Abbasi, M, Abbasi, E & Li, L 2021, 'New transformer‐less DC–DC converter topologies with reduced voltage stress on capacitors and increased voltage conversion ratio', IET Power Electronics, vol. 14, no. 6, pp. 1173-1192.
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AbstractThis article proposes new transformer‐less step‐up/down and step‐up DC–DC topologies providing numerous merits such as less voltage stress on capacitors, lower duty‐cycle, and higher voltage conversion ratio compared to other DC–DC converters. The proposed converters are extendible by benefiting from several switched‐capacitors, making it possible to transfer more power from the converter. The proposed structures are more suitable for modern applications in which it is desirable to achieve high voltage gains by using non‐extreme duty‐cycles. For proving the analysis and claims, the detailed comparison and experimental results are presented. In the experiments, the dynamic performance of the proposed converters is also validated.
Abbasi, M, Abbasi, E & Mohammadi-Ivatloo, B 2021, 'Single and multi-objective optimal power flow using a new differential-based harmony search algorithm', Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 1, pp. 851-871.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. This article proposes a new differential evolutionary-based approach to solve the optimal power flow (OPF) problem in power systems. The proposed approach employs a differential-based harmony search algorithm (DH/best) for optimal settings of OPF control variables. The proposed algorithm benefits from having a more effective initialization method and a better updating procedure in contrast with other algorithms. Here, real power losses minimization, voltage profile improvement, and active power generation minimization are considered as the objectives and formulated in the form of single-objective and multi-objective functions. For proving the performance of the proposed algorithm, comprehensive simulations have been performed by MATLAB software in which IEEE 118-bus and 57-bus systems are considered as the test systems. Besides, thorough comparisons have been performed between the proposed algorithm and other well-known algorithms like PSO, NSGAII, and Harmony search in three different load levels indicating the higher efficiency and robustness of the proposed algorithm in contrast with others.
Abbasi, M, Nazari, Y, Abbasi, E & Li, L 2021, 'A new transformer‐less step‐up DC–DC converter with high voltage gain and reduced voltage stress on switched‐capacitors and power switches for renewable energy source applications', IET Power Electronics, vol. 14, no. 7, pp. 1347-1359.
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AbstractIn many modern applications such as renewable energy sources (RESs), DC–DC step‐up converters can be used to regulate the input variable and/or low voltage to achieve the desired characteristics such as amplitude and ripples at the output voltage. This article proposes a new transformer‐less step‐up DC–DC converter which, compared to previously presented converter topologies in the same class, can provide a higher variable voltage conversion ratio besides benefits such as decreased voltage stress on the switched‐capacitors and power switches. Since the proposed topology is expandable, it can generate much higher voltage conversion ratios with lower, non‐extreme duty‐cycles which can be provided by a simple and cheap control circuit. The aforementioned advantages make the converter a suitable candidate for numerous industrial applications such as RES applications. Besides the voltage regulation applications, the proposed converter can be employed to extract the maximum power from RESs such as photovoltaic panels. To prove the converter performance, comprehensive comparisons and experiments are performed.
Abdo, P & Huynh, BP 2021, 'An experimental investigation of green wall bio-filter towards air temperature and humidity variation', Journal of Building Engineering, vol. 39, pp. 102244-102244.
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Green walls show promise in providing thermal comfort. Their benefits include the reduction of the temperature of air layers around them. They are classified as passive and active systems. Active systems are designed with ventilators that force air through the substrate and plant rooting system of the green wall. With a passive system, air is simply diffused through the green wall substrate and the plant foliage. The current work investigates the effect of green walls on the air temperature and humidity. Temperature and humidity are measured at different locations inside an acrylic chamber where different modules with different plant species are placed. The effect of changing the surrounding ambient conditions is also investigated. Experiments lasted at least 24 h to cover day and night time conditions. For the active modules, lower temperatures in the range of 1–3 °C, along with increased humidity levels have been observed when modules are saturated wet. Passive modules have also provided lower temperatures in the range of 0.5–2 °C. None of the plant species studied showed any preference, indicating that the moisture content of the substrate plays the major role affecting the temperature and humidity variations.
Abdollahi, A & Pradhan, B 2021, 'Integrated technique of segmentation and classification methods with connected components analysis for road extraction from orthophoto images', Expert Systems with Applications, vol. 176, pp. 114908-114908.
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Abdollahi, A & Pradhan, B 2021, 'Integrating semantic edges and segmentation information for building extraction from aerial images using UNet', Machine Learning with Applications, vol. 6, pp. 100194-100194.
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Abdollahi, A & Pradhan, B 2021, 'Urban Vegetation Mapping from Aerial Imagery Using Explainable AI (XAI)', Sensors, vol. 21, no. 14, pp. 4738-4738.
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Urban vegetation mapping is critical in many applications, i.e., preserving biodiversity, maintaining ecological balance, and minimizing the urban heat island effect. It is still challenging to extract accurate vegetation covers from aerial imagery using traditional classification approaches, because urban vegetation categories have complex spatial structures and similar spectral properties. Deep neural networks (DNNs) have shown a significant improvement in remote sensing image classification outcomes during the last few years. These methods are promising in this domain, yet unreliable for various reasons, such as the use of irrelevant descriptor features in the building of the models and lack of quality in the labeled image. Explainable AI (XAI) can help us gain insight into these limits and, as a result, adjust the training dataset and model as needed. Thus, in this work, we explain how an explanation model called Shapley additive explanations (SHAP) can be utilized for interpreting the output of the DNN model that is designed for classifying vegetation covers. We want to not only produce high-quality vegetation maps, but also rank the input parameters and select appropriate features for classification. Therefore, we test our method on vegetation mapping from aerial imagery based on spectral and textural features. Texture features can help overcome the limitations of poor spectral resolution in aerial imagery for vegetation mapping. The model was capable of obtaining an overall accuracy (OA) of 94.44% for vegetation cover mapping. The conclusions derived from SHAP plots demonstrate the high contribution of features, such as Hue, Brightness, GLCM_Dissimilarity, GLCM_Homogeneity, and GLCM_Mean to the output of the proposed model for vegetation mapping. Therefore, the study indicates that existing vegetation mapping strategies based only on spectral characteristics are insufficient to appropriately classify vegetation covers.
Abdollahi, A, Pradhan, B & Alamri, A 2021, 'RoadVecNet: a new approach for simultaneous road network segmentation and vectorization from aerial and google earth imagery in a complex urban set-up', GIScience & Remote Sensing, vol. 58, no. 7, pp. 1151-1174.
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Abdollahi, A, Pradhan, B & Shukla, N 2021, 'Road Extraction from High-Resolution Orthophoto Images Using Convolutional Neural Network', Journal of the Indian Society of Remote Sensing, vol. 49, no. 3, pp. 569-583.
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© 2020, Indian Society of Remote Sensing. Abstract: Two of the major applications in geospatial information system (GIS) and remote sensing fields are object detection and man-made feature extraction (e.g., road sections) from high-resolution remote sensing imagery. Extracting roads from high-resolution remotely sensed imagery plays a crucial role in multiple applications, such as navigation, emergency tasks, land cover change detection, and updating GIS maps. This study presents a deep learning technique based on a convolutional neural network (CNN) to classify and extract roads from orthophoto images. We applied the model on five orthophoto images to specify the superiority of the method for road extraction. First, we used principal component analysis and object-based image analysis for pre-processing to not only obtain spectral information but also add spatial and textural information for enhancing the classification accuracy. Then, the obtained results from the previous step were used as input for the CNN model to classify the images into road and non-road parts and trivial opening and closing operation are applied to extract connected road components from the images and remove holes inside the road parts. For the accuracy assessment of the proposed method, we used measurement factors such as precision, recall, F1 score, overall accuracy, and IOU. Achieved results showed that the average percentages of these factors were 91.09%, 95.32%, 93.15%, 94.44%, and 87.21%. The results were also compared with those of other existing methods. The comparison ascertained the reliability and superior performance of the suggested model architecture for extracting road regions from orthophoto images. Graphic Abstract: [Figure not available: see fulltext.]
Abdollahi, A, Pradhan, B, Sharma, G, Maulud, KNA & Alamri, A 2021, 'Improving Road Semantic Segmentation Using Generative Adversarial Network', IEEE Access, vol. 9, pp. 64381-64392.
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Abdollahi, M, Gao, X, Mei, Y, Ghosh, S, Li, J & Narag, M 2021, 'Substituting clinical features using synthetic medical phrases: Medical text data augmentation techniques', Artificial Intelligence in Medicine, vol. 120, pp. 102167-102167.
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Abdollahi, M, Ni, W, Abolhasan, M & Li, S 2021, 'Software-Defined Networking-Based Adaptive Routing for Multi-Hop Multi-Frequency Wireless Mesh', IEEE Transactions on Vehicular Technology, vol. 70, no. 12, pp. 13073-13086.
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While multi-hop multi-frequency mesh has been extensively studied in the past decades, only several deployable and relatively bulky systems have been developed to support small numbers of hops under stationary settings. This paper presents a new Software-Defined Networking (SDN)-based design of multihop multi-frequency mesh. A new lightweight hardware platform is developed to support adaptive routing and frequency selection, by modifying and integrating commercial-off-the-shelf WiFi modules. We also extend the celebrated Dijkstra’s algorithm in support of the new multi-hop multi-frequency platform, where non-overlapping frequency bands are selected together with the routing paths by maintaining N2 Dijkstra processes for N frequency bands. These processes interact to recursively select the optimal upstream node and frequency for each downstream frequency of a node. Mininet-WiFi is used to evaluate the routing of the new system under dense network settings. The results indicate that our system improves the end-to-end throughput by taking background WiFi traffic into account and adaptively selecting the routes and frequencies, as compared to the shortest-path-based routing strategy.
Abdulganiy, RI, Wen, S, Feng, Y, Zhang, W & Tang, N 2021, 'Adapted block hybrid method for the numerical solution of Duffing equations and related problems', AIMS Mathematics, vol. 6, no. 12, pp. 14013-14034.
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<abstract><p>Problems of non-linear equations to model real-life phenomena have a long history in science and engineering. One of the popular of such non-linear equations is the Duffing equation. An adapted block hybrid numerical integrator that is dependent on a fixed frequency and fixed step length is proposed for the integration of Duffing equations. The stability and convergence of the method are demonstrated; its accuracy and efficiency are also established.</p></abstract>
Abedini, A, Abedin, B & Zowghi, D 2021, 'Adult learning in online communities of practice: A systematic review', British Journal of Educational Technology, vol. 52, no. 4, pp. 1663-1694.
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AbstractAdult learning is a lifelong process whereby knowledge is formed through the transformation of adults' experience. Research on online adult learning has been on the rise in recent years, thanks to the innovative opportunities provided to adults by digital technologies. Online communities of practice (OCOPs) a one of such opportunities, which offer the potential to bring geographically dispersed adult learners together through a common interest. Despite an increased growth in the use of OCOPs by adults in various professional sectors, there is still a lack of understanding of the characteristics of online adult learning in OCOPs, and the facilitators and hinderers influencing engagement in these communities. This paper presents a comprehensive synthesis of research literature on online adult learning in OCOPs to understand its characteristics and what may facilitate or hinder adults' engagement in these communities. A review has been conducted using a systematic, rigorous and standard procedure, aiming to summarise and synthesise existing research on the topic and to provide analytical criticism. In total, thirty‐seven studies were included in this review. Findings revealed that members of OCOPs are independent, experience‐centred, problem‐centred, self‐motivated, goal‐oriented, and lifelong learners with the purpose to achieve professional outcomes. Moreover, the results revealed how the engagement of adults in OCOPs could lead to improving learning processes. Findings also showed that the level of engagement is influenced by aging, fatigue caused by a busy life, resistance process due to learning new technologies, lack of personal evolution, interactive learning settings, motivation, self‐regulation and competition factors. This study revealed facilitators and hinderers of engagement in OCOPs. The study extended andragogy to digital environments and contributes to the theory by making sens...
Abeywickrama, A, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2021, 'LABORATORY INVESTIGATION ON THE USE OF VERTICAL DRAINS TO MITIGATE MUD PUMPING UNDER RAIL TRACKS', Australian Geomechanics Journal, vol. 56, no. 3, pp. 117-126.
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The build-up of excess pore water pressure (EPWP) in undrained soft subgrade under repeated rail loads is the key mechanism causing soil to fluidise, consequently yielding slurry tracks (i.e., mud pumping). This issue has substantially reduced transport efficiency associated with immense cost for track maintenance though considerable effort has been made over the past years. Therefore, this study is carried out to investigate how prefabricated vertical drains (PVDs) can be used to mitigate the accumulated EPWP and associated mud pumping. A series of cyclic triaxial tests including undrained (i.e., without PVDs) and PVD-assisted drained soils are conducted, and their results are compared to evaluate the effect of PVDs on cyclic soil behaviour. In this investigation, subgrade soil collected from a mud pumping site is used while loading parameters including the frequency, confining pressure and cyclic stress ratio (CSR) are considered with respect to heavy rail load condition in the field. The results show that PVDs can help dissipate effectively the accumulated EPWP, thus mitigating soil fluidisation. The current study shows that for undrained condition, lower frequency loading (i.e., slower trains) takes a smaller number of cycles to cause soil failure, whereas for drained cases (i.e., PVDs-assisted specimens), an opposite trend is observed. The study proves that installing PVDs into shallow layer (i.e., 3-5 m depth) is an effective approach to stabilise soft subgrade soil under rail tracks.
Aboulkheyr Es, H, Bigdeli, B, Zhand, S, Aref, AR, Thiery, JP & Warkiani, ME 2021, 'Mesenchymal stem cells induce PD‐L1 expression through the secretion of CCL5 in breast cancer cells', Journal of Cellular Physiology, vol. 236, no. 5, pp. 3918-3928.
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AbstractVarious factors in the tumor microenvironment (TME) regulate the expression of PD‐L1 in cancer cells. In TME, mesenchymal stem cells (MSCs) play a crucial role in tumor progression, metastasis, and drug resistance. Emerging evidence suggests that MSCs can modulate the immune‐suppression capacity of TME through the stimulation of PD‐L1 expression in various cancers; nonetheless, their role in the induction of PD‐L1 in breast cancer remained elusive. Here, we assessed the potential of MSCs in the stimulation of PD‐L1 expression in a low PD‐L1 breast cancer cell line and explored its associated cytokine. We assessed the expression of MSCs‐related genes and their correlation with PD‐L1 across 1826 breast cancer patients from the METABRIC cohort. After culturing an ER+/differentiated/low PD‐L1 breast cancer cells with MSCs conditioned‐medium (MSC‐CM) in a microfluidic device, a variety of in‐vitro assays was carried out to determine the role of MSC‐CM in breast cancer cells' phenotype plasticity, invasion, and its effects on induction of PD‐L1 expression. In‐silico analysis showed a positive association between MSCs‐related genes and PD‐L1 expression in various types of breast cancer. Through functional assays, we revealed that MSC‐CM not only prompts a phenotype switch but also stimulates PD‐L1 expression at the protein level through secretion of various cytokines, especially CCL5. Treatment of MSCs with cytokine inhibitor pirfenidone showed a significant reduction in the secretion of CCL5 and consequently, expression of PD‐L1 in breast cancer cells. We concluded that MSCs‐derived CCL5 may act as a PD‐L1 stimulator in breast cancer.
Aboutorab, H, Hussain, OK, Saberi, M, Hussain, FK & Chang, E 2021, 'A survey on the suitability of risk identification techniques in the current networked environment', Journal of Network and Computer Applications, vol. 178, pp. 102984-102984.
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Abraham, MT, Satyam, N & Pradhan, B 2021, 'Forecasting Landslides Using Mobility Functions: A Case Study from Idukki District, India', Indian Geotechnical Journal, vol. 51, no. 4, pp. 684-693.
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Abraham, MT, Satyam, N, Jain, P, Pradhan, B & Alamri, A 2021, 'Effect of spatial resolution and data splitting on landslide susceptibility mapping using different machine learning algorithms', Geomatics, Natural Hazards and Risk, vol. 12, no. 1, pp. 3381-3408.
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Abraham, MT, Satyam, N, Lokesh, R, Pradhan, B & Alamri, A 2021, 'Factors Affecting Landslide Susceptibility Mapping: Assessing the Influence of Different Machine Learning Approaches, Sampling Strategies and Data Splitting', Land, vol. 10, no. 9, pp. 989-989.
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Data driven methods are widely used for the development of Landslide Susceptibility Mapping (LSM). The results of these methods are sensitive to different factors, such as the quality of input data, choice of algorithm, sampling strategies, and data splitting ratios. In this study, five different Machine Learning (ML) algorithms are used for LSM for the Wayanad district in Kerala, India, using two different sampling strategies and nine different train to test ratios in cross validation. The results show that Random Forest (RF), K Nearest Neighbors (KNN), and Support Vector Machine (SVM) algorithms provide better results than Naïve Bayes (NB) and Logistic Regression (LR) for the study area. NB and LR algorithms are less sensitive to the sampling strategy and data splitting, while the performance of the other three algorithms is considerably influenced by the sampling strategy. From the results, both the choice of algorithm and sampling strategy are critical in obtaining the best suited landslide susceptibility map for a region. The accuracies of KNN, RF, and SVM algorithms have increased by 10.51%, 10.02%, and 4.98% with the use of polygon landslide inventory data, while for NB and LR algorithms, the performance was slightly reduced with the use of polygon data. Thus, the sampling strategy and data splitting ratio are less consequential with NB and algorithms, while more data points provide better results for KNN, RF, and SVM algorithms.
Abraham, MT, Satyam, N, Reddy, SKP & Pradhan, B 2021, 'Runout modeling and calibration of friction parameters of Kurichermala debris flow, India', Landslides, vol. 18, no. 2, pp. 737-754.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Debris flows account for a substantial economy and property loss in Western Ghats of Kerala, India, especially during monsoon seasons. Wayanad district is an active erosion zone in the plateau margins of Western Ghats, and there is a remarkable rise in the number of debris flows since 2018, due to very high-intensity rainfalls in this region. This study comprises geotechnical investigation, runout modeling, and calibration of friction parameters of Kurichermala debris flow, one of the devastating debris flow events that happened in Wayanad, during the 2018 monsoon. The detailed investigation and back analysis of such events are substantial in calibrating the flow parameters for the region. These parameters can be used for predicting the flow paths of possible debris flows and quantitative risk assessment in the future. The geotechnical investigation provided vital information regarding the soil type and shear strength parameters of the debris flow and has helped in understanding the flow behavior. A dynamic numerical model, rapid mass movements (RAMMS), was used for the back analysis of the debris flow, using the shape information of the flow. For precise calibration using statistical comparison, an image processing tool has been developed, to compare the structural similarity of simulated results with the original shape of debris flow. The dry-Coulomb friction coefficient (μ) was calibrated as 0.01 and turbulent friction coefficient (ξ) as 100 m/s2 for the event, using Voellmy-Salm rheology. The shape predicted by the model had a similarity index of 0.626 with the actual shape of debris flow. The results were found to be in accordance with the field and geotechnical observations. Hence, the results can be used to predict the shape of possible debris flows in the study area. The study is the first of its kind for the region and has significant influence in risk assessment for this highly susceptible l...
Abraham, MT, Satyam, N, Shreyas, N, Pradhan, B, Segoni, S, Abdul Maulud, KN & Alamri, AM 2021, 'Forecasting landslides using SIGMA model: a case study from Idukki, India', Geomatics, Natural Hazards and Risk, vol. 12, no. 1, pp. 540-559.
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Abualigah, L, Diabat, A, Mirjalili, S, Abd Elaziz, M & Gandomi, AH 2021, 'The Arithmetic Optimization Algorithm', Computer Methods in Applied Mechanics and Engineering, vol. 376, pp. 113609-113609.
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AbstractThis work proposes a new meta-heuristic method called Arithmetic Optimization Algorithm (AOA) that utilizes the distribution behavior of the main arithmetic operators in mathematics including (Multiplication (), Division (), Subtraction (), and Addition ()). AOA is mathematically modeled and implemented to perform the optimization processes in a wide range of search spaces. The performance of AOA is checked on twenty-nine benchmark functions and several real-world engineering design problems to showcase its applicability. The analysis of performance, convergence behaviors, and the computational complexity of the proposed AOA have been evaluated by different scenarios. Experimental results show that the AOA provides very promising results in solving challenging optimization problems compared with eleven other well-known optimization algorithms. Source codes of AOA are publicly available at and .
Abualigah, L, Diabat, A, Sumari, P & Gandomi, AH 2021, 'A Novel Evolutionary Arithmetic Optimization Algorithm for Multilevel Thresholding Segmentation of COVID-19 CT Images', Processes, vol. 9, no. 7, pp. 1155-1155.
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One of the most crucial aspects of image segmentation is multilevel thresholding. However, multilevel thresholding becomes increasingly more computationally complex as the number of thresholds grows. In order to address this defect, this paper proposes a new multilevel thresholding approach based on the Evolutionary Arithmetic Optimization Algorithm (AOA). The arithmetic operators in science were the inspiration for AOA. DAOA is the proposed approach, which employs the Differential Evolution technique to enhance the AOA local research. The proposed algorithm is applied to the multilevel thresholding problem, using Kapur’s measure between class variance functions. The suggested DAOA is used to evaluate images, using eight standard test images from two different groups: nature and CT COVID-19 images. Peak signal-to-noise ratio (PSNR) and structural similarity index test (SSIM) are standard evaluation measures used to determine the accuracy of segmented images. The proposed DAOA method’s efficiency is evaluated and compared to other multilevel thresholding methods. The findings are presented with a number of different threshold values (i.e., 2, 3, 4, 5, and 6). According to the experimental results, the proposed DAOA process is better and produces higher-quality solutions than other comparative approaches. Moreover, it achieved better-segmented images, PSNR, and SSIM values. In addition, the proposed DAOA is ranked the first method in all test cases.
Abualigah, L, Diabat, A, Sumari, P & Gandomi, AH 2021, 'Applications, Deployments, and Integration of Internet of Drones (IoD): A Review', IEEE Sensors Journal, vol. 21, no. 22, pp. 25532-25546.
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The Internet of Drones (IoD) has become a hot research topic in academia, industry, and management in current years due to its wide potential applications, such as aerial photography, civilian, and military. This paper presents a comprehensive survey of IoD and its applications, deployments, and integration. We focused in this review on two main sides; IoD Applications include smart cities surveillance, cloud and fog frameworks, unmanned aerial vehicles, wireless sensor networks, networks, mobile computing, and business paradigms; integration of IoD includes privacy protection, security authentication, neural network, blockchain, and optimization based-method. A discussion highlights the hot research topics and problems to help researchers interested in this area in their future works. The keywords that have been used in this paper are Internet of Drones.
Abualigah, L, Elaziz, MA, Hussien, AG, Alsalibi, B, Jalali, SMJ & Gandomi, AH 2021, 'Lightning search algorithm: a comprehensive survey', Applied Intelligence, vol. 51, no. 4, pp. 2353-2376.
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The lightning search algorithm (LSA) is a novel meta-heuristic optimization method, which is proposed in 2015 to solve constraint optimization problems. This paper presents a comprehensive survey of the applications, variants, and results of the so-called LSA. In LSA, the best-obtained solution is defined to improve the effectiveness of the fitness function through the optimization process by finding the minimum or maximum costs to solve a specific problem. Meta-heuristics have grown the focus of researches in the optimization domain, because of the foundation of decision-making and assessment in addressing various optimization problems. A review of LSA variants is displayed in this paper, such as the basic, binary, modification, hybridization, improved, and others. Moreover, the classes of the LSA’s applications include the benchmark functions, machine learning applications, network applications, engineering applications, and others. Finally, the results of the LSA is compared with other optimization algorithms published in the literature. Presenting a survey and reviewing the LSA applications is the chief aim of this survey paper.
Abualigah, L, Gandomi, AH, Elaziz, MA, Hamad, HA, Omari, M, Alshinwan, M & Khasawneh, AM 2021, 'Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering', Electronics, vol. 10, no. 2, pp. 101-101.
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This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures. These Artificial Intelligence (AI) algorithms are recognized as promising swarm intelligence methods due to their successful ability to solve machine learning problems, especially text clustering problems. This paper reviews all of the relevant literature on meta-heuristic-based text clustering applications, including many variants, such as basic, modified, hybridized, and multi-objective methods. As well, the main procedures of text clustering and critical discussions are given. Hence, this review reports its advantages and disadvantages and recommends potential future research paths. The main keywords that have been considered in this paper are text, clustering, meta-heuristic, optimization, and algorithm.
Abualigah, L, Yousri, D, Abd Elaziz, M, Ewees, AA, Al-qaness, MAA & Gandomi, AH 2021, 'Aquila Optimizer: A novel meta-heuristic optimization algorithm', Computers & Industrial Engineering, vol. 157, pp. 107250-107250.
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This paper proposes a novel population-based optimization method, called Aquila Optimizer (AO), which is inspired by the Aquila's behaviors in nature during the process of catching the prey. Hence, the optimization procedures of the proposed AO algorithm are represented in four methods; selecting the search space by high soar with the vertical stoop, exploring within a diverge search space by contour flight with short glide attack, exploiting within a converge search space by low flight with slow descent attack, and swooping by walk and grab prey. To validate the new optimizer's ability to find the optimal solution for different optimization problems, a set of experimental series is conducted. For example, during the first experiment, AO is applied to find the solution of well-known 23 functions. The second and third experimental series aims to evaluate the AO's performance to find solutions for more complex problems such as thirty CEC2017 test functions and ten CEC2019 test functions, respectively. Finally, a set of seven real-world engineering problems are used. From the experimental results of AO that compared with well-known meta-heuristic methods, the superiority of the developed AO algorithm is observed. Matlab codes of AO are available at https://www.mathworks.com/matlabcentral/fileexchange/89381-aquila-optimizer-a-meta-heuristic-optimization-algorithm and Java codes are available at https://www.mathworks.com/matlabcentral/fileexchange/89386-aquila-optimizer-a-meta-heuristic-optimization-algorithm.
Aburas, MM, Ho, YM, Pradhan, B, Salleh, AH & Alazaiza, MYD 2021, 'Spatio-temporal simulation of future urban growth trends using an integrated CA-Markov model', Arabian Journal of Geosciences, vol. 14, no. 2.
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Abu-Ulbeh, W, Altalhi, M, Abualigah, L, Almazroi, AA, Sumari, P & Gandomi, AH 2021, 'Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations', Electronics, vol. 10, no. 14, pp. 1670-1670.
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Cyberstalking is a growing anti-social problem being transformed on a large scale and in various forms. Cyberstalking detection has become increasingly popular in recent years and has technically been investigated by many researchers. However, cyberstalking victimization, an essential part of cyberstalking, has empirically received less attention from the paper community. This paper attempts to address this gap and develop a model to understand and estimate the prevalence of cyberstalking victimization. The model of this paper is produced using routine activities and lifestyle exposure theories and includes eight hypotheses. The data of this paper is collected from the 757 respondents in Jordanian universities. This review paper utilizes a quantitative approach and uses structural equation modeling for data analysis. The results revealed a modest prevalence range is more dependent on the cyberstalking type. The results also indicated that proximity to motivated offenders, suitable targets, and digital guardians significantly influences cyberstalking victimization. The outcome from moderation hypothesis testing demonstrated that age and residence have a significant effect on cyberstalking victimization. The proposed model is an essential element for assessing cyberstalking victimization among societies, which provides a valuable understanding of the prevalence of cyberstalking victimization. This can assist the researchers and practitioners for future research in the context of cyberstalking victimization.
Abuzied, SM & Pradhan, B 2021, 'Hydro-geomorphic assessment of erosion intensity and sediment yield initiated debris-flow hazards at Wadi Dahab Watershed, Egypt', Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, vol. 15, no. 3, pp. 221-246.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. This study attempts to assess slope and channel erosion for modelling their implications on debris-flow occurrences in Wadi Dahab Watershed (WDW). Remote sensing and Geographic Information System (GIS) were integrated to appraise erosion rates from a hillslope and channel storage throughout WDW. A mass-wasting database was built initially for modelling hazard zones and validating the final map using a bivariate statistical analysis. An Erosion Hazard Model (EHM) was developed to evaluate the erosion intensity and sediment yield throughout WDW and to prognosticate the hazard zones due to debris-flows. The EHM was developed based on hydrological and geomorphic controls which are responsible for disintegrating bedrocks, delivering detritus downslopes, and accelerating debris through channels. Multi-source datasets, including topographic and geologic maps, climatic, satellite images, aerial photographs, and field-based datasets, were used to derive factors associated with the hydro-geomorphic processes. A spatial prediction of erosion intensity was obtained by the integration of both static and dynamic factors generated hazards in GIS platform. The erosion intensity map classifies WDW relatively to five intensity zones in which the most hazardous zones are distributed in steep sloping terrains and structurally controlled channels covered by metamorphic and clastic rocks. The erosion intensity map was correlated and tested against the debris-flows dataset which was not used during the spatial modelling process. The statistical correlation analysis has confirmed that the debris-flow locations increase exponentially in the high erosion intensity zones. The holistic integration approach provides the promising model for forecasting critical zones prone to erosion intensity and their associated hazards in WDW.
