantly facilitate the efficiency of the navigation system. Motivated by this, we propose a highly efficient point-goal navigation framework, dubbed Geo-Nav. In a nutshell, our Geo-Nav consists of two parts: a visual perception part and a navigation part. In the visual perception part, we firstly propose a Self-supervised Depth Estimation network (SDE) specially tailored for the monocular camera-based navigation agent. Our SDE learns a mapping from an RGB input image to its corresponding depth image by exploring scene geometric constraints in a self-consistency manner. Then, in order to achieve a representative visual representation from the RGB inputs and learned depth images, we propose a Cross-modality Pyramid Fusion module (CPF). Concretely, our CPF computes a patch-wise cross-modality correlation between different modal features and exploits the correlation to fuse and enhance features at each scale. Thanks to the patch-wise nature of our CPF, we can fuse feature maps at high resolution, allowing our visual network to perceive more image details. In the navigation part, our extracted visual representations are fed to a navigation policy network to learn how to map the visual representations to agent actions effectively. Extensive experiments on a widely-used multiple-room environment Gibson demonstrate that Geo-Nav outperforms the state-of-the-art in terms of efficiency and effectiveness.
Tang, W, Long, G, Liu, L, Zhou, T, Blumenstein, M & Jiang, J 1970, 'OMNI-SCALE CNNS: A SIMPLE AND EFFECTIVE KERNEL SIZE CONFIGURATION FOR TIME SERIES CLASSIFICATION', Iclr 2022 10th International Conference on Learning Representations.
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The Receptive Field (RF) size has been one of the most important factors for One Dimensional Convolutional Neural Networks (1D-CNNs) on time series classification tasks. Large efforts have been taken to choose the appropriate size because it has a huge influence on the performance and differs significantly for each dataset. In this paper, we propose an Omni-Scale block (OS-block) for 1D-CNNs, where the kernel sizes are decided by a simple and universal rule. Particularly, it is a set of kernel sizes that can efficiently cover the best RF size across different datasets via consisting of multiple prime numbers according to the length of the time series. The experiment result shows that models with the OS-block can achieve a similar performance as models with the searched optimal RF size and due to the strong optimal RF size capture ability, simple 1D-CNN models with OS-block achieves the state-of-the-art performance on four time series benchmarks, including both univariate and multivariate data from multiple domains. Comprehensive analysis and discussions shed light on why the OS-block can capture optimal RF sizes across different datasets. Code available here.
Tapas, MJ, Yan, A, Thomas, P & Sirivivatnanon, V 1970, 'Effect of Carbonation on Compressive Strength Development of High-Slag Mortars', 76th RILEM Annual Week 2022 and International Conference on Regeneration and Conservation of Structures, 76th RILEM Annual Week 2022 and International Conference on Regeneration and Conservation of Structures, Kyoto, Japan.
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This study investigates the effect of carbonation on the compressive strength development of OPC and OPC+slag mortars (50% and 70% slag replacement) exposed to 2%CO2, 50%RH 23°C as well as the effect of increasing slag replacement level on carbonation resistance. As expected, results showed that OPC has the highest carbonation resistance and that the higher the slag replacement level, the poorer the carbonation resistance. Compressive strength results up to 112 days show that carbonation has no detrimental effect nor benefit to the compressive strength development of high-slag mortars. Mortars cured in accelerated carbonation conditions however show slower strength development than those cured under natural carbonation conditions up to 28 days. This indicates that CO2 curing does not accelerate strength development.
Thalakotuna, DN, Esselle, KP & Koli, NY 1970, 'A Planar Patch Antenna Array for 5G Millimeter Wave Extender', 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/USNC-URSI), IEEE, pp. 866-867.
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A low cost, simple to fabricate, multi-layer printed patch antenna array that can be used as the outdoor antenna system in the 5G Millimeter wave Extender. The antenna is designed to operate in the n258 (24.25 GHz-27.5 GHz) and has a size of 2.3λx2.3λ. The antenna shows 94% efficiency with typical gain of 15.5 dBi with only a 0.6dB gain variation in the band of interest.
Tian, L, Zhang, X & Lau, JH 1970, 'DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks', Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics.
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Tian, Y, Do, T-TN, Wang, Y-K & Lin, C-T 1970, 'The effect of different sensory modalities on inattentional blindness in a virtual environment for attentional loss improvement', 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), IEEE, pp. 1-6.
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Failure to notice salient objects even looking directly at them happens when individuals' attention is preoccupied, known as inattentional blindness (IB). As a form of attentional loss, IB occurrence might cause severe outcomes due to limited cognitive resources. Varied methods have been explored to reduce the IB effect and avoid neglect of critical information. Attenuating attentional loss via aided guidance with different sensory modalities intervention could be a possible way to address this issue. This study investigates how different sensory modalities affect the cognitive performance and IB effect from behaviour and neural changes in the human brain and how could we apply this in attention training for attentional loss improvement. Two experimental sessions were conducted, with a multisensory oddball task designed in virtual reality (VR) as the main task to attract individuals' attention. In session 1, participants responded to the main task without being informed of the unexpected task-irrelevant patterns in the background, while in session 2, they were informed of the unexpected patterns but still attended to the main task. Thus, participants were divided into IB (unaware of the pattern) and Aware (aware of the pattern) groups based on their awareness of patterns in the first session. Our results revealed that this VR-based design successfully induced the IB occurrence, with four out of nine participants reporting being unaware of the unexpected patterns. Further, the multisensory oddball task showed better performance in cross-modal stimuli (visual-auditory, VA) with higher accuracy and shorter reaction time than in uni-modal (A or V) conditions. Interestingly, in session 1, the IB group showed better performance than the Aware group, indicating that the IB group was not distracted during the task since they were unaware of the patterns. These findings supported our aims to explore the impact of different sensory modalities on cognitive perform...
Tian, Z, Zhang, C, Cui, L & Yu, S 1970, 'GSMI: A Gradient Sign Optimization Based Model Inversion Method', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 34th Australasian Joint Conference on Artificial Intelligence (AI), Springer International Publishing, Univ Technol Sydney, ELECTR NETWORK, pp. 67-78.
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The vulnerabilities of deep learning models on security and privacy have attracted a lot of attentions. Researchers have revealed the possibility of reconstructing training data of a target model. However, the performances of current works are highly rely on auxiliary datasets. In this paper, we investigate the model inversion problem under a strict restriction, where the adversary aims to reconstruct plausible samples of the target class without help of auxiliary information. To solve this challenge, we propose a Gradient Sign Model Inversion (GSMI) method based on the idea of adversarial examples generation. Specifically, we make three modifications on a popular adversarial examples generation method i-FGSM to generate plausible samples. 1) increasing the number of attack iterations and 2) superposing noises to reveal more obvious features learned by target model. 3) removing subtle noises to make reconstructed samples more plausible. However, we find samples generated by GSMI still contain noisy components. Furthermore, we adopt the idea of image adjacent regions to design a two-pass components selection algorithm to generate more reasonable sample of the target class. Through experiments, we find that the inversion samples of GSMI are close to real target class samples with some fluctuations on different classes. In addition, we also provide detail analysis for reasons of limitations on the optimization-based model inversion methods.
Tofigh, F, Sepehrirahnama, S, Lai, JCS & Oberst, S 1970, 'CHARACTERISING AND CALIBRATING PIEZO ACTUATORS FOR MICRO-EXCITATION FOR VIBRATION PLAYBACK IN BI-OASSAYS OF INSECTS', Proceedings of the International Congress on Sound and Vibration, 28th Intenational Congress on Sound and Vibration, Singapore.
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Micro-vibration signals in bioassays under controlled environmental conditions in biotremology require a device that can generate a similar level of vibration response as caused by the insect. Since bioassays often need to be run in environmental cabinets, the space available is limited, and structures to be excited should not be mass loaded. Considering the properties of piezo actuators in generating very short strokes with high frequency and fast response times, stacked arrangements were found suitable for micro-excitation based on a given approximation of a Dirac delta impulse, approximating in the first instance the impact signal of a walking insect. However, at below the current limit of miniaturised force and displacement actuators, it is essential to characterise and calibrate the piezo actuators to ensure they are producing the desired signal at the point of contact on a given structure. Here we established a methodology for driving piezo actuators at the order of μm/s to generate low-amplitude impulsive excitations. The methodology includes finding the transfer function of the piezo actuator and an aluminum and a wood beam (Pinus radiata) of 20x10mm2 cross section and 200mm length. The reaction force from the piezo actuator was measured from about 40mN down to 2mΝ for travel ranges between 1.2μm and 11μm. The results showed that the force varies linearly from 5-19μm for the ceramic, and 0.6μm to 1.4μm for the PI and the MTK actuators with an input voltage ranging from 2-10V. The measurement setup improved using an anechoic chamber to reduce the noise level by one order of magnitude, compared to reported results in literature, and ensure excitation amplitudes as low as ±10nm/s can be measured. The presented methodology allows developing affordable micro-excitors in the future for playback bioassays in confined spaces which cause minimal mass loading on the test specimen.
Tomidei, L, Sick, N, Deuse, J & Clemon, L 1970, 'Production Flow Analysis in the Era of Industry 4.0 : How Digital Technologies can Support Decision-Making in the Factory of the Future', 2022 Portland International Conference on Management of Engineering and Technology (PICMET), 2022 Portland International Conference on Management of Engineering and Technology (PICMET), IEEE, pp. 1-15.
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Tomidei, L, Sick, N, Guertler, M, Frijat, L, Carmichael, M, Paul, G, Wambsganss, A, Moreno, VH & Hussain, S 1970, 'BEYOND TECHNOLOGY - THE COGNITIVE AND ORGANISATIONAL IMPACTS OF COBOTS', Australasian Conference on Robotics and Automation Acra, Australasian Conference on Robotics and Automation, Brisbane.
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Work environments are radically changing with the adoption of new technologies. As the trend for automation grows collaborative robots or 'cobots' are being increasingly adopted by organisations from various industries. As opposed to traditional industrial robots, collaborative robots are complex socio-technical systems that allow close interaction between robots and humans. As a result, these systems can have significant impact on the physical and mental well-being of individuals, and safety can be ensured only by addressing physical, cognitive, and organisational factors. This study aims to provide an understanding of the work practices and behaviours in relation to the cognitive and organisational impact of cobots in Australian industries. By raising awareness of the key challenges and possible solutions to address them, this study provides contributions to academia and industry practice.
Tong, M, Huang, X & Zhang, JA 1970, 'Frame-based Decision Directed Successive Interference Cancellation for FTN Signaling', 2022 IEEE Globecom Workshops (GC Wkshps), 2022 IEEE Globecom Workshops (GC Wkshps), IEEE, pp. 1670-1674.
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In this paper, we propose a frame-based decision directed successive interference cancellation to improve the detection performance of Faster-than-Nyquist (FTN) signaling. The main idea of this method is to directly decide all data symbols in a complete transmission frame after minimum-mean-square-error (MMSE) equalization and regenerate the noise-free signal with the decided symbols. The difference between the equalized and regenerated signals represents the residual inter-symbol interference (ISI) which depends on the bit-error-rate (BER) of the decision. After adding the normalized residual ISI to the decided symbols, the date symbols in the transmission frame are decided recursively, leading to a decision directed successive interference cancellation (DDSIC) scheme. The simulation results in both Gaussian and multipath fading channels demonstrate that our proposed method enables lower complexity and better performance FTN systems compared with existing symbol-by-symbol interference cancellation methods.
Torpy, F, Irga, P, Fleck, R, Matheson, S & Douglas, A 1970, 'Indoor plants, our environment and happiness', Happiness & Its Causes, Happiness & Its Causes, Pyrmont Theatre, ICC Sydney.
Torpy, F, Irga, P, Fleck, R, Matheson, S, Smith, H, Duani, G, Surawski, N, Douglas, A & Lyu, L 1970, 'Phytoremediation of air pollution', 26th International Clean Air and Environment Conference, 26th International Clean Air and Environment Conference, Adelaide, SA.
Uddin Murad, MA, Kozanoglu, DC & Chakraborty, S 1970, 'Public Procurement, Big Data Analytics Capabilities, and Healthcare Supply Chain Sustainability', Proceedings of the Annual Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, University of Hawaii, Hawai, pp. 296-303.
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Big data analytics (BDA) is considered the most critical supply chain activity for organizations. Implementing BDA requires specialized infrastructure coupled with specialized analytical expertise. Most of the existing research focuses on building BDA capabilities or perceived benefits of organizations' BDA capabilities. However, the benefits of having BDA capabilities, neither immediately visible nor straightforward. Optimizing procurement is one of the many intermediate factors that influence BDA capabilities' impact on the supply chain's sustainability performance. This paper has analyzed the existing literature to develop a conceptual framework to investigate the relationships among procurement optimization, BDA capabilities, and healthcare sustainable supply chain.
Uddin, F, Lavorante, MJ, Popa-Simil, L, Xie, G, Mucha, J, Dong, J, Ho, LN, Albayrak, O, Gomaa, M, Mahdipour, SA, Abd, E-MTT, Leong, C, Gan, Nacer, R, Nurdin, I, Mazilu, T, Ong, HC, Tariq, GH, Quesada, DE, Adiguzel, O, Majewska, K, Jung, DW, Yap, TC, Mateev, V, Tin, HHK, Reda, Boutagouga, D, Khitab, A, Mimoune, S, Kassmi, K, Dumitriu, M, Macioszek, E, Chi, W, Zahari, NM, Kisielewicz, T, M-Ridha, MJ, Yu, T, Buonomenna, MG, Liu, W, Chen, C, Schwingenschlögl, UE, Wang, HE, Dobrotă, D, Ramos, J, Memon, I, Khalil, RA, Woroniak, G, Issaadi, W, Tsai, CT, Aissaoui, AG, Doroftei, C, Iyer, VG, Lin, YS, Čubrić, IS, Kyzioł, K, Shakir, RR, Marko, O, Akyol, A, Ileana, PC & Dastjerdi, R 1970, 'Preface', Journal of Physics: Conference Series, IOP Publishing, pp. 011001-011001.
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Because of the travel restrictions between China and other countries of our keynote speaker, the 9th annual 2021 International Conference on Material Science and Environmental Engineering [MSEE2021] was held on November 27th, 2021 (Virtual Conference). The conference was held via Tencent Meeting Application. MSEE2021 aims to bring researchers, engineers, and students to the areas of Material Science and Environmental Engineering. MSEE2021 features unique mixed topics of Material Science and Advanced Materials, Material Engineering and Application, Environmental Science and Engineering and Mechanical Design and Technology. We received over 197 submissions from various parts of the world. The Technical Program Committee worked very hard to have all manuscripts reviewed before the review deadline. All the accepted papers have been submitted to strict peer-review, and selected based on originality, significance and clarity for the purpose of the conference. The conference program is extremely profound and featuring high-impact presentations of selected papers and additional late-breaking contributions. We sincerely hope that the conference would not only show the participants a broad overview of the latest research results on related fields, but also provide them with a significant platform for academic connection and exchange. There are two keynote speakers and four invited sessions. The keynote speakers are internationally recognized leading experts in their research fields, who have demonstrated outstanding proficiency and have achieved distinction in their profession. The proceedings would be published by IOP Journal of Physics Conference Series. We would like to express our sincere gratitude to all the members of Technical Program Committee and organizers for their enthusiasm, time, and expertise. Our deep thanks also go to many volunteers and staffs for the long hours and hard wor...
Vahdati, F, Atif, A & Saberi, M 1970, 'A machine learning-based depression detection on social media platforms for adolescents: A work in progress narrative review', 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), IEEE, Gold Coast, pp. 1-6.
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A rise in depression episodes has prompted an increased focus on depression detection. This research paper aims to review the literature to discover the pros and cons of proposed solutions for this critical social problem. In this
narrative review, specifically, we looked at machine learning
(ML) based techniques that analyse text data from social media to diagnose depression symptoms. A thorough search technique across several databases for relevant articles, specifically Google Scholar, PubMed, Medline, ERIC, PsycINFO, and BioMed databases, were used to perform a narrative evaluation. Terms and definitions were used to filter the article titles, abstracts, and full texts. Approaches based on machine learning and text data from social media may be helpful in the diagnosis of depression and might be used in conjunction with other mental health services.
Varghese, A, Jawahar, M, Prince, AA & Gandomi, AH 1970, 'Texture Analysis on Digital Microscopic Leather Images For Species Identification', 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), IEEE, pp. 223-227.
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Vettori, L, Joukhdar, H, Anh, HT, Sarmast, HMS, Filipe, E, Cox, T, Kabakova, I, Rnjak-Kovacina, J & Gentile, C 1970, 'Silk fibroin as a bioink for cardiovascular applications', TISSUE ENGINEERING PART A, Tissue-Engineering-and-Regenerative-Medicine-International-Society-Asia-Pacific-Chapter Conference (TERMIS-AP), MARY ANN LIEBERT, INC, SOUTH KOREA, Jeju, pp. 264-264.
Vickery, NEM & Wyeth, P 1970, 'Exploration in Open-World Videogames: Environment, Items, Locations, Quests, and Combat in The Witcher 3', Proceedings of the 34th Australian Conference on Human-Computer Interaction, OzCHI '22: 34th Australian Conference on Human-Computer Interaction, ACM, pp. 310-318.
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Viswanathan, S, ragunanthan, K, Ravichandran, KS & Gandomi, A 1970, 'Understanding the Effects of Ant Algorithms on Path Planning with Gain-Ant Colony Optimization', Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, ISMSI 2022: 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, ACM, pp. 50-54.
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With the advent of more automated and unmanned systems, there is an increasing need for path planners. Intelligent path planners play an important role in the navigation of automated systems. In this work, the performance of an enhanced gain-ant colony optimization has been tested with the most popularly used ant algorithms-Ant system, Ant colony system and Min-Max ant system in the application of path planning. The pheromone update mechanism of traditional ant metaheuristic is enhanced with a local optimization mechanism and simulated with popular ant algorithms for an efficient choice of update rule. Evaluation is done using performance measures like path length and computation time taken. The results are statistically verified and analyzed. Path planned by proposed algorithm was found to be 3.25% shorter than existing algorithms.
Vizcarra, GC, Muniz, L, Gonçalves, T & Nimbalkar, S 1970, 'Railway Subgrade Characterization Through Repeated Loading Triaxial Testing', Lecture Notes in Civil Engineering, Springer International Publishing, pp. 327-335.
