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 Science, 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, 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, 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, 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, Kabir, M, Zuhara, FT, Mehjabin, A, Tasannum, N, Hoang, AT, Kabir, Z & Mofijur, M 2022, 'Threats, challenges and sustainable conservation strategies for freshwater biodiversity', Environmental Research, vol. 214, no. Pt 1, pp. 113808-113808.
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Increasing human population, deforestation and man-made climate change are likely to exacerbate the negative effects on freshwater ecosystems and species endangerment. Consequently, the biodiversity of freshwater continues to dwindle at an alarming rate. However, this particular topic lacks sufficient attention from conservation ecologists and policymakers, resulting in a dearth of data and comprehensive reviews on freshwater biodiversity, specifically. Despite the widespread awareness of risks to freshwater biodiversity, organized action to reverse this decline has been lacking. This study reviews prospective conservation and management strategies for freshwater biodiversity and their associated challenges, identifying current key threats to freshwater biodiversity. Engineered nanomaterials pose a significant threat to aquatic species, and will make controlling health risks to freshwater biodiversity increasingly challenging in the future. When fish are exposed to nanoparticles, the surface area of their respiratory and ion transport systems can decline to 60% of their total surface area, posing serious health risks. Also, about 50% of freshwater fish species are threatened by climate change, globally. Freshwater biodiversity that is heavily reliant on calcium perishes when the calcium content of their environments degrades, posing another severe threat to world biodiversity. To improve biodiversity, variables such as species diversity, population and water quality, and habitat are essential components that must be monitored continuously. Existing research on freshwater biota and ecosystems is still lacking. Therefore, data collection and the establishment of specialized policies for the conservation of freshwater biodiversity should be prioritized.
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, Ahmed, B, Mehnaz, T, Mehejabin, F, Maliat, D, Hoang, AT & Shafiullah, GM 2022, 'Nanomaterials as a sustainable choice for treating wastewater', Environmental Research, vol. 214, no. Pt 1, pp. 113807-113807.
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Wastewater containing toxic substances is a major threat to the health of both aquatic and terrestrial ecosystems. In order to treat wastewater, nanomaterials are currently being studied intensively due to their unprecedented properties. The unique features of nanoparticles are prompting an increasing number of studies into their use in wastewater treatment. Although several studies have been undertaken in recent years, most of them did not focus on some of the nanomaterials that are now often utilized for wastewater treatment. It is essential to investigate the most recent advances in all the types of nanomaterials that are now frequently employed for wastewater treatment. The recent advancements in common nanomaterials used for sustainable wastewater treatment is comprehensively reviewed in this paper. This paper also thoroughly assesses unique features, proper utilization, future prospects, and current limitations of green nanotechnology in wastewater treatment. Zero-valent metal and metal oxide nanoparticles, especially iron oxides were shown to be more effective than traditional carbon nanotubes (CNTs) for recovering heavy metals in wastewater. Iron oxide achieved 75.9% COD (chemical oxygen demand) removal efficiency while titanium oxide (TiO2) achieved 75.5% COD. Iron nanoparticles attained 72.1% methyl blue removal efficiency. However, since only a few types of nanomaterials have been commercialized, it is important to also focus on the economic feasibility of each nanomaterial. This study found that the large surface area, high reactivity, and strong mechanical properties of nanoparticles means they can be considered as a promising option for successful wastewater treatment.
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, Nuzhat, S, Rafa, N, Musharrat, A, Lam, SS & Boretti, A 2022, 'Sustainable hydrogen production: Technological advancements and economic analysis', International Journal of Hydrogen Energy, vol. 47, no. 88, pp. 37227-37255.
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Hydrogen (H2) is pivotal to phasing out fossil fuel-based energy systems. It can be produced from different sources and using different technologies. Very few studies comprehensively discuss all available state-of-the-art technologies for H2 production, the challenges facing each process, and their economic feasibility and sustainability. The current study thus addresses these gaps to effectively direct future research towards improving H2 production techniques. Many conventional methods contribute to large greenhouse gas footprints, with high production costs and low efficiency. Steam methane reforming and coal gasification dominate the supply side of H2, due to their low production costs (<$3.50/kg). Water-splitting offers one of the most environmentally benign production methods when integrated with renewable energy sources. However, it is considerably expensive and ridden with the flaw of production of harmful by-products that affect efficiency. Fossil fuel processing technologies remain one of the most efficient forms of H2 production sources, with yields exceeding 80% and reaching up to 100%, with the lowest cost despite their high reliance on expensive catalysts. Whereas solar-driven power systems cost slightly less than $10 kg−1, coal gasification and steam reforming cost below $3.05 kg−1. Future research thus needs to be directed towards cost reduction of renewable energy-based H2 production systems, as well as in their decarbonization and designing more robust H2 storage systems that are compatible with long-distance distribution networks with adequate fuelling stations.
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.