Accordini, D, Cagno, E & Trianni, A 2021, 'Identification and characterization of decision-making factors over industrial energy efficiency measures in electric motor systems', Renewable and Sustainable Energy Reviews, vol. 149, pp. 111354-111354.
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Energy efficiency measures in electric motor systems are scarcely implemented and previous literature has largely overlooked the characterizing factors responsible for their adoption in industrial operations. The present study, after a comprehensive literature review, aims at supporting research by offering a framework for the identification of the factors that should be assessed when considering the adoption of electric motor systems' energy efficiency measures. The proposed factors are clustered in ten categories, namely: contextual factors, compatibility, economy, energy savings, production-related factors, operations-related factors, synergies, complexity, personnel and additional technical factors. After a preliminary empirical validation, the proposed framework has been applied in a selected sample of manufacturing firms. Findings show that factors more closely related to the firm's production and operations result most critical for the adoption of energy efficiency measures. However, the adoption process is also deeply influenced by their complexity or compatibility to the specific context application, therefore calling for an exhaustive assessment. The adoption of the framework would have reversed some firm's decisions over the initial uptake of energy efficiency measures that proved to have critical issues for their implementation. Therefore, the proposed framework provides additional support and further value to decision-makers especially for non-energy intensive firms, where the impact on non-energy production resources becomes more important, and small-medium enterprises usually present greater difficulties for a holistic assessment of energy efficiency measures. The study concludes with main implications for research and policy-making from the present study as well as suggestions for future research.
Afsari, M, Shon, HK & Tijing, LD 2021, 'Janus membranes for membrane distillation: Recent advances and challenges', Advances in Colloid and Interface Science, vol. 289, pp. 102362-102362.
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© 2021 Elsevier B.V. Membrane distillation (MD) is a promising hybrid thermal-membrane separation technology that can efficiently produce freshwater from seawater or contaminated wastewater. However, the relatively low flux and the presence of fouling or wetting agents in feed solution negate the applicability of MD for long term operation. In recent years, ‘two-faced’ membranes or Janus membranes have shown promising potential to decrease wetting and fouling problem of common MD system as well as enhance the flux performance. In this review, a comprehensive study was performed to investigate the various fabrication, modification, and novel design processes to prepare Janus membranes and discuss their performance in desalination and wastewater treatment utilizing MD. The promising potential, challenges and future prospects relating to the design and use of Janus membranes for MD are also tackled in this review.
Aftab, MU, Oluwasanmi, A, Alharbi, A, Sohaib, O, Nie, X, Qin, Z & Son, NT 2021, 'Secure and dynamic access control for the Internet of Things (IoT) based traffic system.', PeerJ Comput. Sci., vol. 7, pp. e471-e471.
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Today, the trend of the Internet of Things (IoT) is increasing through the use of smart devices, vehicular networks, and household devices with internet-based networks. Specifically, the IoT smart devices and gadgets used in government and military are crucial to operational success. Communication and data sharing between these devices have increased in several ways. Similarly, the threats of information breaches between communication channels have also surged significantly, making data security a challenging task. In this context, access control is an approach that can secure data by restricting unauthorized users. Various access control models exist that can effectively implement access control yet, and there is no single state-of-the-art model that can provide dynamicity, security, ease of administration, and rapid execution all at once. In combating this loophole, we propose a novel secure and dynamic access control (SDAC) model for the IoT networks (smart traffic control and roadside parking management). Our proposed model allows IoT devices to communicate and share information through a secure means by using wired and wireless networks (Cellular Networks or Wi-Fi). The effectiveness and efficiency of the proposed model are demonstrated using mathematical models and discussed with many example implementations.
Afzal, MU, Lalbakhsh, A & Esselle, KP 2021, 'Method to Enhance Directional Propagation of Circularly Polarized Antennas by Making Near-Electric Field Phase More Uniform', IEEE Transactions on Antennas and Propagation, vol. 69, no. 8, pp. 4447-4456.
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A new approach to significantly increase the uniformity in aperture phase distribution, through time synchronization in near-electric field, of circularly polarized (CP) antennas is presented. The method uses the phase of the CP electric field vectors, obtained through full-wave numerical simulations, and does not rely on any approximation such as ray tracing. The near-field data is post-processed to extract the relative phase difference that exist due to the unsynchronized rotations of the electric field vectors in a plane parallel to the antenna aperture. The phase delay is compensated with a thin time-synchronizing metasurface (TSM) that has a 2D array of time-delay cells. The method is demonstrated with a prototype made of two-port patch antenna, which is fed through a hybrid junction, and a TSM that is placed at one wavelength spacing above the patch. When TSM is used with patch antenna, its uniform phase area increases manyfold thus increasing far-field directivity from 6.8 dBic to 22 dBic.
Agarwal, A, Foster, SJ & Stewart, MG 2021, 'Model error and reliability of reinforced concrete beams in shear designed according to the Modified Compression Field Theory', Structural Concrete, vol. 22, no. 6, pp. 3711-3726.
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AbstractModel error (or model uncertainty) were probabilistically characterized for modified compression field theory (MCFT) Simplified and General Method approaches using experimental databases that contained reinforced concrete (RC) beams having shear failures with and without stirrups (168 and 368 specimens, respectively). It was found that when compared to the design shear model currently used in ACI‐318, the General Method produced low model error variability indicating better consistency for the determination of shear strength. Structural reliabilities were then calculated for RC beams in shear designed to MCFT General Method (AASHTO LRFD, CSA A23.3‐14, AS3600‐2018) for a live‐to‐dead load ratio between 0 and 5, and for capacity reduction factor ϕ = 0.70, 0.75, and 0.80. It was concluded that the ϕ‐factor for shear failure for Australian standards can be increased from 0.70 to 0.75 for RC beams with stirrups, providing a 7.1% increase in the design shear capacity and contributing to sustainable design and reduction in greenhouse gas emissions due to more efficient usage of materials.
Aghlmandi, A, Nikshad, A, Safaralizadeh, R, Warkiani, ME, Aghebati-Maleki, L & Yousefi, M 2021, 'Microfluidics as efficient technology for the isolation and characterization of stem cells.', EXCLI J, vol. 20, pp. 426-443.
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The recent years have been passed with significant progressions in the utilization of microfluidic technologies for cellular investigations. The aim of microfluidics is to mimic small-scale body environment with features like optical transparency. Microfluidics can screen and monitor different cell types during culture and study cell function in response to stimuli in a fully controlled environment. No matter how the microfluidic environment is similar to in vivo environment, it is not possible to fully investigate stem cells behavior in response to stimuli during cell proliferation and differentiation. Researchers have used stem cells in different fields from fundamental researches to clinical applications. Many cells in the body possess particular functions, but stem cells do not have a specific task and can turn into almost any type of cells. Stem cells are undifferentiated cells with the ability of changing into specific cells that can be essential for the body. Researchers and physicians are interested in stem cells to use them in testing the function of the body's systems and solving their complications. This review discusses the recent advances in utilizing microfluidic techniques for the analysis of stem cells, and mentions the advantages and disadvantages of using microfluidic technology for stem cell research.
Agyekum, EB, Kumar, NM, Mehmood, U, Panjwani, MK, Haes Alhelou, H, Adebayo, TS & Al-Hinai, A 2021, 'Decarbonize Russia — A Best–Worst Method approach for assessing the renewable energy potentials, opportunities and challenges', Energy Reports, vol. 7, pp. 4498-4515.
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Ahammad, NA, Badruddin, IA, Kamangar, S, Khaleed, HMT, Saleel, CA & Mahlia, TMI 2021, 'Heat Transfer and Entropy in a Vertical Porous Plate Subjected to Suction Velocity and MHD', Entropy, vol. 23, no. 8, pp. 1069-1069.
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This article presents an investigation of heat transfer in a porous medium adjacent to a vertical plate. The porous medium is subjected to a magnetohydrodynamic effect and suction velocity. The governing equations are nondepersonalized and converted into ordinary differential equations. The resulting equations are solved with the help of the finite difference method. The impact of various parameters, such as the Prandtl number, Grashof number, permeability parameter, radiation parameter, Eckert number, viscous dissipation parameter, and magnetic parameter, on fluid flow characteristics inside the porous medium is discussed. Entropy generation in the medium is analyzed with respect to various parameters, including the Brinkman number and Reynolds number. It is noted that the velocity profile decreases in magnitude with respect to the Prandtl number, but increases with the radiation parameter. The Eckert number has a marginal effect on the velocity profile. An increased radiation effect leads to a reduced thermal gradient at the hot surface.
Ahmad, S, Alnowibet, K, Alqasem, L, Merigo, JM & Zaindin, M 2021, 'Generalized OWA operators for uncertain queuing modeling with application in healthcare', Soft Computing, vol. 25, no. 6, pp. 4951-4962.
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© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature. The weighted averaging operators are one of the popular methods for aggregating information. In recent years, ordered weighted averaging operators (OWA) have attained a great attention by researchers. These OWA operators due to their versatility are very useful to model many real world situations. Several extensions of OWA operators are presented in the literature which can handle a situation with uncertainty. Although many queuing models have been proposed in numerous healthcare studies, the inclusion of OWA operators is still rare. In this research study, we propose a novel method using the uncertain generalized ordered weighted average and illustrate its application to the uncertain queue modeling in a hospital emergency room; where incoming flux of patients and the required level of service for each patient is unknown and uncertain. The model with multilateral decision making process has been described which will provide several alternatives to decision makers to select the best alternative for their challenging situations. The proposed method has resulted an improved performance of the queuing system, increased customer satisfaction as well as a significant reduction in the operational cost. This study will enable decision makers to operate a flexible and cost-effective system in the event of uncertainty, uncontrollable and unpredicted situations.
Ahmadi Choukolaei, H, Jahangoshai Rezaee, M, Ghasemi, P & Saberi, M 2021, 'Efficient Crisis Management by Selection and Analysis of Relief Centers in Disaster Integrating GIS and Multicriteria Decision Methods: A Case Study of Tehran', Mathematical Problems in Engineering, vol. 2021, pp. 1-22.
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In Iran, location is usually done by temporary relief organizations without considering the necessary standards or conditions. The inappropriate and unscientific location may have led to another catastrophe, even far greater than the initial tragedy. In this study, the proposed locations of crisis management in the region and the optimal points proposed by the Geographic Information System (GIS), taking into account the opinions of experts and without the opinion of experts, were evaluated according to 18 criteria. First, the optimal areas have been evaluated according to standard criteria extracted by GIS and the intended locations of the region for accommodation in times of crisis. Then, the position of each place is calculated concerning each criterion. The resulting matrix of optimal options was qualitatively entered into the Preference Ranking Organization Method for Evaluation (PROMETHEE) for analysis. The triangular fuzzy aggregation method for weighting and standard classification of criteria for extracting optimal areas using GIS and integrating entropy and the Multiobjective Optimization Based on Ratio Analysis (MOORA) method for prioritizing places in the region are considered in this research. Finally, by applying constraints and using net input and output flows, optimal and efficient options are identified by PROMETHEE V. Among the research options, only four options were optimal and efficient. A case study of the Tehran metropolis is provided to show the ability of the proposed approach for selecting the points in three modes, with/without applying weights and applying crisis management.
Ahmadi, VE, Butun, I, Altay, R, Bazaz, SR, Alijani, H, Celik, S, Warkiani, ME & Koşar, A 2021, 'The effects of baffle configuration and number on inertial mixing in a curved serpentine micromixer: Experimental and numerical study', Chemical Engineering Research and Design, vol. 168, pp. 490-498.
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Recently, the application of micromixers in microfluidic systems including chemical and biological assays has been widely accomplished. Passive micromixers, benefitting from the low-cost and a less-complex fabrication process, rely solely on their geometry. In particular, Dean vortices generated in curved microchannels enhance the mixing performance through chaotic advection. To improve the mixing performance at relatively low Reynolds numbers (i.e. 1 ≤ Re ≤ 50), this study introduces baffles into the side walls of curved serpentine micromixers with curvature angles of 280°, which constantly agitate, stretch and fold the fluids streams. Six different baffle configurations were designed and the effects of geometry and the number of baffles were investigated both experimentally and numerically. According to the experimental results, while the maximum outlet mixing index of the micromixer with no baffles was 0.61, that of the micromixer with quasi-rectangular baffles was 0.98 at a low Reynolds number of 20, indicating the major contribution of the generated chaotic advection by baffles. Furthermore, numerical results, which were in good agreement with experimental results, shed more light onto the mechanisms involved in micromixing in terms of the flow behavior and mixing index.
Ahmadianfar, I, Heidari, AA, Gandomi, AH, Chu, X & Chen, H 2021, 'RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method', Expert Systems with Applications, vol. 181, pp. 115079-115079.
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Ahmed, AA, Pradhan, B, Chakraborty, S & Alamri, A 2021, 'Developing vehicular traffic noise prediction model through ensemble machine learning algorithms with GIS', Arabian Journal of Geosciences, vol. 14, no. 16.
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Ahmed, AA, Pradhan, B, Chakraborty, S, Alamri, A & Lee, C-W 2021, 'An Optimized Deep Neural Network Approach for Vehicular Traffic Noise Trend Modeling', IEEE Access, vol. 9, pp. 107375-107386.
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Ahmed, F, Afzal, MU, Hayat, T, Esselle, KP & Thalakotuna, DN 2021, 'A dielectric free near field phase transforming structure for wideband gain enhancement of antennas', Scientific Reports, vol. 11, no. 1.
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AbstractThe gain of some aperture antennas can be significantly increased by making the antenna near-field phase distribution more uniform, using a phase-transformation structure. A novel dielectric-free phase transforming structure (DF-PTS) is presented in this paper for this purpose, and its ability to correct the aperture phase distribution of a resonant cavity antenna (RCA) over a much wider bandwidth is demonstrated. As opposed to printed multilayered metasurfaces, all the cells in crucial locations of the DF-PTS have a phase response that tracks the phase error of the RCA over a large bandwidth, and in addition have wideband transmission characteristics, resulting in a wideband antenna system. The new DF-PTS, made of three thin metal sheets each containing modified-eight-arm-asterisk-shaped slots, is significantly stronger than the previous DF-PTS, which requires thin and long metal interconnects between metal patches. The third advantage of the new DF-PTS is, all phase transformation cells in it are highly transparent, each with a transmission magnitude greater than − 1 dB at the design frequency, ensuring excellent phase correction with minimal effect on aperture amplitude distribution. With the DF-PTS, RCA gain increases to 20.1 dBi, which is significantly greater than its 10.7 dBi gain without the DF-PTS. The measured 10-dB return loss bandwidth and the 3-dB gain bandwidth of the RCA with DF-PTS are 46% and 12%, respectively.
Ahmed, J, Jaman, MH, Saha, G & Ghosh, P 2021, 'Effect of environmental and socio-economic factors on the spreading of COVID-19 at 70 cities/provinces', Heliyon, vol. 7, no. 5, pp. e06979-e06979.
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Ahmed, MB, Rahman, MS, Alom, J, Hasan, MDS, Johir, MAH, Mondal, MIH, Lee, D-Y, Park, J, Zhou, JL & Yoon, M-H 2021, 'Microplastic particles in the aquatic environment: A systematic review', Science of The Total Environment, vol. 775, pp. 145793-145793.
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Microplastics (MPs) pollution has become one of the most severe environmental concerns today. MPs persist in the environment and cause adverse effects in organisms. This review aims to present a state-of-the-art overview of MPs in the aquatic environment. Personal care products, synthetic clothing, air-blasting facilities and drilling fluids from gas-oil industries, raw plastic powders from plastic manufacturing industries, waste plastic products and wastewater treatment plants act as the major sources of MPs. For MPs analysis, pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS), Py-MS methods, Raman spectroscopy, and FT-IR spectroscopy are regarded as the most promising methods for MPs identification and quantification. Due to the large surface area to volume ratio, crystallinity, hydrophobicity and functional groups, MPs can interact with various contaminants such as heavy metals, antibiotics and persistent organic contaminants. Among different physical and biological treatment technologies, the MPs removal performance decreases as membrane bioreactor (> 99%) > activated sludge process (~98%) > rapid sand filtration (~97.1%) > dissolved air floatation (~95%) > electrocoagulation (> 90%) > constructed wetlands (88%). Chemical treatment methods such as coagulation, magnetic separations, Fenton, photo-Fenton and photocatalytic degradation also show moderate to high efficiency of MP removal. Hybrid treatment technologies show the highest removal efficacies of MPs. Finally, future research directions for MPs are elaborated.
Ahmed, N, Hoque, MA-A, Pradhan, B & Arabameri, A 2021, 'Spatio-Temporal Assessment of Groundwater Potential Zone in the Drought-Prone Area of Bangladesh Using GIS-Based Bivariate Models', Natural Resources Research, vol. 30, no. 5, pp. 3315-3337.
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Ahmed, N, Howlader, N, Hoque, MA-A & Pradhan, B 2021, 'Coastal erosion vulnerability assessment along the eastern coast of Bangladesh using geospatial techniques', Ocean & Coastal Management, vol. 199, pp. 105408-105408.
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Ahmed, SF, Mofijur, M, Nuzhat, S, Chowdhury, AT, Rafa, N, Uddin, MA, Inayat, A, Mahlia, TMI, Ong, HC, Chia, WY & Show, PL 2021, 'Recent developments in physical, biological, chemical, and hybrid treatment techniques for removing emerging contaminants from wastewater', Journal of Hazardous Materials, vol. 416, pp. 125912-125912.
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Emerging contaminants (ECs) in wastewater have recently attracted the attention of researchers as they pose significant risks to human health and wildlife. This paper presents the state-of-art technologies used to remove ECs from wastewater through a comprehensive review. It also highlights the challenges faced by existing EC removal technologies in wastewater treatment plants and provides future research directions. Many treatment technologies like biological, chemical, and physical approaches have been advanced for removing various ECs. However, currently, no individual technology can effectively remove ECs, whereas hybrid systems have often been found to be more efficient. A hybrid technique of ozonation accompanied by activated carbon was found significantly effective in removing some ECs, particularly pharmaceuticals and pesticides. Despite the lack of extensive research, nanotechnology may be a promising approach as nanomaterial incorporated technologies have shown potential in removing different contaminants from wastewater. Nevertheless, most existing technologies are highly energy and resource-intensive as well as costly to maintain and operate. Besides, most proposed advanced treatment technologies are yet to be evaluated for large-scale practicality. Complemented with techno-economic feasibility studies of the treatment techniques, comprehensive research and development are therefore necessary to achieve a full and effective removal of ECs by wastewater treatment plants.
Ahmed, SF, Mofijur, M, Tarannum, K, Chowdhury, AT, Rafa, N, Nuzhat, S, Kumar, PS, Vo, D-VN, Lichtfouse, E & Mahlia, TMI 2021, 'Biogas upgrading, economy and utilization: a review', Environmental Chemistry Letters, vol. 19, no. 6, pp. 4137-4164.
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Biogas production is rising in the context of fossil fuel decline and the future circular economy, yet raw biogas requires purification steps before use. Here, we review biogas upgrading using physical, chemical and biological methods such as water scrubbing, physical absorption, pressure swing adsorption, cryogenic separation, membrane separation, chemical scrubbing, chemoautotrophic methods, photosynthetic upgrading and desorption. We also discuss their techno-economic feasibility. We found that physical and chemical upgrading technologies are near-optimal, but still require high energy and resources. Biological methods are less explored despite their promising potential. High-pressure water scrubbing is more economic for small-sized plants, whereas potassium carbonate scrubbing provides the maximum net value for large-sized plants.
Ahmed, T, Hassan, S, F. Hasan, M, M. Molla, M, A. Taher, M & C. Saha, S 2021, 'Lattice Boltzmann Simulation of Magnetic Field Effect on Electrically Conducting Fluid at Inclined Angles in Rayleigh-B閚ard Convection', Energy Engineering, vol. 118, no. 1, pp. 15-36.
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Akar, S, Taheri, A, Bazaz, R, Warkiani, E & Shaegh, M 2021, 'Twisted architecture for enhancement of passive micromixing in a wide range of Reynolds numbers', Chemical Engineering and Processing - Process Intensification, vol. 160, pp. 108251-108251.
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Micromixers present essential roles in providing homogeneous mixtures in microfluidic systems. It is of critical importance to introduce strategies to increase the mixing efficiency of passive micromixers, capable of operating at high efficiency levels over a wide range of Reynolds (Re) numbers. To this end, a novel design of twisted microstructure for enhancing mixing performance in a wide range of Reynolds numbers was introduced. Incorporating this microstructure with straight and serpentine micromixers was numerically and experimentally investigated. Micromixers with twisted microstructures were fabricated in Poly(methyl methacrylate) (PMMA) using high-precision micromilling. The effects of Reynolds number, pitch number, and channel hydraulic diameter on mixing efficiency and pressure drop were analyzed. Results revealed that the twist architecture could increase mixing efficiency significantly with very low pressure drop of up to 0.89 kPa. The twisted serpentine micromixer could narrow the disparity of mixing efficiency from 87% (Re = 10) to 98% (Re = 400). High mixing efficiency could be achieved within a length of 4.8 mm in the twisted serpentine micromixer with a hydraulic diameter of 300 μm. Taken together, the twisted structure could be incorporated with various geometries to create compact and high efficiency micromixers for operation in a wide range of Re numbers.
Akbari, M, Meshram, SG, Krishna, RS, Pradhan, B, Shadeed, S, Khedher, KM, Sepehri, M, Ildoromi, AR, Alimerzaei, F & Darabi, F 2021, 'Identification of the Groundwater Potential Recharge Zones Using MCDM Models: Full Consistency Method (FUCOM), Best Worst Method (BWM) and Analytic Hierarchy Process (AHP)', Water Resources Management, vol. 35, no. 14, pp. 4727-4745.
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Akella, A, Singh, AK, Leong, D, Lal, S, Newton, P, Clifton-Bligh, R, Mclachlan, CS, Gustin, SM, Maharaj, S, Lees, T, Cao, Z & Lin, C-T 2021, 'Classifying Multi-Level Stress Responses From Brain Cortical EEG in Nurses and Non-Health Professionals Using Machine Learning Auto Encoder', IEEE Journal of Translational Engineering in Health and Medicine, vol. 9, pp. 1-9.
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Objective
Mental stress is a major problem in our society and has become an area of interest for many psychiatric researchers. One primary research focus area is the identification of bio-markers that not only identify stress but also predict the conditions (or tasks) that cause stress. Electroencephalograms (EEGs) have been used for a long time to study and identify bio-markers. While these bio-markers have successfully predicted stress in EEG studies for binary conditions, their performance is suboptimal for multiple conditions of stress.
Methods
To overcome this challenge, we propose using latent based representations of the bio-markers, which have been shown to significantly improve EEG performance compared to traditional bio-markers alone. We evaluated three commonly used EEG based bio-markers for stress, the brain load index (BLI), the spectral power values of EEG frequency bands (alpha, beta and theta), and the relative gamma (RG), with their respective latent representations using four commonly used classifiers.
Results
The results show that spectral power value based bio-markers had a high performance with an accuracy of 83%, while the respective latent representations had an accuracy of 91%.
Akther, N, Kawabata, Y, Lim, S, Yoshioka, T, Phuntsho, S, Matsuyama, H & Shon, HK 2021, 'Effect of graphene oxide quantum dots on the interfacial polymerization of a thin-film nanocomposite forward osmosis membrane: An experimental and molecular dynamics study', Journal of Membrane Science, vol. 630, pp. 119309-119309.
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We report an ultra-low loading of graphene oxide quantum dots (GQDs) into the polyamide (PA) layer of an outer-selective hollow fiber (OSHF) thin-film composite (TFC) membrane using the vacuum-assisted interfacial polymerization (VAIP) technique to improve the water permeability of OSHF TFC membranes without sacrificing membrane selectivity. Experimental results showed that GQD loading in the PA layer influenced membrane performance. The membrane with a GQD loading of 5 mg L (TFN5) demonstrated an optimal water flux of 30.9 L m h and a specific reverse solute flux (SRSF) of 0.12 g L . To investigate the effect of GQDs on the interfacial polymerization (IP) reaction and membrane performance, molecular dynamics (MD) simulation was employed at the water-hexane and water-PA interfaces. The simulation results showed that GQDs decreased the reaction rate during the IP process by reducing the diffusivities of m-phenylenediamine (MPD) and trimesoyl chloride (TMC). Additionally, GQDs reduced water permeability by acting as barriers to water molecules when present at a high concentration near the PA layer surface. At a very high loading, GQDs aggregated at the water-hexane interface and reduced the membrane selectivity by forming non-selective voids at the interface between the PA layer and GQDs. Together with the experimental findings, the MD simulation results delivered a good insight into the GQDs' effect on the TFC membrane's surface and transport properties at both macroscopic and microscopic levels. −1 −2 −1 −1
Akther, N, Lin, Y, Wang, S, Phuntsho, S, Fu, Q, Ghaffour, N, Matsuyama, H & Shon, HK 2021, 'In situ ultrathin silica layer formation on polyamide thin-film composite membrane surface for enhanced forward osmosis performances', Journal of Membrane Science, vol. 620, pp. 118876-118876.
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© 2020 Elsevier B.V. Polyamide (PA) based thin-film composite (TFC) membranes experience a high degree of organic fouling due to their hydrophobic and rough membrane surfaces during forward osmosis (FO) process. In this study, an ultrathin silica layer was grown in situ on the PA surface to enhance the antifouling property of TFC membrane by silicification process. Surface characterization confirmed the development of a silica layer on the PA surface. The superhydrophilic surface of silica-deposited TFC membrane (contact angle of 20°) with 3 h silicification time (STFC-3h) displayed a 53% higher water flux than the pristine TFC membrane without significantly affecting the membrane selectivity. The silica-modified TFC FO membranes also exhibited excellent stability when subjected to long-term cross-flow shear stress rinsing using deionized (DI) water, including exposure to salty, acidic and basic solutions. Moreover, the fouling tests showed that STFC-3h membrane lost only 4.2%, 9.1% and 12.1% of its initial flux with bovine serum albumin (BSA), humic acid (HA) and sodium alginate (SA), respectively, which are considerably lower compared to the pristine TFC FO membrane where flux losses were 18.7%, 23.2% and 37.2%, respectively. The STFC-3h membrane also revealed higher flux recovery ratio (FRR) of 99.6%, 96.9% and 94.4% with BSA, HA and SA, respectively, after physical cleaning than the pristine membrane (91.4%, 88.7%, and 81.2%, respectively). Overall, the in situ formation of an ultrathin hydrophilic silica layer on the PA surface reported in this work shows that the TFC membrane's water flux and antifouling property could be improved without diminishing the membrane selectivity.
Akther, N, Sanahuja-Embuena, V, Górecki, R, Phuntsho, S, Helix-Nielsen, C & Shon, HK 2021, 'Employing the synergistic effect between aquaporin nanostructures and graphene oxide for enhanced separation performance of thin-film nanocomposite forward osmosis membranes', Desalination, vol. 498, pp. 114795-114795.
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Al zahrani, S, Islam, MS & Saha, SC 2021, 'Heat transfer enhancement investigation in a novel flat plate heat exchanger', International Journal of Thermal Sciences, vol. 161, pp. 106763-106763.
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Al zahrani, S, Islam, MS & Saha, SC 2021, 'Heat transfer enhancement of modified flat plate heat exchanger', Applied Thermal Engineering, vol. 186, pp. 116533-116533.
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Al-Abadi, AM, Fryar, AE, Rasheed, AA & Pradhan, B 2021, 'Assessment of groundwater potential in terms of the availability and quality of the resource: a case study from Iraq', Environmental Earth Sciences, vol. 80, no. 12, pp. 1-22.
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A semi-confined aquifer from Kirkuk Governorate, northern Iraq was taken as a case study to map groundwater potential in terms of both the availability and quality of the resource. In terms of quantity, five machine learning (ML) algorithms were used to model the relationship between locations of 1031 wells with specific-capacity data and nine influential groundwater occurrence factors. The algorithms used were linear discriminant analysis, classification and regression trees, linear vector quantization, random forest, and K-nearest neighbor. The groundwater occurrence factors used were elevation, slope, curvature, aspect, aquifer transmissivity, specific storage, soil, geology, and groundwater depth. Analysis of the worthiness of the factors used in the analysis by the information gain ratio indicated that five out of nine factors were worthy (average merit > 0): groundwater depth, elevation, transmissivity, specific storage, and soil. The remaining factors were non-worthy (average merit = 0) and thus they were removed from the analysis. The performance of the five ML algorithms was investigated using accuracy and kappa as evaluation metrics. Applying the models in the carte package of R software indicated that random forest was the best model. The probability values of this model were used for mapping quantitative groundwater potential after classification into three zones: poor, moderate, and excellent. Groundwater quality for drinking was modeled using the water quality index and the weights of the chemical constituents used (pH, TDS, Ca2+, Mg2+, Na+, SO42-, Cl -, and NO3-) were assigned using entropy information theory. A map of the groundwater quality index revealed five classes: < 50 (excellent), 50–100 (good), 100–150 (moderate), 150–200 (poor), and > 200 (extremely poor). Combining the groundwater quality index map with the groundwater potential map using summation operators revealed three zones of groundwater potential: poor, moderate, and exc...