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Currently, the improvement of means of transportation is a great challenge. Brazil has a large ore production, which will continue in the next decades, and seeks to reduce the transportation times between production and export centers, as well as reduce the emission of contaminants to the environment. In this sense, railways are a more efficient and environmentally friendly means of land transportation, and their proper conservation and operability affect the net gains that Brazil receives from the export of commodities. The implementation of this program proposed in the engineering practice would allow taking more precise decisions regarding the activities of maintenance of railroads, generating significant savings. The first step of the research is the analysis and interpretation of results of repeated load triaxial tests carried out in Brazil on railway subgrade soils. An engineering methodology is presented considering the geotechnical properties of the foundation soil obtained through field and laboratory tests for performing of geotechnical analysis. To ensure the railway stability, criteria of bearing capacity, elastic deflection and permanent deformation for the railway substructure must be met. A prediction model of permanent deformation is used, as well as the influence of moisture on the behavior of the foundation soil. This study aims to contribute to the finding of a comprehensive methodology for evaluating the useful service life of the track substructure so that the most appropriate material can be selected for use as a railroad formation material in order to limit stresses on the railway subgrade, which in turn cause progressive loss of geometric profile of the railway, and to maintain a safe operation of the trains. This will allow significant savings in the periodic maintenance of the substructure, which are one of the activities to restore the track geometry of railways.
Vo, H, Tang, M, Zheng, XJ & Yu, S 1970, 'BI-GAN', Proceedings of the 17th ACM Workshop on Mobility in the Evolving Internet Architecture, ACM MobiCom '22: The 28th Annual International Conference on Mobile Computing and Networking, ACM, pp. 31-36.
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Federated Learning is a growing advanced collaborative machine learning framework that aims to preserve user-privacy data. However, multiple researchers have investigated attack methods from the server side via gradient inversion techniques or Generative Adversarial Networks (GAN) to reconstruct the raw data distributions from users. In this paper, we propose Batch Inversion GAN (BI-GAN), a novel membership inference attack that can recover user-level batch images from local updates, utilizing both gradient inversion techniques and GAN. Our attack is more stealthy since it only requires access to gradients and does not interfere with the global model performance and is more robust in terms of image batch recovery and victim classification. The experiments show that our attack recovers higher quality images of the victim with higher accuracy compared to other attacks.
Vu, TT, Hoang, DT, Phan, KT, Nguyen, DN & Dutkiewicz, E 1970, 'Energy-based Proportional Fairness for Task Offloading and Resource Allocation in Edge Computing', ICC 2022 - IEEE International Conference on Communications, ICC 2022 - IEEE International Conference on Communications, IEEE, Seoul, Korea, Republic of, pp. 1912-1917.
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By executing offloaded tasks from mobile users, edge computing augments mobile devices with computing/communications resources from edge nodes (ENs), enabling new services/applications (e.g., real-time gaming, virtual/augmented reality). However, despite being more resourceful than mobile devices, allocating ENs’ computing/communications resources to given favorable sets of users may block other devices from their service. This is often the case for most existing task offloading and resource allocation approaches that only aim to maximize the network social welfare (e.g., minimizing the total energy consumption) but not consider the computing/battery status of each mobile device. This work develops a proportional fair task offloading and resource allocation framework for a multi-layer cooperative edge computing network to serve all user equipment (UEs) while considering both their service requirements and individual energy/battery levels. The resulting optimization involves both binary (offloading decisions) and real variables (resource allocations), making it NP-hard. To tackle it, we leverage the fact that the relaxed problem is convex and propose a distributed algorithm, namely the dynamic branchand-bound Benders decomposition (DBBD). DBBD decomposes the original problem into a master problem (MP) for the offloading decision and subproblems (SPs) for resource allocation. The SPs can either find their closed-form solutions or be solved in parallel at ENs, thus help reduce the complexity. The numerical results show that the DBBD returns the optimal solution of the problem maximizing the fairness between UEs. The DBBD has higher fairness indexes, i.e., Jain’s index and min-max ratio, in comparing with the existing ones that minimize the total consumed energy.
Waheed, N, Ikram, M, Hashmi, SS, He, X & Nanda, P 1970, 'An Empirical Assessment of Security and Privacy Risks of Web-Based Chatbots', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 325-339.
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Web-based chatbots provide website owners with the benefits of increased sales, immediate response to their customers, and insight into customer behaviour. While Web-based chatbots are getting popular, they have not received much scrutiny from security researchers. The benefits to owners come at the cost of users’ privacy and security. Vulnerabilities, such as tracking cookies and third-party domains, can be hidden in the chatbot’s iFrame script. This paper presents a large-scale analysis of five Web-based chatbots among the top 1-million Alexa websites. Through our crawler tool, we identify the presence of chatbots in these 1-million websites. We discover that 13,392 out of the top 1- million Alexa websites (1.58%) use one of the five analysed chatbots. Our analysis reveals that the top 300k Alexa ranking websites are dominated by Intercom chatbots that embed the least number of third-party domains. LiveChat chatbots dominate the remaining websites and embed the highest samples of third-party domains. We also find that 721 (5.38%) web-based chatbots use insecure protocols to transfer users’ chats in plain text. Furthermore, some chatbots heavily rely on cookies for tracking and advertisement purposes. More than two-thirds (68.92%) of the identified cookies in chatbot iFrames are used for ads and tracking users. Our results show that, despite the promises for privacy, security, and anonymity given by most websites, millions of users may unknowingly be subject to poor security guarantees by chatbot service providers.
Wakulicz, J, Brian Lee, KM, Yoo, C, Vidal-Calleja, T & Fitch, R 1970, 'Informative Planning for Worst-Case Error Minimisation in Sparse Gaussian Process Regression', 2022 International Conference on Robotics and Automation (ICRA), 2022 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Philadelphia, pp. 11066-11072.
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Wambsganss, A, Diederich, A, Sick, N, Salomo, S & Broering, S 1970, 'Drivers and patterns of industry convergence A literature-based framework', Australian and New Zealand Academy of Management, Gold Coast.
Wan, Y, He, Y, Bi, Z, Zhang, J, Sui, Y, Zhang, H, Hashimoto, K, Jin, H, Xu, G, Xiong, C & Yu, PS 1970, 'NaturalCC: An Open-Source Toolkit for Code Intelligence', 2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), 2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), IEEE, pp. 149-153.
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Wan, Y, Zhang, S, Zhang, H, Sui, Y, Xu, G, Yao, D, Jin, H & Sun, L 1970, 'You see what I want you to see: poisoning vulnerabilities in neural code search', Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE '22: 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ACM, pp. 1233-1245.
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Wan, Y, Zhao, W, Zhang, H, Sui, Y, Xu, G & Jin, H 1970, 'What do they capture?', Proceedings of the 44th International Conference on Software Engineering, ICSE '22: 44th International Conference on Software Engineering, ACM, pp. 2377-2388.
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Wang, H, Hu, R, Zhang, Y, Qin, L, Wang, W & Zhang, W 1970, 'Neural Subgraph Counting with Wasserstein Estimator.', SIGMOD Conference, International Conference on Management of Data (SIGMOD), ACM, Philadelphia, PA, pp. 160-175.
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Subgraph counting is a fundamental graph analysis task which has been widely used in many applications. As the problem of subgraph counting is NP-complete and hence intractable, approximate solutions have been widely studied, which fail to work with large and complex query graphs. Alternatively, Machine Learning techniques have been recently applied for this problem, yet the existing ML approaches either only support very small data graphs or cannot make full use of the data graph information, which inherently limits their scalability, estimation accuracies and robustness. In this paper, we propose a novel approximate subgraph counting algorithm, NeurSC, that can exploit and combine information from both the query graphs and the data graphs effectively and efficiently. It consists of two components: (1) an extraction module that adaptively generates simple yet representative substructures from data graph for each query graph and (2) an estimator WEst that first computes the representations from individual and joint distributions of query and data graphs and then estimates subgraph counts with the learned representations. Furthermore, we design a novel Wasserstein discriminator in WEst to minimize the Wasserstein distance between query and data graphs by updating the parameters in network with the vertex correspondence relationship between query and data graphs. By doing this, WEst can better capture the correlation between query and data graphs which is essential to the quality of the estimation. We conduct experimental studies on seven large real-life labeled graphs to demonstrate the superior performance of NeurSC in terms of estimation accuracy and robustness.
Wang, H, Zhang, Y, Qin, L, Wang, W, Zhang, W & Lin, X 1970, 'Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching.', CoRR, 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, pp. 245-258.
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Subgraph matching is a fundamental problem in various fields that use graph structured data. Subgraph matching algorithms enumerate all isomorphic embeddings of a query graph q in a data graph G. An important branch of matching algorithms exploit the backtracking search approach which recursively extends intermediate results following a matching order of query vertices. It has been shown that the matching order plays a critical role in time efficiency of these backtracking based subgraph matching algorithms. In recent years, many advanced techniques for query vertex ordering (i.e., matching order generation) have been proposed to reduce the unpromising intermediate results according to the preset heuristic rules. In this paper, for the first time we apply the Reinforcement Learning (RL) and Graph Neural Networks (GNNs) techniques to generate the high-quality matching order for subgraph matching algorithms. Instead of using the fixed heuristics to generate the matching order, our model could capture and make full use of the graph information, and thus determine the query vertex order with the adaptive learning-based rule that could significantly reduces the number of redundant enumerations. With the help of the reinforcement learning framework, our model is able to consider the long-term benefits rather than only consider the local information at current ordering step. Extensive experiments on six real-life data graphs demonstrate that our proposed matching order generation technique could reduce up to two orders of magnitude of query processing time compared to the state-of-the-art algorithms.
Wang, K, Zhang, W, Lin, X, Qin, L & Zhou, A 1970, 'Efficient Personalized Maximum Biclique Search.', ICDE, 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, pp. 498-511.
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Wang, K, Zhang, W, Lin, X, Zhang, Y & Li, S 1970, 'Discovering Hierarchy of Bipartite Graphs with Cohesive Subgraphs', 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, Kuala Lumpur, Malaysia, pp. 2291-2305.
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Bipartite graph is a widely used model to describe relationships between two different types of entities Exploring graph hierarchy with cohesive subgraphs has been extensively studied on unipartite graphs while only a few works focus on bipartite graphs In this paper we propose the bipartite hierarchy which is the first model to discover the hierarchical structure of bipartite graphs based on the concept of alpha 2 beta core and graph connectivity Notably alpha beta text core is a vertex centric model that conforms to the special structure of bipartite graphs i e formed by two different vertex layers Accordingly the bipartite hierarchy has two parts i e the upper and lower hierarchies to record the hierarchical relationships among upper and lower vertices respectively We theoretically prove that the bipartite hierarchy is space efficient i e its space cost is linear to the graph size and clearly illustrate its structure via visualization In addition efficient algorithms for building the bipartite hierarchy are proposed by utilizing the nested property of alpha beta text core Since bipartite graphs can be dynamically changed in real world scenarios we also study the bipartite hierarchy maintenance algorithms against the edge insertion deletion cases These algorithms can effectively identify the affected regions to limit computation scope and avoid re building the bipartite hierarchy from scratch Extensive experiments on 10 real world graphs not only demonstrate the effectiveness of the proposed bipartite hierarchy but also validate the efficiency of our hierarchy construction and maintenance algorithms
Wang, L, Wang, H, Luo, X & Sui, Y 1970, 'MalWhiteout: Reducing Label Errors in Android Malware Detection', Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, ASE '22: 37th IEEE/ACM International Conference on Automated Software Engineering, ACM, pp. 1-13.
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Machine learning based Android malware detection has attracted a great deal of research work in recent years. A reliable malware dataset is critical to evaluate the effectiveness of malware detection approaches. Unfortunately, existing malware datasets used in our community are mainly labelled by leveraging existing anti-virus services (i.e., VirusTotal), which are prone to mislabelling. This, however, would lead to the inaccurate evaluation of the malware detection techniques. Removing label noises from Android malware datasets can be quite challenging, especially at a large data scale. To address this problem, we propose an effective approach called MalWhiteout to reduce label errors in Android malware datasets. Specifically, we creatively introduce Confident Learning (CL), an advanced noise estimation approach, to the domain of Android malware detection. To combat false positives introduced by CL, we incorporate the idea of ensemble learning and inter-app relation to achieve a more robust capability in noise detection. We evaluate MalWhiteout on a curated large-scale and reliable benchmark dataset. Experimental results show that MalWhiteout is capable of detecting label noises with over 94% accuracy even at a high noise ratio (i.e., 30%) of the dataset. MalWhiteout outperforms the state-of-the-art approach in terms of both effectiveness (8% to 218% improvement) and efficiency (70 to 249 times faster) across different settings. By reducing label noises, we show that the performance of existing malware detection approaches can be improved.
Wang, Q, Liu, F, Zhang, Y, Zhang, J, Gong, C, Liu, T & Han, B 1970, 'Watermarking for Out-of-distribution Detection', Advances in Neural Information Processing Systems.
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Out-of-distribution (OOD) detection aims to identify OOD data based on representations extracted from well-trained deep models. However, existing methods largely ignore the reprogramming property of deep models and thus may not fully unleash their intrinsic strength: without modifying parameters of a well-trained deep model, we can reprogram this model for a new purpose via data-level manipulation (e.g., adding a specific feature perturbation to the data). This property motivates us to reprogram a classification model to excel at OOD detection (a new task), and thus we propose a general methodology named watermarking in this paper. Specifically, we learn a unified pattern that is superimposed onto features of original data, and the model's detection capability is largely boosted after watermarking. Extensive experiments verify the effectiveness of watermarking, demonstrating the significance of the reprogramming property of deep models in OOD detection. The code is publicly available at: github.com/qizhouwang/watermarking.
Wang, R, Wang, S, Lu, W & Peng, X 1970, 'News Recommendation Via Multi-Interest News Sequence Modelling', ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 7942-7946.
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Wang, S, Li, L, Ding, Y & Yu, X 1970, 'One-Shot Talking Face Generation from Single-Speaker Audio-Visual Correlation Learning', Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), pp. 2531-2539.
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Audio-driven one-shot talking face generation methods are usually trained on video resources of various persons. However, their created videos often suffer unnatural mouth shapes and asynchronous lips because those methods struggle to learn a consistent speech style from different speakers. We observe that it would be much easier to learn a consistent speech style from a specific speaker, which leads to authentic mouth movements. Hence, we propose a novel one-shot talking face generation framework by exploring consistent correlations between audio and visual motions from a specific speaker and then transferring audio-driven motion fields to a reference image. Specifically, we develop an Audio-Visual Correlation Transformer (AVCT) that aims to infer talking motions represented by keypoint based dense motion fields from an input audio. In particular, considering audio may come from different identities in deployment, we incorporate phonemes to represent audio signals. In this manner, our AVCT can inherently generalize to audio spoken by other identities. Moreover, as face keypoints are used to represent speakers, AVCT is agnostic against appearances of the training speaker, and thus allows us to manipulate face images of different identities readily. Considering different face shapes lead to different motions, a motion field transfer module is exploited to reduce the audio-driven dense motion field gap between the training identity and the one-shot reference. Once we obtained the dense motion field of the reference image, we employ an image renderer to generate its talking face videos from an audio clip. Thanks to our learned consistent speaking style, our method generates authentic mouth shapes and vivid movements. Extensive experiments demonstrate that our synthesized videos outperform the state-of-the-art in terms of visual quality and lip-sync.
Wang, S, Liu, N, Zhang, X, Wang, Y, Ricci, F & Mobasher, B 1970, 'Data Science and Artificial Intelligence for Responsible Recommendations', Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 4904-4905.
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Wang, S, Liu, Y, Chen, L & Zhang, C 1970, 'Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection', 2022 International Joint Conference on Neural Networks (IJCNN), 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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Unsupervised anomaly detection (AD) is a challenging task in realistic applications. Recently, there is an increasing trend to detect anomalies with deep neural networks (DNN). However, most popular deep AD detectors cannot protect the network from learning contaminated information brought by anomalous data, resulting in unsatisfactory detection performance and overfitting issues. In this work, we identify one reason that hinders most existing DNN-based anomaly detection methods from performing is the wide adoption of the Empirical Risk Minimization (ERM). ERM assumes that the performance of an algorithm on an unknown distribution can be approximated by averaging losses on the known training set. This averaging scheme thus ignores the distinctions between normal and anomalous instances. To break through the limitations of ERM, we propose a novel Diminishing Empirical Risk Minimization (DERM) framework. Specifically, DERM adaptively adjusts the impact of individual losses through a well-devised aggregation strategy. Theoretically, our proposed DERM can directly modify the gradient contribution of each individual loss in the optimization process to suppress the influence of outliers, leading to a robust anomaly detector. Empirically, DERM outperformed the state-of-the-art on the unsupervised AD benchmark consisting of 18 datasets.
Wang, S, Xu, X, Zhang, X, Wang, Y & Song, W 1970, 'Veracity-aware and Event-driven Personalized News Recommendation for Fake News Mitigation', Proceedings of the ACM Web Conference 2022, WWW '22: The ACM Web Conference 2022, ACM, ELECTR NETWORK, pp. 3673-3684.
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Despite the tremendous efforts by social media platforms and fact-check services for fake news detection, fake news and misinformation still spread wildly on social media platforms (e.g., Twitter). Consequently, fake news mitigation strategies are urgently needed. Most of the existing work on fake news mitigation focuses on the overall mitigation on a whole social network while ignoring developing concrete mitigation strategies to deter individual users from sharing fake news. In this paper, we propose a novel veracity-aware and event-driven recommendation model to recommend personalised corrective true news to individual users for effectively debunking fake news. Our proposed model Rec4Mit (Recommendation for Mitigation) not only effectively captures a user's current reading preference with a focus on which event, e.g., US election, from her/his recent reading history containing true and/or fake news, but also accurately predicts the veracity (true or fake) of candidate news. As a result, Rec4Mit can recommend the most suitable true news to best match the user's preference as well as to mitigate fake news. In particular, for those users who have read fake news of a certain event, Rec4Mit is able to recommend the corresponding true news of the same event. Extensive experiments on real-world datasets show Rec4Mit significantly outperforms the state-of-the-art news recommendation methods in terms of the capability to recommend personalized true news for fake news mitigation.
Wang, S, Zhang, Q, Hu, L, Zhang, X, Wang, Y & Aggarwal, C 1970, 'Sequential/Session-based Recommendations', Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 3425-3428.
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In recent years, sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations. Although SRSs and SBRSs have been extensively studied, there are many inconsistencies in this area caused by the diverse descriptions, settings, assumptions and application domains. There is no work to provide a unified framework and problem statement to remove the commonly existing and various inconsistencies in the area of SR/SBR. There is a lack of work to provide a comprehensive and systematic demonstration of the data characteristics, key challenges, most representative and state-of-the-art approaches, typical real- world applications and important future research directions in the area. This work aims to fill in these gaps so as to facilitate further research in this exciting and vibrant area.
Wang, S, Zhao, G, Xu, C, Han, Z & Yu, S 1970, 'A NTRU-Based Access Authentication Scheme for Satellite Terrestrial Integrated Network', GLOBECOM 2022 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, IEEE, pp. 3629-3634.