Ahmed, SF, Rafa, N, Mehnaz, T, Ahmed, B, Islam, N, Mofijur, M, Hoang, AT & Shafiullah, GM 2022, 'Integration of phase change materials in improving the performance of heating, cooling, and clean energy storage systems: An overview', Journal of Cleaner Production, vol. 364, pp. 132639-132639.
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Phase change materials (PCMs) have garnered significant attention as low-cost thermal energy storage systems that efficiently capture and store solar energy. Recent review works have largely focused only on thermal conductivity enhancement techniques, and/or applications of PCMs, while others have mainly discussed the performance enhancement of either heating, cooling, or clean energy storage systems integrating with PCMs. However, not enough studies recently reviewed all of these techniques/systems comprehensively to provide insights into them. This paper thus comprehensively reviews the integration of PCMs as an enhancement to most types of heating, cooling, and clean energy storage system performance, and the techniques to enhance thermal conductivity. The integration of PCMs with these systems has shown promising performance. For instance, an improvement of 13.5% is found in the efficiency of photovoltaic (PV) system when it is integrated with PCM/Al2O3 nanoparticles. In addition, the solar air heater's daily energy efficiency reaches 17% on its own, but when combined with PCM, it reaches 33%. However, the major drawback of using PCM–TES (thermal energy storage) for cooling is that PCM does not entirely solidify at night. The literature also shows that the issues related to PCMs' low thermal conductivity, phase separation, and subcooling/supercooling, their poor compatibility with other materials, and the environmental hazards they pose hinder their application on a large scale. It is necessary to implement international standards for assessing the thermophysical properties of PCMs and compile data to better facilitate the utilization of PCMs by end-users.
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.
Alam, MA, Wan, C, Tran, DT, Mofijur, M, Ahmed, SF, Mehmood, MA, Shaik, F, Vo, D-VN & Xu, J 2022, 'Microalgae binary culture for higher biomass production, nutrients recycling, and efficient harvesting: a review', Environmental Chemistry Letters, vol. 20, no. 2, pp. 1153-1168.
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Microalgae are photosynthetic cell factories of global interest for fuels, food, feed, bioproducts, carbon sequestration, waste mitigation, and environmental remediation. Actually, microalgal monocultures are used for biomass production and pollutant removal, yet are limited by moderate production and contaminations. Here we review binary cultures of autotrophic microalgae with bacteria, yeast, fungi, and heterotrophic microalgae, with focus on growth, lipid accumulation, bioremediation, wastewater treatment, and cost-effective harvesting. We found that a controlled, symbiotic binary culture facilitates waste bioremediation and biomass harvesting, with 96% efficiency, and reduces cost by 20–30%. Noteworthy, in binary or polyculture systems, autotrophic microalgae often develop a symbiosis by exchanging nutrients and metabolites with heterotrophic microalgae, bacteria, yeast, fungi, which may help to achieve higher biomass production.
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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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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The 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 (Formula presented.) 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.
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...
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.
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 compute a spanning forest of H.
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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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.
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...
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.
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.
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, 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, 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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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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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.
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, 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.
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.
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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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.
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.
Canning, J, Guo, Y & Chaczko, Z 2022, '(INVITED)Sustainability, livability and wellbeing in a bionic internet-of-things', Optical Materials: X, vol. 16, pp. 100204-100204.
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Cao, H-L, Nguyen, HAD, Luu, TH, Vu, HTT, Pham, D, Vu, VTN, Le, HH, Nguyen, DXB, Truong, TT, Nguyen, H-D & Nguyen, C-N 2022, 'Localized automation solutions in response to the first wave of COVID-19: a story from Vietnam', International Journal of Pervasive Computing and Communications, vol. 18, no. 5, pp. 548-562.
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PurposeCOVID-19 hits every country’s health-care system and economy. There is a trend toward using automation technology in response to the COVID-19 crisis not only in developed countries but also in those with lower levels of technology development. However, current studies mainly focus on the world level, and only a few ones report deployments at the country level. The purpose of this paper is to investigate the use of automation solutions in Vietnam with locally available materials mainly in the first wave from January to July 2020.Design/methodology/approachThe authors collected COVID-related automation solutions during the first wave of COVID-19 in Vietnam from January to July 2020 through a search process. The analysis and insights of a panel consisting of various disciplines (i.e. academia, health care, government, entrepreneur and media) aim at providing a clear picture of how and to what extent these solutions have been deployed.FindingsThe authors found seven groups of solutions from low to high research and development (R&D) levels deployed across the country with various funding sources. Low R&D solutions were widely spread owing to simplicity and affordability. High R&D solutions were mainly deployed in big cities. Most of the solutions were deployed during the first phases when international supply chains were limited with a significant contribution of the media. Higher R&D solutions have opportunities to be deployed in the reopening phase. However, challenges can be listed as limited interdisciplinary research teams, market demand, the local supporting industry, end-user validation and social-ethical issues.
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, '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 Learning', IEEE Intelligent Systems, vol. 37, no. 4, pp. 3-4.