Alabsi, MI & Gill, AQ 2021, 'A Review of Passenger Digital Information Privacy Concerns in Smart Airports', IEEE Access, vol. 9, no. 99, pp. 33769-33781.
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Alanazi, F, Gay, V, N., M & Alturki, R 2021, 'Modelling Health Process and System Requirements Engineering for Better e-Health Services in Saudi Arabia', International Journal of Advanced Computer Science and Applications, vol. 12, no. 1, pp. 549-559.
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This systematic review aimed to examine the published works on e-health modelling system requirements and suggest one applicable to Saudi Arabia. PRISMA method was adopted to search, screen and select the papers to be included in this review. Google Scholar was used as the search engine to collect relevant works. From an initial 74 works, 20 were selected after all screening procedures as per PRISMA flow diagram. The 20 selected works were discussed under various sections. The review revealed that goal setting is the first step. Using the goals, a model can be created based on which system requirements can be elicited. Different research used different approaches within this broad framework and applied the procedures to varying healthcare contexts. Based on the findings, an attempt has been made to set the goals and elicit the system requirements for a diabetes self-management model for the entire country in Saudi Arabian context. This is a preliminary model which needs to be tested, improved and then implemented.
Al-Bared, MAM, Mustaffa, Z, Armaghani, DJ, Marto, A, Yunus, NZM & Hasanipanah, M 2021, 'Application of hybrid intelligent systems in predicting the unconfined compressive strength of clay material mixed with recycled additive', Transportation Geotechnics, vol. 30, pp. 100627-100627.
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Al-Bawi, AJ, Al-Abadi, AM, Pradhan, B & Alamri, AM 2021, 'Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers', Geomatics, Natural Hazards and Risk, vol. 12, no. 1, pp. 3035-3062.
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Gully erosion is an erosive process that contributes considerably to the shape of the earth’s surface and is a major contributor to land degradation and soil loss. This study applied a methodology for mapping gully erosion susceptibility using only topographic related attributes derived from a medium-resolution digital elevation model (DEM) and a hybrid analytical hierarchy process (AHP) and the technique for an order of preference by similarity to ideal solutions (TOPSIS) and compare the results with naïve Bayes (NB) and support vector machine learning (SVM) algorithms. A transboundary sub-basin in an arid area of southern Iraq was selected as a case study. The performance of the developed models was compared using the receiver operating characteristic curve (ROC). Results showed that the areas under the ROC were 0.933, 0.936, and 0.955 for AHP-TOPSIS, NB, and SVM with radial basis function, respectively, which indicated that the performance of simply derived AHP-TOPSIS model is similar to sophisticated NB and SVM models. Findings indicated that a medium resolution DEM and AHP-TOPSIS are a promising tool for mapping of gully erosion susceptibility.
Alderighi, T, Malomo, L, Bickel, B, Cignoni, P & Pietroni, N 2021, 'Volume decomposition for two-piece rigid casting', ACM Transactions on Graphics, vol. 40, no. 6, pp. 1-14.
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We introduce a novel technique to automatically decompose an input object's volume into a set of parts that can be represented by two opposite height fields. Such decomposition enables the manufacturing of individual parts using two-piece reusable rigid molds. Our decomposition strategy relies on a new energy formulation that utilizes a pre-computed signal on the mesh volume representing the accessibility for a predefined set of extraction directions. Thanks to this novel formulation, our method allows for efficient optimization of a fabrication-aware partitioning of volumes in a completely automatic way. We demonstrate the efficacy of our approach by generating valid volume partitionings for a wide range of complex objects and physically reproducing several of them.
Alderighi, T, Malomo, L, Bickel, B, Cignoni, P & Pietroni, N 2021, 'Volume decomposition for two-piece rigid casting.', ACM Trans. Graph., vol. 40, pp. 272:1-272:1.
Aldhshan, SRS, Abdul Maulud, KN, Wan Mohd Jaafar, WS, Karim, OA & Pradhan, B 2021, 'Energy Consumption and Spatial Assessment of Renewable Energy Penetration and Building Energy Efficiency in Malaysia: A Review', Sustainability, vol. 13, no. 16, pp. 9244-9244.
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The development of sustainable energy systems is very important to addressing the economic, environmental, and social pressures of the energy sector. Globally, buildings consume up to 40% of the world’s total energy. By 2030, it is expected to increase to 50%. Therefore, the world is facing a great challenge to overcome these problems related to global energy production. Malaysia is one of the top consumers of primary energy in Asia. In 2018, primary energy consumption for Malaysia was 3.79 quadrillion btu at an average annual rate of 4.58%. In this paper, we have carried out a detailed literature review on several previous studies of energy consumption in the world, especially in Malaysia, and how geographical information system (GIS) methods have been used for the spatial assessment of energy efficiency. Indeed, strategies of energy efficiency are essential in energy policy that could be created using various approaches used for energy savings in buildings. The findings of this review reveal that, for estimating energy consumption, exploring renewable energy sources, and investigating solar radiation, several geographic information system techniques such as multiple criteria decision analysis (MCDA), machine learning (ML), and deep learning (DL) are mainly utilized. The result indicates that the fuzzy DS method can more reliably determine the optimal PV farm locations. The 3D models are also regarded as an effective tool for estimating solar radiation, since this method generates a 3D model exportable to software tools. In addition, GIS and 3D can contribute to several purposes, such as sunlight access to buildings in urban areas, city growth prediction models and analysis of the habitability of public places.
Alempijevic, A, Vidal-Calleja, T, Falque, R, Quin, P, Toohey, E, Walmsley, B & McPhee, M 2021, 'Lean meat yield estimation using a prototype 3D imaging approach', Meat Science, vol. 181, pp. 108470-108470.
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Alfaro-García, VG, Merigó, JM, Gil-Lafuente, AM & Monge, RG 2021, 'Group-decision making with induced ordered weighted logarithmic aggregation operators', Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 1761-1772.
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This paper presents the induced generalized ordered weighted logarithmic aggregation (IGOWLA) operator, this operator is an extension of the generalized ordered weighted logarithmic aggregation (GOWLA) operator. It uses order-induced variables that modify the reordering process of the arguments included in the aggregation. The principal advantage of the introduced induced mechanism is the consideration of highly complex attitude from the decision makers. We study some families of the IGOWLA operator as measures for the characterization of the weighting vector. This paper presents the general formulation of the operator and some special cases, including the induced ordered weighted logarithmic geometric averaging (IOWLGA) operator and the induced ordered weighted logarithmic aggregation (IOWLA). Further generalizations using quasi-arithmetic mean are also proposed. Finally, an illustrative example of a group decision-making procedure using a multi-person analysis and the IGOWLA operator in the area of innovation management is analyzed.
Alfouneh, M, Ji, J & Luo, Q 2021, 'Damping design of harmonically excited flexible structures with graded materials to minimize sound pressure and radiation', Engineering Optimization, vol. 53, no. 2, pp. 348-367.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. Topology optimization is an effective method in the design of acoustic media. This article presents optimization for graded damping materials to minimize sound pressure at a reference point or sound power radiation under harmonic excitation. The Helmholtz integral equation is used to calculate an acoustic field to satisfy the Sommerfeld conditions. The equation of motion is solved using a unit dynamic load method. Formulations for the sound pressure or sound power radiation in an integral form are derived in terms of mutual kinetic and strain energy densities. These integrals lead to novel physical response functions for solving the proposed optimization problem to design graded damping materials. The response function derived for individual frequency is utilized to solve the multi-objective optimization problem of minimizing sound pressure at the reference point for excitations with a range of frequencies. Numerical examples are presented to verify the efficiency of the present formulations.
Al-Fugara, A, Mabdeh, AN, Ahmadlou, M, Pourghasemi, HR, Al-Adamat, R, Pradhan, B & Al-Shabeeb, AR 2021, 'Wildland Fire Susceptibility Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Whale Optimization Algorithm and Simulated Annealing', ISPRS International Journal of Geo-Information, vol. 10, no. 6, pp. 382-382.
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Fires are one of the most destructive forces in natural ecosystems. This study aims to develop and compare four hybrid models using two well-known machine learning models, support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS), as well as two meta-heuristic models, the whale optimization algorithm (WOA) and simulated annealing (SA) to map wildland fires in Jerash Province, Jordan. For modeling, 109 fire locations were used along with 14 relevant factors, including elevation, slope, aspect, land use, normalized difference vegetation index (NDVI), rainfall, temperature, wind speed, solar radiation, soil texture, topographic wetness index (TWI), distance to drainage, and population density, as the variables affecting the fire occurrence. The area under the receiver operating characteristic (AUROC) was used to evaluate the accuracy of the models. The findings indicated that SVR-based hybrid models yielded a higher AUROC value (0.965 and 0.949) than the ANFIS-based hybrid models (0.904 and 0.894, respectively). Wildland fire susceptibility maps can play a major role in shaping firefighting tactics.
Al-Hadhrami, Y & Hussain, FK 2021, 'DDoS attacks in IoT networks: a comprehensive systematic literature review', World Wide Web, vol. 24, no. 3, pp. 971-1001.
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The Internet of Things (IoT) is a rapidly emerging technology in the consumer and industrial market. This technology has the potential to radically transform the consumer experience, as it will change our daily scenes, starting from the way we drink coffee to how smart objects interact with industrial applications. Such rapid development and deployment face multifarious challenges, including the sheer amount of data generated, network scale, network heterogeneity, as well as security and privacy concerns. In recent years, Distributed Denial-of-Service (DDoS) attacks in IoT networks are considered one of the growing challenges that need to be shed light on. DDoS attacks utilize the limited resources in IoT devices, such as storage limitation and network capacity, that cause this issue in the IoT application. This paper comprehensively reviews the attacks that can lead to DDoS, which eventually will cause serious damage to existing systems. Additionally, the paper investigates the available solutions used to counter these attacks and explore their limitations from the perspective of the constrained device. Furthermore, a detailed analysis of the existing solution placement was implemented, including heterogeneity and their performance for IoT based networks. Finally, the paper will reveal and discuss interesting research direction on the future IoT security and current trends.
Alharbi, A & Sohaib, O 2021, 'Technology Readiness and Cryptocurrency Adoption: PLS-SEM and Deep Learning Neural Network Analysis.', IEEE Access, vol. 9, pp. 21388-21394.
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Alharbi, AI, Gay, V, AlGhamdi, MJ, Alturki, R & Alyamani, HJ 2021, 'Towards an Application Helping to Minimize Medication Error Rate', Mobile Information Systems, vol. 2021, pp. 1-7.
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Medication errors related to medication administration done by both doctors and nurses can be considered a vital issue around the world. It is believed that systematisation and the introduction of main documents are done manually, which might increase the opportunities to have inaccuracies and errors because of unexpected wrong actions done by medical practitioners. Experts stated that the lack of pharmacological knowledge is one of the key factors, which play an important role in causing such errors. Doctors and nurses may face problems when they move from one unit to another and the medication administration list has changed. However, promoting public health activities and recent AI-enabled applications can provide general information about medication that helps both doctors and nurses administer the right medication. However, such an application can require a lot of time and effort to search and then find a medication. Therefore, this article aims to investigate whether AI-enabled applications can help avoid or at least minimize medication error rates.
Ali, A, Fathalla, A, Salah, A, Bekhit, M & Eldesouky, E 2021, 'Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models', Computational Intelligence and Neuroscience, vol. 2021, no. 1, pp. 1-13.
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Nowadays, ocean observation technology continues to progress, resulting in a huge increase in marine data volume and dimensionality. This volume of data provides a golden opportunity to train predictive models, as the more the data is, the better the predictive model is. Predicting marine data such as sea surface temperature (SST) and Significant Wave Height (SWH) is a vital task in a variety of disciplines, including marine activities, deep‐sea, and marine biodiversity monitoring. The literature has efforts to forecast such marine data; these efforts can be classified into three classes: machine learning, deep learning, and statistical predictive models. To the best of the authors’ knowledge, no study compared the performance of these three approaches on a real dataset. This paper focuses on the prediction of two critical marine features: the SST and SWH. In this work, we proposed implementing statistical, deep learning, and machine learning models for predicting the SST and SWH on a real dataset obtained from the Korea Hydrographic and Oceanographic Agency. Then, we proposed comparing these three predictive approaches on four different evaluation metrics. Experimental results have revealed that the deep learning model slightly outperformed the machine learning models for overall performance, and both of these approaches greatly outperformed the statistical predictive model.
Ali, JSM, Siddique, MD, Mekhilef, S, Yang, Y, Siwakoti, YP & Blaabjerg, F 2021, 'Experimental validation of nine-level switched-capacitor inverter topology with high voltage gain.', Int. J. Circuit Theory Appl., vol. 49, no. 8, pp. 2479-2493.
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This paper proposes a new switched-capacitor nine-level (9L) inverter with reduced switch count. In the proposed topology, floating capacitor (FC) is employed as a voltage booster, and it does not need any additional sensors to maintain the voltage across the FC. Due to additional FC, the number of dc sources and voltage stress on switches is reduced. Moreover, the proposed topology can be cascaded to achieve more voltage levels. Various parameters are considered in the comparison of the proposed topology with other recent switched-capacitor topologies. Simulation and experimental results demonstrate the performance with different load and modulation index variations.
Ali, SM, Im, S-J, Jang, A, Phuntsho, S & Shon, HK 2021, 'Forward osmosis system design and optimization using a commercial cellulose triacetate hollow fibre membrane module for energy efficient desalination', Desalination, vol. 510, pp. 115075-115075.
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This study is aimed at developing system mathematical design models to simulate and optimize a full scale forward osmosis (FO) for a hollow fibre membrane module for energy efficient desalination. Experimental data from a commercial outer selective CTA hollow fibre FO membrane module was used for validation. Less than 10% difference between the simulation and experimental results were observed which validated the reliability of the models. Simulation and design were performed for a 1000 m3/day FO plant using 0.6 M NaCl as draw solution (DS) (~seawater) and 0.02 M NaCl feed solution (FS) (~MBR effluent) to produce 0.25, 0.2 and 0.15 M NaCl diluted seawater to reduce the energy consumption of downstream pressure driven desalination process. A single element parallel module arrangement was found more suitable for this commercial hollow fibre membrane element. Finally, the numerical simulations revealed that to achieve 0.25, 0.20 and 0.15 M final DS concentrations, the optimum number of modules required were 370, 435 and 555 respectively considering membrane cost and energy consumption. The FO system using the commercial CTA hollow fibre module was found more energy efficient than a commercial TFC spiral wound membrane module.
Ali, SM, Kim, Y, Qamar, A, Naidu, G, Phuntsho, S, Ghaffour, N, Vrouwenvelder, JS & Shon, HK 2021, 'Dynamic feed spacer for fouling minimization in forward osmosis process', Desalination, vol. 515, pp. 115198-115198.
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In this study, a dynamic feed spacer is used to minimize the fouling problem of forward osmosis (FO) membrane process. The conceptual design of the spacer consists of a series of microturbines assembled in ladder type filament cells and termed as turbospacer. It exploits the kinetic energy of the flowing feed solution to rotate the turbines and creates high flow turbulence in the feed channel to prevent the accumulation of foulants and related performance decline. This proof of concept study employed a 3D printed prototype of the proposed spacer in a lab-scale FO experimental setup to compare their performances with a symmetric non-woven spacer of the same thickness under the same operating condition as a reference. Primary effluent from municipal wastewater treatment plant was used as feed solution for a short term (6 days) fouling experiment in this study. Outcomes of the FO fouling experiment revealed that the turbospacer resulted in (i) a factor 2 lower spacer channel pressure drop built-up, and (ii) a 15% reduction in flux decline compared to the reference symmetric spacer. Almost 2.5 times lower foulant resistance was obtained by using the turbospacer at the end of the fouling experiment. In addition, the analysis of the foulant layer growth over a particular position of the membrane surface captured by an optical coherence tomography (OCT) device at different stages of the experiment exhibited that the turbospacer produced a thinner foulant layer. In summary, the turbospacer demonstrated better fouling prevention and control in the FO process.
Ali, SMN, Sharma, V, Hossain, MJ, Mukhopadhyay, SC & Wang, D 2021, 'Optimized Energy Control Scheme for Electric Drive of EV Powertrain Using Genetic Algorithms', Energies, vol. 14, no. 12, pp. 3529-3529.
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Automotive applications often experience conflicting-objective optimization problems focusing on performance parameters that are catered through precisely developed cost functions. Two such conflicting objectives which substantially affect the working of traction machine drive are maximizing its speed performance and minimizing its energy consumption. In case of an electric vehicle (EV) powertrain, drive energy is bounded by battery dynamics (charging and capacity) which depend on the consumption of drive voltage and current caused by driving cycle schedules, traffic state, EV loading, and drive temperature. In other words, battery consumption of an EV depends upon its drive energy consumption. A conventional control technique improves the speed performance of EV at the cost of its drive energy consumption. However, the proposed optimized energy control (OEC) scheme optimizes this energy consumption by using robust linear parameter varying (LPV) control tuned by genetic algorithms which significantly improves the EV powertrain performance. The analysis of OEC scheme is conducted on the developed vehicle simulator through MATLAB/Simulink based simulations as well as on an induction machine drive platform. The accuracy of the proposed OEC is quantitatively assessed to be 99.3% regarding speed performance which is elaborated by the drive speed, voltage, and current results against standard driving cycles.
Alibeikloo, M, Khabbaz, H, Fatahi, B & Le, TM 2021, 'Reliability Assessment for Time-Dependent Behaviour of Soft Soils Considering Cross Correlation between Visco-Plastic Model Parameters', Reliability Engineering & System Safety, vol. 213, pp. 107680-107680.
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An elastic visco-plastic creep model was combined with the Monte-Carlo probabilistic method incorporating multivariate copula and nonlinear analysis to investigate the effects of uncertainties in the elastic visco-plastic model parameters on time-dependent settlement and the distribution of excess pore water pressure in soft soils under applied loads. The elastic-plastic model parameter (λ/V) and creep coefficient (ψ0/V) were considered as random variables with lognormal distribution while considering the cross correlation between these two random variables. When λ/V and ψ0/V were used as random variables, the coefficient of variation of time-dependent deformation gradually decreased approximately 25% over time until reaching an asymptote. By adopting over 50 years of monitoring data from the case study of Väsby test fill and results from the settlement ratio, the most appropriate cross correlation coefficient between selected random variables was introduced. The results revealed that increasing the cross correlation coefficient between λ/V and ψ0/V increased the standard deviation and the coefficient of variation of settlement up to 40%. Meanwhile, the corresponding statistical features for the predicted excess pore water pressure decreased as the cross correlation coefficient increased. This study also provides a practical insight into selecting the most suitable cross correlation coefficient between elastic visco-plastic model parameters, while adopting reliability-based design approach that captures the time-dependent deformation of embankments and structures built on soft soils.
Aljarajreh, H, Lu, DD-C, Siwakoti, YP, Aguilera, RP & Tse, CK 2021, 'A Method of Seamless Transitions Between Different Operating Modes for Three-Port DC-DC Converters.', IEEE Access, vol. 9, pp. 59184-59195.
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This paper presents the design of three-port converters (TPCs) for smooth transitions (i.e., fast settling time, and no obvious overshoot/undershoot) of 7 distinctive operating modes, depending on sources and loads scheduling. Two viable converter configurations have been identified and selected for further analysis and design of PV-battery systems. Conventionally, mode transition is achieved by assigning specific switching patterns through feedback signals and appropriate control algorithms. This incurs a delay in the response and unavoidable noise in the circuit. Additionally, in TPCs, three voltage sensors and three current sensors are generally required for decision making in mode selection, where errors in sensors may lead to an inaccurate response. This paper presents a new control strategy where the number of switching patterns is significantly reduced to 3 patterns instead of minimum 5 patterns for existing reported topologies. Therefore, decisions are simplified so that the transition occurs naturally based on the power availability and load demand but not deliberately as in the conventional method. In addition, instead of six sensors, three voltage sensors and only one current sensor are required to achieve all the necessary operations, namely, MPPT, battery protection, and output regulation. Moreover, these sensors do not participate in mode selection decision, which leads to seamless and fast mode transition. In addition, this work considers two bidirectional ports as compared with only one bidirectional port in most reported topologies. This configuration enables both standalone and DC grid-connected applications. Experimental results are reported to verify the proposed solution.
Aljarajreh, H, Lu, DD-C, Siwakoti, YP, Tse, CK & See, KW 2021, 'Synthesis and Analysis of Three-Port DC/DC Converters with Two Bidirectional Ports Based on Power Flow Graph Technique', Energies, vol. 14, no. 18, pp. 5751-5751.
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This paper presents a systematic topological study to derive all possible basic and non-isolated three-port converters (TPCs) using power flow diagrams. Unlike most reported TPCs with one bidirectional port, this paper considers up to two bidirectional ports and provides a comprehensive analytical tool. This tool acts as a framework for all power flow combinations, selection, and design. Some viable converter configurations have been identified and selected for further analysis.
Allioux, F-M, Merhebi, S, Tang, J, Zhang, C, Merenda, A, Cai, S, Ghasemian, MB, Rahim, MA, Maghe, M, Lim, S, Zhang, J, Hyde, L, Mayyas, M, Cunning, BV, Ruoff, RS & Kalantar-Zadeh, K 2021, 'Carbonization of low thermal stability polymers at the interface of liquid metals', Carbon, vol. 171, pp. 938-945.
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Almansor, EH, Hussain, FK & Hussain, OK 2021, 'Supervised ensemble sentiment-based framework to measure chatbot quality of services', Computing, vol. 103, no. 3, pp. 491-507.
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© 2020, Springer-Verlag GmbH Austria, part of Springer Nature. Developing an intelligent chatbot has evolved in the last few years to become a trending topic in the area of computer science. However, a chatbot often fails to understand the user’s intent, which can lead to the generation of inappropriate responses that cause dialogue breakdown and user dissatisfaction. Detecting the dialogue breakdown is essential to improve the performance of the chatbot and increase user satisfaction. Recent approaches have focused on modeling conversation breakdown using serveral approaches, including supervised and unsupervised approaches. Unsupervised approach relay heavy datasets, which make it challenging to apply it to the breakdown task. Another challenge facing predicting breakdown in conversation is the bias of human annotation for the dataset and the handling process for the breakdown. To tackle this challenge, we have developed a supervised ensemble automated approach that measures Chatbot Quality of Service (CQoS) based on dialogue breakdown. The proposed approach is able to label the datasets based on sentiment considering the context of the conversion to predict the breakdown. In this paper we aim to detect the affect of sentiment change of each speaker in a conversation. Furthermore, we use the supervised ensemble model to measure the CQoS based on breakdown. Then we handle this problem by using a hand-over mechanism that transfers the user to a live agent. Based on this idea, we perform several experiments across several datasets and state-of-the-art models, and we find that using sentiment as a trigger for breakdown outperforms human annotation. Overall, we infer that knowledge acquired from the supervised ensemble model can indeed help to measure CQoS based on detecting the breakdown in conversation.
Almotairy, SM, Boostani, AF, Hassani, M, Wei, D & Jiang, ZY 2021, 'Mechanical Properties of Aluminium Metal Matrix Nanocomposites Manufactured by Assisted-Flake Powder Thixoforming Process', Metals and Materials International, vol. 27, no. 5, pp. 851-859.
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© 2019, The Korean Institute of Metals and Materials. Abstract: This study discusses the superior effect of thixoforming process on enhancing the tensile properties of aluminium matrix composite produced using flake metallurgy route. The flake metallurgy process was utilised to manufacture aluminium matrix composites followed by thixoforming process. Microstructural investigations carried out using transmission electron microscope have shown the synergic effect of thixoforming process on rendering uniform distribution of SiC nanoparticles associated with lower porosity content. X-ray diffraction characterisations have revealed the promising effect of uniform dispersion of SiC nanoparticles on restricting the grain growth of aluminium matrix within nanoscale regime (90 nm) even at high semi-solid thixoforming temperatures (575 °C). The achieved results of tensile tests have shown a profound effect of flake metallurgy of aluminium powder through dual speed ball milling. These results are higher than those achieved by low speed and high speed even with higher SiC content. This was attributed to the uniform confinement of SiC nanoparticles within the samples produced using flake-assisted forming process compared to the ones manufactured using ball milling-assisted processes. Graphic abstract: [Figure not available: see fulltext.].
Almuntashiri, A, Hosseinzadeh, A, Volpin, F, Ali, SM, Dorji, U, Shon, H & Phuntsho, S 2021, 'Removal of pharmaceuticals from nitrified urine', Chemosphere, vol. 280, pp. 130870-130870.
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In this study, granular activated carbon (GAC) was examined for the removal of five of the most commonly detected pharmaceuticals (naproxen, carbamazepine, acetaminophen, ibuprofen and metronidazole) from a nitrified urine to make the urine-derived fertiliser nutrient safe for food crops. Batch experiments were conducted to investigate the adsorption kinetics that described the removal of micropollutants (equal concentrations of 0.2 mM) from the synthetic nitrified urine at different GAC dosages (10-3000 mg/L). Artificial neural network modelling was also used to predict and simulate the removal of pharmaceuticals from nitrified urine. Langmuir and Freundlich isotherm models described the equilibrium data, with the Langmuir model providing slightly higher correlations. At the highest dose of 3000 mg/L GAC, all the pharmaceuticals showed a removal rates of over 90% after 1 h of adsorption time and 99% removal rates after 6 h of adsorption time. This study concludes that GAC is able to remove the targeted xenobiotics without affecting the concentration of N and P in the urine, suggesting that nitrified urine could be safely used as a nutrient product in future.
Al-Najjar, HAH & Pradhan, B 2021, 'Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks', Geoscience Frontiers, vol. 12, no. 2, pp. 625-637.
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In recent years, landslide susceptibility mapping has substantially improved with advances in machine learning. However, there are still challenges remain in landslide mapping due to the availability of limited inventory data. In this paper, a novel method that improves the performance of machine learning techniques is presented. The proposed method creates synthetic inventory data using Generative Adversarial Networks (GANs) for improving the prediction of landslides. In this research, landslide inventory data of 156 landslide locations were identified in Cameron Highlands, Malaysia, taken from previous projects the authors worked on. Elevation, slope, aspect, plan curvature, profile curvature, total curvature, lithology, land use and land cover (LULC), distance to the road, distance to the river, stream power index (SPI), sediment transport index (STI), terrain roughness index (TRI), topographic wetness index (TWI) and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands. To show the capability of GANs in improving landslide prediction models, this study tests the proposed GAN model with benchmark models namely Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF) and Bagging ensemble models with ANN and SVM models. These models were validated using the area under the receiver operating characteristic curve (AUROC). The DT, RF, SVM, ANN and Bagging ensemble could achieve the AUROC values of (0.90, 0.94, 0.86, 0.69 and 0.82) for the training; and the AUROC of (0.76, 0.81, 0.85, 0.72 and 0.75) for the test, subsequently. When using additional samples, the same models achieved the AUROC values of (0.92, 0.94, 0.88, 0.75 and 0.84) for the training and (0.78, 0.82, 0.82, 0.78 and 0.80) for the test, respectively. Using the additional samples improved the test accuracy of all the models except SVM. As a result, in data-scarce e...
Al-Najjar, HAH, Pradhan, B, Kalantar, B, Sameen, MI, Santosh, M & Alamri, A 2021, 'Landslide Susceptibility Modeling: An Integrated Novel Method Based on Machine Learning Feature Transformation', Remote Sensing, vol. 13, no. 16, pp. 3281-3281.
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Landslide susceptibility modeling, an essential approach to mitigate natural disasters, has witnessed considerable improvement following advances in machine learning (ML) techniques. However, in most of the previous studies, the distribution of input data was assumed as being, and treated, as normal or Gaussian; this assumption is not always valid as ML is heavily dependent on the quality of the input data. Therefore, we examine the effectiveness of six feature transformations (minimax normalization (Std-X), logarithmic functions (Log-X), reciprocal function (Rec-X), power functions (Power-X), optimal features (Opt-X), and one-hot encoding (Ohe-X) over the 11conditioning factors (i.e., altitude, slope, aspect, curvature, distance to road, distance to lineament, distance to stream, terrain roughness index (TRI), normalized difference vegetation index (NDVI), land use, and vegetation density). We selected the frequent landslide-prone area in the Cameron Highlands in Malaysia as a case study to test this novel approach. These transformations were then assessed by three benchmark ML methods, namely extreme gradient boosting (XGB), logistic regression (LR), and artificial neural networks (ANN). The 10-fold cross-validation method was used for model evaluations. Our results suggest that using Ohe-X transformation over the ANN model considerably improved performance from 52.244 to 89.398 (37.154% improvement).