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The Satellite Terrestrial Integrated Network(STIN) has become an indispensable part of the future network. However, the limited resources, long delay communications and highly exposed channels of SGIN are vulnerable to network attacks, which make the access authentication scheme as the first line of defense in network security. Most of the existing access authentication schemes are based on discrete logarithm and large integer factorization problems, which cannot resist quantum attacks, and the number of interactions between entities are too large. Therefore, we propose a lightweight and certificateless anonymous access authentication scheme based on lattice to solve this problem. We introduce Number Theory Research Unit (NTRU) scheme into the key generation process, to improve the utilization rate of equipment resources and ensure the legitimacy of the communication entity. The performance evaluation results demonstrate that our scheme only needs twice satellite-ground interactions to complete mutual authentication.
Wang, W, Liu, S, Liu, A, Liang, CJ & Yu, S 1970, 'Locally Random Sampling for Practical Privacy Protection in Federated Learning', GLOBECOM 2022 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, IEEE, pp. 528-533.
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Federated learning (FL) is an emerging solution for machine learning model training in edge/fog computing systems. Unlike traditional systems that collect and train models on clouds, FL allows multiple edge/fog nodes to train a global model collaboratively without revealing their local data to clouds. Compared with traditional systems, it is inherited with better privacy protection ability. Although the basic privacy protection is inherited in FL, the privacy leakage from shard models is still unsolved. Existing solutions attempt to enhance the privacy of shared model parameters by adding differential privacy (DP) noise. However, these solutions all suffer from accuracy loss and convergence problems owing to the injected noise. In this paper, we propose a novel federated learning protocol to solve the above problem. The model trained on a carefully selected sampling subset can achieve the same level privacy protection as DP while preserving the model accuracy. Experimentally, we proved that our protocol achieves better model accuracy in the same privacy guarantee compared with noise injecting DP methods.
Wang, X, Sun, G, Fang, X, Yang, J & Wang, S 1970, 'Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation', Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}, International Joint Conferences on Artificial Intelligence Organization, pp. 3530-3536.
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Point-of-interest (POI) recommendations can help users explore attractive locations, which is playing an important role in location-based social networks (LBSNs). In POI recommendations, the results are largely impacted by users' preferences. However, the existing POI methods model user and location almost separately, which cannot capture users' personal and dynamic preferences to location. In addition, they also ignore users' acceptance to distance/time of location. To overcome the limitations of the existing methods, we first introduce Knowledge Graph with temporal information (known as TKG) into POI recommendation, including both user and location with timestamps. Then, based on TKG, we propose a Spatial-Temporal Graph Convolutional Attention Network (STGCAN), a novel network that learns users' preferences on TKG by dynamically capturing the spatial-temporal neighbourhoods. Specifically, in STGCAN, we construct receptive fields on TKG to aggregate neighbourhoods of user and location respectively at each timestamp. And we measure the spatial-temporal interval as users' acceptance to distance/time with self-attention. Experiments on three real-world datasets demonstrate that the proposed model outperforms the state-of-the-art POI recommendation approaches.
Wang, X, Wen, D, Qin, L, Chang, L & Zhang, W 1970, 'ScaleG: A Distributed Disk-based System for Vertex-centric Graph Processing (Extended Abstract).', ICDE, 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, pp. 1511-1512.
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Designing disk-based distributed graph systems has drawn a lot of research due to the strong expressiveness of the graph model and rapidly increasing graph volume. However, several challenges still exist in achieving both high computational efficiency and low network communication under the limitation of memory. In this paper, we design a novel distributed disk-based graph processing system, ScaleG, with a series of user-friendly programming interfaces. We propose several techniques to reduce both disk I/Os in each machine and message I/Os via the network. We manage all messages in memory and bound the volume of all messages by the number of vertices. We also carefully design the data structure to support partial computation and automatic vertex activation. We conduct extensive experiments on real-world big graphs to show the high efficiency of our system.
Wang, Y, Hespanhol, L, Worrall, S & Tomitsch, M 1970, 'Pedestrian-Vehicle Interaction in Shared Space: Insights for Autonomous Vehicles', Proceedings of the 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI '22: 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, ACM, pp. 330-339.
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Wang, Y, Peng, X, Clarke, A, Schlegel, C & Jiang, J 1970, 'Machine Teaching-Based Efficient Labelling for Cross-unit Healthcare Data Modelling', AI 2021: Advances in Artificial Intelligence, Springer International Publishing, pp. 320-331.
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A data custodian of a big organization (such as a Commonwealth Data Integrating Authority), namely teacher, can easily build an intelligent model which is well trained by comprehensive data collected from multiple sources. However, due to information security and privacy-related regulation requirements, full access to the well-trained intelligent model and the comprehensive training data is usually limited to the teacher only and not available to any unit (or branch) of that organization. Therefore, if a unit, namely student, needs an intelligent function similar to the trained intelligent model, the student has to train a similar model from scratch using the student’s own dataset. Such a dataset is usually unlabelled, requiring a big workload on labelling. Inspired by the Iterative Machine Teaching, we propose a novel collaboration pipeline. It enables the teacher to iteratively guide the student to select samples that are most worth labelling from the student’s own dataset, which significantly reduces the requirement for human labelling and, at the same time, prevents regulation and information security breaches. The effectiveness and efficiency of the proposed pipeline is empirically demonstrated on two publicly available healthcare datasets in comparison with baseline methods. This work has broad implications for the healthcare sector to facilitate data modelling in instances where the large labelled datasets are not accessible to each unit.
Wang, Y, Vickery, NEM, Tarlinton, D, Ploderer, B, Knight, L, Blackler, A & Wyeth, P 1970, 'Exploring the Affordances of Digital Toys for Young Children's Active Play', Proceedings of the 34th Australian Conference on Human-Computer Interaction, OzCHI '22: 34th Australian Conference on Human-Computer Interaction, ACM, pp. 325-337.
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Wang, Z & Long, G 1970, 'Positive Unlabeled Learning by Sample Selection and Prototype Refinement', Springer Nature Switzerland, pp. 304-318.
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Wang, Z, Jiang, J & Long, G 1970, 'Positive Unlabeled Learning by Semi-Supervised Learning', 2022 IEEE International Conference on Image Processing (ICIP), 2022 IEEE International Conference on Image Processing (ICIP), IEEE, pp. 2976-2980.
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Wang, Z, Liu, L, Duan, Y, Kong, Y & Tao, D 1970, 'Continual Learning with Lifelong Vision Transformer', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 171-181.
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Continual learning methods aim at training a neural network from sequential data with streaming labels, relieving catastrophic forgetting. However, existing methods are based on and designed for convolutional neural networks (CNNs), which have not utilized the full potential of newly emerged powerful vision transformers. In this paper, we propose a novel attention-based framework Lifelong Vision Transformer (LVT), to achieve a better stability-plasticity trade-off for continual learning. Specifically, an inter-task attention mechanism is presented in LVT, which implicitly absorbs the previous tasks' information and slows down the drift of important attention between previous tasks and the current task. LVT designs a dual-classifier structure that independently injects new representation to avoid catas-trophic interference and accumulates the new and previous knowledge in a balanced manner to improve the overall performance. Moreover, we develop a confidence-aware memory update strategy to deepen the impression of the previous tasks. The extensive experimental results show that our approach achieves state-of-the-art performance with even fewer parameters on continual learning benchmarks.
Wang, Z, Ma, B, Zeng, Y, Lin, X, Shi, K & Wang, Z 1970, 'Differential Preserving in XGBoost Model for Encrypted Traffic Classification', 2022 International Conference on Networking and Network Applications (NaNA), 2022 International Conference on Networking and Network Applications (NaNA), IEEE, Urumqi, Xinjiang, China., pp. 220-225.
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The classification of encrypted traffic is becoming ever more relevant in the field of network security management and cybersecurity. Most users are currently using encrypted traffic, which can easily lead to privacy threats, and attackers can identify user behavior through the information obtained. VPN encrypted tunnel is the most popular encrypted tunnel method at present. This paper proposes to use the XGBoost model to classify VPNs and Non-VPNs, normalizing the features extracted from encrypted traffic. Experiments are performed on the public dataset ISCX VPN-nonVPN, and the results show that the XGBoost model has an accuracy of 92.4%. To illustrate the advantages of this model, it is compared with the other 5 classification algorithms. At the same time, this paper applies differential privacy technology to the classification model of encrypted traffic and reduces privacy threats by obfuscating data features.
Wang, Z, Ma, B, Zeng, Y, Lin, X, Shi, K & Wang, Z 1970, 'Differential Preserving in XGBoost Model forEncrypted Traffic Classification', 2022 International Conference on Networking and Network Applications, 2022 International Conference on Networking and Network Applications, Urumqi, Xinjiang, China.
Watterson, P 1970, 'Numerical Modelling of Rotating Magnet Electric Field Generation in the Wrist', Neuromodulation Society of Australia and New Zealand 15th Annual Scientific Meeting, Melbourne.
Webster, C & Reid, W 1970, 'A Comparative Rover Mobility Evaluation for Traversing Permanently Shadowed Regions on the Moon', 2022 IEEE Aerospace Conference (AERO), 2022 IEEE Aerospace Conference (AERO), IEEE, pp. 1-15.
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Webster, C, Kong, FH & Fitch, R 1970, 'Bio-inspired 2D Vertical Climbing with a Novel Tripedal Robot', 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 1239-1246.
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Wen, D, Zhu, L, Xu, J & Cai, Z 1970, 'Elastic Container Scheduling for Stochastically Arrived Workflows in Cloud and Edge Computing', COMPUTER SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING, CHINESECSCW 2021, PT I, 16th CCF Conference on Computer Supported Cooperative Work and Social Computing (ChineseCSCW), Springer Nature Singapore, PEOPLES R CHINA, CCF Tech Comm Cooperat Comp, Xiangtan, pp. 44-58.
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Wen, S & Wen, G 1970, 'Preface', Journal of Physics: Conference Series, IOP Publishing, pp. 011001-011001.
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Abstract Organized by Beijing Jiaotong University, the 2021 2nd International Symposium on Automation, Information and Computing (ISAIC 2021) was held successfully online from December 3rd-6th, 2021. The technical program of ISAIC 2021 comprised one plenary session with 10 plenary speeches (40 minutes for each including 3-5 minutes of Q&A), 9 parallel oral sessions including 15 invited speeches (25 minutes for each including 3-5 minutes of Q&A) and 73 online live presentations (15 minutes for each including 3-5 minutes of Q&A), 54 pre-recorded video presentations (15-20 minutes) and 13 e-poster presentations. The ISAIC conference series aims to provide an academic platform for researchers and scholars to present and discuss their latest findings about automation, information and computing. ISAIC 2021 gathered over 197 participants from 28 different countries and areas. The main subjects of the conference were artificial intelligence, electronic and electric systems, information communication technology, information security, mathematics and system engineering. This volume records the proceedings of ISAIC 2021 and contains 134 manuscripts that in accordance with the Journal’s Peer Review Policy were strictly selected based on originality, significance, relevance, and contribution to the area after being peer-reviewed. List of General Chairs, Co-Chairs, Technical Program Committee are available in this pdf.
Wen, Y & Qin, P-Y 1970, 'Yagi-Uda Monopoles with Elevated-Angle Suppression for Endfire Radiation', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 417-418.
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Weng, Y, Pan, Z, Han, M, Chang, X & Zhuang, B 1970, 'An Efficient Spatio-Temporal Pyramid Transformer for Action Detection', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature Switzerland, pp. 358-375.
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The task of action detection aims at deducing both the action category and localization of the start and end moment for each action instance in a long, untrimmed video. While vision Transformers have driven the recent advances in video understanding, it is non-trivial to design an efficient architecture for action detection due to the prohibitively expensive self-attentions over a long sequence of video clips. To this end, we present an efficient hierarchical Spatio-Temporal Pyramid Transformer (STPT) for action detection, building upon the fact that the early self-attention layers in Transformers still focus on local patterns. Specifically, we propose to use local window attention to encode rich local spatio-temporal representations in the early stages while applying global attention modules to capture long-term space-time dependencies in the later stages. In this way, our STPT can encode both locality and dependency with largely reduced redundancy, delivering a promising trade-off between accuracy and efficiency. For example, with only RGB input, the proposed STPT achieves 53.6% mAP on THUMOS14, surpassing I3D+AFSD RGB model by over 10% and performing favorably against state-of-the-art AFSD that uses additional flow features with 31% fewer GFLOPs, which serves as an effective and efficient end-to-end Transformer-based framework for action detection. Code is available at https://github.com/ziplab/STPT.
WenSheng, X, You, B, YongBo, C, YuJie, Z, RunLing, L, ShiJin, D & JiongHan, Y 1970, 'Mark Damage Phenomenon Caused by Superimposed CMP Dishing on Large-Area STI Regions', 2022 China Semiconductor Technology International Conference (CSTIC), 2022 China Semiconductor Technology International Conference (CSTIC), IEEE, pp. 1-3.
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West, N, Schwenken, J & Deuse, J 1970, 'Comparative Study of Methods for the Real-Time Detection of Dynamic Bottlenecks in Serial Production Lines', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE), Springer International Publishing, Kitakyushu, JAPAN, pp. 3-14.
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Capacity-limiting bottlenecks in manufacturing systems form the ideal starting point for measures of improvement. However, the inherent variability of modern systems leads to dynamic bottleneck behavior, causing them to shift between stations. Numerous methods for the detection of shifting bottlenecks exist in literature. In this paper, we present and compare three methods: Bottleneck Walk (BNW), Active Period Method (APM), and an adaptation of Interdeparture Time Variances (ITV). The comparative study deploys the methods in a serial production line with seven stations and eight buffers. We vary the individual locations of the bottlenecks by adding more process time. To compare the methods, we determine the overall average ratio of agreement between the three detection methods. APM and ITV have the highest agreement at an average of 80.10%. Pairings with BNW achieve significantly lower rates of agreement, with 56.33% for ITV, and 62.03%% when compared to the APM.
West, N, Syberg, M & Deuse, J 1970, 'A Holistic Methodology for Successive Bottleneck Analysis in Dynamic Value Streams of Manufacturing Companies', Lecture Notes in Mechanical Engineering, Springer International Publishing, pp. 612-619.
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Numerous methods for bottleneck detection, along novel approaches for bottleneck prediction, are available in literature. To facilitate the development and application of such methods, this paper proposes a holistic methodology for Bottleneck Analysis in dynamic value streams. Analogous to established data analytics levels, namely descriptive, diagnostic, predictive, and prescriptive analytics, the methodology specifies objectives for data-driven Bottleneck Analysis. Based on state-of-the-art bottleneck detection methods, the methodology provides measures for the diagnosis of bottleneck severity and frequency. Additionally, it considers prediction methods to anticipate emerging bottlenecks, depending on available databases. Finally, the methodology provides a context for the yet unexplored field of bottleneck prescription, which aims to mitigate bottleneck effects by data-driven control recommendations. Further practical application of the methodology has to confirm its suitability as a holistic framework for analyzing bottlenecks in dynamic value streams.
Wickramanayake, B, Ouyang, C, Moreira, C & Xu, Y 1970, 'Generating Purpose-Driven Explanations: The Case of Process Predictive Model Inspection', Springer International Publishing, pp. 120-129.
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Wickramanayake, S, Thiyagarajan, K & Kodagoda, S 1970, 'Deep Learned Ground Penetrating Radar Subsurface Features for Robot Localization', 2022 IEEE Sensors, 2022 IEEE Sensors, IEEE, pp. 1-4.
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Willey, K, Goldsmith, R, Machet, T, Daniel, S, Gardner, A & Langie, G 1970, 'Supporting the Transition to Engineering Education Research: growing the community through the AAEE Winter School', 33rd Australasian Association for Engineering Education Conference, AAEE, Sydney, Australia.
Willey, K, Lowe, D, Tilley, E, Roach, K & Machet, T 1970, 'Undergraduate Student’s perceptions of factors that enable and inhibit their professional skill development', 9th Research in Engineering Education Symposium (REES 2021) and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021), 9th Research in Engineering Education Symposium & 32nd Australasian Association for Engineering Education Conference, Research in Enineering Education Network (REEN), Perth, pp. 878-886.
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CONTEXT The need for Engineering graduates who can balance strong technical competencies with broader professional and transversal capabilities is well recognised. In response to calls from industry and recognition by universities of underdeveloped professional skills in their students, there has been a move towards a more integrated approach to preparing undergraduate students for professional practice. This often involves the integration of professional skills development within the more traditional engineering science curricula. PURPOSE OR GOAL Engineering students tend to have diverse reactions to the teaching of these broader professional competencies with many students reacting negatively. This study explored the nature of these reactions and in particular aims to move past the common assumption that students’ attitudes relate to students feeling like professional elements are not “real engineering”. Understanding students’ views on these competencies will enable universities to adapt their curriculum to maximise the quality of demonstrated learning related to professional skill development learning outcomes. APPROACH OR METHODOLOGY/METHODS As part of a broader survey on student reactions to the development of professional skills (with N ≈ 500), we asked an open-ended question seeking the respondents’ comments that “might be helpful to us in understanding your views and experiences?”. After undertaking a survey that required students to reflect on and think about their views in regard to their professional learning and development, this question captured those aspects that students thought were the most important additional information. We identified the dominant themes that emerged from these comments using a thematic analysis. OUTCOMES A number of dominant and often interconnected themes were observed. These ranged from a perception that professional skills cannot be taught at university and must be learned through workplace practice, to the view th...
Williams, L & Ball, JE 1970, 'Hydraulic Flood Modelling Sensitivity Analysis of Cross Drainage Structure Blockage Factors and Blockage Timing', Proceedings of the IAHR World Congress, pp. 6274-6281.
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Estimation of design floods remains a significant problem for many managers of riverine systems. Design floods are the result of the joint probability of both climatic factors and catchment factors. One of the catchment factors that influences floods is the interaction between debris material and cross-drainage structures like cross drainage structures and bridges. As the purpose of design flood estimation is the prediction of both flood magnitude and flood likelihood, there is a need to understand how blockage of cross-drainage structures influences the predicted flood levels and flows. The study presented herein is based on design flood predictions obtained from a 2-D model of flows in rivers and floodplains that considered the impact of cross-drainage structure blockage on both the predicted flood level and the predicted flood flow. Factors considered in the study were the timing and magnitude of blockage, the peak flow of the flood hydrograph, and the available catchment storage upstream of the cross-drainage structure. These factors were modified to assess the sensitivity of design flood predictions. It was found that blockage results in changes to the flood profile and downstream discharge. The magnitude of these changes was found to be dependent on the factors considered. However, the common assumption of frequency translation between rainfall and flow or level is suspect when blockage is considered.