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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, '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, Lv, T & Ni, W 2022, 'Two-Timescale Optimization for Intelligent Reflecting Surface-Assisted MIMO Transmission in Fast-Changing Channels', IEEE Transactions on Wireless Communications, vol. 21, no. 12, pp. 10424-10437.
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The application of intelligent reflecting surface (IRS) depends on the knowledge of channel state information (CSI), and has been hindered by the heavy overhead of channel training, estimation, and feedback in fast-changing channels. This paper presents a new two-timescale beamforming approach to maximizing the average achievable rate (AAR) of IRS-assisted MIMO systems, where the IRS is configured relatively infrequently based on statistical CSI (S-CSI) and the base station precoder and power allocation are updated frequently based on quickly outdated instantaneous CSI (I-CSI). The key idea is that we first reveal the optimal small-timescale power allocation based on outdated I-CSI yields a water-filling structure. Given the optimal power allocation, a new mini-batch sampling (mbs)-based particle swarm optimization (PSO) algorithm is developed to optimize the large-timescale IRS configuration with reduced channel samples. Another important aspect is that we develop a model-driven PSO algorithm to optimize the IRS configuration, which maximizes a lower bound of the AAR by only using the S-CSI and eliminates the need of channel samples. The model-driven PSO serves as a dependable lower bound for the mbs-PSO. Simulations corroborate the superiority of the new two-timescale beamforming strategy to its alternatives in terms of the AAR and efficiency, with the benefits of the IRS demonstrated.
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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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 ...
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.
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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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.
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, '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, 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-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, Wang, Y, Wang, L, Ji, Y, Wang, Q, Li, M & Gao, L 2022, 'Emerging investigator series: effects of sediment particle size on the spatial distributions of contaminants and bacterial communities in the reservoir sediments', Environmental Science: Water Research & Technology, vol. 8, no. 5, pp. 957-967.
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This study investigates the effects of sediment particle size on the spatial distributions of contaminants and bacterial communities in the reservoir sediments, which can guide the implementation of partial desilting in the reservoirs.
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, 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, 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, Wang, Q, Choi, S, Zeng, H, Takahashi, K, Qian, C & Yu, X 2022, 'Focal fMRI signal enhancement with implantable inductively coupled detectors', NeuroImage, vol. 247, pp. 118793-118793.
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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, 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, H-C, Gao, L & Hsieh, M-H 2022, 'Properties of Noncommutative Rényi and Augustin Information', Communications in Mathematical Physics, vol. 390, no. 2, pp. 501-544.
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Rényi and Augustin information are generalizations of mutual information defined via the Rényi divergence, playing a significant role in evaluating the performance of information processing tasks by virtue of its connection to the error exponent analysis. In quantum information theory, there are three generalizations of the classical Rényi divergence—the Petz’s, sandwiched, and log-Euclidean versions, that possess meaningful operational interpretation. However, the associated quantum Rényi and Augustin information are much less explored compared with their classical counterpart, and lacking crucial properties hinders applications of these quantities to error exponent analysis in the quantum regime. The goal of this paper is to analyze fundamental properties of the Rényi and Augustin information from a noncommutative measure-theoretic perspective. Firstly, we prove the uniform equicontinuity for all three quantum versions of Rényi and Augustin information, and it hence yields the joint continuity of these quantities in order and prior input distributions. Secondly, we establish the concavity of the scaled Rényi and Augustin information in the region of s∈ (- 1 , 0) for both Petz’s and the sandwiched versions. This completes the open questions raised by Holevo (IEEE Trans Inf Theory 46(6):2256–2261, 2000), and Mosonyi and Ogawa (Commun Math Phys 355(1):373–426, 2017). For the applications, we show that the strong converse exponent in classical-quantum channel coding satisfies a minimax identity, which means that the strong converse exponent can be attained by the best constant composition code. The established concavity is further employed to prove an entropic duality between classical data compression with quantum side information and classical-quantum channel coding, and a Fenchel duality in joint source-channel coding with quantum side information.
Cheng, H-C, Hanson, EP, Datta, N & Hsieh, M-H 2022, 'Duality Between Source Coding With Quantum Side Information and Classical-Quantum Channel Coding', IEEE Transactions on Information Theory, vol. 68, no. 11, pp. 7315-7345.
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In this paper, we establish an interesting duality between two different quantum information-processing tasks, namely, classical source coding with quantum side information, and channel coding over classical-quantum channels. The duality relates the optimal error exponents of these two tasks, generalizing the classical results of Ahlswede and Dueck [IEEE Trans. Inf. Theory, 28(3):430-443, 1982]. We establish duality both at the operational level and at the level of the entropic quantities characterizing these exponents. For the latter, the duality is given by an exact relation, whereas for the former, duality manifests itself in the following sense: an optimal coding strategy for one task can be used to construct an optimal coding strategy for the other task. Along the way, we derive a bound on the error exponent for classical-quantum channel coding with constant composition codes which might be of independent interest. Finally, we consider the task of variable-length classical compression with quantum side information, and a duality relation between this task and classical-quantum channel coding can also be established correspondingly. Furthermore, we study the strong converse of this task, and show that the strong converse property does not hold even in the i.i.d. scenario.