Al-Najjar, HAH, Pradhan, B, Sarkar, R, Beydoun, G & Alamri, AM 2021, 'A New Integrated Approach for Landslide Data Balancing and Spatial Prediction Based on Generative Adversarial Networks (GAN).', Remote. Sens., vol. 13, pp. 4011-4011.
Aloqaily, AA, Tafavogh, S, Harvey, BL, Catchpoole, DR & Kennedy, PJ 2021, 'Feature prioritisation on big genomic data for analysing gene-gene interactions', International Journal of Bioinformatics Research and Applications, vol. 17, no. 2, pp. 158-158.
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Complex diseases are not caused by single genes but result from intricate non-linear interactions among them. There is a critical need to implement approaches that take into account non-linear gene-gene interactions in searching for markers that jointly cause diseases. Determining the interaction between more than two single nucleotide polymorphisms (SNP) within the whole genome data is computationally expensive and often infeasible. In this paper, we develop an approach to classify patients with Acute Lymphoblastic Leukaemia by analysing multiple SNP interactions. A novel feature prioritisation algorithm called interaction effect quantity (IEQ) selects SNPs with high potential of interaction by analysing their distribution throughout the genomic data and enables deeper analysis of non-linear interactions within large datasets. We show that IEQ enables analyses of interactions between up to four SNPs, with F-measure for classification greater than 89% obtained. Such an analysis is typically much more computationally challenging if IEQ is not implemented.
Alqahtani, M & Braun, R 2021, 'Reviewing Influence of UTAUT2 Factors on Cyber Security Compliance: A Literature Review', Journal of Information Assurance & Cybersecurity, vol. 2021, pp. 1-15.
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Evidence suggests that, regardless of the number of technical controls in place, organizations will still experience security breaches. Organizations spend millions of dollars on their cyber security infrastructure that includes technical and non-technical measures but mostly disregarded the most important asset and vulnerability the human.
Alqaisi, R, Le, TM & Khabbaz, H 2021, 'Combined effects of eggshell powder and hydrated lime on the properties of expansive soils', Australian Geomechanics Journal, vol. 56, no. 1, pp. 107-118.
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This study involves the utilization of eggshell powder (ESP) as a supplementary additive to lime stabilization of expansive soil and evaluates its potential in enhancing the performance of expansive soil treated with lime. Eggshell is a waste material obtained from several sources. Some of the challenges associated with dumping eggshell are odour, insect growth, disposal costs and availability of disposal sites. In order to reduce these environmental issues, eggshells can be processed into ESP and play a role as a soil stabilizing agent. Calcium oxide is considered to be the main ingredient of the ESP. Therefore, an experimental program is carried out to test a mixture of kaolinite, bentonite and Sydney fine sand, which is simulated to be as an artificial expansive soil. The eggshell powder was used as an additive to 5% lime in four percentages of 5%, 10%, 15% and 20% by total dry weight of the soil mass. Results of linear shrinkage, proctor compaction, and unconfined compressive strength tests after various curing time are presented in detail and compared with untreated soil samples. The outcomes of these experimental investigations indicated that the combination of eggshell powder and hydrated lime led to a further decrease in linear shrinkage and the maximum dry density of expansive soil samples. It was found that the improved geotechnical characteristics were more pronounced for 5% ESP treated expansive soil. At this percentage, the compressive strength at failure and the corresponding strain increased slightly by 18% and 9%, respectively, compared to the untreated expansive soil after 28 days of curing. Moreover, in comparison with lime (5%) only stabilized expansive soil, the combined lime (5%) and ESP (5%), induced approximately 15% build-up in the compressive strength of samples. Based on the reasonable laboratory test results, this addition is recommended to improve the shrinkage properties and stabilize the expansive soils where the high perfo...
Alsahafi, YA & Gay, V 2021, 'Erratum to ‘An overview of electronic personal health records’ [Health Policy and Technology 7 (2018) 427-432]', Health Policy and Technology, vol. 10, no. 4, pp. 100566-100566.
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Al-Shetwi, AQ, Hannan, MA, Abdullah, MA, Rahman, MSA, Ker, PJ, Alkahtani, AA, Mahlia, TMI & Muttaqi, KM 2021, 'Utilization of Renewable Energy for Power Sector in Yemen: Current Status and Potential Capabilities', IEEE Access, vol. 9, pp. 79278-79292.
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A severe energy crisis has plagued Yemen for decades, and most of the population lack access to electricity. This has harmed the country’s economic, social, and industrial growth. Yemen generates electricity mainly from fossil fuels, despite having a high potential for renewable energy. Unfortunately, the situation has recently been compounded by the country’s continuing war, which has been ongoing since early 2015. It has impacted the country’s energy infrastructure negatively, resulting in power outages. Therefore, this paper aims to provide an updated perspective on Yemen’s current energy crisis and explain its key issues and potential solutions. Besides, it examines the potential, development, and current state of renewable energy sources, such as solar, wind, geothermal, and biomass. Based on the findings, Yemen is one of the world’s wealthiest countries in terms of sunlight and wind speed, and these two resources are abundant in all regions of the country. In addition, this paper sheds light on the solar energy revolution that has arisen since the war started due to the complete outage of the national electricity. Within a few years, solar energy in Yemen has increased its capacity by 50 times and has recently become the primary source of electricity for most Yemenis. Furthermore, the paper discusses the difficulties and challenges that face the implementation of renewable energy investment projects. Numerous recommendations for potential improvements in Yemen’s widespread use of renewable energy are also provided in this paper. All of the ideas presented in this paper are hoped to increase the efforts to grow renewable energy production in Yemen, thereby solving the issues of energy poverty and reducing environmental effects. The presented analysis can be used as a scientific reference for researchers and industrial companies looking for suitable solutions to advance Yemen’s renewable energy.
Alzahrani, AS, Gay, V, Alturki, R & AlGhamdi, MJ 2021, 'Towards Understanding the Usability Attributes of AI-Enabled eHealth Mobile Applications', Journal of Healthcare Engineering, vol. 2021, pp. 1-8.
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Mobile application (app) use is increasingly becoming an essential part of our daily lives. Due to their significant usefulness, people rely on them to perform multiple tasks seamlessly in almost all aspects of everyday life. Similarly, there has been immense progress in artificial intelligence (AI) technology, especially deep learning, computer vision, natural language processing, and robotics. These technologies are now actively being implemented in smartphone apps and healthcare, providing multiple healthcare services. However, several factors affect the usefulness of mobile healthcare apps, and usability is an important one. There are various healthcare apps developed for each specific task, and the success of these apps depends on their performance. This study presents a systematic review of the existing apps and discusses their usability attributes. It highlights the usability models, outlines, and guidelines proposed in previous research for designing apps with improved usability characteristics. Thirty-nine research articles were reviewed and examined to identify the usability attributes, framework, and app design conducted. The results showed that satisfaction, efficiency, and learnability are the most important usability attributes to consider when designing eHealth mobile apps. Surprisingly, other significant attributes for healthcare apps, such as privacy and security, were not among the most indicated attributes in the studies.
Al-Zahrani, S, Islam, MS & Saha, SC 2021, 'Comparison of flow resistance and port maldistribution between novel and conventional plate heat exchangers', International Communications in Heat and Mass Transfer, vol. 123, pp. 105200-105200.
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AlZainati, N, Saleem, H, Altaee, A, Zaidi, SJ, Mohsen, M, Hawari, A & Millar, GJ 2021, 'Pressure retarded osmosis: Advancement, challenges and potential', Journal of Water Process Engineering, vol. 40, pp. 101950-101950.
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An excessive amount of renewable energy could be possibly produced when solutions of dissimilar salinities are combined simultaneously in a semipermeable membrane. The aforestated energy harnessing for transformation into power could be achieved through the pressure retarded osmosis (PRO) process. The PRO system utilizes a semipermeable membrane for separating a low concentration solution from a pressurized-high concentrated solution. This work examines the recent developments and applications of the PRO process and potential energy that could be conceivably harvested from salinity gradient resources in a single-stage and multi-stage PRO processes. One of the existing challenges for this process is finding a commercial membrane that combines characteristics of the forward osmosis membrane (for reducing the phenomenon of concentration polarization) and the reverse osmosis membrane (to withstand high hydraulic pressure). For addressing this challenge, details about the commercial PRO membranes and the innovative laboratory fabricated PRO membranes are introduced. The potential of the PRO process is presented by elucidating salinity gradient resources, the energy of Pretreatment, the process design, PRO-desalination systems, and dual-stage PRO (DSPRO). It is anticipated that this paper can assist in widely understanding the PRO process and thus deliver important data for activating additional research and development.
Alzoubi, Y & Gill, A 2021, 'The Critical Communication Challenges Between Geographically Distributed Agile Development Teams: Empirical Findings', IEEE Transactions on Professional Communication, vol. 64, no. 4, pp. 322-337.
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Background: Although a number of empirical studies have investigated communication challenges during recent years, we still need to discover the most critical challenges that face communication when agile development is geographically distributed. We also need to discover how successful geographically distributed agile development (GDAD) organizations deal with these challenges. Literature review: Most previous studies reported that the critical challenges facing GDAD communication can be categorized into five themes: differences in cultures, different time zones, different spoken languages, different personal skills, and the efficiency and effectiveness of communication tools used. Research questions: 1. What are the challenges of communication between GDAD teams? 2. How can the impact of GDAD communication challenges be mitigated? Methodology: Data were collected by interviewing 12 members of a three-team organization using distributed agile development. These teams are distributed over three countries; the main team located in Australia, the developers' team located in China, and the testers' team located in India. A thematic analysis technique was used to identify communication challenges and practices used to mitigate the effect of these challenges. Results: Our findings reveal that the five challenges are still critical to GDAD. Moreover, we report a new critical challenge of communication in GDAD, the insufficient documentation provided by distributed teams and members. In addition, we recommend several practices to mitigate the impact of these challenges. Conclusions: Communication among distributed agile development teams still faces several critical challenges, and the solutions to these challenges provided in recent years have not been sufficient. This fact prompts the need for more research on how the impact of these challenges can be lessened.
Amin, BMR, Taghizadeh, S, Maric, S, Hossain, MJ & Abbas, R 2021, 'Smart Grid Security Enhancement by Using Belief Propagation', IEEE Systems Journal, vol. 15, no. 2, pp. 2046-2057.
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Amin, U, Hossain, MJ, Tushar, W & Mahmud, K 2021, 'Energy Trading in Local Electricity Market With Renewables—A Contract Theoretic Approach', IEEE Transactions on Industrial Informatics, vol. 17, no. 6, pp. 3717-3730.
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Emerging smart grid technologies and increased penetration of renewable energy sources (RESs) direct the power sector to focus on RESs as an alternative to meet both baseload and peak load demands in a cost-efficient way. A key issue in such schemes is the design and analysis of energy trading techniques involving complex interactions between an aggregator and multiple electricity suppliers (ESs) with RESs fulfilling a certain demand. This is challenging because ESs can be of various categories, such as small/medium/large scale, and they are self-interested and generally have different preferences toward trading based on their types and constraints. This article introduces a new contract theoretic framework to tackle this challenge by designing optimal contracts for ESs. To this end, a dynamic pricing scheme is developed such that the aggregator can utilize to incentivize the ESs to contribute to both baseload and peak load demands according to their categories. An algorithm is proposed that can be implemented in a distributed manner by trading partners to enable energy trading. It is shown that the trading strategy under a baseload scenario is feasible, and the aggregator only needs to consider the per unit generation cost of ESs to decide on its strategy. The trading strategy for a peak load scenario, however, is complex and requires consideration of different factors, such as variations in the wholesale price and its effect on the selling price of ESs, and the uncertainty of energy generation from RESs. Simulation results demonstrate the effectiveness of the proposed scheme for energy trading in the local electricity market.
Amirgholipour, S, Jia, W, Liu, L, Fan, X, Wang, D & He, X 2021, 'PDANet: Pyramid density-aware attention based network for accurate crowd counting', Neurocomputing, vol. 451, pp. 215-230.
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Crowd counting, i.e., estimating the number of people in crowded areas, has attracted much interest in the research community. Although many attempts have been reported, crowd counting remains an open real-world problem due to the vast density variations and severe occlusion within the interested crowd area. In this paper, we propose a novel Pyramid Density-Aware Attention based network, abbreviated as PDANet, which leverages the attention, pyramid scale feature, and two branch decoder modules for density-aware crowd counting. The PDANet utilizes these modules to extract features of different scales while focusing on the relevant information and suppressing the misleading information. We also address the variation of crowdedness levels among different images with a Density-Aware Decoder (DAD) modules. For this purpose, a classifier is constructed to evaluate the density level of the input features and then passes them to the corresponding high and low density DAD modules. Finally, we generate an overall density map by considering the summation of low and high crowdedness density maps. Meanwhile, we employ different losses aiming to achieve a precise density map for the input scene. Extensive evaluations conducted on the challenging benchmark datasets well demonstrate the superior performance of the proposed PDANet in terms of the accuracy of counting and generated density maps over the well-known state-of-the-art approaches.
Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 2021, 'Soil moisture remote sensing using SIW cavity based metamaterial perfect absorber', Scientific Reports, vol. 11, no. 1, pp. 1-17.
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AbstractContinuous and accurate sensing of water content in soil is an essential and useful measure in the agriculture industry. Traditional sensors developed to perform this task suffer from limited lifetime and also need to be calibrated regularly. Further, maintenance, support, and deployment of these sensors in remote environments provide additional challenges to the use of conventional soil moisture sensors. In this paper, a metamaterial perfect absorber (MPA) based soil moisture sensor is introduced. The ability of MPAs to absorb electromagnetic signals with near 100% efficiency facilitates the design of highly accurate and low-profile radio frequency passive sensors. MPA based sensor can be fabricated from highly durable materials and can therefore be made more resilient than traditional sensors. High resolution sensing is achieved through the creation of physical channels in the substrate integrated waveguide (SIW) cavity. The proposed sensor does not require connection for both electromagnetic signals or for adding a testing sample. Importantly, an external power supply is not needed, making the MPA based sensor the perfect solution for remote and passive sensing in modern agriculture. The proposed MPA based sensor has three absorption bands due to the various resonance modes of the SIW cavity. By changing the soil moisture level, the absorption peak shifts by 10 MHz, 23.3 MHz, and 60 MHz, which is correlated with the water content percentage at the first, second and third absorption bands, respectively. Finally, a $$6 \times 6$$ 6 × 6 cell array with...
Amiri, M, Tofigh, F, Shariati, N, Lipman, J & Abolhasan, M 2021, 'Review on Metamaterial Perfect Absorbers and Their Applications to IoT', IEEE Internet of Things Journal, vol. 8, no. 6, pp. 4105-4131.
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Future Internet of Things (IoT) devices are expected to be fully ubiquitous. To achieve this vision, a new generation of IoT devices needs to be developed, which can operate autonomously. To achieve autonomy, IoT devices must be completely wireless, both in terms of transmission and power. Further, accurate sensing is another crucial parameter of autonomy. Several wireless standards have been developed for improving the efficiency of IoT applications. However, the powering of IoT devices, sensor accuracy, and efficiency of electronic devices are open research problems in literature. With the advent of metamaterial perfect absorbers (MPAs), electromagnetic waves can be used as a source of energy, to enable sensing of the phenomenon and as a carrier for exchanging data. In this article, an extensive application-based investigation has been conducted on design principles and various methods of enhancing MPA characteristics. Moreover, the current applications that benefit from MPA, such as absorption of undesired frequencies, optical switching, energy harvesting, and sensing, are investigated. Finally, some implemented examples of MPA in industrial applications are provided along with possible directions for future work and open research areas.
Amjadipour, M, Bradford, J, Zebardastan, N, Motta, N & Iacopi, F 2021, 'MoS2/Epitaxial graphene layered electrodes for solid-state supercapacitors', Nanotechnology, vol. 32, no. 19, pp. 195401-195401.
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Abstract The potential of transition metal dichalcogenides such as MoS2 for energy storage has been significantly limited so far by the lack of conductivity and structural stability. Employing highly conductive, graphitic materials in combination with transition metal dichalcogenides can address this gap. Here, we explore the use of a layered electrode structure for solid-state supercapacitors, made of MoS2 and epitaxial graphene (EG) on cubic silicon carbide for on-silicon energy storage. We show that the energy storage of the solid-state supercapacitors can be significantly increased by creating layered MoS2/graphene electrodes, yielding a substantial improvement as compared to electrodes using either EG or MoS2 alone. We conclude that the conductivity of EG and the growth morphology of MoS2 on graphene play an enabling role in the successful use of transition metal dichalcogenides for on-chip energy storage.
An, N, Zhang, H, Zhu, X & Xu, F 2021, 'A Hybrid Approach for the Dynamic Instability Analysis of Single-Layer Latticed Domes with Uncertainties', International Journal of Structural Stability and Dynamics, vol. 21, no. 06, pp. 2150082-2150082.
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Currently, there is no unified criterion to evaluate the failure of single-layer latticed domes, and an accurate nonlinear time-history analysis (NTHA) is generally required; however, this does not consider the uncertainties found in practice. The seismic instability of domes subjected to earthquake ground motions has not been thoroughly investigated. In this paper, a new approach is developed to automatically capture the instability points in the incremental dynamic analysis (IDA) of single-layer lattice domes by integrating different efficient and robust methods. First, a seismic fragility analysis with instability parameters is performed using the bootstrap calibration method for the perfect dome. Second, based on the Sobol sequence, the quasi-Monte Carlo (QMC) sampling method is used to efficiently calculate the failure probability of the dome with uncertain parameters, in which the truncated distributions of random parameters are considered. Third, the maximum entropy principle (MEP) method is used to improve the computational efficiency in the analyses of structures with uncertainties. Last, the uncertain interval of the domes is determined based on the IDA method. The proposed method has been used to investigate the instability of single-layer lattice domes with uncertain parameters. The results show that it can determine the probability of structural failure with high efficiency and reliability. Additionally, the limitations of the proposed method for parallel computation are discussed.
An, Y, Wang, J, Lu, H & Zhao, W 2021, 'Research of a combined wind speed model based on multi‐objective ant lion optimization algorithm', International Transactions on Electrical Energy Systems, vol. 31, no. 12.
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Andaryani, S, Nourani, V, Ball, J, Jahanbakhsh Asl, S, Keshtkar, H & Trolle, D 2021, 'A comparison of frameworks for separating the impacts of human activities and climate change on river flow in existing records and different near‐future scenarios', Hydrological Processes, vol. 35, no. 7.
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AbstractSeparating the effects of human activities/climate change on lotic ecosystems is one of the important components of environmental management as well as water resources maintenance. A Mann–Kendall analysis of hydro‐climatic parameters and vegetation cover (VC), calculated using normalized difference vegetation index (NDVI), during the period 1985–2014 suggested a significant decrease and increase of river flow and temperature at p < 0.01, as well as an insignificant decline and increment of precipitation and VC, respectively within the arid and semi‐arid region, that is, Zilbier River basin in north‐western Iran. A separation of human activities/climate change effects on the reduction of river flow was carried out using three alternative approaches: a simple eco‐hydrological method (coupled water‐energy budget (ECH)), elasticity‐based analysis (Budyko framework (EBA)), and a process‐based watershed model based on the Soil and Water Assessment Tool (SWAT). The efficiency of these approaches was assessed over the periods 1985–1994, 1995–2014, and under five potential near‐future human activities/climate change scenarios (S1–S5) by 2030. The results indicated that the climate change impacts on river flow was more severe than those of human activities. Climate change contributed to an average of 83.6% and 77.0% reduction in river flow in the past and the realistic future scenarios (i.e., S4 and S5), respectively, while human activities accounted for 16.4% and 30%. According to our findings, despite the fact that ECH results are more in line with the SWAT model, in case of physical characterization inaccessibility, ECH and EBA (as simple descriptive and quantitative models, respectively) can be used to separate, simulate and project the impacts of human activities and climate changes on river flow.
Anderson, GE, Bell, ME, Stevens, J, Aksulu, MD, Miller-Jones, JCA, van der Horst, AJ, Wijers, RAMJ, Rowlinson, A, Bahramian, A, Hancock, PJ, Macquart, J-P, Ryder, SD & Plotkin, RM 2021, 'Rapid-response radio observations of short GRB 181123B with the Australia Telescope Compact Array', Monthly Notices of the Royal Astronomical Society, vol. 503, no. 3, pp. 4372-4386.
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ABSTRACT We introduce the Australia Telescope Compact Array (ATCA) rapid-response mode by presenting the first successful trigger on the short-duration gamma-ray burst (GRB) 181123B. Early-time radio observations of short GRBs may provide vital insights into the radio afterglow properties of Advanced LIGO- and Virgo-detected gravitational wave events, which will in turn inform follow-up strategies to search for counterparts within their large positional uncertainties. The ATCA was on target within 12.6 h post-burst, when the source had risen above the horizon. While no radio afterglow was detected during the 8.3 h observation, we obtained force-fitted flux densities of 7 ± 12 and $15 \pm 11\, \mu$Jy at 5.5 and 9 GHz, respectively. Afterglow modelling of GRB 181123B showed that the addition of the ATCA force-fitted radio flux densities to the Swift X-ray Telescope detections provided more stringent constraints on the fraction of thermal energy in the electrons (log $\epsilon _e = -0.75^{+0.39}_{-0.40}$ rather than log $\epsilon _e = -1.13^{+0.82}_{-1.2}$ derived without the inclusion of the ATCA values), which is consistent with the range of typical ϵe derived from GRB afterglow modelling. This allowed us to predict that the forward shock may have peaked in the radio band ∼10 d post-burst, producing detectable radio emission ≳3–4 d post-burst. Overall, we demonstrate the potential for extremely rapid radio follow-up of transients and the importance of triggered radio observations for constraining GRB blast wave properties, regardless of whether there is a detection, via the inclusion of force-fitted radio flux densities in afterglow modelling efforts.
Anderson, GE, Hancock, PJ, Rowlinson, A, Sokolowski, M, Williams, A, Tian, J, Miller-Jones, JCA, Hurley-Walker, N, Bannister, KW, Bell, ME, James, CW, Kaplan, DL, Murphy, T, Tingay, SJ, Meyers, BW, Johnston-Hollitt, M & Wayth, RB 2021, 'Murchison Widefield Array rapid-response observations of the short GRB 180805A', Publications of the Astronomical Society of Australia, vol. 38.
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AbstractHere we present stringent low-frequency (185 MHz) limits on coherent radio emission associated with a short-duration gamma-ray burst (SGRB). Our observations of the short gamma-ray burst (GRB) 180805A were taken with the upgraded Murchison Widefield Array (MWA) rapid-response system, which triggered within 20s of receiving the transient alert from theSwiftBurst Alert Telescope, corresponding to 83.7 s post-burst. The SGRB was observed for a total of 30 min, resulting in a$3\sigma$persistent flux density upper limit of 40.2 mJy beam–1. Transient searches were conducted at theSwiftposition of this GRB on 0.5 s, 5 s, 30 s and 2 min timescales, resulting in$3\sigma$limits of 570–1 830, 270–630, 200–420, and 100–200 mJy beam–1, respectively. We also performed a dedispersion search for prompt signals at the position of the SGRB with a temporal and spectral resolution of 0.5 s and 1.28 MHz, respectively, resulting in a$6\sigma$fluence upper-limit range from 570 Jy ms at DMIEEE Antennas and Propagation Magazine, vol. 63, no. 6, pp. 4-4.
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Anjum, M, Voinov, A, Taghikhah, F & Pileggi, SF 2021, 'Discussoo: Towards an intelligent tool for multi-scale participatory modeling', Environmental Modelling & Software, vol. 140, pp. 105044-105044.
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Ansari, M, Jones, B, Zhu, H, Shariati, N & Guo, YJ 2021, 'A Highly Efficient Spherical Luneburg Lens for Low Microwave Frequencies Realized With a Metal-Based Artificial Medium', IEEE Transactions on Antennas and Propagation, vol. 69, no. 7, pp. 3758-3770.
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IEEE This paper describes a novel spherical lens antenna constructed of planar layers of light-weight foam with equally spaced conducting inclusions of varying sizes on an orthogonal grid. This construction largely overcomes the problems of weight and cost that have tended to make larger low frequency Luneburg lenses impractical. A penalty for this type of design is that some anisotropy exists in the lens’s dielectric. This effect is examined using both ray tracing techniques and full-wave simulation and it is found that the principal consequence is that the focal length of the lens varies in different directions. Methods for mitigating the effect are proposed. A prototype lens antenna intended for cellular use in the band 3.3 – 3.8 GHz with dual linear slant polarized feeds was designed and constructed to confirm the findings. Measured results show a peak gain of 23 dBi which is less than 1 dB lower than the maximum possible directivity from the lens’s cross section area. Scanning loss is less than 0.8 dB over the whole sphere. Simulated and measured performance show excellent agreement over the whole sphere. The overall performance of the prototype lens antenna demonstrates that this type of lens should be very suitable for use in high-gain multibeam antennas at lower microwave frequencies.
Anwar, MJ, Gill, AQ, Hussain, FK & Imran, M 2021, 'Secure big data ecosystem architecture: challenges and solutions.', EURASIP J. Wirel. Commun. Netw., vol. 2021, pp. 130-130.
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Ao, S, Zhou, T, Long, G, Lu, Q, Zhu, L & Jiang, J 2021, 'CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum', Advances in Neural Information Processing Systems, vol. 13, pp. 10444-10456.
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Goal-conditioned reinforcement learning (RL) usually suffers from sparse reward and inefficient exploration in long-horizon tasks. Planning can find the shortest path to a distant goal that provides dense reward/guidance but is inaccurate without a precise environment model. We show that RL and planning can collaboratively learn from each other to overcome their own drawbacks. In “CO-PILOT”, a learnable path-planner and an RL agent produce dense feedback to train each other on a curriculum of tree-structured sub-tasks. Firstly, the planner recursively decomposes a long-horizon task to a tree of sub-tasks in a top-down manner, whose layers construct coarse-to-fine sub-task sequences as plans to complete the original task. The planning policy is trained to minimize the RL agent’s cost of completing the sequence in each layer from top to bottom layers, which gradually increases the sub-tasks and thus forms an easy-to-hard curriculum for the planner. Next, a bottom-up traversal of the tree trains the RL agent from easier sub-tasks with denser rewards on bottom layers to harder ones on top layers and collects its cost on each sub-task train the planner in the next episode. CO-PILOT repeats this mutual training for multiple episodes before switching to a new task, so the RL agent and planner are fully optimized to facilitate each other’s training. We compare CO-PILOT with RL (SAC, HER, PPO), planning (RRT*, NEXT, SGT), and their combination (SoRB) on navigation and continuous control tasks. CO-PILOT significantly improves the success rate and sample efficiency. Our code is available at https://github.com/Shuang-AO/CO-PILOT.
Arabameri, A, Chandra Pal, S, Costache, R, Saha, A, Rezaie, F, Seyed Danesh, A, Pradhan, B, Lee, S & Hoang, N-D 2021, 'Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms', Geomatics, Natural Hazards and Risk, vol. 12, no. 1, pp. 469-498.
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Spatial modelling of gully erosion at regional level is very relevant for local authorities to establish successful counter-measures and to change land-use planning. This work is exploring and researching the potential of a genetic algorithm-extreme gradient boosting (GE-XGBoost) hybrid computer education solution for spatial mapping of the susceptibility of gully erosion. The new machine learning approach is to combine the extreme gradient boosting machine (XGBoost) and the genetic algorithm (GA). The GA metaheuristic is being used to improve the efficiency of the XGBoost classification approach. A GIS database has been developed that contains recorded instances of gully erosion incidents and 18 conditioning variables. These parameters are used as predictive variables used to assess the condition of non-erosion or erosion in a given region within the Kohpayeh-Sagzi River Watershed research area in Iran. Exploratory results indicate that the proposed GE-XGBoost model is superior to the other benchmark solution with the desired predictive precision (89.56%). Therefore, the newly built model may be a promising method for large-scale mapping of gully erosion susceptibility.
Arandiyan, H, S. Mofarah, S, Sorrell, CC, Doustkhah, E, Sajjadi, B, Hao, D, Wang, Y, Sun, H, Ni, B-J, Rezaei, M, Shao, Z & Maschmeyer, T 2021, 'Defect engineering of oxide perovskites for catalysis and energy storage: synthesis of chemistry and materials science', Chemical Society Reviews, vol. 50, no. 18, pp. 10116-10211.
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The present work provides a critical review of the science and technological state-of-the-art of defect engineering applied to oxide perovskites in thermocatalytic, electrocatalytic, photocatalytic, and energy-storage applications.
Arivalagan, J, Rujikiatkamjorn, C, Indraratna, B & Warwick, A 2021, 'The role of geosynthetics in reducing the fluidisation potential of soft subgrade under cyclic loading', Geotextiles and Geomembranes, vol. 49, no. 5, pp. 1324-1338.