Wong, HX, Koh, Y, Goh, DJ, Sharma, J, Merugu, S & Lee, JE-Y 1970, 'Silicon Electrothermal Microactuators as Zero Standby Power Local Temperature Switches', 2022 IEEE Sensors, 2022 IEEE Sensors, IEEE, Dallas, TX, USA, pp. 1-4.
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The bent beam structure is a well known elec trothermal actuator based on asymmetric thermal expansion but is less commonly used for sensors In this work a silicon based bent beam structure was fabricated on Silicon on Insulator SOl wafers to realise a local temperature sensitive switch The novel sensor switch closes a 0 7 mu mathrm m gap when locally heated above 80 C yet is unperturbed when the ambient temperature is raised up to 150 C We used thermal imaging to analyse the temperature distributions for different heating schemes and modelled the results with finite element simulations The results show that these structures are attractive candidates for zero standby power surface sensitive temperature switches
Woolfrey, J & Liu, D 1970, 'An Optimal Dynamic Control Method for Robots with Virtual Links', 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 12843-12848.
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Virtual links and virtual joints can be appended to the kinematic chain of a robot arm to assist in modelling and control of certain tasks. Activities such as spray painting, sand blasting, or scanning with a laser or camera can be enhanced by modelling the fluid stream, light beam, or field of view using a virtual link. Virtual joints can be used to allow movement in semi-redundant degrees of freedom of the task space. This can can be exploited to optimize the control of the real robot. A prudent choice is to minimize the effort required by the manipulator to execute the task. This often requires the inversion of the inertia matrix. However, virtual links have no inertia so the inverse does not exist. This paper first explores methods of adding virtual mass or modifying the inertia matrix to allow inversion and the consequences. Then an optimal control problem is proposed that minimizes kinetic energy in the real manipulator and maximizes use of the virtual joints. In doing so, we only need the real inertia matrix which is always invertible. The method is validated in a case study for high pressure water blasting. It is shown to reduce the dynamic torque norm compared to a minimum velocity controller.
Wöstmann, R, Borggräfe, T, Janßen, S, Kimberger, J, Ould, S, Bennett, N, Moreno, VH & Deuse, J 1970, 'Data-driven recipe optimisation based on unified digital twins and shared prediction models', Proceedings of the European Modeling & Simulation Symposium, EMSS, The 34th European Modeling & Simulation Symposium, CAL-TEK srl.
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The importance of cross-process multivariate data analysis for improving products and processes is continuously increasing. Artificial intelligence and machine learning offer new possibilities to represent complex cause-effect relationships in models and to use them for optimisation. For consistent and scalable usage, unified data structures and representations of products, processes and resources are required in order to be able to use larger data populations as well as deploy these models in different application contexts. The paper presents an approach of shared prediction models for recipe optimisation based on unified digital twins in the beverage industry. For this purpose, a central generic data model was created, which is the basis for unified digital twins and thus the integration of physical and digital entities, as well as the foundation for cross-process data analysis.
Wu, G, Li, Z, Shen, Y, Zhang, H, Shen, S & Yu, S 1970, 'A Deep Reinforcement Learning Approach to Edge-based IDS Packets Sampling', 2022 5th International Conference on Data Science and Information Technology (DSIT), 2022 5th International Conference on Data Science and Information Technology (DSIT), IEEE, pp. 1-6.
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Edge computing expands the Internet of Things (IoT) by allowing partial computing tasks to be migrated to edge servers and alleviate the computing pressure of terminal devices. However, the edge servers will be attacked through malicious network traffic. A well-designed edge-based IDS plays a significant role in protecting from malicious attacks. In this paper, we employ a gated recurrent unit classifier to perform intrusion detection due to its characteristics on long-term memory of inputs and light structure. Furthermore, to reduce the cost of performing intrusion detection when facing a large volume of data, we propose an actor-critic network based on deep reinforcement learning for packets sampling to work on some packets by ignoring the others. The system we designed can achieve great classification performance through partial packets. We use dataset CIC-IDS-2017 to evaluate our model, and the accuracy of classification reaches 97% while the proportion of detection packets is 26% and below. Evaluations of our approach on UNSW-NB15 and CIC-IDS-2017 show that our model maintains under 26% and 18% of the selected packets, respectively.
Wu, G, Zhao, Y, Shen, Y, Zhang, H, Shen, S & Yu, S 1970, 'DRL-based Resource Allocation Optimization for Computation Offloading in Mobile Edge Computing', IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, ELECTR NETWORK, pp. 1-6.
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Mobile edge computing (MEC) provides a new development direction for emerging computing-intensive applications because it can improve computing performance and lower the threshold for users to use such applications. However, designing an effective computation offloading strategy to determine which tasks should be uninstalled to an edge server is still a crucial challenge. To this end, we propose a computation offload scheme based on dynamic resource allocation to optimize computing performance and energy consumption in MEC systems. We further formulate the resource allocation as a partially observable Markov decision process, which is solved by a policy gradient deep reinforcement learning method. Compared with other existing solutions, simulation results show that our proposal reduces the computational latency and energy consumption.
Wu, K, Qin, P & Chen, S-L 1970, 'A High-Efficiency 3D-Printed E-Band Dielectric Transmitarray For Integrated Space and Terrestrial Networks', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 319-320.
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Wu, K, Zhang, JA, Huang, X & Guo, YJ 1970, 'Removing False Targets For Cyclic Prefixed OFDM Sensing With Extended Ranging', 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring), IEEE, Helsinki, Finland, pp. 1-5.
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Employing cyclic prefixed OFDM (CP-OFDM) communication waveform for sensing has attracted extensive attention in vehicular integrated sensing and communications (ISAC). A unified sensing framework is developed recently, greatly extending the ranging capability of CP-OFDM sensing. However, a false target issue still remains unsolved. In this paper, we investigate and solve this issue. Specifically, we unveil that false targets are caused by periodic cyclic prefixes (CPs) in CP-OFDM waveform. We also derive the relation between the locations of false and true targets, and other features, e.g., strength, of false targets. Moreover, we develop an effective solution to removing false targets. Simulations are provided to confirm the validity of our analysis and the effectiveness of the proposed solution.
Wu, M, Zhang, Y & Li, X 1970, 'Exploring Associations within Disease-Gene Pairs: Bibliometrics, Word Embedding, and Network Analytics', 2022 Portland International Conference on Management of Engineering and Technology (PICMET), 2022 Portland International Conference on Management of Engineering and Technology (PICMET), IEEE, Portland, OR, USA, pp. 1-7.
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Topic extraction and relationship identification are attracting increasing interests from the bibliometric community, as well as from relevant fields in biomedicine. Recently many biomedical studies reveal the pairwise associations between various genes and diseases, which lead to the problem of predicting and investigating new emerging pairs. This paper proposes a method to generate disease-gene pair prediction and ranking, based on both semantic similarities between textual contexts and topological similarities between nodes within a disease-gene network. Specifically, genes and diseases are identified via a term clumping process and the association strengths are calculated based on co-occurrence frequency and a pre-trained Word2Vec model. Meanwhile, an integrated disease-gene network is constructed and we capture potential emerging disease-gene pairs through a modified link prediction approach. We applied the proposed method to a dataset with 27,727 scientific articles in the atrial fibrillation area to demonstrate the reliability of the model. The empirical insights derived from the case highlight implicit associations within those highly ranked disease-gene pairs and provide references for stakeholders in cardiovascular areas.
Wu, M, Zhang, Y, Markley, M, Cassidy, C, Newman, N & Porter, A 1970, 'Covid-19 Knowledge Deconstruction and Retrieval: Solutions of Intelligent Bibliometrics', Ceur Workshop Proceedings, pp. 92-103.
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Covid-19 is an unprecedented challenge that disruptively reshapes societies and scientific research communities. Facing the knowledge flood brought by the overwhelming volume of research efforts, there still lacks a platform to link those to previous knowledge foundations and efficiently visualize and understand them. Aiming to fill this gap, we propose a research framework in this paper to assist scientists in identifying, retrieving, and visualizing the emerging Covid-19 knowledge. The proposed framework incorporates principal topic decomposition (PCD), text analytics-based knowledge model (KM), and the hierarchical topic tree (HTT) method to profile the research landscape, retrieve knowledge of specific interest, and visualize the knowledge structures. Initially, our topic analysis of 127, 971 research papers published during 2020-2021 identified 35 research hotspots. Furthermore, we built up a knowledge model on the topic of vaccination and retrieved 92, 286 research papers from the entire PubMed database as the knowledge foundation of this topic. Lastly, the HTT results of the retrieved papers highlighted multiple relevant disciplines, from whose branches we identified four future research directions: Monoclonal antibody treatments, vaccination in diabetic patients, vaccination effectiveness in SARS-CoV-2 antigenic drift, and vaccination-related allergic sensitization.
Wu, X, Qi, L, Xu, X, Yu, S, Dou, W & Zhang, X 1970, 'Crowdsourcing-based Multi-Device Communication Cooperation for Mobile High-Quality Video Enhancement', Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, ACM, ELECTR NETWORK, pp. 1140-1148.
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The widespread use of mobile devices propels the development of new-fashioned video applications like 3D (3-Dimensional) stereo video and mobile cloud game via web or App, exerting more pressure on current mobile access network. To address this challenge, we adopt the crowdsourcing paradigm to offer some incentive for guiding the movement of recruited crowdsourcing users and facilitate the optimization of the movement control decision. In this paper, based on a practical 4G (4th-Generation) network throughput measurement study, we formulate the movement control decision as a cost-constrained user recruitment optimization problem. Considering the intractable complexity of this problem, we focus first on a single crowdsourcing user case and propose a pseudo-polynomial time complexity optimal solution. Then, we apply this solution to solve the more general problem of multiple users and propose a graph-partition-based algorithm. Extensive experiments show that our solutions can improve the efficiency of real-time D2D communication for mobile videos.
Xia, J, Qu, W, Huang, W, Zhang, J, Wang, X & Xu, M 1970, 'Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.
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Xia, X, Yin, H, Yu, J, Wang, Q, Xu, G & Nguyen, QVH 1970, 'On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation', Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 546-555.
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Xiang, S, Cheng, D, Shang, C, Zhang, Y & Liang, Y 1970, 'Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction', Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM '22: The 31st ACM International Conference on Information and Knowledge Management, ACM, pp. 3584-3593.
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The price movement prediction of stock market has been a classical yet challenging problem, with the attention of both economists and computer scientists. In recent years, graph neural network has significantly improved the prediction performance by employing deep learning on company relations. However, existing relation graphs are usually constructed by handcraft human labeling or nature language processing, which are suffering from heavy resource requirement and low accuracy. Besides, they cannot effectively response to the dynamic changes in relation graphs. Therefore, in this paper, we propose a temporal and heterogeneous graph neural network-based (THGNN) approach to learn the dynamic relations among price movements in financial time series. In particular, we first generate the company relation graph for each trading day according to their historic price. Then we leverage a transformer encoder to encode the price movement information into temporal representations. Afterward, we propose a heterogeneous graph attention network to jointly optimize the embeddings of the financial time series data by transformer encoder and infer the probability of target movements. Finally, we conduct extensive experiments on the stock market in the United States and China. The results demonstrate the effectiveness and superior performance of our proposed methods compared with state-of-the-art baselines. Moreover, we also deploy the proposed THGNN in a real-world quantitative algorithm trading system, the accumulated portfolio return obtained by our method significantly outperforms other baselines.
Xiang, S, Cheng, D, Zhang, J, Ma, Z, Wang, X & Zhang, Y 1970, 'Efficient Learning-based Community-Preserving Graph Generation', 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, Kuala Lumpur, Malaysia, pp. 1982-1994.
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Graph generation is beneficial to comprehend the creation of meaningful structures of networks in a broad spec trum of applications such as social networks and biological net works Recent studies tend to leverage deep learning techniques to learn the topology structures in graphs However we notice that the community structure which is one of the most unique and prominent features of the graph cannot be well captured by the existing graph generators Moreover the existing advanced deep learning based graph generators are not efficient and scalable which can only handle small graphs In this paper we propose a novel community preserving generative adversarial network CPGAN for effective and efficient scalable graph simulation We employ graph convolution networks in the encoder and share parameters in the generation process to transmit information about community structures and preserve the permutation invariance in CPGAN We conducted extensive experiments on benchmark datasets including six sets of real life graphs The results demonstrate that CPGAN can achieve a good trade off between efficiency scalability and graph simulation quality for real life graph simulation compared with state of the art baselines
Xiao, D, Ni, W, Zhang, JA, Liu, R, Chen, S & Qu, Y 1970, 'AI-Enabled Automated and Closed-Loop Optimization Algorithms for Delay-Aware Network', 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Austin, TX, pp. 806-811.
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Network slicing is one of the core techniques of the current 5G networks. To accommodate as many network slices as possible with limited hardware resources, service providers need to avoid over-provisioning of resources. In this paper, we first propose a Deep Q-Network (DQN) based network slicing algorithm to maximize the acceptance ratio and ensure prior placement of higher-priority requests for Ultra-Reliable Low-Latency Communication (URLLC) services. Specifically, we model the network slicing as a Markov Decision Process (MDP), where we consider Virtual Network Function (VNF) placements to be the actions of the MDP, and define a reward function based on service priority. For every service request, we use the DQN to choose an MDP action for performing the VNF placement. The placement results in an MDP reward that we can use to train the DQN. Once trained, the DQN approximates the optimal solution of the MDP. Considering the over-provisioning of resources, we then propose a Binary Search Assisted Transfer Learning algorithm (BSATL), in which the available hardware resources are scaled down/up and the knowledge learned from the source task is transferred to the target task in each iteration, to achieve automated and closed-loop optimization for the ever changing infrastructure, a scenario of 6G Event Defined uRLLC (EDuRLLC). Numerical evaluations show that our proposed scheme can significantly improve cost-utility while maintaining the optimal acceptance ratio.
Xiao, S, Wang, Y, Dong, D & Zhang, J 1970, 'Two-stage solution of quantum process tomography in the natural basis', 2022 IEEE 61st Conference on Decision and Control (CDC), 2022 IEEE 61st Conference on Decision and Control (CDC), IEEE, pp. 5807-5812.
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Xiao, T, Halkon, B, Oberst, S, Wang, S & Qiu, X 1970, 'SOUND FIELD MEASUREMENT AT AN ENCLOSURE OPENING USING REFRACTO-VIBROMETRY', Proceedings of the International Congress on Sound and Vibration, International Congress on Sound and Vibration, Singapore.
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A sound field can be measured by an array of microphones distributed across the area of interest or by moving a smaller number of microphones sequentially. Such procedures can be time-consuming and expensive when high spatial resolution is required. Furthermore, the presence of physical microphones might disturb the sound field. Refracto-vibrometry is based on the acousto-optic effect. It can serve as an alternative method to measure sound pressure at all the points of interest without disturbing the sound field. In this paper, three methods, the filtered back-projection, the truncated singular value decomposition and the Tikhonov regularisation methods, are used to evaluate the sound field at an enclosure opening. Comparison with a microphone array shows that the Tikhonov regularisation method yields the best result.
Xie, F, Zhang, Y, Wei, H & Bai, G 1970, 'UQ-AAS21: A Comprehensive Dataset of Amazon Alexa Skills', Springer International Publishing, pp. 159-173.
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Xie, F, Zhang, Y, Yan, C, Li, S, Bu, L, Chen, K, Huang, Z & Bai, G 1970, 'Scrutinizing Privacy Policy Compliance of Virtual Personal Assistant Apps', Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, ASE '22: 37th IEEE/ACM International Conference on Automated Software Engineering, ACM, pp. 1-13.
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Xie, M, Jiang, J, Shen, T, Wang, Y, Gerrard, L & Clarke, A 1970, 'A Green Pipeline for Out-of-Domain Public Sentiment Analysis', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 190-202.
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In the changing social and economic environment, organisations are keen to act promptly and appropriately to changes. Sentiment analysis can be applied to social media data to capture timely information of new events and the corresponding public opinions. However, currently both the social topics and trending words are changing just as rapidly as the target topics and domains that organisations are interested in investigating. Therefore, there is a need for a well-trained sentiment analysis model able to handle out-of-domain input. Current solutions mainly focus on using domain adaptation techniques, but these solutions require domain-specific data and inevitably introduce extra overheads. To tackle this challenge, we propose a green Artificial Intelligence (AI) solution for a sentiment analysis pipeline (GreenSAP) to gain a better understanding of the changing public opinions on social media. Specifically, we propose to leverage the expressively powerful capability of the pre-trained Transformer encoder, and make use of several publicly-available sentiment analysis datasets from various domains and scenarios to develop a pipeline model. A sarcasm detection model is also included to eliminate false positive predictions. In experiments, this model significantly outperforms its competitors on three public benchmark datasets and on two of our labelled out-of-domain datasets for real-world applications.
Xing, B & Tsang, I 1970, 'DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition', Findings of the Association for Computational Linguistics: ACL 2022, Findings of the Association for Computational Linguistics: ACL 2022, Association for Computational Linguistics, pp. 3611-3621.
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The task of joint dialog sentiment classification (DSC) and act recognition (DAR) aims to simultaneously predict the sentiment label and act label for each utterance in a dialog. In this paper, we put forward a new framework which models the explicit dependencies via integrating prediction-level interactions other than semantics-level interactions, more consistent with human intuition. Besides, we propose a speaker-aware temporal graph (SATG) and a dual-task relational temporal graph (DRTG) to introduce temporal relations into dialog understanding and dual-task reasoning. To implement our framework, we propose a novel model dubbed DARER, which first generates the context-, speaker- and temporal-sensitive utterance representations via modeling SATG, then conducts recurrent dual-task relational reasoning on DRTG, in which process the estimated label distributions act as key clues in prediction-level interactions. Experiment results show that DARER outperforms existing models by large margins while requiring much less computation resource and costing less training time. Remarkably, on DSC task in Mastodon, DARER gains a relative improvement of about 25% over previous best model in terms of F1, with less than 50% parameters and about only 60% required GPU memory.
Xing, B & Tsang, I 1970, 'Neural Subgraph Explorer: Reducing Noisy Information via Target-oriented Syntax Graph Pruning', Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}, International Joint Conferences on Artificial Intelligence Organization, Vienna, Austria, pp. 4425-4431.
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Recent years have witnessed the emerging success of leveraging syntax graphs for the target sentiment classification task.However, we discover that existing syntax-based models suffer from two issues:noisy information aggregation and loss of distant correlations.In this paper, we propose a novel model termed Neural Subgraph Explorer, which (1) reduces the noisy information via pruning target-irrelevant nodes on the syntax graph;(2) introduces beneficial first-order connections between the target and its related words into the obtained graph.Specifically, we design a multi-hop actions score estimator to evaluate the value of each word regarding the specific target.The discrete action sequence is sampled through Gumble-Softmax and then used for both of the syntax graph and the self-attention graph.To introduce the first-order connections between the target and its relevant words, the two pruned graphs are merged.Finally, graph convolution is conducted on the obtained unified graph to update the hidden states.And this process is stacked with multiple layers.To our knowledge, this is the first attempt of target-oriented syntax graph pruning in this task.Experimental results demonstrate the superiority of our model, which achieves new state-of-the-art performance.