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.
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, S, Zeng, H, Chen, Y, Sobczak, F, Qian, C & Yu, X 2022, 'Laminar-specific functional connectivity mapping with multi-slice line-scanning fMRI', Cerebral Cortex, vol. 32, no. 20, pp. 4492-4501.
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AbstractDespite extensive studies detecting laminar functional magnetic resonance imaging (fMRI) signals to illustrate the canonical microcircuit, the spatiotemporal characteristics of laminar-specific information flow across cortical regions remain to be fully investigated in both evoked and resting conditions at different brain states. Here, we developed a multislice line-scanning fMRI (MS-LS) method to detect laminar fMRI signals in adjacent cortical regions with high spatial (50 μm) and temporal resolution (100 ms) in anesthetized rats. Across different trials, we detected either laminar-specific positive or negative blood-oxygen-level-dependent (BOLD) responses in the surrounding cortical region adjacent to the most activated cortex under the evoked condition. Specifically, in contrast to typical Layer (L) 4 correlation across different regions due to the thalamocortical projections for trials with positive BOLD, a strong correlation pattern specific in L2/3 was detected for trials with negative BOLD in adjacent regions, which indicated brain state-dependent laminar-fMRI responses based on corticocortical interaction. Also, in resting-state (rs-) fMRI study, robust lag time differences in L2/3, 4, and 5 across multiple cortices represented the low-frequency rs-fMRI signal propagation from caudal to rostral slices. In summary, our study provided a unique laminar fMRI mapping scheme to better characterize trial-specific intra- and inter-laminar functional connectivity in evoked and resting-state MS-LS.
Choo, Y, Hwa, Y & Cairns, EJ 2022, 'A review of the rational interfacial designs and characterizations for solid‐state lithium/sulfur cells', Electrochemical Science Advances, vol. 2, no. 6.
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AbstractThe high theoretical specific energy of lithium/sulfur (Li/S) cells (2600 Wh/kg) has positioned the Li/S cell as one of the most promising candidates for the beyond lithium‐ion cell. Despite the evident advantages, there are remaining problems mainly associated with the unique solution‐based reaction chemistry involving lithium polysulfide (Li‐PS) that hinder the commercialization of the Li/S cells. Incorporating solid‐state electrolytes (SSEs) can avoid the Li‐PS shuttle problem while preserving the benefits of Li/S cells, but it introduces other challenges related to the electrode/electrolyte solid interfaces. This topical review summarizes the current status of solid‐state Li/S cells and their major challenges and discusses the recent efforts to improve cell performance and durability. Various solid‐state electrolytes, including oxides, sulfides, and solid polymer electrolytes, are briefly reviewed. In particular, we focus on the recent progress to improve the interfacial properties by two major approaches, morphological and chemical modifications of the electrode/electrolyte interfaces. The design strategy and implementation to overcome the prominent issues associated with sulfur electrodes are critically discussed. Also, several electrochemical and physicochemical characterization methods to examine the electron/ion transport at the interface are outlined. Given the superior theoretical physicochemical properties of the Li/S cells, we emphasize that the inappropriate interfacial design of the solid‐state Li/S cells is the major challenge to bring solid‐state Li/S cells to a commercially attractive level.
Choo, Y, Snyder, RL, Shah, NJ, Abel, BA, Coates, GW & Balsara, NP 2022, 'Complete Electrochemical Characterization and Limiting Current of Polyacetal Electrolytes', Journal of The Electrochemical Society, vol. 169, no. 2, pp. 020538-020538.
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We investigate a polyacetal-based electrolyte, poly(1,3,6-trioxocane) (P(2EO-MO)) mixed with lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) salt, and report full electrochemical characterization of the transport parameters and a thermodynamic property in comparison to the previously reported poly(ethylene oxide) (PEO) electrolyte data [D. Gribble et al., J. Electrochem. Soc., 166, A3228 (2019)]. While the steady-state current fraction (ρ +) of P(2EO-MO) electrolyte is greater than that of PEO electrolyte in the entire salt concentration window we explored, the rigorously defined transference number using Newman’s concentrated solution theory ( t + 0 ) appears to be similar to that of PEO electrolyte. On the basis of full electrochemical characterization, we calculate the salt concentration profile as a function of position in the cell and predict limiting current density (i L ...
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.
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.
Crowther, CA, Samuel, D, McCowan, LME, Edlin, R, Tran, T & McKinlay, CJ 2022, 'Lower versus Higher Glycemic Criteria for Diagnosis of Gestational Diabetes', New England Journal of Medicine, vol. 387, no. 7, pp. 587-598.