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The instability of railway tracks including mud pumping, ballast degradation, and differential settlement on weak subgrade soils occurs due to cyclic stress from heavy haul trains. Although geotextiles are currently being used as a separator in railway and highway embankments, their ability to prevent the migration of fine particles and reduce cyclic pore pressure has to be investigated under adverse hydraulic conditions to prevent substructure failures. This study primarily focuses on using geosynthetics to mitigate the migration of fine particles and the accumulation of excess pore pressure (EPP) due to mud pumping (subgrade fluidisation) using dynamic filtration apparatus. The role that geosynthetics play in controlling and preventing mud pumping is analysed by assessing the development of EPP, the change in particle size distribution and the water content of subgrade soil. Using 3 types of geotextiles, the potential for fluidisation is assessed by analysing the time-dependent excess pore pressure gradient (EPPG) inside the subgrade. The experimental results are then used to evaluate the performance of selected geotextiles under heavy haul loading.
Ariyarathna, T, Kularatna, N, Gunawardane, K, Jayananda, D & Steyn-Ross, DA 2021, 'Development of Supercapacitor Technology and Its Potential Impact on New Power Converter Techniques for Renewable Energy', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 2, no. 3, pp. 267-276.
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Armaghani, DJ & Asteris, PG 2021, 'A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength', Neural Computing and Applications, vol. 33, no. 9, pp. 4501-4532.
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Armaghani, DJ, Bayat, V, Koopialipoor, M & Pham, BT 2021, 'Investigating the effect of jointed environment on the cracked concrete arch dam in 3D conditions using FEM', Bulletin of Engineering Geology and the Environment, vol. 80, no. 1, pp. 55-70.
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Armaghani, DJ, Mamou, A, Maraveas, C, Roussis, PC, Siorikis, VG, Skentou, AD & Asteris, PG 2021, 'Predicting the unconfined compressive strength of granite using only two non-destructive test indexes', Geomechanics and Engineering, vol. 25, no. 4, pp. 317-330.
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This paper reports the results of advanced data analysis involving artificial neural networks for the prediction of the unconfined compressive strength of granite using only two non-destructive test indexes. A data-independent site-independent unbiased database comprising 182 datasets from non-destructive tests reported in the literature was compiled and used to train and develop artificial neural networks for the prediction of the unconfined compressive strength of granite. The results show that the optimum artificial network developed in this research predicts the unconfined compressive strength of weak to very strong granites (20.3-198.15 MPa) with less than ±20% deviation from the experimental data for 70% of the specimen and significantly outperforms a number of available models available in the literature. The results also raise interesting questions with regards to the suitability of the Pearson correlation coefficient in assessing the prediction accuracy of models.
Armaghani, DJ, Yagiz, S, Mohamad, ET & Zhou, J 2021, 'Prediction of TBM performance in fresh through weathered granite using empirical and statistical approaches', Tunnelling and Underground Space Technology, vol. 118, pp. 104183-104183.
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Arunachalam, S, Chakraborty, S, Lee, T, Paraashar, M & de Wolf, R 2021, 'Two new results about quantum exact learning', Quantum, vol. 5, pp. 587-587.
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We present two new results about exact learning by quantum computers. First, we show how to exactly learn a k-Fourier-sparse n-bit Boolean function from O(k1.5(logk)2) uniform quantum examples for that function. This improves over the bound of Θ~(kn) uniformly random classical examples (Haviv and Regev, CCC'15). Additionally, we provide a possible direction to improve our O~(k1.5) upper bound by proving an improvement of Chang's lemma for k-Fourier-sparse B...
Asatullaeva, Z, Aghdam, RFZ, Ahmad, N & Tashpulatova, L 2021, 'The impact of foreign aid on economic development: A systematic literature review and content analysis of the top 50 most influential papers', Journal of International Development, vol. 33, no. 4, pp. 717-751.
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AbstractThe effectiveness of foreign aid in stimulating economic development is a topic of intense debate in the scientific community and among policy analysts. Numerous empirical studies are devoted to investigating the impact of foreign aid on the economic growth/development of recipient countries. This study reviews the literature relevant to this debate using the bibliometric data of scholarly papers in the Scopus database. Our intention is to identify the trends of publications, their geographical distribution and the most influential journals, authors and articles in this field of research.
Ashraf, A, Naz, S, Shirazi, SH, Razzak, I & Parsad, M 2021, 'Deep transfer learning for alzheimer neurological disorder detection', Multimedia Tools and Applications, vol. 80, no. 20, pp. 30117-30142.
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Asteris, PG, Koopialipoor, M, Armaghani, DJ, Kotsonis, EA & Lourenço, PB 2021, 'Prediction of cement-based mortars compressive strength using machine learning techniques', Neural Computing and Applications, vol. 33, no. 19, pp. 13089-13121.
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Asteris, PG, Mamou, A, Hajihassani, M, Hasanipanah, M, Koopialipoor, M, Le, T-T, Kardani, N & Armaghani, DJ 2021, 'Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks', Transportation Geotechnics, vol. 29, pp. 100588-100588.
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Atiqah Rochin Demong, N, Lu, J & Khadeer Hussain, F 2021, 'An Adaptive Personalized Property Investment Risk Analysis Method Based on Data-Driven Approach', International Journal of Information Technology & Decision Making, vol. 20, no. 02, pp. 671-706.
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Risk assessment analysis for investment decisions largely depends on expert judgment using traditional approaches and is lacking in considering investors’ different preferences and limitations. This paper proposes an adaptive personalized property investment risk analysis (APPIRA) method to identify the property investment determinants using a data-driven and personalized approach to weight the risk factors using the multicriteria decision model for optimal solutions. Result for predictive modeling using value prediction technique that measures the median house price depicts that the best method used was nonseasonal ARIMA. Furthermore, classification technique indicates that in each of the three selected suburbs, different property characteristics determined the rental properties desirable. As shown in result, for the investors who plan to invest in property for rental purposes, they need to choose townhouse type or property to make it rentable while for Vaucluse, terrace houses. These results can be applied into practice and will benefit the property industry directly.
Augustine, R, Dan, P, Hasan, A, Khalaf, IM, Prasad, P, Ghosal, K, Gentile, C, McClements, L & Maureira, P 2021, 'Stem cell-based approaches in cardiac tissue engineering: controlling the microenvironment for autologous cells', Biomedicine & Pharmacotherapy, vol. 138, pp. 111425-111425.
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Avilés-Ochoa, E, Flores-Sosa, M & Merigó, JM 2021, 'A bibliometric overview of volatility', Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 1997-2009.
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Price volatility is a matter of importance for making decisions in the finance world. The growing studies regarding volatility have focused on minimizing the risks through modeling, estimating and forecasting. This paper presents a bibliometric overview of the most important authors, institutions and countries that work on the topic. Additionally, a historical analysis of how the agents have interrelated is presented. For the purposes of the analysis and the design of tables and graphics, tools from the Web of Science Core Collection and the VOSviewer software were used. The results show the importance of volatility in the study of business economics and decision making.
Awang, MSN, Mohd Zulkifli, NW, Abbas, MM, Amzar Zulkifli, S, Kalam, MA, Ahmad, MH, Mohd Yusoff, MNA, Mazlan, M & Daud, WMAW 2021, 'Effect of Addition of Palm Oil Biodiesel in Waste Plastic Oil on Diesel Engine Performance, Emission, and Lubricity', ACS Omega, vol. 6, no. 33, pp. 21655-21675.
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Azadi, S, Tafazzoli Shadpour, M & Warkiani, ME 2021, 'Characterizing the effect of substrate stiffness on the extravasation potential of breast cancer cells using a 3D microfluidic model', Biotechnology and Bioengineering, vol. 118, no. 2, pp. 823-835.
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AbstractDifferent biochemical and biomechanical cues from tumor microenvironment affect the extravasation of cancer cells to distant organs; among them, the mechanical signals are poorly understood. Although the effect of substrate stiffness on the primary migration of cancer cells has been previously probed, its role in regulating the extravasation ability of cancer cells is still vague. Herein, we used a microfluidic device to mimic the extravasation of tumor cells in a 3D microenvironment containing cancer cells, endothelial cells, and the biological matrix. The microfluidic‐based extravasation model was utilized to probe the effect of substrate stiffness on the invasion ability of breast cancer cells. MCF7 and MDA‐MB‐231 cancer cells were cultured among substrates with different stiffness which followed by monitoring their extravasation capability through the microfluidic device. Our results demonstrated that acidic collagen at a concentration of 2.5 mg/ml promotes migration of cancer cells. Additionally, the substrate softening resulted in up to 46% reduction in the invasion of breast cancer cells. The substrate softening not only affected the number of extravasated cells but also reduced their migration distance up to 53%. We further investigated the secreted level of matrix metalloproteinase 9 (MMP9) and identified that there is a positive correlation between substrate stiffening, MMP9 concentration, and extravasation of cancer cells. These findings suggest that the substrate stiffness mediates the cancer cells extravasation in a microfluidic model. Changes in MMP9 level could be one of the possible underlying mechanisms which need more investigations to be addressed thoroughly.
Azeez, OS, Pradhan, B & Jena, R 2021, 'Urban tree classification using discrete-return LiDAR and an object-level local binary pattern algorithm', Geocarto International, vol. 36, no. 16, pp. 1785-1803.
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Urban trees have the potential to mitigate some of the harm brought about by rapid urbanization and population growth, as well as serious environmental degradation (e.g. soil erosion, carbon pollution and species extirpation), in cities. This paper presents a novel urban tree extraction modelling approach that uses discrete laser scanning point clouds and object-based textural analysis to (1) develop a model characterised by four sub-models, including (a) height-based split segmentation, (b) feature extraction, (c) texture analysis and (d) classification, and (2) apply this model to classify urban trees. The canopy height model is integrated with the object-level local binary pattern algorithm (LBP) to achieve high classification accuracy. The results of each sub-model reveal that the classification of urban trees based on the height at 47.14 (high) and 2.12 m (low), respectively, while based on crown widths were highest and lowest at 22.5 and 2.55 m, respectively. Results also indicate that the proposed algorithm of urban tree modelling is effective for practical use.
Azzam, R, Alkendi, Y, Taha, T, Huang, S & Zweiri, Y 2021, 'A Stacked LSTM-Based Approach for Reducing Semantic Pose Estimation Error', IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-14.
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© 1963-2012 IEEE. Achieving high estimation accuracy is significant for semantic simultaneous localization and mapping (SLAM) tasks. Yet, the estimation process is vulnerable to several sources of error, including limitations of the instruments used to perceive the environment, shortcomings of the employed algorithm, environmental conditions, or other unpredictable noise. In this article, a novel stacked long short-term memory (LSTM)-based error reduction approach is developed to enhance the accuracy of semantic SLAM in presence of such error sources. Training and testing data sets were constructed through simulated and real-time experiments. The effectiveness of the proposed approach was demonstrated by its ability to capture and reduce semantic SLAM estimation errors in training and testing data sets. Quantitative performance measurement was carried out using the absolute trajectory error (ATE) metric. The proposed approach was compared with vanilla and bidirectional LSTM networks, shallow and deep neural networks, and support vector machines. The proposed approach outperforms all other structures and was able to significantly improve the accuracy of semantic SLAM. To further verify the applicability of the proposed approach, it was tested on real-time sequences from the TUM RGB-D data set, where it was able to improve the estimated trajectories.
Azzam, R, Kong, FH, Taha, T & Zweiri, Y 2021, 'Pose-Graph Neural Network Classifier for Global Optimality Prediction in 2D SLAM', IEEE Access, vol. 9, pp. 80466-80477.
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Ba, X, Wang, P, Zhang, C, Zhu, JG & Guo, Y 2021, 'Improved Deadbeat Predictive Current Control to Enhance the Performance of the Drive System of Permanent Magnet Synchronous Motors', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-4.
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Badeti, U, Pathak, NK, Volpin, F, Dorji, U, Freguia, S, Shon, HK & Phuntsho, S 2021, 'Impact of source-separation of urine on effluent quality, energy consumption and greenhouse gas emissions of a decentralized wastewater treatment plant', Process Safety and Environmental Protection, vol. 150, pp. 298-304.
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The impact of urine diversion on the biological treatment processes at a decentralized wastewater treatment plant (WWTP) was investigated. BioWin software was used for the simulations, and the model was firstly validated with data from a real WWTP. The simulations showed that upto 82 % N, 30 % P, 6% chemical oxygen demand (COD) load to the WWTP can be reduced by complete urine diversion but effluent N reduction was notable up to 75 % urine diversion. Under the current WWTP operating conditions, the simulations suggest that 33 % of aeration energy can be saved by 90 % urine diversion. Direct N2O and CO2 emissions in the treatment processes can also be reduced by 98 % and 25 % respectively. Indirect green house gas emissions can also be reduced by 20 %. Overall, the reduction in the discharge of nutrients and in the operation of blowers was found to contribute to a 22 % reduction in the operating costs (on energy consumption and nutrient discharge).
Bagheri, S, Huang, Y, Walker, PD, Zhou, JL & Surawski, NC 2021, 'Strategies for improving the emission performance of hybrid electric vehicles', Science of The Total Environment, vol. 771, pp. 144901-144901.
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Low emission vehicle technologies need widespread adoption in the transport sector to overcome its significant decarbonisation challenges. Hybrid Electric Vehicles (HEVs) represent an intermediate technology between pure electric vehicles and internal combustion engines that have proven capability in reducing petroleum consumption. HEV customers often cite improved fuel economy as a major benefit from adopting this technology; however, outstanding questions remain regarding their respective emission levels. Through an extensive literature study, we show that several issues remain with HEV emissions performance which stem from frequent high-power cold starts, engine calibration issues and inefficient operating conditions for catalytic converters. HEVs have more NOx, HC, CO and particle number emissions compared to conventional vehicles by up to 21.0, 5.8, 9.0 and 23.3 times, respectively. Improved engine control algorithms, after-treatment design and thermal design of three-way catalysts emerge as research priorities for improving the emissions performance of HEVs.
Bai, F, Vidal-Calleja, T & Grisetti, G 2021, 'Sparse Pose Graph Optimization in Cycle Space', IEEE Transactions on Robotics, vol. 37, no. 5, pp. 1381-1400.
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The state-of-the-art modern pose-graph optimization (PGO) systems are vertex based. In this context, the number of variables might be high, albeit the number of cycles in the graph (loop closures) is relatively low. For sparse problems particularly, the cycle space has a significantly smaller dimension than the number of vertices. By exploiting this observation, in this article, we propose an alternative solution to PGO that directly exploits the cycle space. We characterize the topology of the graph as a cycle matrix, and reparameterize the problem using relative poses, which are further constrained by a cycle basis of the graph. We show that by using a minimum cycle basis, the cycle-based approach has superior convergence properties against its vertex-based counterpart, in terms of convergence speed and convergence to the global minimum. For sparse graphs, our cycle-based approach is also more time efficient than the vertex-based. As an additional contribution of this work, we present an effective algorithm to compute the minimum cycle basis. Albeit known in computer science, we believe that this algorithm is not familiar to the robotics community. All the claims are validated by experiments on both standard benchmarks and simulated datasets. To foster the reproduction of the results, we provide a complete open-source C++ implementation (1) of our approach.
Bai, J, Liu, Y, Ren, Y, Nie, Z & Guo, YJ 2021, 'Efficient Synthesis of Linearly Polarized Shaped Patterns Using Iterative FFT via Vectorial Least-Square Active Element Pattern Expansion', IEEE Transactions on Antennas and Propagation, vol. 69, no. 9, pp. 6040-6045.
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Bai, L, Yao, L, Wang, X, Li, C & Zhang, X 2021, 'Deep spatial–temporal sequence modeling for multi-step passenger demand prediction', Future Generation Computer Systems, vol. 121, pp. 25-34.
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Bai, X, Hou, S, Wang, X, Hao, D, Sun, B, Jia, T, Shi, R & Ni, B-J 2021, 'Mechanism of surface and interface engineering under diverse dimensional combinations: the construction of efficient nanostructured MXene-based photocatalysts', Catalysis Science & Technology, vol. 11, no. 15, pp. 5028-5049.
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Proposed scheme of the surface and interface engineering to improve the charge separation efficiency of MXene-based photocatalysts.
Bai, X, Jia, T, Wang, X, Hou, S, Hao, D & Bingjie-Ni 2021, 'High carrier separation efficiency for a defective g-C3N4 with polarization effect and defect engineering: mechanism, properties and prospects', Catalysis Science & Technology, vol. 11, no. 16, pp. 5432-5447.
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Different types of defects in g-C3N4 induce polarization effect to promote the separation of charge carriers and improve the photocatalytic efficiency.
Bai, X, Ni, J, Beretov, J, Wasinger, VC, Wang, S, Zhu, Y, Graham, P & Li, Y 2021, 'Activation of the eIF2α/ATF4 axis drives triple-negative breast cancer radioresistance by promoting glutathione biosynthesis', Redox Biology, vol. 43, pp. 101993-101993.
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Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype. Radiotherapy is an effective option for the treatment of TNBC; however, acquired radioresistance is a major challenge to the modality. In this study, we show that the integrated stress response (ISR) is the most activated signaling pathway in radioresistant TNBC cells. The constitutive phosphorylation of eIF2α in radioresistant TNBC cells promotes the activation of ATF4 and elicits the transcription of genes implicated in glutathione biosynthesis, including GCLC, SLC7A11, and CTH, which increases the intracellular level of reduced glutathione (GSH) and the scavenging of reactive oxygen species (ROS) after irradiation (IR), leading to a radioresistant phenotype. The cascade is significantly up-regulated in human TNBC tissues and is associated with unfavorable survival in patients. Dephosphorylation of eIF2α increases IR-induced ROS accumulation in radioresistant TNBC cells by disrupting ATF4-mediated GSH biosynthesis and sensitizes them to IR in vitro and in vivo. These findings reveal ISR as a vital mechanism underlying TNBC radioresistance and propose the eIF2α/ATF4 axis as a novel therapeutic target for TNBC treatment.
Baier-Fuentes, H, Merigó, J, Miranda, L & Martínez-López, F 2021, 'Strategic planning research through fifty years of long range planning: A bibliometric overview', Strategic Management, vol. 26, no. 1, pp. 3-25.
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Long Range Planning (LRP) is the first journal focused on strategic planning. It was created in 1968 by the Long Range Planning Society, and it celebrated its 50 th anniversary in 2018. This event led to the presentation of a complete bibliometric study aimed at identifying the most significant results that occurred in the journal during this period. For this purpose, bibliometric data were collected from the Web of Science Core Collection database, and two bibliometric approaches were used to analyze the journal's publications: a performance analysis and a graphical mapping of the literature. The first of these uses a wide range of productivity and influence indicators that include the number of publications and citations, the h-index, and citations by paper, among others. The second approach uses the VOSviewer software to deliver a graphical view of the various intellectual connections within LRP. The results of both bibliometric approaches are consistent and confirm LRP as a leading journal in strategic planning and management, with increasing participation of authors and universities from countries around the world.
Bailo, F & Goldsmith, BE 2021, 'No paradox here? Improving theory and testing of the nuclear stability–instability paradox with synthetic counterfactuals', Journal of Peace Research, vol. 58, no. 6, pp. 1178-1193.
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This article contributes to both the theoretical elaboration and empirical testing of the ‘stability–instability paradox’, the proposition that while nuclear weapons deter nuclear war, they also increase conventional conflict among nuclear-armed states. Some recent research has found support for the paradox, but quantitative studies tend to pool all international dyads while qualitative and theoretical studies focus almost exclusively on the USA–USSR and India–Pakistan dyads. This article argues that existing empirical tests lack clearly relevant counterfactual cases, and are vulnerable to a number of inferential problems, including selection on the dependent variable, unintentionally biased inference, and extrapolation from irrelevant cases. The limited evidentiary base coincides with a lack of consideration of the theoretical conditions under which the paradox might apply. To address these issues this article theorizes some scope conditions for the paradox. It then applies synthetic control, a quantitative method for valid comparison when appropriate counterfactual cases are lacking, to model international conflict between India–Pakistan, China–India, and North Korea–USA, before and after nuclearization. The article finds only limited support for the paradox when considered as a general theory, or within the theorized scope conditions based on the balance of resolve and power within each dyad.
Bailo, F, Meese, J & Hurcombe, E 2021, 'The Institutional Impacts of Algorithmic Distribution: Facebook and the Australian News Media', Social Media + Society, vol. 7, no. 2, pp. 205630512110249-205630512110249.
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Since changing its algorithm in January 2018 to boost the content of family and friends over other content (including news), Facebook has signaled that it is less interested in news. However, the field is still trying to understand the long-term impacts of this change for news publishers. This is a problem because policymakers and legislators across the world are becoming concerned about the relationship between platforms and publishers. In particular, there are worries that platforms’ ability to make unilateral decisions about how their algorithms operate may harm the economic sustainability of journalism. This article provides some clarity around the relationship between these two parties through a longitudinal study of the Australian news media sector’s relationship with Facebook from 2014 to 2020, with a particular focus on the January 2018 algorithm change. We do this by analyzing Facebook data (2,082,804 posts from CrowdTangle) and external traffic data from 32 major Australian news outlets. These data are contextualized by additional desk research. We identify a range of trends including the decline of news sharing, the collapse in the performance of “social news,” the variable position of social media as a source of referral traffic, and, most critically, the diffused nature of the 2018 algorithm change. Our approach cannot make direct causal inferences. We can only identify trends in on-platform performance and referral traffic, which we then contextualize with industry reportage. However, the data provide vital longitudinal insights into the performance and responses of individual media outlets, news categories, and the Australian media sector as a whole during a critical moment of algorithmic change.
Bakirov, R, Fay, D & Gabrys, B 2021, 'Automated adaptation strategies for stream learning', Machine Learning, vol. 110, no. 6, pp. 1429-1462.
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AbstractAutomation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism.
Balakrishnan, HK, Badar, F, Doeven, EH, Novak, JI, Merenda, A, Dumée, LF, Loy, J & Guijt, RM 2021, '3D Printing: An Alternative Microfabrication Approach with Unprecedented Opportunities in Design', Analytical Chemistry, vol. 93, no. 1, pp. 350-366.
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In the past decade, 3D printing technologies have been adopted for the fabrication of microfluidic devices. Extrusion-based approaches including fused filament fabrication (FFF), jetting technologies including inkjet 3D printing, and vat photopolymerization techniques including stereolithography (SLA) and digital light projection (DLP) are the 3D printing methods most frequently adopted by the microfluidic community. Each printing technique has merits toward the fabrication of microfluidic devices. Inkjet printing offers a good selection of materials and multimaterial printing, and the large build space provides manufacturing throughput, while FFF offers a great selection of materials and multimaterial printing but at lower throughput compared to inkjet 3D printing. Technical and material developments adopted from adjacent research fields and developed by the microfluidic community underpin the printing of sub-100 μm enclosed microchannels by DLP, but challenges remain in multimaterial printing throughput. With the feasibility of 3D printed microfluidics established, we look ahead at trends in 3D printing to gain insights toward the future of this technology beyond the sole prism of being an alternative fabrication approach. A shift in emphasis from using 3D printing for prototyping, to mimic conventionally manufactured outputs, toward integrated approaches from a design perspective is critically developed.
Balakrishnan, HK, Doeven, EH, Merenda, A, Dumée, LF & Guijt, RM 2021, '3D printing for the integration of porous materials into miniaturised fluidic devices: A review', Analytica Chimica Acta, vol. 1185, pp. 338796-338796.
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Porous materials facilitate the efficient separation of chemicals and particulate matter by providing selectivity through structural and surface properties and are attractive as sorbent owing to their large surface area. This broad applicability of porous materials makes the integration of porous materials and microfluidic devices important in the development of more efficient, advanced separation platforms. Additive manufacturing approaches are fundamentally different to traditional manufacturing methods, providing unique opportunities in the fabrication of fluidic devices. The complementary 3D printing (3DP) methods are each accompanied by unique opportunities and limitations in terms of minimum channel size, scalability, functional integration and automation. This review focuses on the developments in the fabrication of 3DP miniaturised fluidic devices with integrated porous materials, focusing polymer-based methods including fused filament fabrication (FFF), inkjet 3D printing and digital light projection (DLP). The 3DP methods are compared based on resolution, scope for multimaterial printing and scalability for manufacturing. As opportunities for printing pores are limited by resolution, the focus is on approaches to incorporate materials with sub-micron pores to be used as membrane, sorbent or stationary phase in separation science using Post-Print, Print-Pause-Print and In-Print processes. Technical aspects analysing the efficiency of the fabrication process towards scalable manufacturing are combined with application aspects evaluating the separation and/or extraction performance. The review is concluded with an overview on achievements and opportunities for manufacturable 3D printed membrane/sorbent integrated fluidic devices.
Bane, O, Said, D, Weiss, A, Stocker, D, Kennedy, P, Hectors, SJ, Khaim, R, Salem, F, Delaney, V, Menon, MC, Markl, M, Lewis, S & Taouli, B 2021, '4D flow MRI for the assessment of renal transplant dysfunction: initial results', European Radiology, vol. 31, no. 2, pp. 909-919.
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Banerjee, S, Lyu, J, Huang, Z, Leung, HFF, Lee, TT-Y, Yang, D, Su, S, Zheng, Y & Ling, S-H 2021, 'Light-Convolution Dense Selection U-Net (LDS U-Net) for Ultrasound Lateral Bony Feature Segmentation', Applied Sciences, vol. 11, no. 21, pp. 10180-10180.
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Scoliosis is a widespread medical condition where the spine becomes severely deformed and bends over time. It mostly affects young adults and may have a permanent impact on them. A periodic assessment, using a suitable modality, is necessary for its early detection. Conventionally, the usually employed modalities include X-ray and MRI, which employ ionising radiation and are expensive. Hence, a non-radiating 3D ultrasound imaging technique has been developed as a safe and economic alternative. However, ultrasound produces low-contrast images that are full of speckle noise, and skilled intervention is necessary for their processing. Given the prevalent occurrence of scoliosis and the limitations of scalability of human expert interventions, an automatic, fast, and low-computation assessment technique is being developed for mass scoliosis diagnosis. In this paper, a novel hybridized light-weight convolutional neural network architecture is presented for automatic lateral bony feature identification, which can help to develop a fully-fledged automatic scoliosis detection system. The proposed architecture, Light-convolution Dense Selection U-Net (LDS U-Net), can accurately segment ultrasound spine lateral bony features, from noisy images, thanks to its capabilities of smartly selecting only the useful information and extracting rich deep layer features from the input image. The proposed model is tested using a dataset of 109 spine ultrasound images. The segmentation result of the proposed network is compared with basic U-Net, Attention U-Net, and MultiResUNet using various popular segmentation indices. The results show that LDS U-Net provides a better segmentation performance compared to the other models. Additionally, LDS U-Net requires a smaller number of parameters and less memory, making it suitable for a large-batch screening process of scoliosis without a high computational requirement.
Bano, M, Zowghi, D & Arora, C 2021, 'Requirements, Politics, or Individualism: What Drives the Success of COVID-19 Contact-Tracing Apps?', IEEE Softw., vol. 38, no. 1, pp. 7-12.
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The year 2020 brought us the global pandemic of COVID-19, which is not just a health crisis but a disruption to the fabric of society around the world. With no vaccine yet approved, other measures have been taken all over the world related to lockdowns, social distancing, and contact tracing to quarantine the infected individuals and suppress community transmission. The numerous challenges presented by this novel coronavirus, such as the incubation period, various symptoms, and asymptomatic superspreaders, have exacerbated the challenges of manual contact tracing.
Bao, T, Damtie, MM, Wei, W, Phong Vo, HN, Nguyen, KH, Hosseinzadeh, A, Cho, K, Yu, ZM, Jin, J, Wei, XL, Wu, K, Frost, RL & Ni, B-J 2021, 'Simultaneous adsorption and degradation of bisphenol A on magnetic illite clay composite: Eco-friendly preparation, characterizations, and catalytic mechanism', Journal of Cleaner Production, vol. 287, pp. 125068-125068.
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Excess bisphenol A (BPA) is a pollutant of concern in different water sources. In this work, magnetic illite clay-composite material (Fe3O4@illite) was synthesized via the coprecipitation method by loading Fe3O4 nanoparticles (nano-Fe3O4) onto the surfaces of illite clay. Results from different characterizations showed that nano-Fe3O4 was embedded into illite clay nanosheets and existed on the surfaces of illite clay, thereby reducing the degree of agglomeration and improving dispersibility. The catalytic BPA degradation of Fe3O4@illite and nano-Fe3O4 confirmed the superior performance of Fe3O4@illite compared with that of nano-Fe3O4. The optimum operating parameters for degradation were 0.3 mL of H2O2 at pH of 3 in the presence of Fe3O4@illite, which provided a maximum degradation capacity up to 816, 364, 113, and 68 mg/g for epoxy BPA concentration of resin wastewater (266 mg/L), synthetic wastewater (80 mg/L), Hefei City swan lake (25 mg/L), and Hefei University lake wastewater (14.94 mg/L), respectively, in 180 min reaction time. The degradation data conformed to the pseudo-first-order kinetic model. The degradation pathways and mineralization study revealed that the adsorption-Fenton-like reaction was the principal mechanism that demonstrated 100% degradation efficiency of Fe3O4@illite even after nine successive runs. The regeneration and reusability tendency analysis ensured that Fe3O4@illite can be easily separated by using magnets. Therefore, Fe3O4@illite composite with H2O2 Fenton-like technology was a promising method for BPA degradation.