Xing, Y, Zhao, G, Xu, C, Cheng, K & Yut, S 1970, 'The Converged Scheduling for Time Sensitive Mission in Satellite Formation Flying', GLOBECOM 2022 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, IEEE, pp. 4691-4696.
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Xompero, A, Pang, YL, Patten, T, Prabhakar, A, Calli, B & Cavallaro, A 1970, 'Audio-Visual Object Classification for Human-Robot Collaboration', ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Singapore, Singapore, pp. 9137-9141.
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Human robot collaboration requires the contactless estimation of the physical properties of containers manipulated by a person for example while pouring content in a cup or moving a food box Acoustic and visual signals can be used to estimate the physical properties of such objects which may vary substantially in shape material and size and also be occluded by the hands of the person To facilitate comparisons and stimulate progress in solving this problem we present the CORSMAL challenge and a dataset to assess the performance of the algorithms through a set of well defined performance scores The tasks of the challenge are the estimation of the mass capacity and dimensions of the object container and the classification of the type and amount of its content A novel feature of the challenge is our real to simulation framework for visualising and assessing the impact of estimation errors in human to robot handovers
Xu, C, Qu, Y, Xiang, Y, Gao, L, Smith, D & Yu, S 1970, 'BASS: Blockchain-Based Asynchronous SignSGD for Robust Collaborative Data Mining', 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, Shenzhen, China, pp. 1-7.
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Federated learning (FL) is a machine learning framework for collaborative data mining in many scenarios (e.g. Internet of Things) due to its privacy-preserving feature. However, various attacks arise security concerns of FL, such as poisoning, backdoor, and DDoS attacks. Several blockchain-based FL schemes strengthen credibility and security without considering the increased communication overhead. Some existing work compresses local updated gradients to sign vectors to lower communication overhead at the expense of model accuracy. To address the above concerns, this paper offers a blockchain-based asynchronous SignSGD (BASS) scheme. A novel asynchronous sign aggregation algorithm is introduced to ensure model accuracy even if the local updated gradients are compressed to sign vectors. Considering the unstable network connection on IoT, a consensus algorithm that elects multiple leader nodes enables reliable global model aggregation. The introduced blockchain improves credibility and security without downgrading efficiency. Empirical studies show that BASS outperforms other schemes in efficiency, model accuracy, and security.
Xu, D, Yang, H, Rizoiu, M-A & Xu, G 1970, 'Being Automated or Not? Risk Identification of Occupations with Graph Neural Networks', Advanced Data Mining and Applications, Springer Nature Switzerland, pp. 520-534.
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The rapid advances in automation technologies, such as artificialintelligence (AI) and robotics, pose an increasing risk of automation foroccupations, with a likely significant impact on the labour market. Recentsocial-economic studies suggest that nearly 50\% of occupations are at highrisk of being automated in the next decade. However, the lack of granular dataand empirically informed models have limited the accuracy of these studies andmade it challenging to predict which jobs will be automated. In this paper, westudy the automation risk of occupations by performing a classification taskbetween automated and non-automated occupations. The available information is910 occupations' task statements, skills and interactions categorised byStandard Occupational Classification (SOC). To fully utilize this information,we propose a graph-based semi-supervised classification method named\textbf{A}utomated \textbf{O}ccupation \textbf{C}lassification based on\textbf{G}raph \textbf{C}onvolutional \textbf{N}etworks (\textbf{AOC-GCN}) toidentify the automated risk for occupations. This model integrates aheterogeneous graph to capture occupations' local and global contexts. Theresults show that our proposed method outperforms the baseline models byconsidering the information of both internal features of occupations and theirexternal interactions. This study could help policymakers identify potentialautomated occupations and support individuals' decision-making before enteringthe job market.
Xu, H, Nanda, P, Liang, J & He, X 1970, 'The Force of Compensation, a Multi-stage Incentive Mechanism Model for Federated Learning', Springer Nature Switzerland, pp. 357-373.
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Xu, L, He, G, Zhou, J, Lei, J, Xie, W, Li, Y & Tai, Y-W 1970, 'Transcoded Video Restoration by Temporal Spatial Auxiliary Network', Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), pp. 2875-2883.
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In most video platforms, such as Youtube, Kwai, and TikTok, the played videos usually have undergone multiple video encodings such as hardware encoding by recording devices, software encoding by video editing apps, and single/multiple video transcoding by video application servers. Previous works in compressed video restoration typically assume the compression artifacts are caused by one-time encoding. Thus, the derived solution usually does not work very well in practice. In this paper, we propose a new method, temporal spatial auxiliary network (TSAN), for transcoded video restoration. Our method considers the unique traits between video encoding and transcoding, and we consider the initial shallow encoded videos as the intermediate labels to assist the network to conduct self-supervised attention training. In addition, we employ adjacent multi-frame information and propose the temporal deformable alignment and pyramidal spatial fusion for transcoded video restoration. The experimental results demonstrate that the performance of the proposed method is superior to that of the previous techniques. The code is available at https://github.com/icecherylXuli/TSAN.
Xu, M, Zhao, L, Huang, S & Hao, Q 1970, 'Active SLAM in 3D deformable environments', 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 7952-7958.
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This paper considers active SLAM problem for 3D deformable environments where the trajectory of the robot is planned to optimize the SLAM results. A planning strategy combining an efficient global planner with an accurate local planner is proposed to solve the problem. Simulation results under different scenarios have shown that the proposed active SLAM algorithm provides a good balance between accuracy and efficiency as compared to the local planner and the global planner. The MATLAB code of this first active SLAM algorithm for 3D deformable environments is made publicly available4.
Xu, X, Zhang, J, Liu, F, Sugiyama, M & Kankanhalli, M 1970, 'Adversarial Attack and Defense for Non-Parametric Two-Sample Tests', Proceedings of Machine Learning Research, pp. 24743-24769.
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Non-parametric two-sample tests (TSTs) that judge whether two sets of samples are drawn from the same distribution, have been widely used in the analysis of critical data. People tend to employ TSTs as trusted basic tools and rarely have any doubt about their reliability. This paper systematically uncovers the failure mode of non-parametric TSTs through adversarial attacks and then proposes corresponding defense strategies. First, we theoretically show that an adversary can upper-bound the distributional shift which guarantees the attack's invisibility. Furthermore, we theoretically find that the adversary can also degrade the lower bound of a TST's test power, which enables us to iteratively minimize the test criterion in order to search for adversarial pairs. To enable TST-agnostic attacks, we propose an ensemble attack (EA) framework that jointly minimizes the different types of test criteria. Second, to robustify TSTs, we propose a max-min optimization that iteratively generates adversarial pairs to train the deep kernels. Extensive experiments on both simulated and real-world datasets validate the adversarial vulnerabilities of non-parametric TSTs and the effectiveness of our proposed defense. Source code is available at https://github.com/GodXuxilie/Robust-TST.git.
Xu, Y, Fang, M, Chen, L, Du, Y, Zhou, J & Zhang, C 1970, 'Perceiving the World: Question-guided Reinforcement Learning for Text-based Games', Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, pp. 538-560.
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Text-based games provide an interactive way to study natural language processing. While deep reinforcement learning has shown effectiveness in developing the game playing agent, the low sample efficiency and the large action space remain to be the two major challenges that hinder the DRL from being applied in the real world. In this paper, we address the challenges by introducing world-perceiving modules, which automatically decompose tasks and prune actions by answering questions about the environment. We then propose a two-phase training framework to decouple language learning from reinforcement learning, which further improves the sample efficiency. The experimental results show that the proposed method significantly improves the performance and sample efficiency. Besides, it shows robustness against compound error and limited pre-training data.
Xu, Z, Khabbaz, H, Fatahi, B, Lee, J & Bhandari, S 1970, 'Numerical Assessment of Impacts of Vibrating Roller Characteristics on Acceleration Response of Drum Used for Intelligent Compaction', Lecture Notes in Civil Engineering, 4th International Conference on Transportation Geotechnics (ICTG), Springer International Publishing, ELECTR NETWORK, pp. 231-245.
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Intelligent compaction (IC) is an emerging technology for efficient and optimized ground compaction. IC combines the roller-integrated measurements with the Global Positioning System (GPS), which performs the real-time quality control and assurance during the compaction work. Indeed, IC technology is proven to be capable of providing a detailed control system for compaction process with real-time feedback and adjustment on full-area of compaction. Although roller manufacturers offer typical recommended settings for rollers in various soils, there are still some areas needing further improvement, particularly on the selection of vibration frequency and amplitude of the roller in soils experiencing significant nonlinearity and plasticity during compaction. In this paper, the interaction between the road subgrade and the vibrating roller is simulated, using the three-dimensional finite element method capturing the dynamic responses of the soil and the roller. The developed numerical model is able to simulate the nonlinear behavior of soil subjected to dynamic loading, particularly variations of soil stiffness and damping with the cyclic shear strain induced by the applied load. In this study, the dynamic load of the roller is explicitly applied to the simulated cylindrical roller drum. Besides, the impact of the frequency and amplitude on the level of subgrade compaction is discussed based on the detailed numerical analysis. The adopted constitutive model allows to assess the progressive settlement of ground subjected to cyclic loading. The results based on the numerical modeling reveal that the roller vibration characteristics can impact the influence depth as well as the level of soil compaction and its variations with depth. The results of this study can be used as a potential guidance by practicing engineers and construction teams on selecting the best choice of roller vibration frequency and amplitude to achieve high-quality compaction.
Yang, C, Wang, X, Yao, L, Jiang, J & Xu, G 1970, 'An Explanation Module for Deep Neural Networks Facing Multivariate Time Series Classification', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 3-14.
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Deep neural networks currently achieve state-of-the-art performance in many multivariate time series classification (MTSC) tasks, which are crucial for various real-world applications. However, the black-box characteristic of deep learning models impedes humans from obtaining insights into the internal regulation and decisions made by classifiers. Existing explainability research generally requires constructing separate explanation models to work with deep learning models or process their results, thus calling for additional development efforts. We propose a novel explanation module pluggable into existing deep neural networks to explore variable importance for explaining MTSC. We evaluate our module with popular deep neural networks on both real-world and synthetic datasets to demonstrate its effectiveness in generating explanations for MTSC. Our experiments also show the module improves the classification accuracy of existing models due to the comprehensive incorporation of temporal features.
Yang, C, Wang, X, Yao, L, Jiang, J & Xu, G 1970, 'Pluggable Explanation for Deep Neural Networks-based Multivariate Time Series Classification', Australasian Joint Conference on Artificial Intelligence, Australasian Joint Conference on Artificial Intelligence, hybrid (Sydney, online).
Yang, C, Wang, X, Yao, L, Long, G, Jiang, J & Xu, G 1970, 'Attentional Gated Res2net for Multivariate Time Series Classification', ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Singapore, pp. 3308-3312.
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Yang, H, Chen, H, Pan, S, Li, L, Yu, PS & Xu, G 1970, 'Dual Space Graph Contrastive Learning', Proceedings of the ACM Web Conference 2022, WWW '22: The ACM Web Conference 2022, ACM, pp. 1238-1247.
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Unsupervised graph representation learning has emerged as a powerful tool to address real-world problems and achieves huge success in the graph learning domain. Graph contrastive learning is one of the unsupervised graph representation learning methods, which recently attracts attention from researchers and has achieved state-of-the-art performances on various tasks. The key to the success of graph contrastive learning is to construct proper contrasting pairs to acquire the underlying structural semantics of the graph. However, this key part is not fully explored currently, most of the ways generating contrasting pairs focus on augmenting or perturbating graph structures to obtain different views of the input graph. But such strategies could degrade the performances via adding noise into the graph, which may narrow down the field of the applications of graph contrastive learning. In this paper, we propose a novel graph contrastive learning method, namely Dual Space Graph Contrastive (DSGC) Learning, to conduct graph contrastive learning among views generated in different spaces including the hyperbolic space and the Euclidean space. Since both spaces have their own advantages to represent graph data in the embedding spaces, we hope to utilize graph contrastive learning to bridge the spaces and leverage advantages from both sides. The comparison experiment results show that DSGC achieves competitive or better performances among all the datasets. In addition, we conduct extensive experiments to analyze the impact of different graph encoders on DSGC, giving insights about how to better leverage the advantages of contrastive learning between different spaces.
Yang, J & Lin, C-T 1970, 'Multi-View Adjacency-Constrained Nearest Neighbor Clustering (Student Abstract)', Proceedings of the AAAI Conference on Artificial Intelligence, 36th AAAI Conference on Artificial Intelligence / 34th Conference on Innovative Applications of Artificial Intelligence / 12th Symposium on Educational Advances in Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), ELECTR NETWORK, pp. 13097-13098.
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Most existing multi-view clustering methods have problems with parameter selection and high computational complexity, and there have been very few works based on hierarchical clustering to learn the complementary information of multiple views. In this paper, we propose a Multi-view Adjacency-constrained Nearest Neighbor Clustering (MANNC) and its parameter-free version (MANNC-PF) to overcome these limitations. Experiments tested on eight real-world datasets validate the superiority of the proposed methods compared with the 13 current state-of-the-art methods.
Yang, L, Xie, Y, Islam, MR & Xu, G 1970, 'Big Data and Artificial Intelligence (AI) to Detect Glaucoma', 2022 9th International Conference on Behavioural and Social Computing (BESC), 2022 9th International Conference on Behavioural and Social Computing (BESC), IEEE, pp. 1-6.
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Glaucoma is an eye condition that is one of the most prevalent causes of blindness due to damage to the optic nerve. According to existing studies, it is the second most common reason for vision loss worldwide. With the advanced development of artificial intelligence (AI), it is necessary to develop a glaucoma detection system for diagnosis. Therefore, the primary objective of this paper is to create a system for detecting glaucoma using retinal fundus images, which can help determine if the patient was affected by glaucoma. Although several methods have been applied to detect glaucoma in the past decades, it is essential to use an advanced AI technique with a glaucoma detection system. Thus, in this paper, we divided our task into threefold: 1) segmentation, 2) classification, and 3) deployment. The U-Net architecture is implemented for segmentation. The pretrained GC-Net model is proposed for classification. Finally, based on the segmentation and classification, we developed a glaucoma detection system for diagnosis. This study uses ACRIMA datasets for training and testing. The result of this model is evaluated using various parameters such as accuracy, sensitivity, specificity, f1-score, and auc score. The output is compared to deep learning models such as ResNet, CNN, Inception V3, and TB-Net. The proposed model achieved 96% accuracy in training and 93% accuracy in testing. Overall, the performance of the proposed model is better in all the analyses.
Yang, P, Wang, H, Lian, D, Zhang, Y, Qin, L & Zhang, W 1970, 'TMN: Trajectory Matching Networks for Predicting Similarity.', ICDE, 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, pp. 1700-1713.
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Trajectory similarity computation is the cornerstone of many applications in the field of trajectory data analysis. To cope with the high time complexity of calculating exact similarity between trajectories, learning-based models have been developed for a good trade-off between the similarity computing time and the accuracy of the learned similarity. As each trajectory can be represented by a fixed-length vector regardless of the size of the trajectory, the similarity computation among the trajectories is highly time-efficient. Nevertheless, we observe that these learning-based models are designed based on recurrent neural networks (RNN), which cannot properly capture the correlations among the trajectories. Moreover, these learning-based models simply use the similarity scores of the pairs of trajectories in the training for a specific similarity metric, while a vital piece of information is neglected: the mappings of the points between two trajectories are readily available when the similarity score is calculated. These motivate us to design a new learning-based model, named TMN, based on attention networks, aiming to significantly improve the accuracy such that a better trade-off between the similarity computing time and the accuracy can be achieved. The proposed matching mechanism associates points across trajectories by computing attention weights of point pairs so that TMN learns to simulate similarity computation between the trajectory pair. Apart from taking interactions between trajectories into consideration, the sequential information of each individual trajectory is also considered, thereby making full use of spatial features of a pair of trajectories. We evaluate various approaches on real-life datasets under extensive trajectory distance metrics. Experimental results demonstrate that TMN outperforms state-of-the-art methods in terms of accuracy. Besides, ablation studies prove the effectiveness of our novel matching mechanism.
Yang, S, Sun, P, Jiang, Y, Xia, X, Zhang, R, Yuan, Z, Wang, C, Luo, P & Xu, M 1970, 'OBJECTS IN SEMANTIC TOPOLOGY', Iclr 2022 10th International Conference on Learning Representations.
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A more realistic object detection paradigm, Open-World Object Detection, has arised increasing research interests in the community recently. A qualified open-world object detector can not only identify objects of known categories, but also discover unknown objects, and incrementally learn to categorize them when their annotations progressively arrive. Previous works rely on independent modules to recognize unknown categories and perform incremental learning, respectively. In this paper, we provide a unified perspective: Semantic Topology. During the life-long learning of an open-world object detector, all object instances from the same category are assigned to their corresponding pre-defined node in the semantic topology, including the 'unknown' category. This constraint builds up discriminative feature representations and consistent relationships among objects, thus enabling the detector to distinguish unknown objects out of the known categories, as well as making learned features of known objects undistorted when learning new categories incrementally. Extensive experiments demonstrate that semantic topology, either randomly-generated or derived from a well-trained language model, could outperform the current state-of-the-art open-world object detectors by a large margin, e.g., the absolute open-set error (the number of unknown instances that are wrongly labeled as known) is reduced from 7832 to 2546, exhibiting the inherent superiority of semantic topology on open-world object detection.
Yang, S, Yang, E, Han, B, Liu, Y, Xu, M, Niu, G & Liu, T 1970, 'Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network', Proceedings of Machine Learning Research, pp. 25302-25312.
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In label-noise learning, estimating the transition matrix is a hot topic as the matrix plays an important role in building statistically consistent classifiers. Traditionally, the transition from clean labels to noisy labels (i.e., clean-label transition matrix (CLTM)) has been widely exploited to learn a clean label classifier by employing the noisy data. Motivated by that classifiers mostly output Bayes optimal labels for prediction, in this paper, we study to directly model the transition from Bayes optimal labels to noisy labels (i.e., Bayes-label transition matrix (BLTM)) and learn a classifier to predict Bayes optimal labels. Note that given only noisy data, it is ill-posed to estimate either the CLTM or the BLTM. But favorably, Bayes optimal labels have less uncertainty compared with the clean labels, i.e., the class posteriors of Bayes optimal labels are one-hot vectors while those of clean labels are not. This enables two advantages to estimate the BLTM, i.e., (a) a set of examples with theoretically guaranteed Bayes optimal labels can be collected out of noisy data; (b) the feasible solution space is much smaller. By exploiting the advantages, we estimate the BLTM parametrically by employing a deep neural network, leading to better generalization and superior classification performance.