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BACKGROUND: Treatment of gestational diabetes improves maternal and infant health, although diagnostic criteria remain unclear. METHODS: We randomly assigned women at 24 to 32 weeks' gestation in a 1:1 ratio to be evaluated for gestational diabetes with the use of lower or higher glycemic criteria for diagnosis. The lower glycemic criterion was a fasting plasma glucose level of at least 92 mg per deciliter (≥5.1 mmol per liter), a 1-hour level of at least 180 mg per deciliter (≥10.0 mmol per liter), or a 2-hour level of at least 153 mg per deciliter (≥8.5 mmol per liter). The higher glycemic criterion was a fasting plasma glucose level of at least 99 mg per deciliter (≥5.5 mmol per liter) or a 2-hour level of at least 162 mg per deciliter (≥9.0 mmol per liter). The primary outcome was the birth of an infant who was large for gestational age (defined as a birth weight above the 90th percentile according to Fenton-World Health Organization standards). Secondary outcomes were maternal and infant health. RESULTS: A total of 4061 women underwent randomization. Gestational diabetes was diagnosed in 310 of 2022 women (15.3%) in the lower-glycemic-criteria group and in 124 of 2039 women (6.1%) in the higher-glycemic-criteria group. Among 2019 infants born to women in the lower-glycemic-criteria group, 178 (8.8%) were large for gestational age, and among 2031 infants born to women in the higher-glycemic-criteria group, 181 (8.9%) were large for gestational age (adjusted relative risk, 0.98; 95% confidence interval, 0.80 to 1.19; P = 0.82). Induction of labor, use of health services, use of pharmacologic agents, and neonatal hypoglycemia were more common in the lower-glycemic-criteria group than in the higher-glycemic-criteria group. The results for the other secondary outcomes were similar in the two trial groups, and there were no substantial between-group differences in adverse events. Among the women in both groups who had glucose test results that fell betwe...
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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Dayarathne, HNP, 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 (
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, Wu, H, Mu, H, Jiang, L, Xi, W, Xu, X & Zheng, W 2022, 'Preparation and properties of electrospun NaYF4: Yb3+, Er3+‐PLGA‐gelatin nanofibers', Journal of Applied Polymer Science, vol. 139, no. 26.
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AbstractThe synthesis of composite nanofiber often requires complex reaction conditions and the dimensions of the synthesized composite nanofiber are difficult to control. Electrospinning technique could tackle the issue. In this work, we firstly prepare the NaYF4 up‐conversion material composed of double doped rare earth ions of Er3+ and Yb3+. Then, the up‐conversion luminescent NaYF4: Yb3+, Er3+ nanoparticles (NaYF4 NPs) are encapsulated into poly(lactide‐co‐glycolide)‐gelatin (NaYF4‐PLGA‐gelatin) using one‐step electrospinning process. The effect of NaYF4 NPs on morphology, up‐conversion emission spectra, hydrophilicity, mechanical property and degradation of the electrospun NaYF4‐PLGA‐gelatin nanofiber are studied in detail. The highest luminescent intensity of the electrospun NaYF4‐PLGA‐gelatin nanofiber is achieved when the encapsulated content of NaYF4 NPs is 5 mg/ml. Meanwhile, the mechanical properties of the nanofibers with this encapsulated content are also averagely higher than that of the nanofibers with other concentrations. In addition, the electrospun NaYF4‐PLGA‐gelatin nanofibers with a variety of NaYF4 NPs contents present great hydrophilicity and degradation rates. Therefore, this work provides an effective approach for the design of up‐conversion composite nanofibers and can further exploit the applications in in vivo biological imaging and tissue engineering.
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
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.
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, Y, Wu, Y, Huang, C, Tang, S, Wu, F, Yang, Y, Zhu, W & Zhuang, Y 2022, 'NAP: Neural architecture search with pruning', Neurocomputing, vol. 477, pp. 85-95.
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There has been continuously increasing attention attracted by Neural Architecture Search (NAS). Due to its computational efficiency, gradient-based NAS methods like DARTS have become the most popular framework for NAS tasks. Nevertheless, as the search iterates, the derived model in previous NAS frameworks becomes dominated by skip-connects, causing the performance downfall. In this work, we present a novel approach to alleviate this issue, named Neural Architecture search with Pruning (NAP). Unlike prior differentiable architecture search works, our approach draws the idea from network pruning. We first train an over-parameterized network, including all candidate operations. Then we propose a criterion to prune the network. Based on a newly designed relaxation of architecture representation, NAP can derive the most potent model by removing trivial and redundant edges from the whole network topology. Experiments show the effectiveness of our proposed approach. Specifically, the model searched by NAP achieves state-of-the-art performances (2.48% test error) on CIFAR-10. We transfer the model to ImageNet and obtains a 25.1% test error with only 5.0 M parameters, which is on par with modern NAS methods.
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, 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.
Dou, Y, Cheng, X, Miao, M, Wang, T, Hao, T, Zhang, Y, Li, Y, Ning, X & Wang, Q 2022, 'The impact of chlorination on the tetracycline sorption behavior of microplastics in aqueous solution', Science of The Total Environment, vol. 849, pp. 157800-157800.