Baral, P, Indraratna, B, Rujikiatkamjorn, C, Kelly, R & Vincent, P 2021, 'Consolidation by Vertical Drains beneath a Circular Embankment under Surcharge and Vacuum Preloading', Journal of Geotechnical and Geoenvironmental Engineering, vol. 147, no. 8, pp. 05021004-05021004.
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A membrane-type vacuum consolidation system, including surcharge loading and prefabricated vertical drains, was applied to rapidly consolidate soft clay beneath a circular embankment located at the National Field Testing Facility (NFTF) at Ballina, New South Wales (NSW), Australia. Most previous studies were devoted to multidrain systems corresponding to an embankment strip loading in two-dimensional (2D) plane strain. So far, no case study has been investigated using vacuum consolidation via prefabricated vertical drains (PVDs) beneath a circular loaded area, where the system conforms to an axisymmetric problem. This paper outlines the site investigation, construction technique, and installation of a suite of instrumentation on this circular embankment. It also describes and discusses consolidation during and after the construction of this embankment in terms of settlement, excess pore water pressure, lateral deformation, and water flow relationships as they pertain to prediction embankment with vertical drains and surcharge only. The case study demonstrates that a loss of vacuum pressure can be prevented using the proposed approach in a membrane system. Treatment of water extracted using the vacuum consolidation technique, especially in acid-sulfate terrain, is also presented.
Baral, P, Rujikiatkamjorn, C, Indraratna, B, Leroueil, S & Yin, J-H 2021, 'Closure to “Radial Consolidation Analysis Using Delayed Consolidation Approach” by Pankaj Baral, Cholachat Rujikiatkamjorn, Buddhima Indraratna, Serge Leroueil, and Jian-Hua Yin', Journal of Geotechnical and Geoenvironmental Engineering, vol. 147, no. 1, pp. 07020025-07020025.
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Barani, A, Mosaddegh, P, Haghjooy Javanmard, S, Sepehrirahnama, S & Sanati-Nezhad, A 2021, 'Numerical and experimental analysis of a hybrid material acoustophoretic device for manipulation of microparticles', Scientific Reports, vol. 11, no. 1.
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AbstractAcoustophoretic microfluidic devices have been developed for accurate, label-free, contactless, and non-invasive manipulation of bioparticles in different biofluids. However, their widespread application is limited due to the need for the use of high quality microchannels made of materials with high specific acoustic impedances relative to the fluid (e.g., silicon or glass with small damping coefficient), manufactured by complex and expensive microfabrication processes. Soft polymers with a lower fabrication cost have been introduced to address the challenges of silicon- or glass-based acoustophoretic microfluidic systems. However, due to their small acoustic impedance, their efficacy for particle manipulation is shown to be limited. Here, we developed a new acoustophoretic microfluid system fabricated by a hybrid sound-hard (aluminum) and sound-soft (polydimethylsiloxane polymer) material. The performance of this hybrid device for manipulation of bead particles and cells was compared to the acoustophoretic devices made of acoustically hard materials. The results show that particles and cells in the hybrid material microchannel travel to a nodal plane with a much smaller energy density than conventional acoustic-hard devices but greater than polymeric microfluidic chips. Against conventional acoustic-hard chips, the nodal line in the hybrid microchannel could be easily tuned to be placed in an off-center position by changing the frequency, effective for particle separation from a host fluid in parallel flow stream models. It is also shown that the hybrid acoustophoretic device deals with smaller temperature rise which is safer for the actuation of bioparticles. This new device eliminates the limitations of each sound-soft and sound-hard materials in terms of cost, adjusting the position of nodal plane, temperature rise, fragility, production cost and disposability, making it desirable for developing the next gene...
Barbar, M & Sui, Y 2021, 'Compacting points-to sets through object clustering', Proceedings of the ACM on Programming Languages, vol. 5, no. OOPSLA, pp. 1-27.
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Inclusion-based set constraint solving is the most popular technique for whole-program points-to analysis whereby an analysis is typically formulated as repeatedly resolving constraints between points-to sets of program variables. The set union operation is central to this process. The number of points-to sets can grow as analyses become more precise and input programs become larger, resulting in more time spent performing unions and more space used storing these points-to sets. Most existing approaches focus on improving scalability of precise points-to analyses from an algorithmic perspective and there has been less research into improving the data structures behind the analyses. Bit-vectors as one of the more popular data structures have been used in several mainstream analysis frameworks to represent points-to sets. To store memory objects in bit-vectors, objects need to mapped to integral identifiers. We observe that this object-to-identifier mapping is critical for a compact points-to set representation and the set union operation. If objects in the same points-to sets (co-pointees) are not given numerically close identifiers, points-to resolution can cost significantly more space and time. Without data on the unpredictable points-to relations which would be discovered by the analysis, an ideal mapping is extremely challenging. In this paper, we present a new approach to inclusion-based analysis by compacting points-to sets through object clustering. Inspired by recent staged analysis where an auxiliary analysis produces results approximating a more precise main analysis, we formulate points-to set compaction as an optimisation problem solved by integer programming using constraints generated from the auxiliary analysis’s results in order to produce an effective mapping. We then develop a more approximate mapping, yet much more efficiently, using hierarchical clustering to compact bit-v...
Barbieri, DM, Lou, B, Passavanti, M, Hui, C, Hoff, I, Lessa, DA, Sikka, G, Chang, K, Gupta, A, Fang, K, Banerjee, A, Maharaj, B, Lam, L, Ghasemi, N, Naik, B, Wang, F, Foroutan Mirhosseini, A, Naseri, S, Liu, Z, Qiao, Y, Tucker, A, Wijayaratna, K, Peprah, P, Adomako, S, Yu, L, Goswami, S, Chen, H, Shu, B, Hessami, A, Abbas, M, Agarwal, N & Rashidi, TH 2021, 'Impact of COVID-19 pandemic on mobility in ten countries and associated perceived risk for all transport modes', PLOS ONE, vol. 16, no. 2, pp. e0245886-e0245886.
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The restrictive measures implemented in response to the COVID-19 pandemic have triggered sudden massive changes to travel behaviors of people all around the world. This study examines the individual mobility patterns for all transport modes (walk, bicycle, motorcycle, car driven alone, car driven in company, bus, subway, tram, train, airplane) before and during the restrictions adopted in ten countries on six continents: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the United States. This cross-country study also aims at understanding the predictors of protective behaviors related to the transport sector and COVID-19. Findings hinge upon an online survey conducted in May 2020 (N = 9,394). The empirical results quantify tremendous disruptions for both commuting and non-commuting travels, highlighting substantial reductions in the frequency of all types of trips and use of all modes. In terms of potential virus spread, airplanes and buses are perceived to be the riskiest transport modes, while avoidance of public transport is consistently found across the countries. According to the Protection Motivation Theory, the study sheds new light on the fact that two indicators, namely income inequality, expressed as Gini index, and the reported number of deaths due to COVID-19 per 100,000 inhabitants, aggravate respondents’ perceptions. This research indicates that socio-economic inequality and morbidity are not only related to actual health risks, as well documented in the relevant literature, but also to the perceived risks. These findings document the global impact of the COVID-19 crisis as well as provide guidance for transportation practitioners in developing future strategies.
Bardhan, A, Samui, P, Ghosh, K, Gandomi, AH & Bhattacharyya, S 2021, 'ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions', Applied Soft Computing, vol. 110, pp. 107595-107595.
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This study proposes novel integration of extreme learning machine (ELM) and adaptive neuro swarm intelligence (ANSI) techniques for the determination of California bearing ratio (CBR) of soils for the subgrade layers of railway tracks, a critical real-time problem of geotechnical engineering. Particle swarm optimization (PSO) with adaptive and time-varying acceleration coefficients (TAC) was employed to optimize the learning parameters of ELM. Three novel ELM-based ANSI models, namely ELM coupled-modified PSO (ELM-MPSO), ELM coupled-TAC PSO (ELM-TPSO), and ELM coupled-improved PSO (ELM-IPSO) were developed for predicting the CBR of soils in soaked conditions. Compared to standard PSO (SPSO), the modified and improved version of PSO are capable of converging to a high-quality solution at early iterations. A detailed comparison was made between the proposed models and other conventional soft computing techniques, such as conventional ELM, artificial neural network, genetic programming, support vector machine, group method of data handling, and three ELM-based swarm intelligence optimized models (ELM-based grey wolf optimization, ELM-based slime mould algorithm, and ELM-based Harris hawks optimization). Experimental results reveal that the proposed ELM-based ANSI models can attain the most accurate prediction and confirm the dominance of MPSO over SPSO. Considering the consequences and robustness of the proposed models, it can be concluded that the newly constructed ELM-based ANSI models, especially ELM-MPSO, can solve the difficulties in tuning the acceleration coefficients of SPSO by the trial-and-error method for predicting the CBR of soils and be further applied to other real-time problems of geotechnical engineering.
Barua, PD, Dogan, S, Tuncer, T, Baygin, M & Acharya, UR 2021, 'Novel automated PD detection system using aspirin pattern with EEG signals', Computers in Biology and Medicine, vol. 137, pp. 104841-104841.
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Barzegarkhoo, R, Lee, SS, Khan, SA, Siwakoti, Y & Lu, DD-C 2021, 'A Novel Generalized Common-Ground Switched-Capacitor Multilevel Inverter Suitable for Transformerless Grid-Connected Applications', IEEE Transactions on Power Electronics, vol. 36, no. 9, pp. 10293-10306.
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Recent research on common-ground switched-capacitor transformerless (CGSC-TL) inverters shows some intriguing features, such as integrated voltage boosting ability, possible multilevel output voltage generation, and nullification of the leakage current issue. However, the number of output voltage levels and also the overall voltage boosting ratio of most of the existing CGSC-TL inverters are limited to five and two, respectively. This article presents a generalized circuit configuration of such converters capable of higher voltage gain and output voltage levels generation. A basic five-level (5L) CGSC-TL inverter is first proposed using eight power switches and two self-balanced dc-link capacitors. A generalized extension of the circuit for any output voltage levels and voltage gain is then presented while keeping all the traits of the proposed basic 5L-CGSC-TL inverter. The circuit descriptions, control strategy, design guidelines, comparative study, and the relevant simulation and experimental results for the proposed 5L-CGSC-TL inverters and its seven-level derived topology are presented to validate the effectiveness and feasibility of this proposal.
Barzegarkhoo, R, Lee, SS, Siwakoti, YP, Khan, SA & Blaabjerg, F 2021, 'Design, Control, and Analysis of a Novel Grid-Interfaced Switched-Boost Dual T-Type Five-Level Inverter With Common-Ground Concept.', IEEE Trans. Ind. Electron., vol. 68, no. 9, pp. 8193-8206.
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Barzegarkhoo, R, Mojallali, H, Shahalami, SH & Siwakoti, YP 2021, 'A novel common‐ground switched‐capacitor five‐level inverter with adaptive hysteresis current control for grid‐connected applications', IET Power Electronics, vol. 14, no. 12, pp. 2084-2098.
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AbstractThe aim of this paper is to present a new topology of single‐phase transformerless inverters, which can be tied to the local grid as a low‐scaled ac module system. The proposed topology offers a common ground between the neutral point of the ac grid and the negative terminal of the dc supply, and can properly alleviate the concern of variable common mode voltage and leakage current problems. This promising feature is acquired by the aim of both the switched‐capacitor and charge pumped circuit cells. In order to inject a tightly controlled ac current to the grid, an adaptive hysteresis current controller scheme is also presented, which can guarantee almost fixed switching frequency operation of the involved power switches. A complete theoretical analysis, comparative study, and the relevant experimental results are also given to confirm the superior performance of the proposed topology.
Barzegarkhoo, R, Siwakoti, YP, Aguilera, RP, Khan, MNH, Lee, SS & Blaabjerg, F 2021, 'A Novel Dual-Mode Switched-Capacitor Five-Level Inverter With Common-Ground Transformerless Concept', IEEE Transactions on Power Electronics, vol. 36, no. 12, pp. 13740-13753.
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Transformerless (TL) grid-connected photovoltaic (PV) inverters with a common-ground (CG) circuit architecture exhibit some excellent features in removing the leakage current concern and improving the overall efficiency. However, the ability to cope with a wide range of input voltage changes while maintaining the output voltage in a single power conversion stage is a key technological challenge. Considering this, the article at hand proposes a novel dual-mode switched-capacitor five-level (DMSC5L)-TL inverter with a CG feature connected to the grid. The proposed topology is comprised of a single dc source and power diode, three capacitors, four unidirectional, and three bidirectional power switches. Based on the series-parallel switching conversion of the involved switches, the proposed DMSC5L-TL inverter can generate five distinctive output voltage levels during both the boost and buck operation modes with a self-voltage balancing operation for the involved capacitors. A simple dead-beat continuous current controller (DB3C) modulation technique is also used to handle both the active and reactive power exchange while ensuring a fixed switching frequency operation. The proposed circuit description with its DB3C details, the design guidelines with a comparative study, and some experimental results are also given to show the feasibility of the proposed solution for the practical applications.
Barzegarkhoo, R, Siwakoti, YP, Vosoughi, N & Blaabjerg, F 2021, 'Six-Switch Step-Up Common-Grounded Five-Level Inverter With Switched-Capacitor Cell for Transformerless Grid-Tied PV Applications.', IEEE Trans. Ind. Electron., vol. 68, no. 2, pp. 1374-1387.
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Basack, S, Goswami, G & Nimbalkar, S 2021, 'Analytical and Numerical Solutions to Selected Research Problems in Geomechanics and Geohydraulics', WSEAS TRANSACTIONS ON APPLIED AND THEORETICAL MECHANICS, vol. 16, pp. 222-231.
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Geomechanical and geohydraulic engineering is a promising study area with several emerging research concerns. Most of such problems requires advanced level of mathematics to arrive at specific solutions. A wide range of approaches includes several analytical and numerical techniques for better understanding of such problems. In this paper, a few selected research problems are identified, and their solution techniques are demonstrated. The specific areas relevant to such problems are soil-structure interaction, ground improvement and groundwater hydraulics. This paper presents the problem identification, their mathematical solutions and results as well as pertinent analyses and useful interpretations to practice.
Basack, S, Goswami, G, Khabbaz, H, Karakouzian, M, Baruah, P & Kalita, N 2021, 'A Comparative Study on Soil Stabilization Relevant to Transport Infrastructure using Bagasse Ash and Stone Dust and Cost Effectiveness', Civil Engineering Journal, vol. 7, no. 11, pp. 1947-1963.
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Soft ground improvement to provide stable foundations for infrastructure is national priority for most countries. Weak soil may initiate instability to foundations reducing their lifespan, which necessitates the adoption of a suitable soil stabilization method. Amongst various soil stabilization techniques, using appropriate admixtures is quite popular. The present study aims to investigate the suitability of bagasse ash and stone dust as the admixtures for stabilizing soft clay, in terms of compaction and penetration characteristics. The studies were conducted by means of a series of laboratory experimentations with standard Proctor compaction and CBR tests. From the test results it was observed that adding bagasse ash and stone dust significantly upgraded the compaction and penetration properties, specifically the values of optimum moisture content, maximum dry density and CBR. Comparison of test results with available data on similar experiments conducted by other researchers were also performed. Lastly, a study on the cost effectiveness for transport embankment construction with the treated soils, based on local site conditions in the study area of Assam, India, was carried out. The results are analyzed and interpreted, and the relevant conclusions are drawn therefrom. The limitations and recommendations for future research are also included. Doi: 10.28991/cej-2021-03091771 Full Text: PDF
Basaglia, BM, Li, J, Shrestha, R & Crews, K 2021, 'Response Prediction to Walking-Induced Vibrations of a Long-Span Timber Floor', Journal of Structural Engineering, vol. 147, no. 2, pp. 1-15.
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Long-span timber floors are susceptible to annoying floor vibrations caused by human activities, which, in many cases, govern the timber floor design. Consequently, a reliable prediction of floor vibration responses under human activities, which relies on appropriate walking load models, can be crucial in the design to keep timber floors remaining competitive in the commercial building market. Much of the current design guidance for timber floor vibrations have been established from short-span floors in a residential context, and as a result, many designers refer to established design methods formulated for use with concrete and steel-framed buildings. These guidelines predict the floor response based on a deterministic single-person walking load model that differs depending on the classification of the floor as either a high- or low-frequency floor, which are assumed as a transient or resonant floor response, respectively. Recent advances in modeling human walking have been made, including a single footfall trace load that avoids the need to classify the floor, as well as load models that incorporate a probabilistic approach. To date, an investigation on different walking load models to predict the vibration response of long-span timber floors has not been undertaken, partially due to the fact that there are limited examples in practice. This paper presents the results of a recently completed state-of-the-art research project involving full-scale testing of long-span timber floors and the development of novel numerical models to investigate the applicability of the deterministic walking load model used in current floor vibration design guides, as well as two innovative single-person walking load models for predicting the floor responses of a single long-span timber cassette floor. The numerical investigation was carried out with a finite-element model calibrated with experimentally obtained modal properties. The comparison between the predicted respons...
Basha, JS, Jafary, T, Vasudevan, R, Bahadur, JK, Ajmi, MA, Neyadi, AA, Soudagar, MEM, Mujtaba, MA, Hussain, A, Ahmed, W, Shahapurkar, K, Rahman, SMA & Fattah, IMR 2021, 'Potential of Utilization of Renewable Energy Technologies in Gulf Countries', Sustainability, vol. 13, no. 18, pp. 10261-10261.
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This critical review report highlights the enormous potentiality and availability of renewable energy sources in the Gulf region. The earth suffers from extreme air pollution, climate changes, and extreme problems due to the enormous usage of underground carbon resources applications materialized in industrial, transport, and domestic sectors. The countries under Gulf Cooperation Council, i.e., Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates, mainly explore those underground carbon resources for crude oil extraction and natural gas production. As a nonrenewable resource, these are bound to be exhausted in the near future. Hence, this review discusses the importance and feasibility of renewable sources in the Gulf region to persuade the scientific community to launch and explore renewable sources to obtain the maximum benefit in electric power generation. In most parts of the Gulf region, solar and wind energy sources are abundantly available. However, attempts to harness those resources are very limited. Furthermore, in this review report, innovative areas of advanced research (such as bioenergy, biomass) were proposed for the Gulf region to extract those resources at a higher magnitude to generate surplus power generation. Overall, this report clearly depicts the current scenario, current power demand, currently installed capacities, and the future strategies of power production from renewable power sources (viz., solar, wind, tidal, biomass, and bioenergy) in each and every part of the Gulf region.
Basirun, C, Ferlazzo, ML, Howell, NR, Liu, G-J, Middleton, RJ, Martinac, B, Narayanan, SA, Poole, K, Gentile, C & Chou, J 2021, 'Microgravity × Radiation: A Space Mechanobiology Approach Toward Cardiovascular Function and Disease', Frontiers in Cell and Developmental Biology, vol. 9, p. 750775.
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In recent years, there has been an increasing interest in space exploration, supported by the accelerated technological advancements in the field. This has led to a new potential environment that humans could be exposed to in the very near future, and therefore an increasing request to evaluate the impact this may have on our body, including health risks associated with this endeavor. A critical component in regulating the human pathophysiology is represented by the cardiovascular system, which may be heavily affected in these extreme environments of microgravity and radiation. This mini review aims to identify the impact of microgravity and radiation on the cardiovascular system. Being able to understand the effect that comes with deep space explorations, including that of microgravity and space radiation, may also allow us to get a deeper understanding of the heart and ultimately our own basic physiological processes. This information may unlock new factors to consider with space exploration whilst simultaneously increasing our knowledge of the cardiovascular system and potentially associated diseases.
Bayazidy-Hasanabad, M, Vayghan, SS, Ghasemkhani, N, Pradhan, B & Alamri, A 2021, 'Developing a volunteered geographic information-based system for rapidly estimating damage from natural disasters', Arabian Journal of Geosciences, vol. 14, no. 17.
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Baygin, M, Dogan, S, Tuncer, T, Datta Barua, P, Faust, O, Arunkumar, N, Abdulhay, EW, Emma Palmer, E & Rajendra Acharya, U 2021, 'Automated ASD detection using hybrid deep lightweight features extracted from EEG signals', Computers in Biology and Medicine, vol. 134, pp. 104548-104548.
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Baygin, M, Yaman, O, Tuncer, T, Dogan, S, Barua, PD & Acharya, UR 2021, 'Automated accurate schizophrenia detection system using Collatz pattern technique with EEG signals', Biomedical Signal Processing and Control, vol. 70, pp. 102936-102936.
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Begum, H, Qian, J & Lee, JEY 2021, 'Fully differential higher order transverse mode piezoelectric membrane resonators for enhanced liquid-phase quality factors', Journal of Micromechanics and Microengineering, vol. 31, no. 10, pp. 104004-104004.
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Begum, H, Qian, J & Lee, JE-Y 2021, 'Piezoelectric Elliptical Plate Micromechanical Resonator With Low Motional Resistance for Resonant Sensing in Liquid', IEEE Sensors Journal, vol. 21, no. 6, pp. 7339-7347.
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Behera, A, Panigrahi, TK, Pati, SS, Ghatak, S, Ramasubbareddy, S & Gandomi, AH 2021, 'A hybrid evolutionary algorithm for stability analysis of 2-area multi-non-conventional system with communication delay and energy storage', International Journal of Electrical Power & Energy Systems, vol. 130, pp. 106823-106823.
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Behera, TM, Nanda, S, Mohapatra, SK, Samal, UC, Khan, MS & Gandomi, AH 2021, 'CH Selection via Adaptive Threshold Design Aligned on Network Energy', IEEE Sensors Journal, vol. 21, no. 6, pp. 8491-8500.
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Energy consumption in Wireless Sensor Networks (WSN) involving multiple sensor nodes is a crucial parameter in many applications like smart healthcare systems, home automation, environmental monitoring, and industrial use. Hence, an energy-efficient cluster-head (CH) selection strategy is imperative in a WSN to improve network performance. So to balance the harsh conditions in the network with fast changes in the energy dynamics, a novel energy-efficient adaptive fuzzy-based CH selection approach is projected. Extensive simulations exploited various real-time scenarios, such as varying the optimal position of the location of the base station and network energy. Additionally, the results showed an improved performance in the throughput (46%) and energy consumption (66%), which demonstrated the robustness and efficacy of the proposed model for the future designs of WSN applications.
Behmanesh, R, Rahimi, I & Gandomi, AH 2021, 'Evolutionary Many-Objective Algorithms for Combinatorial Optimization Problems: A Comparative Study', Archives of Computational Methods in Engineering, vol. 28, no. 2, pp. 673-688.
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© 2020, CIMNE, Barcelona, Spain. Many optimization problems encountered in the real-world have more than two objectives. To address such optimization problems, a number of evolutionary many-objective optimization algorithms were developed recently. In this paper, we tested 18 evolutionary many-objective algorithms against well-known combinatorial optimization problems, including knapsack problem (MOKP), traveling salesman problem (MOTSP), and quadratic assignment problem (mQAP), all up to 10 objectives. Results show that some of the dominance and reference-based algorithms such as non-dominated sort genetic algorithm (NSGA-III), strength Pareto-based evolutionary algorithm based on reference direction (SPEA/R), and Grid-based evolutionary algorithm (GrEA) are promising algorithms to tackle MOKP and MOTSP with 5 and 10 while increasing the number of objectives. Also, the dominance-based algorithms such as MaOEA-DDFC as well as the indicator-based algorithms such as HypE are promising to solve mQAP with 5 and 10 objectives. In contrast, decomposition based algorithms present the best on almost problems at saving time. For example, t-DEA displayed superior performance on MOTSP for up to 10 objectives.
Bei, X, Chen, S, Guan, J, Qiao, Y & Sun, X 2021, 'From Independent Sets and Vertex Colorings to Isotropic Spaces and Isotropic Decompositions: Another Bridge between Graphs and Alternating Matrix Spaces', SIAM Journal on Computing, vol. 50, no. 3, pp. 924-971.
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In the 1970s, Lovász built a bridge between graphs and alternating matrix spaces, in the context of perfect matchings [Proceedings of FCT, 1979, pp. 565-574]. A similar connection between bipartite graphs and matrix spaces plays a key role in the recent resolutions of the noncommutative rank problem [A. Garg et al., Proceedings of FOCS, 2016, pp. 109-117; G. Ivanyos, Y. Qiao, and K. V. Subrahmanyam, Comput. Complexity, 26 (2017), pp. 717-763]. In this paper, we lay the foundation for another bridge between graphs and alternating matrix spaces, in the context of independent sets and vertex colorings. The corresponding structures in alternating matrix spaces are isotropic spaces and isotropic decompositions, both useful structures in group theory and manifold theory. We first show that the maximum independent set problem and the vertex c-coloring problem reduce to the maximum isotropic space problem and the isotropic c-decomposition problem, respectively. Next, we show that several topics and results about independent sets and vertex colorings have natural correspondences for isotropic spaces and decompositions. These include algorithmic problems, such as the maximum independent set problem for bipartite graphs, and exact exponential-time algorithms for the chromatic number, as well as mathematical questions, such as the number of maximal independent sets, and the relation between the maximum degree and the chromatic number. These connections lead to new interactions between graph theory and algebra. Some results have concrete applications to group theory and manifold theory, and we initiate a variant of these structures in the context of quantum information theory. Finally, we propose several open questions for further exploration.
Béjanin, JH, Earnest, CT, Sanders, YR & Mariantoni, M 2021, 'Resonant Coupling Parameter Estimation with Superconducting Qubits', PRX Quantum, vol. 2, no. 4, pp. 1-18.
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Today’s quantum computers are composed of tens of qubits interacting with each other and the environment in increasingly complex networks. To achieve the best possible performance when operating such systems, it is necessary to have accurate knowledge of all parameters in the quantum computer Hamiltonian. In this paper, we demonstrate theoretically and experimentally a method to efficiently learn the parameters of resonant interactions for quantum computers consisting of frequency-tunable superconducting qubits. Such interactions include, for example, those with other qubits, resonators, two-level systems, or other wanted or unwanted modes. Our method is based on a significantly improved swap spectroscopy calibration and consists of an offline data collection algorithm, followed by an online Bayesian learning algorithm. The purpose of the offline algorithm is to detect and coarsely estimate resonant interactions from a state of zero knowledge. It produces a quadratic speedup in the scaling of the number of measurements. The online algorithm subsequently refines the estimate of the parameters to accuracy comparable with that of traditional swap spectroscopy calibration but in constant time. We perform an experiment implementing our technique with a superconducting qubit. By combining both algorithms, we observe a reduction of the calibration time by 1 order of magnitude. Our method will improve present medium-scale superconducting quantum computers and will also scale up to larger systems. Finally, the two algorithms presented here can be readily adopted by communities working on different physical implementations of quantum computing architectures.
Belotti, Y, McGloin, D & Weijer, CJ 2021, 'Effects of spatial confinement on migratory properties of Dictyostelium discoideum cells', Communicative & Integrative Biology, vol. 14, no. 1, pp. 5-14.
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Berger, PR, Hussain, MM, Iacopi, F, Schulze, J, Ye, P, Rachmady, W, Wen, H-C & Krishnan, S 2021, 'Foreword Special Issue on Low-Temperature Processing of Electronic Materials for Cutting Edge Devices', IEEE Transactions on Electron Devices, vol. 68, no. 7, pp. 3138-3141.
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Bharathy, G, Christian Prasana, J, Muthu, S, Irfan, A, Basha Asif, F, Saral, A, Aayisha, S & Niranjana devi, R 2021, 'Evaluation of electronic and biological interactions between N-[4-(Ethylsulfamoyl)phenyl]acetamide and some polar liquids (IEFPCM solvation model) with Fukui function and molecular docking analysis', Journal of Molecular Liquids, vol. 340, pp. 117271-117271.
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Bhol, P, Yadav, S, Altaee, A, Saxena, M, Misra, PK & Samal, AK 2021, 'Graphene-Based Membranes for Water and Wastewater Treatment: A Review', ACS Applied Nano Materials, vol. 4, no. 4, pp. 3274-3293.
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Bhuiyan, RA, Tarek, S & Tian, H 2021, 'Enhanced bag-of-words representation for human activity recognition using mobile sensor data', Signal, Image and Video Processing, vol. 15, no. 8, pp. 1739-1746.