Yang, Y, Jiang, J, Wang, Z, Duan, Q & Shi, Y 1970, 'BiES: Adaptive Policy Optimization for Model-Based Offline Reinforcement Learning', Springer International Publishing, pp. 570-581.
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Yang, Y, Jiang, J, Zhou, T, Ma, J & Shi, Y 1970, 'PARETO POLICY POOL FOR MODEL-BASED OFFLINE REINFORCEMENT LEARNING', ICLR 2022 - 10th International Conference on Learning Representations.
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Online reinforcement learning (RL) can suffer from poor exploration, sparse reward, insufficient data, and overhead caused by inefficient interactions between an immature policy and a complicated environment. Model-based offline RL instead trains an environment model using a dataset of pre-collected experiences so online RL methods can learn in an offline manner by solely interacting with the model. However, the uncertainty and accuracy of the environment model can drastically vary across different state-action pairs, so the RL agent may achieve a high model return but perform poorly in the true environment. Unlike previous works that need to carefully tune the trade-off between the model return and uncertainty in a single objective, we study a bi-objective formulation for model-based offline RL that aims at producing a pool of diverse policies on the Pareto front performing different levels of trade-offs, which provides the flexibility to select the best policy for each realistic environment from the pool. Our method, “Pareto policy pool (P3)”, does not need to tune the trade-off weight but can produce policies allocated at different regions of the Pareto front. For this purpose, we develop an efficient algorithm that solves multiple bi-objective optimization problems with distinct constraints defined by reference vectors targeting diverse regions of the Pareto front. We theoretically prove that our algorithm can converge to the targeted regions. In order to obtain more Pareto optimal policies without linearly increasing the cost, we leverage the achieved policies as initialization to find more Pareto optimal policies in their neighborhoods. On the D4RL benchmark for offline RL, P3 substantially outperforms several recent baseline methods over multiple tasks, especially when the quality of pre-collected experiences is low.
Yang, Y, Li, M, Esselle, K & Thalakotuna, D 1970, '3D Printed Millimetre-Wave and Sub-Terahertz Devices: Prospects, Challenges, and Solutions', 2022 16th European Conference on Antennas and Propagation (EuCAP), 2022 16th European Conference on Antennas and Propagation (EuCAP), IEEE, pp. 1-4.
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Advanced additive manufacturing (AM) technology enjoys the advantages of fast-prototyping, low-entry-cost, and in-house short-run manufacturing, which empowers millions of start-ups and companies with demanding confidentiality and accelerated innovation. The advancement of AM technology will circumvent the limitations of traditional 3D printed microwave circuits and antennas. This talk aims to present the fundamental knowledge about AM technology and its capability in microwave/millimeter-wave/terahertz circuits and antenna designs. This article presents the state-of-the-art 3D printing technologies and their applications in the millimeter-wave, and sub-terahertz designs, including meta lenses, frequency selective surfaces, waveguides, and antennas.
Yang, Z, Zheng, B, Wang, X, Li, G & Zhou, X 1970, 'minIL: A Simple and Small Index for String Similarity Search with Edit Distance', 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, Kuala Lumpur, Malaysia, pp. 565-577.
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Yao, G, Wu, H, Yuan, Y, Li, L, Zhou, K & Yu, X 1970, 'Learning Implicit Body Representations from Double Diffusion Based Neural Radiance Fields', Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}, International Joint Conferences on Artificial Intelligence Organization, pp. 1566-1572.
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In this paper, we present a novel double diffusion based neural radiance field, dubbed DD-NeRF, to reconstruct human body geometry and render the human body appearance in novel views from a sparse set of images. We first propose a double diffusion mechanism to achieve expressive representations of input images by fully exploiting human body priors and image appearance details at two levels. At the coarse level, we first model the coarse human body poses and shapes via an unclothed 3D deformable vertex model as guidance. At the fine level, we present a multi-view sampling network to capture subtle geometric deformations and image detailed appearances, such as clothing and hair, from multiple input views. Considering the sparsity of the two level features, we diffuse them into feature volumes in the canonical space to construct neural radiance fields. Then, we present a signed distance function (SDF) regression network to construct body surfaces from the diffused features. Thanks to our double diffused representations, our method can even synthesize novel views of unseen subjects. Experiments on various datasets demonstrate that our approach outperforms the state-of-the-art in both geometric reconstruction and novel view synthesis.
Yao, K, Chang, L & Qin, L 1970, 'Computing Maximum Structural Balanced Cliques in Signed Graphs.', ICDE, 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, pp. 1004-1016.
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Yao, Y, Wang, X, Ma, Y, Fang, H, Wei, J, Chen, L, Anaissi, A & Braytee, A 1970, 'Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks', 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp. 1-10.
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Class imbalance occurs in many real-world applications, including image classification, where the number of images in each class differs significantly. With imbalanced data, the generative adversarial networks (GANs) leans to majority class samples. The two recent methods, Balancing GAN (BAGAN) and improved BAGAN (BAGAN-GP), are proposed as an augmentation tool to handle this problem and restore the balance to the data. The former pre-trains the autoencoder weights in an unsupervised manner. However, it is unstable when the images from different categories have similar features. The latter is improved based on BAGAN by facilitating supervised autoencoder training, but the pre-training is biased towards the majority classes. In this work, we propose a novel Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks (CAPGAN) as an augmentation tool to generate realistic synthetic images. In particular, we utilize a conditional convolutional variational autoencoder with supervised and balanced pre-training for the GAN initialization and training with gradient penalty. Our proposed method presents a superior performance of other state-of-the-art methods on the highly imbalanced version of MNIST, Fashion-MNIST, CIFAR-10, and two medical imaging datasets. Our method can synthesize high-quality minority samples in terms of Fréchet inception distance, structural similarity index measure and perceptual quality. The source code is available at https://github.com/alibraytee/CAPGAN.
Yazdani, D & Yao, X 1970, 'Evolutionary continuous dynamic optimization', Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '22: Genetic and Evolutionary Computation Conference, ACM, pp. 1230-1242.
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Ye, K, Ji, JC & Hu, D 1970, 'Dynamic Analysis of a Novel Zero-Stiffness Vibration Isolator by Considering Frictional Force Involved', Lecture Notes in Electrical Engineering, Springer Singapore, pp. 544-553.
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This study proposes a novel zero-stiffness vibration isolator and investigates its dynamic responses under micro-oscillation with a friction consideration. The novel vibration isolator is based on the mechanism of a cam-roller Quasi-Zero-Stiffness (QZS) system while with improvement by reducing its system components. The proposed vibration isolator consists of a designed bearing, which can provide stiffness responses in the radial direction, and an inserted rod with curved surface. Without the precise cooperation between the positive and negative stiffness systems required in a typical QZS isolator, the designed single stiffness system can provide the high-static-low-dynamic stiffness characteristic directly. The static characteristics of the stiffness performance are numerically confirmed, and then the dynamic responses with friction consideration at the contact surfaces are discussed. The displacement transmissibility in low frequency range is numerically validated when applying harmonic excitation on the base. The analysis results of this study reveal a unique vibration isolating performance of the zero-stiffness system under frication consideration.
Yin, Q, Wang, Z, Song, Y, Xu, Y, Niu, S, Bai, L, Guo, Y & Yang, X 1970, 'Improving Deep Embedded Clustering via Learning Cluster-level Representations', Proceedings - International Conference on Computational Linguistics, COLING, pp. 2226-2236.
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Driven by recent advances in neural networks, various Deep Embedding Clustering (DEC) based short text clustering models are being developed. In these works, latent representation learning and text clustering are performed simultaneously. Although these methods are becoming increasingly popular, they use pure cluster-oriented objectives, which can produce meaningless representations. To alleviate this problem, several improvements have been developed to introduce additional learning objectives in the clustering process, such as models based on contrastive learning. However, existing efforts rely heavily on learning meaningful representations at the instance level. They have limited focus on learning global representations, which are necessary to capture the overall data structure at the cluster level. In this paper, we propose a novel DEC model, which we named the deep embedded clustering model with cluster-level representation learning (DECCRL) to jointly learn cluster and instance level representations. Here, we extend the embedded topic modelling approach to introduce reconstruction constraints to help learn cluster-level representations. Experimental results on real-world short text datasets demonstrate that our model produces meaningful clusters.
Yousefi, M, Tabatabaei, SH, Pour, AB & Pradhan, B 1970, 'DPSB model-based clustering algorithm for mineral mapping in hyperspectral imagery', IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IEEE, pp. 5470-5472.
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Yu, D, Li, Q, Wang, X, Wang, Z, Cao, Y & Xu, G 1970, 'Semantics-Guided Disentangled Learning for Recommendation', Advances in Knowledge Discovery and Data Mining, Springer International Publishing, pp. 249-261.
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Although traditional recommendation methods trained on observational interaction information have engendered a significant impact in real-world applications, it is challenging to disentangle users’ true interests from interaction data. Recent disentangled learning methods emphasize on untangling users’ true interests from historical interaction records, which however overlook auxiliary information to correct bias. In this paper, we design a novel method called SeDLR (Semantics Disentangled Learning Recommendation) to bridge this gap. Particularly, by leveraging rich heterogeneous information networks (HIN), SeDLR is capable of untangling high-order user-item relationships into multiple independent components according to their semantic user intents. In addition, SeDLR offers reliable explanations for the disentangled graph embeddings by the designed Monte Carlo edge-drop component. Finally, we conduct extensive experiments on two benchmark datasets and achieve state-of-the-art performance compared against recent strong baselines.
Yu, N 1970, 'Towards Efficient Reasoning of Quantum Programs', Springer Nature Switzerland, pp. 10-15.
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Yu, X, Serra, T, Ramalingam, S & Zhe, S 1970, 'The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks', Proceedings of Machine Learning Research, pp. 25668-25683.
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Neural networks tend to achieve better accuracy with training if they are larger - even if the resulting models are overparameterized. Nevertheless, carefully removing such excess of parameters before, during, or after training may also produce models with similar or even improved accuracy. In many cases, that can be curiously achieved by heuristics as simple as removing a percentage of the weights with the smallest absolute value - even though absolute value is not a perfect proxy for weight relevance. With the premise that obtaining significantly better performance from pruning depends on accounting for the combined effect of removing multiple weights, we revisit one of the classic approaches for impact-based pruning: the Optimal Brain Surgeon (OBS). We propose a tractable heuristic for solving the combinatorial extension of OBS, in which we select weights for simultaneous removal, and we combine it with a single-pass systematic update of unpruned weights. Our selection method outperforms other methods for high sparsity, and the single-pass weight update is also advantageous if applied after those methods. Source code: github.com/yuxwind/CBS.
Yu, X, Xiao, B, Ni, W & Wang, X 1970, 'Optimal Power Control for Over-The-Air Federated Edge Learning Using Statistical Channel Knowledge', 2022 14th International Conference on Wireless Communications and Signal Processing (WCSP), 2022 14th International Conference on Wireless Communications and Signal Processing (WCSP), IEEE.
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Yu, Y, Wen, D, Zhang, Y, Qin, L, Zhang, W & Lin, X 1970, 'GPU-accelerated Proximity Graph Approximate Nearest Neighbor Search and Construction.', ICDE, 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, pp. 552-564.
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The approximate nearest neighbor (ANN) search in high-dimensional space offers a wide spectrum of applications across many domains such as database, machine learning, multimedia and computer vision. A variety of ANN search algorithms have been proposed in the literature. In recent years, proximity graph-based approaches have attracted considerable attention from both industry and academic settings due to the superior search performance in terms of speed and accuracy. A recent work utilizes a graphics processing unit (GPU) to accelerate the ANN search on proximity graphs. Though significantly reducing the distance computation time by taking advantage of the massive parallelism of GPUs, the algorithm suffers from the high expenses of data structure operations. In this paper, we propose a novel GPU -accelerated algorithm that designs a novel GPU-friendly search framework on proximity graphs to fully exploit the massively parallel processing power of GPUs at key steps of the search. Also, we propose GPU-accelerated proximity graph construction algorithms which can build high-quality representative proximity graphs with efficient parallel implementations. Extensive experiments on benchmark high-dimensional datasets demonstrate the outstanding performance of our proposed algorithms in both ANN search and proximity graph construction.
Yue, Z, Guo, P, Zhang, Y & Liang, C 1970, 'Learning Feature Alignment Architecture for Domain Adaptation', 2022 International Joint Conference on Neural Networks (IJCNN), 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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Yue, Z, Lin, B, Zhang, Y & Liang, C 1970, 'Effective, Efficient and Robust Neural Architecture Search', 2022 International Joint Conference on Neural Networks (IJCNN), 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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Yuksel, B & Kocaballi, AB 1970, 'Conversational Agents to Support Couple Therapy', Proceedings of the 34th Australian Conference on Human-Computer Interaction, OzCHI '22: 34th Australian Conference on Human-Computer Interaction, ACM, Australian Conference on Human-Computer Interaction, pp. 291-297.
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Zahra, H, Shrestha, S, Kiyani, A, Abbas, SM, Mukhopadhyay, S & Esselle, KP 1970, 'Switchable Frequency Selective Surface Based on Polydimethyl-siloxane Composite Flexible Substrate for WLAN and 5G Sub-6GHz Applications', 2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC), 2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC), IEEE, pp. 1-3.
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Zaki, MLHM, Sovuthy, C, Elamvazuthi, I, Prasetyo, T, Su, S & Ali, SSA 1970, 'Drone based Virtual Reality System for Inspection of Oil and Gas Platform', 2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation (ROMA), 2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation (ROMA), IEEE, pp. 1-5.
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Zeng, H, Yu, X, Miao, J & Yang, Y 1970, 'MHR-Net: Multiple-Hypothesis Reconstruction of Non-Rigid Shapes from 2D Views', Computer Vision – ECCV 2022, Springer Nature Switzerland, pp. 1-17.
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We propose MHR-Net, a novel method for recovering Non-Rigid Shapes from Motion (NRSfM). MHR-Net aims to find a set of reasonable reconstructions for a 2D view, and it also selects the most likely reconstruction from the set. To deal with the challenging unsupervised generation of non-rigid shapes, we develop a new Deterministic Basis and Stochastic Deformation scheme in MHR-Net. The non-rigid shape is first expressed as the sum of a coarse shape basis and a flexible shape deformation, then multiple hypotheses are generated with uncertainty modeling of the deformation part. MHR-Net is optimized with reprojection loss on the basis and the best hypothesis. Furthermore, we design a new Procrustean Residual Loss, which reduces the rigid rotations between similar shapes and further improves the performance. Experiments show that MHR-Net achieves state-of-the-art reconstruction accuracy on Human3.6M, SURREAL and 300-VW datasets.
Zhang, C, Chen, H, Zhang, S, Xu, G & Gao, J 1970, 'Geometric Inductive Matrix Completion', Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, ACM, pp. 1337-1346.
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Collaborative filtering is a central task in a broad range of recommender systems. As traditional methods train latent variables for user/item individuals under a transductive setting, it requires re-training for out-of-sample inferences. Inductive matrix completion (IMC) solves this problem by learning transformation functions upon engineered features, but it sacrifices model expressiveness and highly depends on feature qualities. In this paper, we propose Geometric Inductive Matrix Completion (GIMC) by introducing hyperbolic geometry and a unified message passing scheme into this generic task. The proposed method is the earliest attempt utilizing capacious hyperbolic space to enhance the capacity of IMC. It is the first work defining continuous explicit feedback prediction within non-Euclidean space by introducing hyperbolic regression for vertex interactions. This is also the first to provide comprehensive evidence that edge semantics can significantly improve recommendations, which is ignored by previous works. The proposed method outperforms the state-of-the-art algorithms with less than 1% parameters compared to its transductive counterparts. Extensive analysis and ablation studies are conducted to reveal the design considerations and practicability for a positive impact to the research community.
Zhang, C, Mayr, P, Lu, W & Zhang, Y 1970, 'JCDL2022 workshop', Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries, JCDL '22: The ACM/IEEE Joint Conference on Digital Libraries in 2022, ACM, Cologne, GERMANY, pp. 1-2.
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The 3rd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE 2022) was held online at the ACM/IEEE Joint Conference on Digital Libraries (JCDL) 2022. The goal of this workshop series (https://eekeworkshop. github.io/) is to engage the related communities in open problems in the extraction and evaluation of knowledge entities from scientific documents. Participants are encouraged to identify knowledge entities, explore feature of various entities, analyze the relationship between entities, and construct the extraction platform or knowledge base.
Zhang, C, Mayr, P, Lu, W & Zhang, Y 1970, 'Preface to the 3rd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents at JCDL 2022', Ceur Workshop Proceedings, pp. 1-4.
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The 3rd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE 2022) was held online at the ACM/IEEE Joint Conference on Digital Libraries (JCDL) 2022. The goal of this workshop series (https://eeke-workshop.github.io/) is to engage the related communities in open problems in the extraction and evaluation of knowledge entities from scientific documents. Topics of this proceedings include extraction method of knowledge entity, application of knowledge entity extraction, knowledge entity and bibliometrics.
Zhang, C, Zhang, S, Yu, S & Yu, JJQ 1970, 'Graph-Based Traffic Forecasting via Communication-Efficient Federated Learning', 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Austin, TX, pp. 2041-2046.
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The existing Federated Learning (FL) systems encounter an enormous communication overhead when employing GNN-based models for traffic forecasting tasks since these models commonly incorporate enormous number of parameters to be transmitted in the FL systems. In this paper, we propose a FL framework, namely, C lustering-based hierarchical and T wo-step- optimized FL (CTFL), to overcome this practical problem. CTFL employs a divide-and-conquer strategy, clustering clients based on the closeness of their local model parameters. Furthermore, we incorporate the particle swarm optimization algorithm in CTFL, which employs a two-step strategy for optimizing local models. This technique enables the central server to upload only one representative local model update from each cluster, thus reducing the communication overhead associated with model update transmission in the FL. Comprehensive case studies on two real-world datasets and two state-of-the-art GNN-based models demonstrate the proposed framework's outstanding training efficiency and prediction accuracy, and the hyperparameter sensitivity of CTFL is also investigated.
Zhang, G, Lu, W, Peng, X, Wang, S, Kan, B & Yu, R 1970, 'Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension', Proceedings International Conference on Computational Linguistics Coling, pp. 4061-4070.