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Considering the large volumes of treated water and incomplete elimination of pollutants, wastewater treatment plants (WWTPs) remain a considerable source of microplastics (MPs). Chlorine, the most frequently used disinfectant in WWTPs, has a strong oxidizing impact on MPs. However, little is documented, to date, about the impact of chlorination on the transformation of MPs and the subsequent environmental behaviors of the chlorinated MPs when released into the aquatic environment. This study explored the response of the physicochemical properties of specific thermoplastics, namely polyurethane (TPU) MPs and polystyrene (PS) MPs, to chlorination and their emerging pollutant [tetracycline (TC)] adsorption behavior in aqueous solution. The results indicated that the O/C ratio of the MP surface did not significantly change, and that there were increases in the O-containing functional groups of the TPU and PS MPs, after chlorination. The surface area of the chlorinated TPU MPs increased by 45 %, and that of the chlorinated PS increased by 21 %, compared with the pristine ones, which contributed to the TC adsorption. The adsorption isotherm fitting parameters suggested that the chlorinated TPU fitted the multilayer adsorption, and the chlorinated PS was inclined to the monolayer adsorption. The relative abundance of the O-containing functional groups, on the TPU surface, led to the release of CHCl3 molecules, and the clear surface irregularities and fissures occurred after chlorine treatment. No fissures were found on the surface of the chlorinated PS MPs. The hydrophobicity and electrostatic adsorption were proved to be the major impacts on the TC adsorption of the chlorinated MPs, and the subsequently formed hydrogen bonds led to the stronger adsorption capacity of the chlorinated TPU than the chlorinated PS MPs.
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, Hsieh, M-H, Liu, T, You, S & Tao, D 2022, 'Quantum Differentially Private Sparse Regression Learning', IEEE Transactions on Information Theory, vol. 68, no. 8, pp. 5217-5233.
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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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Duy, PQ, Weise, SC, Marini, C, Li, X-J, Liang, D, Dahl, PJ, Ma, S, Spajic, A, Dong, W, Juusola, J, Kiziltug, E, Kundishora, AJ, Koundal, S, Pedram, MZ, Torres-Fernández, LA, Händler, K, De Domenico, E, Becker, M, Ulas, T, Juranek, SA, Cuevas, E, Hao, LT, Jux, B, Sousa, AMM, Liu, F, Kim, S-K, Li, M, Yang, Y, Takeo, Y, Duque, A, Nelson-Williams, C, Ha, Y, Selvaganesan, K, Robert, SM, Singh, AK, Allington, G, Furey, CG, Timberlake, AT, Reeves, BC, Smith, H, Dunbar, A, DeSpenza, T, Goto, J, Marlier, A, Moreno-De-Luca, A, Yu, X, Butler, WE, Carter, BS, Lake, EMR, Constable, RT, Rakic, P, Lin, H, Deniz, E, Benveniste, H, Malvankar, NS, Estrada-Veras, JI, Walsh, CA, Alper, SL, Schultze, JL, Paeschke, K, Doetzlhofer, A, Wulczyn, FG, Jin, SC, Lifton, RP, Sestan, N, Kolanus, W & Kahle, KT 2022, 'Impaired neurogenesis alters brain biomechanics in a neuroprogenitor-based genetic subtype of congenital hydrocephalus', Nature Neuroscience, vol. 25, no. 4, pp. 458-473.
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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 Hammoumi, M, Tubbal, F, El Amrani El Idrissi, N, Raad, R, Theoharis, PI, Lalbakhsh, A & Abulgasem, S 2022, 'A Wideband 5G CubeSat Patch Antenna', IEEE Journal on Miniaturization for Air and Space Systems, vol. 3, no. 2, pp. 47-52.
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El Hassan, M, Assoum, H, Bukharin, N, Al Otaibi, H, Mofijur, M & Sakout, A 2022, 'A review on the transmission of COVID-19 based on cough/sneeze/breath flows', The European Physical Journal Plus, vol. 137, no. 1, p. 1.
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COVID-19 pandemic has recently had a dramatic impact on society. The understanding of the disease transmission is of high importance to limit its spread between humans. The spread of the virus in air strongly depends on the flow dynamics of the human airflows. It is, however, known that predicting the flow dynamics of the human airflows can be challenging due to different particles sizes and the turbulent aspect of the flow regime. It is thus recommended to present a deep analysis of different human airflows based on the existing experimental investigations. A validation of the existing numerical predictions of such flows would be of high interest to further develop the existing numerical model for different flow configurations. This paper presents a literature review of the experimental and numerical studies on human airflows, including sneezing, coughing and breathing. The dynamics of these airflows for different droplet sizes is discussed. The influence of other parameters, such as the viscosity and relative humidity, on the germs transmission is also presented. Finally, the efficacy of using a facemask in limiting the transmission of COVID-19 is investigated.
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.
Esfandiari, M, Lalbakhsh, A, Nasiri Shehni, P, Jarchi, S, Ghaffari-Miab, M, Noori Mahtaj, H, Reisenfeld, S, Alibakhshikenari, M, Koziel, S & Szczepanski, S 2022, 'Recent and emerging applications of Graphene-based metamaterials in electromagnetics', Materials & Design, vol. 221, pp. 110920-110920.