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Human activity recognition based on sensors (e.g., accelerometer and gyroscope) embedded in smartphones is of great significance for many applications under uncontrolled environments. Although significant progress has been noticed in this field, one of the challenges limiting its real-life applications lies in robust feature extraction for efficient activity recognition on smartphones. This study addresses this challenge by proposing an improved bag-of-words representation for activity signal characterization. Specifically, raw activity signals are processed by discrete wavelet transformation to extract local features, which will be clustered using K-means to form a bag-of-words dictionary. The vocabularies in the dictionary are regarded as bin centers for histogram feature construction. For each local feature of an activity signal, its distance from all the bin centers will be measured. To capture higher-order information for feature representation, the frequency for the bin centers corresponding to the minimum n distances will be updated. Moreover, the frequency is increased by a trigonometry constraint cosine value of the corresponding distances to account for activity signals’ structural information. The proposed feature representation has been verified with three well-established classifiers, namely SVM, ANN, and KNN on the UCI-HAR dataset. The consistently good performance validates the effectiveness and robustness of the proposed feature representation. Compared with the state-of-the-art, the experimental results also demonstrate the advantage of the proposed method in terms of accuracy and computational cost.
Bird, T 2021, 'Capacitance Theorem In Electrostatics [Historically Speaking]', IEEE Antennas and Propagation Magazine, vol. 63, no. 3, pp. 142-143.
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Biswas, R, Bardhan, A, Samui, P, Rai, B, Nayak, S & Armaghani, DJ 2021, 'Efficient soft computing techniques for the prediction of compressive strength of geopolymer concrete', Computers and Concrete, vol. 28, no. 2, pp. 221-232.
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In the recent year, extensive researches have been done on fly ash-based geopolymer concrete for its similar properties like Portland cement as well as its environmental sustainability. However, it is difficult to provide a consistent method for geopolymer mix design because of the complexity and uncertainty of its design parameters, such as the alkaline solution concentration, mole ratio, and liquid to fly ash mass ratio. These mix-design parameters, along with the curing time and temperature ominously affect the most significant properties of the geopolymer concrete, i.e., compressive strength. To overcome these difficulties, the paper aims to provide a simple mix-design tool using artificial intelligence (AI) models. Three well-established and efficient AI techniques namely, genetic programming, relevance vector machine, and Gaussian process regression are used. Based on the performance of the developed models, it is understood that all the models have the capability to deliver higher prediction accuracies in the range of 0.9362 to 0.9905 (based on R2 value). Among the employed models, RVM outperformed the other model with R2=0.9905 and RMSE=0.0218. Theodore, the developed RVM model is very potential to be a new alternative to assist engineers to save time and expenditure on account of the trial-and-error process in finding the correct design mix proportions.
Blake, C, Massey, O, Boyd-Moss, M, Firipis, K, Rifai, A, Franks, S, Quigley, A, Kapsa, R, Nisbet, DR & Williams, RJ 2021, 'Replace and repair: Biomimetic bioprinting for effective muscle engineering', APL Bioengineering, vol. 5, no. 3.
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The debilitating effects of muscle damage, either through ischemic injury or volumetric muscle loss (VML), can have significant impacts on patients, and yet there are few effective treatments. This challenge arises when function is degraded due to significant amounts of skeletal muscle loss, beyond the regenerative ability of endogenous repair mechanisms. Currently available surgical interventions for VML are quite invasive and cannot typically restore function adequately. In response to this, many new bioengineering studies implicate 3D bioprinting as a viable option. Bioprinting for VML repair includes three distinct phases: printing and seeding, growth and maturation, and implantation and application. Although this 3D bioprinting technology has existed for several decades, the advent of more advanced and novel printing techniques has brought us closer to clinical applications. Recent studies have overcome previous limitations in diffusion distance with novel microchannel construct architectures and improved myotubule alignment with highly biomimetic nanostructures. These structures may also enhance angiogenic and nervous ingrowth post-implantation, though further research to improve these parameters has been limited. Inclusion of neural cells has also shown to improve myoblast maturation and development of neuromuscular junctions, bringing us one step closer to functional, implantable skeletal muscle constructs. Given the current state of skeletal muscle 3D bioprinting, the most pressing future avenues of research include furthering our understanding of the physical and biochemical mechanisms of myotube development and expanding our control over macroscopic and microscopic construct structures. Further to this, current investigation needs to be expanded from immunocompromised rodent and murine myoblast models to more clinically applicable human cell lines as we move closer to viable therapeutic implementation.
Bliuc, D, Tran, T, Adachi, JD, Atkins, GJ, Berger, C, van den Bergh, J, Cappai, R, Eisman, JA, van Geel, T, Geusens, P, Goltzman, D, Hanley, DA, Josse, R, Kaiser, S, Kovacs, CS, Langsetmo, L, Prior, JC, Nguyen, TV, Solomon, LB, Stapledon, C & Center, JR 2021, 'Cognitive decline is associated with an accelerated rate of bone loss and increased fracture risk in women: a prospective study from the Canadian Multicentre Osteoporosis Study', Journal of Bone and Mineral Research, vol. 36, no. 11, pp. 2106-2115.
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ABSTRACT Cognitive decline and osteoporosis often coexist and some evidence suggests a causal link. However, there are no data on the longitudinal relationship between cognitive decline, bone loss and fracture risk, independent of aging. This study aimed to determine the association between: (i) cognitive decline and bone loss; and (ii) clinically significant cognitive decline (≥3 points) on Mini Mental State Examination (MMSE) over the first 5 years and subsequent fracture risk over the following 10 years. A total of 1741 women and 620 men aged ≥65 years from the population-based Canadian Multicentre Osteoporosis Study were followed from 1997 to 2013. Association between cognitive decline and (i) bone loss was estimated using mixed-effects models; and (ii) fracture risk was estimated using adjusted Cox models. Over 95% of participants had normal cognition at baseline (MMSE ≥ 24). The annual % change in MMSE was similar for both genders (women −0.33, interquartile range [IQR] −0.70 to +0.00; and men −0.34, IQR: −0.99 to 0.01). After multivariable adjustment, cognitive decline was associated with bone loss in women (6.5%; 95% confidence interval [CI], 3.2% to 9.9% for each percent decline in MMSE from baseline) but not men. Approximately 13% of participants experienced significant cognitive decline by year 5. In women, fracture risk was increased significantly (multivariable hazard ratio [HR], 1.61; 95% CI, 1.11 to 2.34). There were too few men to analyze. There was a significant association between cognitive decline and both bone loss and fracture risk, independent of aging, in women. Further studies are needed to determine mechanisms that link these common conditions. © 2021 American Society for Bone and Mineral Research (ASBMR).
Bliuc, D, Tran, T, Alarkawi, D, Chen, W, Blank, RD, Ensrud, KE, Blith, F, March, L & Center, J 2021, 'Multimorbidity Increases Risk of Osteoporosis Under-Diagnosis and Under-Treatment in Patients at High Fracture Risk: 45 and up a Prospective Population Based-Study', Journal of the Endocrine Society, vol. 5, no. Supplement_1, pp. A248-A249.
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Abstract Background: Management of osteoporosis following fracture is suboptimal. Multimorbidity adds to clinical management complexity in the elderly but its contribution to the osteoporosis treatment gap has never been investigated. Objectives: To determine the impact of multimorbidity on fracture risk and on osteoporosis investigation and treatment in patients at high fracture risk. Design and Setting: The 45 and Up Study is a prospective population-based cohort study in NSW, Australia with questionnaire data linked to hospital records by the Centre for Health Record Linkage (CHeReL) and the Pharmaceutical Benefits Scheme (PBS) and Medicare Benefits Scheme (MBS) data provided by Department of Human Services. Fractures identified from hospital records, comorbidities from questionnaires, hospital and PBS records. Bone mineral density (BMD) investigation obtained from MBS and treatment for osteoporosis from PBS. Participants: 16191 women and 9089 men with incident low-trauma fracture (2000 - 2017) classified in a high and low-risk group based on 10-year fracture risk threshold of 20% from the Garvan Fracture Risk Calculator (age, gender, prior fracture and falls). Main Outcome Measurements: Association of Charlson comorbidity index (CCI) with fracture. Likelihood of BMD investigation and treatment initiation. Outcomes ascertained by logistic regression and re-fracture risk by Cox models. Results: Individuals at high fracture risk were significantly older [women (mean age ±SD) 77 ± 10 vs 57 ±4 for high- vs low risk and men 86±5 vs 65±8 for high vs low risk] and had a higher morbidity burden [women, CCI ≥ 2 40% vs 12% for high- vs low-risk and men 53% vs 26% for high vs low risk]. Being in the high-risk group as well as a higher CCI were independently associated with > 2-fold higher risk of re-fracture. However, in the high-risk group, only 28% (48% women and 17% men) had a BMD investigation and 31% (...
Booth, E & Lim, R 2021, 'The Illusion of Inclusion: Disempowered “Diversity” in 2018 Australian Children’s Picture Books', New Review of Children's Literature and Librarianship, vol. 27, no. 2, pp. 122-143.
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Booth, E & Narayan, B 2021, 'Behind Closed Gates: The Barriers to Self-Expression and Publication for Australian Young Adult Authors of OwnVoices Fiction', International Research in Children's Literature, vol. 14, no. 2, pp. 183-198.
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This article based on an empirical study of Australian authors argues that, despite the OwnVoices movement gathering momentum in Australia, there are still barriers and limitations for authors from marginalised communities within the Australian publishing industry. This is due to power imbalances in publishing spaces which silence marginalised writers, limiting the availability of their books to teenage readers.
Booth, E & Narayan, B 2021, 'Identifying Inclusion: Publishing Industry Trends and the Lack of #OwnVoices Australian Young Adult Fiction', Research on Diversity in Youth Literature, vol. 3, no. 1, pp. 64-78.
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Booth, E & Narayan, B 2021, ''That Authenticity is Missing': Australian Authors of #OwnVoices Fiction on Authorship, Identity, and Outsider Writers', The ALAN Review, vol. 48, no. 2, pp. 64-78.
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This paper presents the findings of original, empirical research into the perspectives of authors of #OwnVoices YA fiction on the concept of authenticity and on outsider writers. Through the discussion of data from seven qualitative interviews with Australian authors of#OwnVoices young adult fiction, it contributes an understanding of how authors perceive the relationship between their personal identity and their professional creative practice, as well as their views on how the absence of this identity-practice connection affects the work of outsider writers. More broadly, it provides insight into current global debates about authorship and diversity in literature, while re-centering the voices of authors of #OwnVoices young adult fiction.
Booth, E, Kwaymullina, A & Lim, R 2021, 'Investigating the Publication of OwnVoices Australian Picture Books in 2018', Publishing Research Quarterly, vol. 37, no. 1, pp. 27-40.
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This paper discusses findings from the first OwnVoices count of Australian picture books, as part of a grant-funded partnership with advocacy group Voices from the Intersection (VFTI). The research involved the compilation of a detailed annotated list of all Australian picture books published in 2018; the identities of the characters; and demographic information of authors and illustrators, sourced from publicly available resources where creators freely self-identified as belonging to structurally marginalised communities. A book’s OwnVoices status was determined from this information. Findings indicate OwnVoices picture books are significantly under-published in Australia, and contribute a greater understanding of the level of equity and access in the contemporary Australian publishing industry.
Bore, JC, Li, P, Jiang, L, Ayedh, WMA, Chen, C, Harmah, DJ, Yao, D, Cao, Z & Xu, P 2021, 'A Long Short-Term Memory Network for Sparse Spatiotemporal EEG Source Imaging', IEEE Transactions on Medical Imaging, vol. 40, no. 12, pp. 3787-3800.
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EEG inverse problem is underdetermined, which poses a long standing challenge in Neuroimaging. The combination of source-imaging and analysis of cortical directional networks enables us to noninvasively explore the underlying neural processes. However, existing EEG source imaging approaches mainly focus on performing the direct inverse operation for source estimation, which will be inevitably influenced by noise and the strategy used to find the inverse solution. Here, we develop a new source imaging technique, Deep Brain Neural Network (DeepBraiNNet), for robust sparse spatiotemporal EEG source estimation. In DeepBraiNNet, considering that Recurrent Neural Network (RNN) are usually 'deep' in temporal dimension and thus suitable for time sequence modelling, the RNN with Long Short-Term Memory (LSTM) is utilized to approximate the inverse operation for the lead field matrix instead of performing the direct inverse operation, which avoids the possible effect of the direct inverse operation on the underdetermined lead field matrix prone to be influenced by noise. Simulations on various source patterns and noise conditions confirmed that the proposed approach could actually recover the spatiotemporal sources well, outperforming existing state of-the-art methods. DeepBraiNNet also estimated sparse MI related activation patterns when it was applied to a real Motor Imagery dataset, consistent with other findings based on EEG and fMRI. Based on the spatiotemporal sources estimated from DeepBraiNNet, we constructed MI related cortical neural networks, which clearly exhibited strong contralateral network patterns for the two MI tasks. Consequently, DeepBraiNNet may provide an alternative way different from the conventional approaches for spatiotemporal EEG source imaging.
Boroon, L, Abedin, B & Erfani, E 2021, 'The Dark Side of Using Online Social Networks', Journal of Global Information Management, vol. 29, no. 6, pp. 1-21.
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Research on online social networks (OSNs) has focused overwhelmingly on their benefits and potential, with their negative effects overlooked. This study builds on the limited existing work on the so-called ‘dark side’ of using OSNs. The authors conducted a systematic review of selected databases and identified 46 negative effects of using OSNs from the users’ perspective, which is a rich spectrum of users’ negative experiences. This article then proposed nomenclature and taxonomy for the dark side of using OSNs by grouping these negative effects into six themes: cost of social exchange, cyberbullying, low performance, annoying content, privacy concerns and security threats. This study then conducted structured interviews with experts to confirm the sense-making and validity of the proposed taxonomy. This study discusses the confirmed taxonomy and outlines directions for future research.
Bown, O, Ferguson, S, Dias Pereira Dos Santos, A & Mikolajczyk, K 2021, 'Hacking the Medium: Shaping the creative constraints of network architectures in multiplicitous media artworks', Organised Sound, vol. 26, no. 3, pp. 305-316.
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In this article we discuss our practice-based research into effective architectures and creative workflows for creatively coding massive multidevice light and sound installation artworks. We discuss the challenges of working with networked multidevice systems and illustrate these challenges with examples of the type of content that one may wish to display on these systems. We then consider how the structuring of a creative framework can strongly influence how an artist approaches the creation of such work, eases the process of creative search and discovery and reduces the time cost and risk of solving technical problems of architecture design. We take a design perspective on how to make effective creativity support tools and also consider a holistic perspective on creative practice that attempts to satisfy creative ideals grounded in the reality of practice.
Brohl, AS, Sindiri, S, Wei, JS, Milewski, D, Chou, H-C, Song, YK, Wen, X, Kumar, J, Reardon, HV, Mudunuri, US, Collins, JR, Nagaraj, S, Gangalapudi, V, Tyagi, M, Zhu, YJ, Masih, KE, Yohe, ME, Shern, JF, Qi, Y, Guha, U, Catchpoole, D, Orentas, RJ, Kuznetsov, IB, Llosa, NJ, Ligon, JA, Turpin, BK, Leino, DG, Iwata, S, Andrulis, IL, Wunder, JS, Toledo, SRC, Meltzer, PS, Lau, C, Teicher, BA, Magnan, H, Ladanyi, M & Khan, J 2021, 'Immuno-transcriptomic profiling of extracranial pediatric solid malignancies', Cell Reports, vol. 37, no. 8, pp. 110047-110047.
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We perform an immunogenomics analysis utilizing whole-transcriptome sequencing of 657 pediatric extracranial solid cancer samples representing 14 diagnoses, and additionally utilize transcriptomes of 131 pediatric cancer cell lines and 147 normal tissue samples for comparison. We describe patterns of infiltrating immune cells, T cell receptor (TCR) clonal expansion, and translationally relevant immune checkpoints. We find that tumor-infiltrating lymphocytes and TCR counts vary widely across cancer types and within each diagnosis, and notably are significantly predictive of survival in osteosarcoma patients. We identify potential cancer-specific immunotherapeutic targets for adoptive cell therapies including cell-surface proteins, tumor germline antigens, and lineage-specific transcription factors. Using an orthogonal immunopeptidomics approach, we find several potential immunotherapeutic targets in osteosarcoma and Ewing sarcoma and validated PRAME as a bona fide multi-pediatric cancer target. Importantly, this work provides a critical framework for immune targeting of extracranial solid tumors using parallel immuno-transcriptomic and -peptidomic approaches.
Brzozowska, MM, Tran, T, Bliuc, D, Jorgensen, J, Talbot, M, Fenton-Lee, D, Chen, W, Hong, A, Viardot, A, White, CP, Nguyen, TV, Pocock, N, Eisman, JA, Baldock, PA & Center, JR 2021, 'Roux-en-Y gastric bypass and gastric sleeve surgery result in long term bone loss', International Journal of Obesity, vol. 45, no. 1, pp. 235-246.
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Objectives
Little is known about the long-term skeletal impact of bariatric procedures, particularly the increasingly commonly performed gastric sleeve surgery (GS). We examined bone density (BMD) change following three types of bariatric surgery Roux-en-Y gastric bypass (RYGB), GS and laparoscopic adjustable gastric banding (LAGB), compared with diet, over 36 months.
Methods
Non-randomized, prospective study of participants with severe obesity (n = 52), undergoing weight-loss interventions: RYGB (n = 7), GS (n = 21), LAGB (n = 11) and diet (n = 13). Measurements of calciotropic indices, gut hormones (fasting and post prandial) peptide YY (PYY), glucagon-like peptide 1 (GLP1) and adiponectin together with dual-X-ray absorptiometry and quantitative computed tomography scans were performed thorough the study.
Results
All groups lost weight during the first 12 months. Despite weight stability from 12 to 36 months and supplementation of calcium and vitamin D, there was progressive bone loss at the total hip (TH) over 36 months in RYGB -14% (95% CI: -12, -17) and GS -9% (95% CI: -7, -10). In RYGB forearm BMD also declined over 36 months -9% (95% CI: -6, -12) and LS BMD declined over the first 12 months -7% (95% CI: -3, -12). RYGB and GS groups experienced significantly greater bone loss until 36 months than LAGB and diet groups, which experienced no significant BMD loss. These bone losses remained significant after adjustment for weight loss and age. RYGB and GS procedures resulted in elevated postprandial PYY, adiponectin and bone turnover markers up to 36 months without such changes among LAGB and diet participants.
Conclusions
RYGB and GS but not LAGB resulted in ongoing TH bone loss for three postoperative years. For RYGB, bone loss was also observed at LS and non-weight-bearing forearms. These BMD changes were independent of weight and age differences. We, therefore, recommend close monitoring of bone health following RYGB a...
Bubb, KJ, Tang, O, Gentile, C, Moosavi, SM, Hansen, T, Liu, C-C, Di Bartolo, BA & Figtree, GA 2021, 'FXYD1 Is Protective Against Vascular Dysfunction', Hypertension, vol. 77, no. 6, pp. 2104-2116.
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Nitric oxide (NO) production by eNOS (endothelial NO synthase) is critical for vascular health. Oxidative stress-induced uncoupling of eNOS leads to decreased NO bioavailability, compounded by increased superoxide generation. FXYD1 (FXYD domain containing ion transport regulator 1), a caveolar protein, protects against oxidative inhibition of the Na + -K + -ATPase. We hypothesized that FXYD1 may afford a similar inhibition of oxidative dysregulation of eNOS, providing a broader protection within caveolae. FXYD1-eNOS colocalization was demonstrated by co-immunoprecipitation in heart protein and by proximity ligation assay in human umbilical vein endothelial cells. The functional nature of this partnership was shown by silencing FXYD1 in human umbilical vein endothelial cells, where 50% decreased NO and 2-fold augmented superoxide was shown. Three-dimensional cocultured cardiac spheroids generated from FXYD1 knockout mice were incapable of acetylcholine-induced NO production. Overexpression of FXYD1 in HEK293 cells revealed a possible mechanism, where FXYD1 protected against redox modification of eNOS cysteines. In vivo, vasodilation in response to increasing doses of bradykinin was impaired in knockout mice, and this was rescued in mice by delivery of FXYD1 protein packaged in exosomes. Bloods vessels extracted from knockout mice exhibited increased oxidative and nitrosative stress with evidence of reduce eNOS phosphorylation. Impaired vascular function and augmented superoxide generation were also evident in diabetic knockout mice. Despite this, blood pressure was similar in wildtype and knockout mice, but after chronic angiotensin II infusion, knockout of FXYD1 was associated with a heightened blood pressure response. FXYD1 protects eNOS from dysregulated redox signaling and is protective against both hypertension and diabetic vascular oxidati...
Buchlak, QD, Esmaili, N, Leveque, J-C, Bennett, C, Farrokhi, F & Piccardi, M 2021, 'Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review', Journal of Clinical Neuroscience, vol. 89, pp. 177-198.
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Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. This systematic review synthesizes and analyzes the current state of machine learning applications to glioma MRI data and explores the use of machine learning for systematic review automation. Various datapoints were extracted from the 153 studies that met inclusion criteria and analyzed. Natural language processing (NLP) analysis involved keyword extraction, topic modeling and document classification. Machine learning has been applied to tumor grading and diagnosis, tumor segmentation, non-invasive genomic biomarker identification, detection of progression and patient survival prediction. Model performance was generally strong (AUC = 0.87 ± 0.09; sensitivity = 0.87 ± 0.10; specificity = 0.0.86 ± 0.10; precision = 0.88 ± 0.11). Convolutional neural network, support vector machine and random forest algorithms were top performers. Deep learning document classifiers yielded acceptable performance (mean 5-fold cross-validation AUC = 0.71). Machine learning tools and data resources were synthesized and summarized to facilitate future research. Machine learning has been widely applied to the processing of MRI data in glioma research and has demonstrated substantial utility. NLP and transfer learning resources enabled the successful development of a replicable method for automating the systematic review article screening process, which has potential for shortening the time from discovery to clinical application in medicine.
Budati, AK & Ling, SSH 2021, 'Guest editorial: Machine Learning in Wireless Networks.', CAAI Trans. Intell. Technol., vol. 6, no. 2, pp. 133-134.
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Bui, HT, Hussain, OK, Saberi, M & Hussain, F 2021, 'Assessing the authenticity of subjective information in the blockchain: a survey and open issues', World Wide Web, vol. 24, no. 2, pp. 483-509.
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Bulzinetti, MA, Abraham, MT, Satyam, N, Pradhan, B & Segoni, S 2021, 'Combining rainfall thresholds and field monitoring data for development of LEWS.'.
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<p>Landslide Early Warning Systems (LEWS) can provide enough time to take necessary precautions before the occurrence of landslides and can reduce the risk associated with it. Deriving empirical rainfall thresholds is the conventional approach in developing regional scale LEWS, but the major drawback of this approach is the relatively high number of false alarms. In this study, a prototype method for LEWS is proposed by combining rainfall thresholds and field monitoring data from MicroElectroMechanical Systems (MEMS) units that integrate a tilt sensor, a soil moisture meter and a real-time wireless transmitter. The study was conducted in the Kalimpong district of West Bengal, India. Tilt sensors were installed at different locations on unstable slopes of Kalimpong since July 2017 and the observations from July 2017 to August 2020 were used to enhance the performance of the existing rainfall thresholds.</p><p>During this period, both rainfall thresholds and tilt meters, when used separately, systematically overestimated landslide hazard, producing high false alarm rates. However, it was found that using a decisional algorithm that combines both approaches can reduce the false alarms and improve the overall efficiency of the system from 84 % (based on rainfall thresholds) to 92 % (combined method). The prototype LEWS is found to be promising to be developed as an operational LEWS capable to issue alerts with a lead time of 24 h.&#160;</p><p>The method is simple and can be easy exported to other sites with historical rainfall and landslide data and a network of slope monitoring sensors. Cost of installation of a large number of sensors is a major concern for developing countries like India, hence a cost-effective approach is used in this study: the use of MEMS sensors along with empirical rainfall thresholds is thus a simple and economical approach for the prediction of l...
Cai, X, Li, JJ, Liu, T, Brian, O & Li, J 2021, 'Infectious disease mRNA vaccines and a review on epitope prediction for vaccine design', Briefings in Functional Genomics, vol. 20, no. 5, pp. 289-303.
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AbstractMessenger RNA (mRNA) vaccines have recently emerged as a new type of vaccine technology, showing strong potential to combat the COVID-19 pandemic. In addition to SARS-CoV-2 which caused the pandemic, mRNA vaccines have been developed and tested to prevent infectious diseases caused by other viruses such as Zika virus, the dengue virus, the respiratory syncytial virus, influenza H7N9 and Flavivirus. Interestingly, mRNA vaccines may also be useful for preventing non-infectious diseases such as diabetes and cancer. This review summarises the current progresses of mRNA vaccines designed for a range of diseases including COVID-19. As epitope study is a primary component in the in silico design of mRNA vaccines, we also survey on advanced bioinformatics and machine learning algorithms which have been used for epitope prediction, and review on user-friendly software tools available for this purpose. Finally, we discuss some of the unanswered concerns about mRNA vaccines, such as unknown long-term side effects, and present with our perspectives on future developments in this exciting area.
Cao, D-F, Zhu, H-H, Guo, C-C, Wu, J-H & Fatahi, B 2021, 'Investigating the hydro-mechanical properties of calcareous sand foundations using distributed fiber optic sensing', Engineering Geology, vol. 295, pp. 106440-106440.
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Cao, H, Chen, G, Xia, J, Zhuang, G & Knoll, A 2021, 'Fusion-Based Feature Attention Gate Component for Vehicle Detection Based on Event Camera', IEEE Sensors Journal, vol. 21, no. 21, pp. 24540-24548.
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Cao, J, Gowripalan, N, Sirivivatnanon, V & South, W 2021, 'Accelerated test for assessing the potential risk of alkali-silica reaction in concrete using an autoclave', Construction and Building Materials, vol. 271, pp. 121871-121871.
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To rapidly assess the potential risk of alkali-silica reaction (ASR) in concrete, an accelerated test using an autoclave by adopting multi-cycle 80 °C steam warming at atmospheric pressure is proposed. The influence of autoclave steam warming temperature, cycles/duration, and alkali dosage on expansion of mortar bars and concrete prisms was evaluated. Mechanical properties of concrete under accelerated ASR test were investigated. Furthermore, SEM-EDS analysis confirmed ASR products and indicated that the expansion is caused by ASR. The expansion limits considered for classifying aggregates were discussed. The experimental results demonstrated that the period required for assessing the potential risk of ASR in concrete (dacite aggregate in this study) can be shortened to 37 days.
Cao, L & Zhu, C 2021, 'Table2Vec-automated universal representation learning of enterprise data DNA for benchmarkable and explainable enterprise data science', Scientific Reports, vol. 11, no. 1, p. 23957.
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AbstractEnterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, customer demographics, status, behaviors, interactions and communications with the enterprise, and the consumption and feedback of its products, services, production, marketing, operations, and management, etc. They involve enterprise DNA associated with domain-oriented transactions and master data, informational and operational metadata, and relevant external data. A critical challenge in enterprise data science is to enable an effective ‘whole-of-enterprise’ data understanding and data-driven discovery and decision-making on all-round enterprise DNA. Accordingly, here we introduce a neural encoder Table2Vec for automated universal representation learning of entities such as customers from all-round enterprise DNA with automated data characteristics analysis and data quality augmentation. The learned universal representations serve as representative and benchmarkable enterprise data genomes (similar to biological genomes and DNA in organisms) and can be used for enterprise-wide and domain-specific learning tasks. Table2Vec integrates automated universal representation learning on low-quality enterprise data and downstream learning tasks. Such automated universal enterprise representation and learning cannot be addressed by existing enterprise data warehouses (EDWs), business intelligence and corporate analytics systems, where ‘enterprise big tables’ are constructed with reporting and analytics conducted by specific analysts on respective domain subjects and goals. It addresses critical limitations and gaps of existing representation learning, enterprise analytics and cloud analytics, which are analytical subject, task and data-specific, creating analytical silos in an enterprise. We ill...
Cao, Y, Lv, T, Lin, Z & Ni, W 2021, 'Delay-Constrained Joint Power Control, User Detection and Passive Beamforming in Intelligent Reflecting Surface-Assisted Uplink mmWave System', IEEE Transactions on Cognitive Communications and Networking, vol. 7, no. 2, pp. 482-495.
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While millimeter-wave (mmWave) communications can enjoy abundant bandwidth resource, their high susceptibility to blockage poses serious challenges to low-latency services. In this paper, a novel intelligent reflecting surface (IRS)-assisted mmWave scheme is proposed to overcome the impact of blockage. The scheme minimizes the user power of a multi-user mmWave system by jointly optimizing the transmit powers of the devices, the multi-user detector at the base station, and the passive beamforming at the IRS, subject to delay requirements. An alternating optimization framework is developed to decompose the joint optimization problem into three subproblems iteratively optimized till convergence. In particular, closed-form expressions are devised for the update of the powers and multi-user detector. The IRS configuration is formulated as a sum-of-inverse minimization (SIMin) fractional programming problem and solved by exploiting the alternating direction method of multipliers (ADMM). The configuration is also interpreted as a latency residual maximization problem, and solved efficiently by designing a new complex circle manifold optimization (CCMO) method. Numerical results corroborate the effectiveness of our scheme in terms of power saving, as compared with a semidefinite relaxation-based alternative.