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Word sense disambiguation (WSD), identifying the most suitable meaning of ambiguous words in the given contexts according to a predefined sense inventory, is one of the most classical and challenging tasks in natural language processing. Reformulating WSD as a text span extraction task is an effective approach, which accepts a sentence context of an ambiguous word together with all definitions of its candidate senses simultaneously, and requires to extract the text span corresponding with the right sense. However, the approach merely depends on a short definition to learn sense representation, which neglects abundant semantic knowledge from related senses and leads to data-inefficient learning and suboptimal WSD performance. To address the limitations, we propose a novel WSD method with Knowledge-Enhanced and Local self-attention-based Extractive Sense Comprehension (KELESC). Specifically, a knowledge-enhanced method is proposed to enrich semantic representation by incorporating additional examples and definitions of the related senses in WordNet. Then, in order to avoid the huge computing complexity induced by the additional information, a local self-attention mechanism is utilized to constrain attention to be local, which allows longer input texts without large-scale computing burdens. Extensive experimental results demonstrate that KELESC achieves better performance than baseline models on public benchmark datasets.
Zhang, J, Li, W, Qin, L, Zhang, Y, Wen, D, Cui, L & Lin, X 1970, 'Reachability Labeling for Distributed Graphs.', ICDE, 38th IEEE International Conference on Data Engineering (ICDE), IEEE, ELECTR NETWORK, pp. 686-698.
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Real-world graphs are typically distributed across multiple data centers. When performing reachability queries on these distributed graphs, reachability labeling methods ensure fast query processing by using lightweight indexes. One of the best-known labeling methods is TOL; however, TOL is a serial algorithm and cannot handle distributed graphs. The main goal of this paper is to design new labeling methods that can work in parallel while producing the same index as TOL. To this end, we investigate the limitation of TOL and thus propose a filtering-and-refinement framework for index creation. This framework first obtains a super-set of each vertex's label sets and then eliminates the invalid elements. Based on this framework, we design distributed labeling algorithms and then use batch processing to improve efficiency. Experimental results on real-world graphs show that the proposed algorithms can index distributed graphs efficiently.
Zhang, L, Cook, K, Szmalenberg, A, Liu, B, Ding, L, Wang, F & McGloin, D 1970, 'Dual beam optical fiber traps for aerosols with angular deviation', Complex Light and Optical Forces XVI, Complex Light and Optical Forces XVI, SPIE, pp. 41-41.
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Zhang, M, Liu, J, Liu, C, Wu, T & Peng, X 1970, 'An Efficient CADNet for Classification of High-frequency Oscillations in Magnetoencephalography', 2022 4th International Conference on Robotics and Computer Vision (ICRCV), 2022 4th International Conference on Robotics and Computer Vision (ICRCV), IEEE, pp. 25-30.
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Epilepsy is a chronic neurological disease, and locating the lesions precisely is crucial to the success of epilepsy surgery. The high-frequency oscillations (HFOs) in magnetoencephalography (MEG) of epileptic patients can be used to detect seizures. Due to the inefficient and error-prone operation of traditional HFOs detection, it is necessary to develop an approach for the detection of HFOs, which can automatically classify HFOs in MEG. In this paper, We proposed a novel deep learning-based CADNet for the classification of HFOs in MEG. First, we preprocessed acquired MEG data by short-time Fourier transform (STFT), and the extracted time-frequency domain information was applied for model training after pictorialism. Then, we captured the features from these images through convolutional neural network combined with multi-head self-attention, all these features were input into Dendrite Net for classification. We evaluated our model on MEG dataset, and the accuracy, precision, recall, and F1-score of the optimized model reached 0.97, 0.98, 0.97, 0.97. We compared the proposed CADNet with other deep learning models, the result demonstrates that our model outperforms others.
Zhang, M, Pan, S, Chang, X, Su, S, Hu, J, Haffari, G & Yang, B 1970, 'BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, New Orleans, LA, pp. 11861-11870.
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Differentiable Architecture Search (DARTS) has received massive attention in recent years, mainly because it significantly reduces the computational cost through weight sharing and continuous relaxation. However, more recent works find that existing differentiable NAS techniques struggle to outperform naive baselines, yielding deteriorative architectures as the search proceeds. Rather than directly optimizing the architecture parameters, this paper formulates the neural architecture search as a distribution learning problem through relaxing the architecture weights into Gaussian distributions. By leveraging the natural-gradient variational inference (NGVI), the architecture distribution can be easily optimized based on existing codebases without incurring more memory and computational consumption. We demonstrate how the differentiable NAS benefits from Bayesian principles, enhancing exploration and improving stability. The experimental results on NAS benchmark datasets confirm the significant improvements the proposed framework can make. In addition, instead of simply applying the argmax on the learned parameters, we further leverage the recently-proposed training-free proxies in NAS to select the optimal architecture from a group architectures drawn from the optimized distribution, where we achieve state-of-the-art results on the NAS-Bench-201 and NAS-Bench-1shot1 benchmarks. Our best architecture in the DARTS search space also obtains competitive test errors with 2.37%, 15.72%, and 24.2% on CIFAR-10, CIFAR-100, and ImageNet, respectively.
Zhang, Q, Hu, L, Cao, L, Shi, C, Wang, S & Liu, DD 1970, 'A Probabilistic Code Balance Constraint with Compactness and Informativeness Enhancement for Deep Supervised Hashing', Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}, International Joint Conferences on Artificial Intelligence Organization, pp. 1651-1657.
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Building on deep representation learning, deep supervised hashing has achieved promising performance in tasks like similarity retrieval. However, conventional code balance constraints (i.e., bit balance and bit uncorrelation) imposed on avoiding overfitting and improving hash code quality are unsuitable for deep supervised hashing owing to their inefficiency and impracticality of simultaneously learning deep data representations and hash functions. To address this issue, we propose probabilistic code balance constraints on deep supervised hashing to force each hash code to conform to a discrete uniform distribution. Accordingly, a Wasserstein regularizer aligns the distribution of generated hash codes to a uniform distribution. Theoretical analyses reveal that the proposed constraints form a general deep hashing framework for both bit balance and bit uncorrelation and maximizing the mutual information between data input and their corresponding hash codes. Extensive empirical analyses on two benchmark datasets further demonstrate the enhancement of compactness and informativeness of hash codes for deep supervised hash to improve retrieval performance (code available at: https://github.com/mumuxi/dshwr).
Zhang, Q, Wang, S, Lu, W, Feng, C, Peng, X & Wang, Q 1970, 'Rethinking Adjacent Dependency in Session-Based Recommendations', Advances in Knowledge Discovery and Data Mining, Springer International Publishing, pp. 301-313.
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Session-based recommendations (SBRs) recommend the next item for an anonymous user by modeling the dependencies between items in a session. Benefiting from the superiority of graph neural networks (GNN) in learning complex dependencies, GNN-based SBRs have become the main stream of SBRs in recent years. Most GNN-based SBRs are based on a strong assumption of adjacent dependency, which means any two adjacent items in a session are necessarily dependent here. However, based on our observation, the adjacency does not necessarily indicate dependency due to the uncertainty and complexity of user behaviours. Therefore, the aforementioned assumption does not always hold in the real-world cases and thus easily leads to two deficiencies: (1) the introduction of false dependencies between items which are adjacent in a session but are not really dependent, and (2) the missing of true dependencies between items which are not adjacent but are actually dependent. Such deficiencies significantly downgrade accurate dependency learning and thus reduce the recommendation performance. Aiming to address these deficiencies, we propose a novel review-refined inter-item graph neural network (RI-GNN), which utilizes the topic information extracted from items’ reviews to refine dependencies between items. Experiments on two public real-world datasets demonstrate that RI-GNN outperforms the state-of-the-art methods (The implementation is available at https://github.com/Nishikata97/RI-GNN. ).
Zhang, S, Chen, H, Sun, X, Li, Y & Xu, G 1970, 'Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation', Proceedings of the ACM Web Conference 2022, WWW '22: The ACM Web Conference 2022, ACM, pp. 1322-1330.
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Graph contrastive learning is the state-of-the-art unsupervised graph representation learning framework and has shown comparable performance with supervised approaches. However, evaluating whether the graph contrastive learning is robust to adversarial attacks is still an open problem because most existing graph adversarial attacks are supervised models, which means they heavily rely on labels and can only be used to evaluate the graph contrastive learning in a specific scenario. For unsupervised graph representation methods such as graph contrastive learning, it is difficult to acquire labels in real-world scenarios, making traditional supervised graph attack methods difficult to be applied to test their robustness. In this paper, we propose a novel unsupervised gradient-based adversarial attack that does not rely on labels for graph contrastive learning. We compute the gradients of the adjacency matrices of the two views and flip the edges with gradient ascent to maximize the contrastive loss. In this way, we can fully use multiple views generated by the graph contrastive learning models and pick the most informative edges without knowing their labels, and therefore can promisingly support our model adapted to more kinds of downstream tasks. Extensive experiments show that our attack outperforms unsupervised baseline attacks and has comparable performance with supervised attacks in multiple downstream tasks including node classification and link prediction. We further show that our attack can be transferred to other graph representation models as well.
Zhang, S, Fu, J, Zhang, Z, Yu, S, Mao, S & Lin, Y 1970, 'Maximum Focal Inter-Class Angular Loss with Norm Constraint for Automatic Modulation Classification', GLOBECOM 2022 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, IEEE, pp. 5323-5328.
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Zhang, S, Zhao, L, Huang, S, Wang, H, Luo, Q & Hao, Q 1970, 'SLAM-TKA: Real-time Intra-operative Measurement of Tibial Resection Plane in Conventional Total Knee Arthroplasty', Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, Springer Nature Switzerland, pp. 126-135.
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Total knee arthroplasty (TKA) is a common orthopaedic surgery to replace a damaged knee joint with artificial implants. The inaccuracy of achieving the planned implant position can result in the risk of implant component aseptic loosening, wear out, and even a joint revision, and those failures most of the time occur on the tibial side in the conventional jig-based TKA (CON-TKA). This study aims to precisely evaluate the accuracy of the proximal tibial resection plane intra-operatively in real-time such that the evaluation processing changes very little on the CON-TKA operative procedure. Two X-ray radiographs captured during the proximal tibial resection phase together with a pre-operative patient-specific tibia 3D mesh model segmented from computed tomography (CT) scans and a trocar pin 3D mesh model are used in the proposed simultaneous localisation and mapping (SLAM) system to estimate the proximal tibial resection plane. Validations using both simulation and in-vivo datasets are performed to demonstrate the robustness and the potential clinical value of the proposed algorithm.
Zhang, X, Li, Y, Peng, X, Qiao, X, Zhang, H & Lu, W 1970, 'Correlation Encoder-Decoder Model for Text Generation', 2022 International Joint Conference on Neural Networks (IJCNN), 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-7.
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Text generation is crucial for many applications in natural language processing. With the prevalence of deep learning, the encoder-decoder architecture is dominantly adopted for this task. Accurately encoding the source information is of key importance to text generation, because the target text can be generated only when accurate and complete source information is captured by the encoder and fed into the decoder. However, most existing approaches fail to effectively encode and learn the entire source information, as some features are easy to be missed along with the encoding procedures of the encoder. Similar problems also confuse the implementation of the decoder. How to reduce the problem of information loss in the encoder-decoder model is critical for text generation. To address this issue, we propose a novel correlation encoder-decoder model, which optimizes both the encoder and the decoder to reduce the problem of information loss by enforcing them to minimize the differences between hierarchical layers by maximizing the mutual information. Experimental results on two benchmark datasets demonstrate that the proposed model substantially outperforms the existing state-of-the-art methods. Our source code is publicly available on GitHub1.
Zhang, X, Zhu, X & Li, J 1970, 'Knowledge transfer for structural damage detection using fine-tuning based on FRF', Proceedings of the 3rd International Conference on Structural Engineering Research, the 3rd International Conference on Structural Engineering Research, Sydney, Australia, pp. 184-189.
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The majority of data-driven structural damage detection techniques are created based on the assumption that comprehensive labelled data is available, and the underlying distribution of the training and test sets is the same, which is hard to conduct in real engineering applications. In this work, we propose an approach for structural health monitoring (SHM) based on a convolutional neural network (CNN) that uses frequency response functions (FRF) obtained from the structural response to detect structural damage. This method is capable to identify structural damage based on both severity and localisations in the real structure. Specifically, a numerical model was simulated firstly. The acceleration response was collected to process to the FRF and trained in the designed CNN model. Thus, a complete CNN model was created using the numerical data, and then the pre-trained CNN model was fine-tuned using the limited experimental data to adapt its feature distribution on the fully connected layer for the experimental data. Finally, the performance of this fine-tuned CNN was compared with the original CNN to validate the effectiveness of the proposed methods on damage detection. In addition, when limited damage data are available, this method can be used by utilising the damage information, having studied other structures to detect the damage to the structure. Besides, visualising the characteristics from the last layer can provide a physical understanding of how the network recognises various damage scenarios with different damage severities. It was found that the network extracted feature can represent some physical features of structural behaviours to some extent based on the damage scenarios, implying the accurate results to reveal that the proposed structural detection method outperforms the current method.
Zhang, Y & Wang, C 1970, 'Face Detection Algorithm in Classroom Scene Based on Deep Learning', Springer Nature Switzerland, pp. 255-265.
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Zhang, Y, Li, C, Tsang, IW, Xu, H, Duan, L, Yin, H, Li, W & Shao, J 1970, 'Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations', 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, pp. 2942-2955.
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Cold-start issues have been more and more challenging for providing accurate recommendations with the fast increase of users and items. Most existing approaches attempt to solve the intractable problems via content-aware recommendations based on auxiliary information and/or cross-domain recommendations with transfer learning. Their performances are often constrained by the extremely sparse user-item interactions, unavailable side information, or very limited domain-shared users. Recently, meta-learners with meta-augmentation by adding noises to labels have been proven to be effective to avoid overfitting and shown good performance on new tasks. Motivated by the idea of meta-augmentation, in this paper, by treating a user's preference over items as a task, we propose a so-called Diverse Preference Augmentation framework with multiple source domains based on meta-learning (referred to as MetaDPA) to i) generate diverse ratings in a new domain of interest (known as target domain) to handle overfitting on the case of sparse interactions, and to ii) learn a preference model in the target domain via a meta-learning scheme to alleviate cold-start issues. Specifically, we first conduct multi-source domain adaptation by dual conditional variational autoencoders and impose a Multi-domain InfoMax (MDI) constraint on the latent representations to learn domain-shared and domain-specific preference properties. To avoid overfitting, we add a Mutually-Exclusive (ME) constraint on the output of decoders to generate diverse ratings given content data. Finally, these generated diverse ratings and the original ratings are introduced into the meta-training procedure to learn a preference meta-learner, which produces good generalization ability on cold-start recommendation tasks. Experiments on real-world datasets show our proposed MetaDPA clearly outperforms the current state-of-the-art baselines.
Zhang, Y, Wang, M, Zipperle, M, Abbasi, A & Saberi, M 1970, 'S-index: Significance of Academic Authors to Individual Publication Venues', 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), IEEE, pp. 1-6.
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Zhang, Y, Wu, M, Wang, X & Chen, H 1970, 'Navigating the Trade-offs between Independence and Collaboration: A Network Analytic Method and Case Study', 2022 Portland International Conference on Management of Engineering and Technology (PICMET), 2022 Portland International Conference on Management of Engineering and Technology (PICMET), IEEE, Portland, OR, USA, pp. 1-7.
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Research independence emphasizes the ability to conduct individual work, while collaboration highlights establishing academic connections for team-based activities. Since the two dimensions have become crucial indicators for individual-based research evaluation, how to handle the trade-offs between research independence and collaboration diversity is raised. This paper exploits network analytics to investigate the two indicators via an integrated network that includes an author-term network and a co-authorship network. Specifically, a diffusion-based network analytic model is proposed to evaluate independence by considering the uniqueness of the use of terms, and, in parallel, collaboration diversity is quantified by the strength and breadth of a researcher’s co-authorships. We applied the proposed method to a dataset with 19,612 information science-related articles. The observed results not only well demonstrate the reliability of the proposed method but also empirically uncover a balancing threshold between the two indicators, providing references for researchers and decision-makers in the discipline.
Zhang, Z, Fang, M, Chen, L & Namazi Rad, MR 1970, 'Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics', Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, pp. 3886-3893.
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Recent work incorporates pre-trained word embeddings such as BERT embeddings into Neural Topic Models (NTMs), generating highly coherent topics. However, with high-quality contextualized document representations, do we really need sophisticated neural models to obtain coherent and interpretable topics? In this paper, we conduct thorough experiments showing that directly clustering high-quality sentence embeddings with an appropriate word selecting method can generate more coherent and diverse topics than NTMs, achieving also higher efficiency and simplicity.
Zhang, Z, Wang, X, Yu, G, Ni, W, Liu, RP, Georgalas, N & Reeves, A 1970, 'A Community Detection-Based Blockchain Sharding Scheme', Springer Nature Switzerland, pp. 78-91.
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Zhao, D, Mihaita, A-S, Ou, Y, Shafiei, S, Grzybowska, H, Qin, K, Tan, G, Li, M & Dia, H 1970, 'Traffic disruption modelling with mode shift in multi-modal networks', 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), IEEE, pp. 2428-2435.
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A multi-modal transport system is acknowledged to have robust failure tolerance and can effectively relieve urban congestion issues. However, estimating the impact of disruptions across multi-transport modes is a challenging problem due to a dis-aggregated modelling approach applied to only individual modes at a time. To fill this gap, this paper proposes a new integrated modelling framework for a multi-modal traffic state estimation and evaluation of the disruption impact across all modes under various traffic conditions. First, we propose an iterative trip assignment model to elucidate the association between travel demand and travel behaviour, including a multi-modal origin-to-destination estimation for private and public transport. Secondly, we provide a practical multi-modal travel demand re-adjustment that takes the mode shift of the affected travellers into consideration. The pros and cons of the mode shift strategy are showcased via several scenario-based transport simulating experiments. The results show that a well-balanced mode shift with flexible routing and early announcements of detours so that travellers can plan ahead can significantly benefit all travellers by a delay time reduction of 46%, while a stable route assignment maintains a higher average traffic flow and the inactive mode-route choice help relief density under the traffic disruptions.
Zhao, G, Wang, K, Zhang, W, Lin, X, Zhang, Y & He, Y 1970, 'Efficient Computation of Cohesive Subgraphs in Uncertain Bipartite Graphs', 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, pp. 2333-2345.
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Zhao, S & Burnett, IS 1970, 'Adaptive personal sound zones systems with online plant modelling', Proceedings of the International Congress on Acoustics, The 24th International Congress on Acoustics, Gyeongju, Korea.
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Personal sound zones systems have attracted significant research interest recently due to its broad potential applications in car cabins, mobile devices and public spaces etc. Most existing studies focus on optimal performance in stationary environments assuming that accurate plant from loudspeakers to microphones are known a priori. However, in practical applications, the acoustic environments may change over time and the accurate plant may not be available. This paper presents an adaptive personal sound system with online plant modeling for non-stationary environments. The control filters are updated based on the filtered-x least mean squares algorithm and the plant models are adapted with the recursive least squares algorithm. Simulations with measured impulse responses are performed to evaluate the performance of a ten-loudspeaker array for the creation of two sound zones. Simulation results demonstrate that an adaptive system with online plant modeling converges to the solution with an ideal plant model for both stationary and non-stationary environments.