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Surface Plasmon Polaritons (SPPs) operating in mid-infrared up to terahertz (THz) frequencies have been traditionally manufactured on expensive metals such as gold, silver, etc. However, such metals have poor surface confinement that limits the optical applications of SPPs. The invention of graphene is a breakthrough in plasmon-based devices in terms of design, fabrication and applications, thanks to its plasmonic wave distribution, low-cost prototyping and its inherent reconfigurability. In addition, recent advancements in plasmon-based metamaterials and metasurfaces led to the elimination of the past constraints on regular optical devices, opening a new door in THz devices and applications. This paper provides an operational perspective of the advanced graphene-based electromagnetic devices, with a focus on graphene enabled antennas, absorbers and sensors, analyzing the strengths and limitations of various design methodologies.
Esfandiyari, M, Lalbakhsh, A, Jarchi, S, Ghaffari-Miab, M, Mahtaj, HN & Simorangkir, RBVB 2022, 'Tunable terahertz filter/antenna-sensor using graphene-based metamaterials', Materials & Design, vol. 220, pp. 110855-110855.
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In this paper, a novel tunable graphene-based bandstop filter/antenna-sensor is presented. This structure is an integrated module that can be used to combine filtering and high-gain radiation performance. The initial design of the unit cell consists of four U-shaped stubs loaded, resembling the arms of a ring and a sensing layer in the substrate. The reflection and transmission spectra are obtained for various graphene's chemical potentials and refractive index of sensing layer (Ns) of structure in the range of 1.3–1.6 THz. The proposed structure exhibits the attributes of both dual-band filter and single-band antenna-sensor. The conductivity of graphene and its structural parameters are studied to optimize the component performance. In filtering mode, the first bandstop is from 1.23 to 1.6 THz equal to 26% of fractional bandwidth (FBW) at 1.415 THz. The second stopband is centered at 3.12 THz with FBW of 14% for Ns = 1.6 and 0.6 eV chemical potential. In the antenna mode, a single band of the antenna-sensor is centered at 1.95 THz for the same Ns and same chemical potential. It is shown that a sensitivity of 0.145 THz/RIU is achieved at Ns = 1.5 and chemical potential of 0.6 eV. Additionally, the performance of the proposed filter/antenna-sensor module is investigated for different wave polarizations and oblique angles.
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, Li, J, Zhou, Y, Hsieh, M-H & Poor, HV 2022, 'Entanglement-assisted concatenated quantum codes', Proceedings of the National Academy of Sciences, vol. 119, no. 24.
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Entanglement-assisted concatenated quantum codes (EACQCs), constructed by concatenating two quantum codes, are proposed. These EACQCs show significant advantages over standard concatenated quantum codes (CQCs). First, we prove that, unlike standard CQCs, EACQCs can beat the nondegenerate Hamming bound for entanglement-assisted quantum error-correction codes (EAQECCs). Second, we construct families of EACQCs with parameters better than the best-known standard quantum error-correction codes (QECCs) and EAQECCs. Moreover, these EACQCs require very few Einstein–Podolsky–Rosen (EPR) pairs to begin with. Finally, it is shown that EACQCs make entanglement-assisted quantum communication possible, even if the ebits are noisy. Furthermore, EACQCs can outperform CQCs in entanglement fidelity over depolarizing channels if the ebits are less noisy than the qubits. We show that the error-probability threshold of EACQCs is larger than that of CQCs when the error rate of ebits is sufficiently lower than that of qubits. Specifically, we derive a high threshold of 47% when the error probability of the preshared entanglement is 1% to that of qubits.
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, 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, 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.
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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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.
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, 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.
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, 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.
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, 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, 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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Deep 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, 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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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.
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, 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.
Gil Lafuente, AM, Reverter, SB, Merigó, JM & Martínez, AT 2022, 'Preface', Lecture Notes in Networks and Systems, vol. 388 LNNS, pp. v-vi.
Gilberts, R, McGinnis, E, Ransom, M, Pynn, EV, Walker, B, Brown, S, Trehan, P, Jayasekera, P, 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 2: feasibility data from a multicentre prospective observational cohort study to inform a future randomized controlled trial', Clinical and Experimental Dermatology, vol. 47, no. 10, pp. 1839-1847.
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Abstract Background Compression therapy is considered beneficial for postsurgical lower leg wound healing by secondary intention; however, there is a lack of supportive evidence. To plan a randomized controlled trial (RCT), suitable data are needed. Aim To determine the feasibility of recruitment and estimate recruitment rate; to understand the standard postoperative wound management pathway; to determine uptake of optional additional clinic visits for healing confirmation; and to explore patient acceptability of compression bandaging and plan a future RCT. Methods Participant recruitment was performed from secondary care dermatology clinics, during a period of 22 months. Inclusion criteria were age ≥ 18 years, planned excision of keratinocyte cancer on the lower leg with healing by secondary intention and an ankle–brachial pressure index of ≥ 0.8. Exclusion criteria were planned primary closure/graft or flap; inability to receive, comply with or tolerate high compression; planned compression; or suspected melanoma. Patients were followed up weekly (maximum 6 months) in secondary care clinics and/or by telephone. Information was collected on healthcare resource use, unplanned compression, wound healing and an optional clinic visit to confirm healing. Results This study recruited 58 patients from 9 secondary care dermatology clinics over 22 months. Mean recruitment/centre/month was 0.8 (range 0.1–2.3). Four centres had dedicated Research Nurse support...
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.
Golestanifar, A, Karimi, G & Lalbakhsh, A 2022, 'Varactor-tuned wideband band-pass filter for 5G NR frequency bands n77, n79 and 5G Wi-Fi', Scientific Reports, vol. 12, no. 1, p. 16330.
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AbstractA wide-band band-pass filter (BPF) using coupled lines, rectangular stubs and Stepped-Impedance Resonators (SIRs) is presented in this paper. The proposed BPF operates over a large pass-band from 3.15 to 6.05 GHz covering 5G New Radio (NR) frequency Bands n77, n79 and 5G Wi-Fi, which includes the G band of US (3.3 to 4.2 GHz), 5G band of Japan (4.4 to 5 GHz) and 5G Wi-Fi (5.15 to 5.85 GHz). The presented filter has a maximum pass-band Insertion-Loss (IL) of 2 dB, a sharp roll-off rate and suppresses all the unwanted harmonics from 4.2 GHz up to 12 GHz with a 15 dB attenuation level. The performance of each section can be analyzed based on lumped-element circuit models. The electrical size of the BPF is 0.258 λg × 0.255 λg, where λgis the guided wavelength at the central frequency. The design accuracy is verified through implementing and testing the final BPF. The pass-band band-width can be controlled by adding the varactor diodes. A good relationship between the band-width and the varactor diodes are extracted by the curve fitting technique.
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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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.
Goudarzi, S, Ahmad Soleymani, S, Hossein Anisi, M, Ciuonzo, D, Kama, N, Abdullah, S, Abdollahi Azgomi, M, Chaczko, Z & Azmi, A 2022, 'Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network', Computers, Materials & Continua, vol. 70, no. 1, pp. 715-738.
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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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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, 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, Li, X, Luby, S & Jiang, G 2022, 'Vertical outbreak of COVID-19 in high-rise buildings: The role of sewer stacks and prevention measures', Current Opinion in Environmental Science & Health, vol. 29, pp. 100379-100379.
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COVID-19 outbreaks in high-rise buildings suggested the transmission route of fecal-aerosol-inhalation due to the involvement of viral aerosols in sewer stacks. The vertical transmission is likely due to the failure of water traps that allow viral aerosols to spread through sewer stacks. This process can be further facilitated by the chimney effect in vent stack, extract ventilation in bathrooms, or wind-induced air pressure fluctuations. To eliminate the risk of such vertical disease spread, the installation of protective devices is highly encouraged in high-rise buildings. Although the mechanism of vertical pathogen spread through drainage pipeline has been illustrated by tracer gas or microbial experiments and numerical modeling, more research is needed to support the update of regulatory and design standards for sewerage facilities.
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 Ethics Committee of The ...
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, 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 ...
Hadei, M, Dadashzadeh, G, Torabi, Y & Lalbakhsh, A 2022, 'Terahertz beamforming network with a nonuniform contour', Applied Optics, vol. 61, no. 4, pp. 1087-1087.
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This paper presents a terahertz beamforming network based on a nonlocal lens with a 2D beam-scanning demonstration through leaky-wave antennas. The proposed design methodology is novel, to the best of our knowledge, in the aspect of using unconventional optimization parameters to significantly reduce the phase error associated with this class of beamformers. In this approach, a nonuniform contour defined by Fourier series expansion is used as a new optimization parameter to significantly decrease the phase error over a larger scan-angle than that in the previous works. The proposed system is a good candidate for industrial and security applications such as automotive radar sensors and electromagnetic THz imaging, thanks to its extensive 2D scanning range: − 68 ∘ to 0° in the elevation plane and − 45 ∘ to + 45 ∘
Hagos, FY, Abd Aziz, AR, Zainal, EZ, Mofijur, M & Ahmed, SF 2022, 'Recovery of gas waste from the petroleum industry: a review', Environmental Chemistry Letters, vol. 20, no. 1, pp. 263-281.
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Globally, 150–170 billion cubic metres of gas are being flared annually from petroleum refineries, petrochemical industries and from landfills. In this paper, we critically review the flaring technology, the impact of flare gas on the environment, and recovery options. We also discuss flare gas challenges such as technical, structural, economic, and regulatory challenges. We found that ground flaring is preferred whenever the facility is situated in a highly populated area or close to an aviation zone. Gas flaring produces major pollutants including benzene, benzopyrene and toluene. The recovery of this gas should be intensified to minimise impacts.
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.
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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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