Zhao, S & Burnett, IS 1970, 'Time-Domain Acoustic Contrast Control with A Spatial Uniformity Constraint for Personal Audio Systems', ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Singapore, Singapore, pp. 1061-1065.
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Personal audio systems with multiple sound zones for listeners to enjoy different music/audio contents privately in a shared physical space have attracted great research interest in the past two decades. Acoustic Contrast Control (ACC) is one of the most popular methods for generating multiple personal sound zones because it produces the minimum inter-zone interference. However, the ACC method has been found to be inferior to the pressure matching method in terms of sound quality due to an uneven frequency response and nonuniform spatial sound field distribution in the bright zone. This paper proposes a spatial uniformity constraint on time-domain broadband ACC in addition to the frequency response trend estimation constraint with the aim of ensuring a uniform sound field distribution in the bright zone. Simulation results with measured room impulse responses demonstrate that the proposed algorithm reduces the magnitude variations in the bright zone to be less than 1 dB higher than the just noticeable level difference at a cost of a perceptually negligible degradation in acoustic contrast.
Zhao, S, Zhu, L, Wang, X & Yang, Y 1970, 'CenterCLIP', Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 970-981.
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Zhao, Y, Ma, B, Wang, Z, Liu, Z, Zeng, Y & Ma, J 1970, 'Trajectory Obfuscation and Detection in Internet-of-vehicles', 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), IEEE, pp. 769-774.
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In Internet-of-vehicles, vehicles cooperate with each other by transmitting Internet-of-vehicles and location-based service (LBS) providers optimize services by analyzing trajectory data collected from drivers. Nevertheless, illegal trajectories generated by attackers or malicious drivers can obfuscate the process of analysis and breach the quality of service. Some mechanisms protect drivers' location privacy by using obfuscation-based schemes. Obfuscation-based mechanisms report LBS with obfuscated trajectories data rather than actual trajectories, which increases difficulties to detect illegal trajectories accurately. This paper focuses on detecting illegal trajectories when all drivers employ obfuscation-based mechanisms to protect location privacy. In this paper, we propose a dynamic obfuscation mechanism in road networks based on Geo-indistinguishability to dynamically protect drivers' location privacy. Considering personalization in road networks, we also propose a classification mechanism to detect illegal trajectories in road networks. Illegal trajectories are generated based on real trajectories to simulate actions of malicious drivers and attackers. Experiment results in real road networks show that the classifier can detect illegal obfuscated trajectories with at least 94% Area Under the Curve (AUC) score, which outperforms than existing works in road networks.
Zhao, Y, You, Z, Xing, D, Li, J, Lin, J & Du, Y 1970, 'PORE SIZE OF SCAFFOLDS ENABLES DISTINCT MECHANORESPONSIVE BEHAVIOUR OF OSTEOARTHRITIC CHONDROCYTES', TISSUE ENGINEERING PART A, 6th World Congress of the Tissue-Engineering-and-Regenerative-Medicine-International-Society (TERMIS), MARY ANN LIEBERT, INC, NETHERLANDS, Maastricht, pp. S403-S403.
Zheng, J, Sun, K, Ma, B, Zhu, J & Lei, G 1970, 'An Efficient Decoupling Approach for Non-probabilistic Reliability-Based Design Optimization of Electrical Machines Considering Interval Uncertainties', 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC), 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC), IEEE, pp. 1-2.
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Zheng, J, Sun, K, Ma, B, Zhu, J & Lei, G 1970, 'Non-probabilistic Reliability-based Robust Design Optimization of Electrical Machines Considering Interval Uncertainties', 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC), 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC), IEEE, pp. 1-2.
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Zheng, T, Verma, S & Liu, W 1970, 'Interpretable Binaural Ratio for Visually Guided Binaural Audio Generation', 2022 International Joint Conference on Neural Networks (IJCNN), 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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Zheng, X & Huo, H 1970, 'Enhancing group polarity of temporal patterns for rumour detection on Twitter', 2022 9th International Conference on Behavioural and Social Computing (BESC), 2022 9th International Conference on Behavioural and Social Computing (BESC), IEEE, pp. 1-4.
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Zhong, J, Kirby, R, Karimi, M, Qiu, X & Lu, J 1970, 'Audio sound field generated by a parametric array loudspeaker in a rectangular room with lightly damped walls', Proceedings of the International Congress on Acoustics.
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Parametric array loudspeakers (PALs) are known for their ability of generating highly directional sound beams. Existing research focuses on the generated sound field in free field but pays little attention to the wave propagation in a room. This paper aims to investigate the audio sound generated by a PAL in a two-dimensional rectangular room with lightly damped walls. An expression of the audio sound is derived first based on the normal mode analysis and the quasilinear solution of the Westervelt equation. The nonlinear local effects are then included by adding an algebraic correction to the solution. The simulation results are presented and compared to the audio sound field generated by a PAL in free field. Unlike the PAL in free field, it is found that the local effects cannot be neglected when the PAL is placed in a room. It is also observed that the audio beam still focuses along the radiation axis of the PAL in a room albeit there are fluctuations in the propagating direction.
Zhong, Z, Liu, J, Wu, D, Di, P, Sui, Y & Liu, AX 1970, 'Field-based static taint analysis for industrial microservices', Proceedings of the 44th International Conference on Software Engineering: Software Engineering in Practice, ICSE '22: 44th International Conference on Software Engineering, ACM, pp. 149-150.
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Zhou, C, Lyu, B, Hoang, DT & Gong, S 1970, 'Reconfigurable Intelligent Surface Assisted Secure Symbiotic Radio Multicast Communications', 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), IEEE, London, United Kingdom, pp. 1-6.
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In this paper we propose a reconfigurable intelligent surface RIS assisted secure transmission scheme for a symbiotic radio multicast system where the RIS not only assists the confidential information multicasting from a primary transmitter PT to multiple primary users PUs to against the information interception by eavesdroppers but also delivers its own signal to a secondary user SU by passive reflections We formulate a signal to noise ratio SNR maximization problem for the SU by jointly optimizing the active beamforming at the PT amplitude reflection coefficients and phase shifts of the RIS To address the non convexity of the formulated problem we propose to decompose the original problem into two sub problems and solve them independently in an iteratively alternating manner For the first sub problem we adopt the successive convex approximation SCA and semidefinite relaxation SDR techniques to design the active beamforming by proving the tightness of SDR For the second sub problem the sequential rank one constraint relaxation SROCR technique is adopted to handle the rank one constraint for reflection coefficients optimization Numerical results show that compared to the benchmark schemes the proposed scheme can achieve up to 68 3 performance gain in terms of SNR
Zhou, J, Chen, F & Holzinger, A 1970, 'Towards Explainability for AI Fairness', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 375-386.
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AbstractAI explainability is becoming indispensable to allow users to gain insights into the AI system’s decision-making process. Meanwhile, fairness is another rising concern that algorithmic predictions may be misaligned to the designer’s intent or social expectations such as discrimination to specific groups. In this work, we provide a state-of-the-art overview on the relations between explanation and AI fairness and especially the roles of explanation on human’s fairness judgement. The investigations demonstrate that fair decision making requires extensive contextual understanding, and AI explanations help identify potential variables that are driving the unfair outcomes. It is found that different types of AI explanations affect human’s fairness judgements differently. Some properties of features and social science theories need to be considered in making senses of fairness with explanations. Different challenges are identified to make responsible AI for trustworthy decision making from the perspective of explainability and fairness.
Zhou, J, Li, Z, Xiao, C & Chen, F 1970, 'Does a Compromise on Fairness Exist in Using AI Models?', AI 2022: Advances in Artificial Intelligence, Springer International Publishing, pp. 191-204.
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Zhou, S, Eager, D, Halkon, B, Walker, P, Covey, K & Braiden, S 1970, 'INVESTIGATION AND COMPARISON OF THE SOUND QUALITY OF THE LURES USED FOR GREYHOUND RACING', Proceedings of the International Congress on Sound and Vibration.
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This study investigates and compares the acoustic signatures of a traditional wire-cable pulled lure system and two novel alternative battery-operated lure systems which were developed to eliminate the hazardous steel-wire cable and make the sport of greyhound racing safer for greyhounds, participants, and spectators. The acoustical measurements of these three lure systems were conducted at the Murray Bridge greyhound racing track in South Australia with high-frequency B&K Type 4191 microphones. The microphones were positioned within the starting box and on the track adjacent to the starting boxes, at both the straight track and bending track. The measurements captured the sounds that the greyhounds hear before and after the opening of the starting box gate. The sound quality analysis was conducted to compare the lure sounds. It was found when the battery-lure was installed with all nylon rollers, it presented less sound energy than the traditional wire-cable pulled lure. When two of the nylon rollers were replaced with steel rollers, the battery-operated lure emitted a louder sound than the traditional wire-cable-pulled lure. The different acoustic characteristics of these lure systems suggest future research is warranted on the reaction of greyhounds to different lure sounds, particularly their excitement level within the starting box as the lure approaches. This initial research also suggests some greyhounds may not clearly hear the battery-operated lure with all nylon rollers approaching the starting boxes and the timing of these greyhounds to jump may be delayed, particularly during high wind conditions.
Zhou, S, Eager, D, Halkon, B, Walker, P, Covey, K & Braiden, S 1970, 'Investigation and Comparison of the Sound Quality the Wirecable Lure and Battery-Operated Lure used for Greyhound Racing', International Congress on Sound and Vibration, Singapore.
Zhou, S, Li, G, Dong, D & Kuang, S 1970, 'Rapid Preparation of Bell States Based on an Exponential Quantum Projection Filter', 2022 41st Chinese Control Conference (CCC), 2022 41st Chinese Control Conference (CCC), IEEE, pp. 5664-5669.
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Zhou, Y, Geng, X, Shen, T, Long, G & Jiang, D 1970, 'EventBERT: A Pre-Trained Model for Event Correlation Reasoning', Proceedings of the ACM Web Conference 2022, WWW '22: The ACM Web Conference 2022, ACM, pp. 850-859.
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Event correlation reasoning infers whether a natural language paragraph containing multiple events conforms to human common sense. For example, 'Andrew was very drowsy, so he took a long nap, and now he is very alert'is sound and reasonable. In contrast, 'Andrew was very drowsy, so he stayed up a long time, now he is very alert'does not comply with human common sense. Such reasoning capability is essential for many downstream tasks, such as script reasoning, abductive reasoning, narrative incoherence, story cloze test, etc. However, conducting event correlation reasoning is challenging due to a lack of large amounts of diverse event-based knowledge and difficulty in capturing correlation among multiple events. In this paper, we propose EventBERT, a pre-trained model to encapsulate eventuality knowledge from unlabeled text. Specifically, we collect a large volume of training examples by identifying natural language paragraphs that describe multiple correlated events and further extracting event spans in an unsupervised manner. We then propose three novel event- and correlation-based learning objectives to pre-train an event correlation model on our created training corpus. Experimental results show EventBERT outperforms strong baselines on four downstream tasks, and achieves state-of-the-art results on most of them. Moreover, it outperforms existing pre-trained models by a large margin, e.g., 6.5 23%, in zero-shot learning of these tasks.
Zhou, Y, Liu, J, Yang, Z, Liu, T, Meng, X, Zhou, Z, Anaissi, A & Braytee, A 1970, 'VGG-FusionNet: A Feature Fusion Framework from CT scan and Chest X-ray Images based Deep Learning for COVID-19 Detection', 2022 IEEE International Conference on Data Mining Workshops (ICDMW), 2022 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, pp. 1-9.
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Zhou, Y, Shen, T, Geng, X, Long, G & Jiang, D 1970, 'ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification', Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, pp. 2559-2575.
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Generating new events given context with correlated ones plays a crucial role in many event-centric reasoning tasks. Existing works either limit their scope to specific scenarios or overlook event-level correlations. In this paper, we propose to pre-train a general Correlation-aware context-to-Event Transformer (ClarET) for event-centric reasoning. To achieve this, we propose three novel event-centric objectives, i.e., whole event recovering, contrastive event-correlation encoding and prompt-based event locating, which highlight event-level correlations with effective training. The proposed ClarET is applicable to a wide range of event-centric reasoning scenarios, considering its versatility of (i) event-correlation types (e.g., causal, temporal, contrast), (ii) application formulations (i.e., generation and classification), and (iii) reasoning types (e.g., abductive, counterfactual and ending reasoning). Empirical fine-tuning results, as well as zero- and few-shot learning, on 9 benchmarks (5 generation and 4 classification tasks covering 4 reasoning types with diverse event correlations), verify its effectiveness and generalization ability.
Zhu, F, Yang, Z, Yu, X, Yang, Y & Wei, Y 1970, 'Instance as Identity: A Generic Online Paradigm for Video Instance Segmentation', Computer Vision – ECCV 2022, Springer Nature Switzerland, pp. 524-540.
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Modeling temporal information for both detection and tracking in a unified framework has been proved a promising solution to video instance segmentation (VIS). However, how to effectively incorporate the temporal information into an online model remains an open problem. In this work, we propose a new online VIS paradigm named Instance As Identity (IAI), which models temporal information for both detection and tracking in an efficient way. In detail, IAI employs a novel identification module to predict identification number for tracking instances explicitly. For passing temporal information cross frame, IAI utilizes an association module which combines current features and past embeddings. Notably, IAI can be integrated with different image models. We conduct extensive experiments on three VIS benchmarks. IAI outperforms all the online competitors on YouTube-VIS-2019 (ResNet-101 41.9 mAP) and YouTube-VIS-2021 (ResNet-50 37.7 mAP). Surprisingly, on the more challenging OVIS, IAI achieves SOTA performance (20.3 mAP). Code is available at https://github.com/zfonemore/IAI.
Zhu, H & Guo, YJ 1970, 'Compact and Wideband Filtering Power Dividers with Arbitrary and Constant Output Phase Difference', 2022 Asia-Pacific Microwave Conference (APMC), 2022 Asia-Pacific Microwave Conference (APMC), IEEE, pp. 788-790.
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Zhu, H, Song, L-Z & Guo, YJ 1970, 'Wideband Hybrid Couplers and Their Applications to Multi-beam Antenna Feed Networks', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 411-412.
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Zhu, J, Xiao, G, Zheng, Z & Sui, Y 1970, 'Enhancing Traceability Link Recovery with Unlabeled Data', 2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE), 2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE), IEEE, pp. 446-457.
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Traceability link recovery (TLR) is an important software engineering task for developing trustworthy and reliable software systems. Recently proposed deep learning (DL) models have shown their effectiveness compared to traditional information retrieval-based methods. DL often heavily relies on sufficient labeled data to train the model. However, manually labeling traceability links is time-consuming, labor-intensive, and requires specific knowledge from domain experts. As a result, typically only a small portion of labeled data is accompanied by a large amount of unlabeled data in real-world projects. Our hypothesis is that artifacts are semantically similar if they have the same linked artifact(s). This paper presents TRACEFUN, a new approach to enhance traceability link recovery with unlabeled data. TRACEFUN first measures the similarities between unlabeled and labeled artifacts using two similarity prediction methods (i.e., vector space model and contrastive learning). Then, based on the similarities, newly labeled links are generated between the unlabeled artifacts and the linked objects of the labeled artifacts. Generated links are further used for TLR model training. We have evaluated TRACEFUN on three GitHub projects with two state-of-the-art DL models (i.e., Trace BERT and TraceNN). The results show that TRACEFUN is effective in terms of a maximum improvement of F1-score up to 21% and 1,088%, respectively for Trace BERT and TraceNN.
Zhu, M & Zhao, S 1970, 'Broadband loudspeaker placement optimization for personal sound zones systems', Proceedings of the International Congress on Acoustics, The 24th International Congress on Acoustics, Gyeongju, Korea.
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Personal sound zones system has attracted considerable attention in the past decades due to its potential for private audio generation in public spaces. Various methods have been explored to optimize the driving signals of loudspeakers that are placed to form a regular array, such as circular, linear, and arc-shaped arrays. Recently, loudspeaker placement optimization has been investigated by researchers to reduce the number of loudspeakers without remarkable sacrifice in performance. Existing loudspeaker placement optimization algorithms have been designed in the frequency domain and the optimized loudspeaker arrangements depend on frequency, which is undesirable in practical applications. To overcome this problem, this paper explores broadband loudspeaker placement optimization for multizone sound field reproduction based on a time-domain evolutionary array optimization method. Simulations with measured room impulse responses are performed to select a smaller number of loudspeakers from 60 candidate loudspeakers that are uniformly placed along a circle. Simulation results demonstrate the optimized array achieves a higher acoustic contrast with a lower array effort than the empirical arc-shaped array, when the same number of loudspeakers are selected.
Zhu, W, Tuan, HD & Fang, Y 1970, '2D Beamforming for 3D Full-Dimensional Massive MIMO', 2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS), 2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS), IEEE, pp. 352-357.
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The paper considers the jointly 2D beamforming design for multi-user (MU) full-dimensional (3D) massive multiple-input multiple output (m-MIMO) systems to maximize the geometric mean of users' rate (GM-rate), which yields not only users' fairness in terms of their rate but also rational transmit powers at antennas. We develop a low-complex algorithms, which iterates closed-form expressions for computational solutions of the GM-rate maximization problem. The provided simulations confirm the viability of our development.
Zuo, Y, Peng, X, Lu, W, Wang, S, Li, Z, Zhang, W & Zhai, Y 1970, 'Chinese Sentence Matching with Multiple Alignments and Feature Augmentation', 2022 International Joint Conference on Neural Networks (IJCNN), 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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Chinese sentence matching is a critical and yet challenging task in natural language processing. Recent work on modeling sentence semantic relations with deep neural models has shown its great potential in improving the performance of sentence matching. However, existing sentence matching methods usually focus on generating word-level sentence representation, which neglects the character-level information and leads to weak semantic representations. Also, they usually capture the interactive features with an attention-based alignment, which are typically implemented on sentence level and neglect the interactions among characters, words and sentences. This paper proposes a novel Chinese sentence matching model with Multiple Alignments and Feature Augmentation (MAFA). Specifically, the model first employs the multi-level embedding layer to accept the character and word sequences of sentences, and introduces the multiple alignment layer to capture the interactions among characters, words and sentences in turn. Then, the feature augmentation layer is applied to combine the interactive features to generate the final semantic matching representations. Finally, the prediction layer is utilized to judge the matching degree of the input sentences. Substantial and extensive experiments are conducted on two real-world data sets to show that MAFA significantly outperforms the competing methods and achieve comnarable nerformance with BERT-based methods.