Cao, Y, Lv, T, Ni, W & Lin, Z 2021, 'Sum-Rate Maximization for Multi-Reconfigurable Intelligent Surface-Assisted Device-to-Device Communications', IEEE Transactions on Communications, vol. 69, no. 11, pp. 7283-7296.
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Cao, Y, Sheng, L, Cheng, H, Wang, C, Sun, Y & Fu, Q 2021, 'In situ synthesis of metal‐free N‐GQD@g‐C3N4 photocatalyst for enhancing photocatalytic activity', Micro & Nano Letters, vol. 16, no. 1, pp. 77-82.
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AbstractGraphitic carbon nitride modified with N‐doped graphene quantum dot (N‐GQDs/g‐C3N4) was prepared by an in situ method, in which the g‐C3N4 was synthesized in the presence of N‐GQD. Furthermore, the structure and photocatalytic degradation performance of in situ synthesised N‐GQDs/g‐C3N4 were investigated and compared with N‐GQDs/g‐C3N4 prepared by traditional mixing method. The removal efficiency was about 98.0% for the photocatalytic degradation of RhB after 70 min, which was larger comparing with other GQDs/g‐C3N4 reported in previous works. The result was attributed to uniform distribution of N‐GQDs on surface of g‐C3N4, leading to more photogenerated electrons transfer. This work did not only report a new synthesis method of N‐GQDs/g‐C3N4, but also provided a new method to improve photodegradation performance of photocatalyst based on g‐C3N4.
Cao, Y, Wang, S, Guo, Z, Huang, T & Wen, S 2021, 'Event-based passification of delayed memristive neural networks', Information Sciences, vol. 569, pp. 344-357.
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This paper focuses on the passification issue of delayed memristive neural networks via the event-based control. First, by designing an appropriate controller based on a static event trigger scheme, the passification conditions are deduced for delayed memristive neural networks. Then, under the same controller, the passivity is discussed for the delayed memristive neural network system with a more economical and realistic dynamic event trigger rule. Meanwhile, in order to ensure these two event trigger control schemes are Zeno free, the existence of positive lower bounds are approved for the inter event time. Finally, illustrative examples are elaborated to support the theoretical results.
Cao, Z, John, AR, Chen, H-T, Martens, KE, Georgiades, M, Gilat, M, Nguyen, HT, Lewis, SJG & Lin, C-T 2021, 'Identification of EEG Dynamics During Freezing of Gait and Voluntary Stopping in Patients With Parkinson’s Disease', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 1774-1783.
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Mobility is severely impacted in patients with Parkinson's disease (PD), who often experience involuntary stopping from the freezing of gait (FOG). Understanding the neurophysiological difference between “voluntary stopping” and “involuntary stopping” caused by FOG is vital for the detection of and potential intervention for FOG in the daily lives of patients. This study characterised the electroencephalographic (EEG) signature associated with FOG in contrast to voluntary stopping. The protocol consisted of a timed up-and-go (TUG) task and an additional TUG task with a voluntary stopping component, where participants reacted to verbal “stop” and “walk” instructions by voluntarily stopping or walking. Event-related spectral perturbation (ERSP) analysis was performed to study the dynamics of the EEG spectra induced by different walking phases, including normal walking, voluntary stopping and episodes of involuntary stopping (FOG), as well as the transition windows between normal walking and voluntary stopping or FOG. These results demonstrate for the first time that the EEG signal during the transition from walking to voluntary stopping is distinguishable from that during the transition to involuntary stopping caused by FOG. The EEG signature of voluntary stopping exhibits a significantly decreased power spectrum compared with that of FOG episodes, with distinctly different patterns in the delta and low-beta power in the central area. These findings suggest the possibility of a practical EEG-based tool that can accurately predict FOG episodes, excluding the potential confounding of voluntary stopping.
Cao, Z, Lin, C-T, Deng, Y & Weber, G-W 2021, 'Guest Editorial: Fuzzy Systems Toward Human-Explainable Artificial Intelligence and Their Applications', IEEE Transactions on Fuzzy Systems, vol. 29, no. 12, pp. 3577-3578.
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Cao, Z, Wong, K & Lin, C-T 2021, 'Weak Human Preference Supervision for Deep Reinforcement Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 12, pp. 5369-5378.
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The current reward learning from human preferences could be used to resolve complex reinforcement learning (RL) tasks without access to a reward function by defining a single fixed preference between pairs of trajectory segments. However, the judgment of preferences between trajectories is not dynamic and still requires human input over thousands of iterations. In this study, we proposed a weak human preference supervision framework, for which we developed a human preference scaling model that naturally reflects the human perception of the degree of weak choices between trajectories and established a human-demonstration estimator through supervised learning to generate the predicted preferences for reducing the number of human inputs. The proposed weak human preference supervision framework can effectively solve complex RL tasks and achieve higher cumulative rewards in simulated robot locomotion—MuJoCo games—relative to the single fixed human preferences. Furthermore, our established human-demonstration estimator requires human feedback only for less than 0.01% of the agent’s interactions with the environment and significantly reduces the cost of human inputs by up to 30% compared with the existing approaches. To present the flexibility of our approach, we released a video ( https://youtu.be/jQPe1OILT0M ) showing comparisons of the behaviors of agents trained on different types of human input. We believe that our naturally inspired human preferences with weakly supervised learning are beneficial for precise reward learning and can be applied to state-of-the-art RL systems, such as human-autonomy teaming systems.
Carpentieri, D, Catchpoole, D & Vercauteren, S 2021, 'Special Issue on Biobanking for Pediatric Research', Biopreservation and Biobanking, vol. 19, no. 2, pp. 97-97.
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Cetindamar Kozanoglu, D & Abedin, B 2021, 'Understanding the role of employees in digital transformation: conceptualization of digital literacy of employees as a multi-dimensional organizational affordance', Journal of Enterprise Information Management, vol. 34, no. 6, pp. 1649-1672.
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PurposeMuch of recent academic and professional interest in exploring digital transformation and enterprise systems has focused on the technology or the organizations' external forces, leaving internal factors, in particular employees, overlooked. The purpose of this paper is to explore digital literacy of employees as an organizational affordance to capture contextual factors within which digital technologies are situated and are used.Design/methodology/approachWe used the evidence-based practice for information systems approach, and undertook a systematic literature review of 30 papers coupled with brainstorming with 11 professional experts on the neglected topic of digital literacy and its assessment.FindingsThis paper draws upon affordance theory, and develops a novel framework for conceptualization of digital literacy of employees as an organizational affordance. We do this by distinguishing digital literacy at the individual level and organizational level, and by assessing digital literacy through Information/Cognitive and Social Practice/Articulation affordances.Research limitations/implicationsThe current paper contributes to the notion of organizational affordances by examining the effect of interactions between employee-technology through digital literacy of employees in using digital technologies. We offer a novel conceptualization of digital literacy to improve understanding of the role of employee in digital transformation and utilization of enterprise systems. Thus, our definition of digital literacy offers an extension to the recent discussions in the IS literature regardin...
Cetindamar, D, Katic, M, Burdon, S & Gunsel, A 2021, 'The Interplay among Organisational Learning Culture, Agility, Growth, and Big Data Capabilities', Sustainability, vol. 13, no. 23, pp. 13024-13024.
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This paper examines how an organisational learning culture impacts organisational agility by developing a model based on dynamic capabilities. The model treats agility as a dynamic capability and explains how an organisational learning culture (OLC) triggers a chain reaction through its influence on organisational agility (OA) that ultimately results in company growth. This paper also investigates the role of big data capabilities in transferring learning outcomes into dynamic capabilities. The model is tested through data collected from a survey of 138 Australian companies. Partial least squares structural equation modeling is adopted to empirically demonstrate how agility fully mediates the impact of the learning culture on growth. In addition, this paper further sheds light on the moderating role of big data competencies on the effects of OLC on OA. After presenting the results with implications to theory and practice, the paper ends with suggestions for future studies.
Cetindamar, D, Lammers, T, Kocaoglu, DF & Zhang, Y 2021, 'The Anniversary Tribute of PICMET: 1989-2018.', IEEE Trans. Engineering Management, vol. 68, no. 2, pp. 612-627.
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The Portland International Conference for Management
of Engineering and Technology (PICMET) has become a
world-leading organization in the field of management of engineering
and technology management (MET) since its inception in
1989. PICMET provides a strong platform for academics, industry
professionals, and government representatives to exchange new
knowledge in the field. To celebrate its 30-year journey, this article
examines 20 conferences organized by PICMET covering 6601
accepted papers in order to show the trends in MET research and
implementation through topics, authors, journals, and countries. In
addition to the analysis of the PICMET data, the article delves into
the past ten years (2009–2018) to carry out an in-depth bibliometric
analysis of the citations of more than 3000 PICMET papers available
at Scopus. The detailed analysis sheds light on how PICMET
has developed a rich network of researchers and practitioners
through its conferences over time. PICMET contributes to the
interdisciplinary nature of the MET field and is also affected by
the changes of the field. The article ends with key observations and
a few suggestions for further studies.
Chai, M, Moradi, S, Erfani, E, Asadnia, M, Chen, V & Razmjou, A 2021, 'Application of Machine Learning Algorithms to Estimate Enzyme Loading, Immobilization Yield, Activity Retention, and Reusability of Enzyme–Metal–Organic Framework Biocatalysts', Chemistry of Materials, vol. 33, no. 22, pp. 8666-8676.
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Chai, M, Razavi Bazaz, S, Daiyan, R, Razmjou, A, Ebrahimi Warkiani, M, Amal, R & Chen, V 2021, 'Biocatalytic micromixer coated with enzyme-MOF thin film for CO2 conversion to formic acid', Chemical Engineering Journal, vol. 426, pp. 130856-130856.
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In this study, a novel micromixer with a 3D helical, threaded channel was fabricated via 3D printing. The micromixer can enhance the mass transfer of reactants and product in an enzymatic cascade reaction converting CO2 to formic acid. Two enzymes, including carbonic anhydrase (CA) and formate dehydrogenase (FDH), were biomineralised in a zeolitic imidazolate framework-8 composite thin film on the micromixer channel that has been modified with polydopamine/polyethyleneimine. The biocatalytic performance of the micromixer was evaluated by testing at various liquid flow rates, and an optimum liquid flow rate at 1 mL/min (Rel = 8, Del = 3) was observed as the two-phase flow pattern in the micromixer channel transitioned from slug flow to bubbly flow. A comparison of the micromixer performance with and without threaded channels revealed ~ 170% enhancement in formic acid yield, indicating improved mixing with the presence of threads. In addition, the formic acid production rate for the micromixer with threaded channel was three folds higher than a conventional bubble column, demonstrating the superior performance of the proposed micromixer. The ease of assembling multiple micromixer units in series also enabled the immobilisation of different enzymes in separate units to carry out sequential reactions in a modular system. As a proof of concept, the solution product collected from long term biocatalysis was also tested in a direct formic acid fuel cell, which showed a promising prospect of integrating these two systems for a closed-loop energy generation system.
Chakraborty, S, Dann, C, Mandal, A, Dann, B, Paul, M & Hafeez-Baig, A 2021, 'Effects of rubric quality on marker variation in higher education', Studies in Educational Evaluation, vol. 70, pp. 100997-100997.
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Variation among markers has the potential to disadvantage students by contributing to a discrepancy in assessments in higher education settings. In this study, we extended a previous study that analyzed first-year students’ results in a Business Faculty within an Australian university to understand the extent of variation within and between multiple markers and across multiple courses. The study investigated the potential influence of quality of rubrics and associated documentation provided as marker guidance. Results indicated that specific features of rubrics, such as the inclusion of clear indicators of quality, had an observable effect on marker variation.
Chakraborty, S, Milner, LE, Zhu, X, Sevimli, O, Parker, AE & Heimlich, MC 2021, 'An Edge-Coupled Marchand Balun With Partial Ground for Excellent Balance in 0.13 μm SiGe Technology', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 1, pp. 226-230.
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An edge-coupled meandered three-coupled-line Marchand balun with a partial ground plane implemented in 0.13 μ {m} SiGe Bi-CMOS technology is presented in this brief. The balance performance of the designed balun is significantly improved by creating a 'no ground plane' beneath the coupled-line structure, which is demonstrated by simulating two baluns: one with a partial ground and the other with a solid ground underneath. The measured amplitude and phase imbalances are less than 0.4 dB and 2.5°, across the 3-dB bandwidth from 21.5 to 95 GHz, surpassing previously reported results of edge-coupled Marchand baluns. The balun occupies 230 μ \text{m}\,\,×370\,\,μ {m}.
Chan, Y, Mehta, M, Paudel, KR, Madheswaran, T, Panneerselvam, J, Gupta, G, Su, QP, Hansbro, PM, MacLoughlin, R, Dua, K & Chellappan, DK 2021, 'Versatility of Liquid Crystalline Nanoparticles in Inflammatory Lung Diseases', Nanomedicine, vol. 16, no. 18, pp. 1545-1548.
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Chan, Y, Ng, SW, Chellappan, DK, Madheswaran, T, Zeeshan, F, Kumar, P, Pillay, V, Gupta, G, Wadhwa, R, Mehta, M, Wark, P, Hsu, A, Hansbro, NG, Hansbro, PM, Dua, K & Panneerselvam, J 2021, 'Celastrol-loaded liquid crystalline nanoparticles as an anti-inflammatory intervention for the treatment of asthma', International Journal of Polymeric Materials and Polymeric Biomaterials, vol. 70, no. 11, pp. 754-763.
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The present study aimed to formulate celastrol into liquid crystalline nanoparticles (LCNPs) through ultrasonication to enhance its therapeutic efficacy in the treatment of asthma. The physiochemical characteristics, in-vitro release studies were determined along with molecular simulations. Celastrol-loaded LCNPs showed the mean particle size of 194.1 ± 9.78 nm and high entrapment efficiency of 99.1 ± 0.02%. TEM revealed cubical-like structure of the nanoparticles and in-vitro release study demonstrated sustained drug release. They also demonstrated significant activity in reducing IL-1β production, when evaluated using immortalized human bronchial epithelial cell lines (BCi-NS1.1), that may help alleviate the symptoms of asthma.
Chandran, D & Alammari, AM 2021, 'Influence of Culture on Knowledge Sharing Attitude among Academic Staff in eLearning Virtual Communities in Saudi Arabia', Information Systems Frontiers, vol. 23, no. 6, pp. 1563-1572.
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Knowledge sharing is a significant component of success in knowledge management. In Saudi Arabia, knowledge management is often lacking when it comes to knowledge sharing adoption, especially between academic staff. This research aims to investigate various factors of knowledge sharing adoption for eLearning communities in Saudi Arabia and to examine the effect of culture as a moderating role on the relationships between these factors and academics’ attitude. Therefore, a framework is aimed at sharing knowledge within the eLearning communities is developed. Data has been collected from public universities in Saudi Arabia. Partial Least Square approach has been applied to analyse the data. The results show individual factors (such as openness in communication, interpersonal trust) and technology acceptance factors (perceived usefulness and perceived ease of use) significantly influence knowledge sharing attitude, while the relationship between people self-motivation and knowledge sharing attitude is insignificant. Subjective norm and attitude significantly impact behavioral intention toward knowledge sharing adoption in Saudi universities’ eLearning communities.
Chandran, M, Mitchell, PJ, Amphansap, T, Bhadada, SK, Chadha, M, Chan, D-C, Chung, Y-S, Ebeling, P, Gilchrist, N, Habib Khan, A, Halbout, P, Hew, FL, Lan, H-PT, Lau, TC, Lee, JK, Lekamwasam, S, Lyubomirsky, G, Mercado-Asis, LB, Mithal, A, Nguyen, TV, Pandey, D, Reid, IR, Suzuki, A, Chit, TT, Tiu, KL, Valleenukul, T, Yung, CK & Zhao, YL 2021, 'Development of the Asia Pacific Consortium on Osteoporosis (APCO) Framework: clinical standards of care for the screening, diagnosis, and management of osteoporosis in the Asia-Pacific region', Osteoporosis International, vol. 32, no. 7, pp. 1249-1275.
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Guidelines for doctors managing osteoporosis in the Asia-Pacific region vary widely. We compared 18 guidelines for similarities and differences in five key areas. We then used a structured consensus process to develop clinical standards of care for the diagnosis and management of osteoporosis and for improving the quality of care. PURPOSE: Minimum clinical standards for assessment and management of osteoporosis are needed in the Asia-Pacific (AP) region to inform clinical practice guidelines (CPGs) and to improve osteoporosis care. We present the framework of these clinical standards and describe its development. METHODS: We conducted a structured comparative analysis of existing CPGs in the AP region using a '5IQ' model (identification, investigation, information, intervention, integration, and quality). One-hundred data elements were extracted from each guideline. We then employed a four-round Delphi consensus process to structure the framework, identify key components of guidance, and develop clinical care standards. RESULTS: Eighteen guidelines were included. The 5IQ analysis demonstrated marked heterogeneity, notably in guidance on risk factors, the use of biochemical markers, self-care information for patients, indications for osteoporosis treatment, use of fracture risk assessment tools, and protocols for monitoring treatment. There was minimal guidance on long-term management plans or on strategies and systems for clinical quality improvement. Twenty-nine APCO members participated in the Delphi process, resulting in consensus on 16 clinical standards, with levels of attainment defined for those on identification and investigation of fragility fractures, vertebral fracture assessment, and inclusion of quality metrics in guidelines. CONCLUSION: The 5IQ analysis confirmed previous anecdotal observations of marked heterogeneity of osteoporosis clinical guidelines in the AP region. The Framework provides practical, clear, and feasible recommendations...
Chang, W, Zhang, Q, Fu, C, Liu, W, Zhang, G & Lu, J 2021, 'A cross-domain recommender system through information transfer for medical diagnosis', Decision Support Systems, vol. 143, pp. 113489-113489.
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The electronic diagnostic records of patients, primarily collected by hospitals, comprise valuable data for the development of recommender systems to support physicians in predicting the risks associated with various diseases. For some diseases, the diagnostic record data are not sufficient to train a prediction model to generate recommendations; this is referred to as the data sparsity problem. Cross-domain recommender systems offer a solution to this problem by transferring knowledge from a similar domain (source domain) with sufficient data for modeling to facilitate prediction in the current domain (target domain). However, building a cross-domain recommender system for medical diagnosis presents two challenges: (1) uncertain representations, such as the symptoms characterized by interval numbers, are often used in medical records, and (2) given two different diseases, the feature spaces of the two diagnostic domains are often disparate because the diseases are only likely to share a few symptoms. This study addresses these challenges by proposing a cross-domain recommender system, named information transfer for medical diagnosis (ITMD), to provide physicians with personalized recommendations for disease risks. In ITMD, a novel dissimilarity measurement was performed for diagnosis, represented as interval numbers. The space alignment technique eliminated the feature space divergence caused by different symptoms between two diseases, and the development of collective matrix factorization enabled knowledge transfer between the source and target domains. Experiments and a case study using real-world data demonstrated that ITMD outperforms four baselines and improves the accuracy of recommendations for disease risks in patients to support physicians in determining a final medical diagnosis.
Chang, Y-C, Shi, Y, Dostovalova, A, Cao, Z, Kim, J, Gibbons, D & Lin, C-T 2021, 'Interpretable Fuzzy Logic Control for Multirobot Coordination in a Cluttered Environment', IEEE Transactions on Fuzzy Systems, vol. 29, no. 12, pp. 3676-3685.
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Mobile robot navigation is an essential problem in robotics. We propose a method for constructing and training fuzzy logic controllers (FLCs) to coordinate a robotic team performing collision-free navigation and arriving simultaneously at a target location in an unknown environment. Our FLCs are organized in a multilayered architecture to reduce the number of tunable parameters and improve the scalability of the solution. In addition, in contrast to simple traditional switching mechanisms between target seeking and obstacle avoidance, we develop a novel rule-embedded FLC to improve the navigation performance. Moreover, we design a grouping and merging mechanism to obtain transparent fuzzy sets and integrate this mechanism into the training process for all FLCs, thus increasing the interpretability of the fuzzy models. We train the proposed FLCs using a novel multiobjective hybrid approach combining a genetic algorithm and particle swarm optimization. Simulation results demonstrate the effectiveness of our algorithms in reliably solving the proposed navigation problem.
Chang, Y-C, Wang, Y-K, Pal, NR & Lin, C-T 2021, 'Exploring Covert States of Brain Dynamics via Fuzzy Inference Encoding', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 2464-2473.
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Human brain inherently exhibits latent mental processes which are likely to change rapidly over time. A framework that adopts a fuzzy inference system is proposed to model the dynamics of the human brain. The fuzzy inference system is used to encode real-world data to represent the salient features of the EEG signals. Then, an unsupervised clustering is conducted on the extracted feature space to identify the brain (external and covert) states that respond to different cognitive demands. To understand the human state change, a state transition diagram is introduced, allowing visualization of connectivity patterns between every pair of states. We compute the transition probability between every pair of states to represent the relationships between the states. This state transition diagram is named as the Fuzzy Covert State Transition Diagram (FCOSTD), which helps the understanding of human states and human performance. We then apply FCOSTD on distracted driving experiments. FCOSTD successfully discovers the external and covert states, faithfully reveals the transition of the brain between states, and the route of the state change when humans are distracted during a driving task. The experimental results demonstrate that different subjects have similar states and inter-state transition behaviour (establishing the consistency of the system) but different ways to allocate brain resources as different actions are being taken.
Chaturvedi, K, Vishwakarma, DK & Singh, N 2021, 'COVID-19 and its impact on education, social life and mental health of students: A survey', Children and Youth Services Review, vol. 121, pp. 105866-105866.
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The outbreak of COVID-19 affected the lives of all sections of society as people were asked to self-quarantine in their homes to prevent the spread of the virus. The lockdown had serious implications on mental health, resulting in psychological problems including frustration, stress, and depression. In order to explore the impacts of this pandemic on the lives of students, we conducted a survey of a total of 1182 individuals of different age groups from various educational institutes in Delhi - National Capital Region (NCR), India. The article identified the following as the impact of COVID-19 on the students of different age groups: time spent on online classes and self-study, medium used for learning, sleeping habits, daily fitness routine, and the subsequent effects on weight, social life, and mental health. Moreover, our research found that in order to deal with stress and anxiety, participants adopted different coping mechanisms and also sought help from their near ones. Further, the research examined the student's engagement on social media platforms among different age categories. This study suggests that public authorities should take all the necessary measures to enhance the learning experience by mitigating the negative impacts caused due to the COVID-19 outbreak.
Chaudhary, P, Gupta, BB, Chang, X, Nedjah, N & Chui, KT 2021, 'Enhancing big data security through integrating XSS scanner into fog nodes for SMEs gain', Technological Forecasting and Social Change, vol. 168, pp. 120754-120754.
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Advancement in embedded Nano-technologies empowers IoT technology which serves as the backbone technology for many small and medium enterprises. Evolution of smart devices proved as a supreme source of data, denoted as Big data, that is analyzed to instantly extricate constructive and valuable information that helps the organization in many ways. As it may embrace sensitive information, hence, it turned out to be the fascinating target of multitude of attacks that aims at stealing the information, resulting into privacy breaches. XSS attack is one such security attack that supports the attacker to intrude into user's personal space. Therefore, this paper is focused on designing an approach that detects XSS attack in IoT network to protect the data privacy. It employs Convolution Neural Network (CNN) to detect the XSS attack payload after applying certain data preparation methods. This approach helps in preventing privacy breaches which eventually helps the enterprises in strengthening their bond with their users. The experimental results unveiled that the approach achieves a detection accuracy of 99% after the successful execution of data preparation methods.
Chen, C-S, Chen, S-K, Lai, C-C & Lin, C-T 2021, 'Sequential Motion Primitives Recognition of Robotic Arm Task via Human Demonstration Using Hierarchical BiLSTM Classifier', IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 502-509.
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IEEE Learning from demonstration (LfD) is an intuitive teaching technology without extensive programming for an operator. In recently LfD research, machine vision is usually used to capture the human-robot interaction. However, it's not reliable during the machining process. In this paper, a novel intuitive high-level kinesthetic teaching technology is proposed by reconstructing the recorded motion information during a human-guided robotic arm. A hierarchical BiLSTM-based machine learning algorithm is proposed in this paper to recognize and segment motion primitives according to the therblig definition. The hybrid sensing interface is used to record and extract the motion features, the velocity profile, force/torque, and gripper information. The motion features, output data via the hybrid sensing interface, are finally used to classify into the target motion primitive by the proposed classifier. The experimental results and comparisons with the state-of-the-art algorithm show that the proposed method can correctly and efficiently synthesize the recorded motion features into a motion primitive sequence. Finally, the recognition results of real-world tasks show that the proposed algorithm can be used to reconstruct the human-guided task and further used to robot command for the KUKA robot. The experimental results of the reconstructed trajectory show that a real-world task can represent and maintain the accuracy in 2.37 mm using the proposed algorithm.
Chen, C-Y, Chen, W-H, Lim, S, Ong, HC & Ubando, AT 2021, 'Synergistic interaction and biochar improvement over co-torrefaction of intermediate waste epoxy resins and fir', Environmental Technology & Innovation, vol. 21, pp. 101218-101218.
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This study investigated the synergistic effect of co-torrefaction with intermediate waste epoxy resins and fir in a batch-type reactor towards biochar improvement. The synergistic effect ratio was used to judge the interaction between the two materials assisted by statistical tools. The main interaction between the feedstocks was the catalytic reaction and blocking effect. Sodium presented in the intermediate waste had a pronounced catalytic effect on the liquid products during torrefaction. It successfully enhanced the volatile matter emissions and exhibited an antagonistic effect on the solid yield. Different from the catalytic reaction that occurred during short retention time, the blocking effect was more noticeable with a longer duration, showing a synergistic effect on the solid yield. Alternatively, a significantly antagonistic effect was exerted on oxygen content, while the carbon content displayed a converse trend. This gave rise to a major antagonistic effect on the O/C ratio which was closer to coal for pure materials torrefaction. The other spotlight in this study was to reuse the tar as a heating value additive. After coating it onto the biochar, the higher heating value could be increased by up to 5.4%. Although tar is considered as an unwanted byproduct of torrefaction treatment, the presented data show its high potential to be recycled into useful calorific value enhancer. It also fulfills the scope of waste-to-energy in this study.
Chen, D, Liu, Y, Chen, S-L, Qin, P-Y & Guo, YJ 2021, 'A Wideband High-Gain Multilinear Polarization Reconfigurable Antenna', IEEE Transactions on Antennas and Propagation, vol. 69, no. 7, pp. 4136-4141.
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IEEE In this Communication, a wideband low-profile antenna with switchable multi-linear polarizations (MLPs) is proposed. An odd number of dipoles with trapezoidal-shaped arms printed on both sides of a substrate are adopted as reconfigurable radiators, which provides a much smaller polarization interval than using an adjacent even number of dipoles. PIN diodes with simple DC biasing lines are loaded to reconfigure the polarization states. A circular-contoured artificial magnetic conductor (AMC) reflector using hexagon-patch cells is employed to reduce the antenna profile. The whole multiple dipole structure is rotationally invariant which provides almost rotationally invariant antenna performance for different LPs. In addition, the antenna can be easily re-designed when adjusting the number of dipoles for different LPs. A seven-LP reconfigurable antenna working in 2:85 GHz to 3:40 GHz is used as an example to give the detailed parameters study and performance analysis. Three antennas with 5, 7 and 9 reconfigurable LPs are designed and measured. With 0:035λ height, they achieve the measured overlapped bandwidths of 20:6%, 17:6% and 15:9% for 5, 7 and 9 LPs, respectively, and their measured peak gains are ranging from 8:3 to 8:5 dBi.
Chen, J, Indraratna, B, Vinod, JS, Ngo, NT, Gao, R & Liu, Y 2021, 'Stress-dilatancy behaviour of fouled ballast: experiments and DEM modelling', Granular Matter, vol. 23, no. 4.
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This paper presents a study of the mechanical behaviour of ballast contaminated by different fouling agents such as coal and subgrade clay. Large-scale direct shear tests were carried out to examine the strength and deformation properties for coal-fouled and clay-fouled ballast. The experimental results show that fouled ballast (both clay and coal) exhibits a lower peak shear strength and decreased dilation during shearing. The clay-fouled ballast shows higher shear strength and smaller dilation compared to coal-fouled ballast. The relationship between shear stress and dilatancy of ballast under different fouling conditions is reported in this paper, where the numerical predictions are made using the discrete element method (DEM). The DEM simulations show that with the increase of fouling level, the coordination number, the average contact force, the particle rotation and the velocity decrease for ballast aggregates. The results indicate that coal-fouled ballast exhibits a smaller average contact forces with less stress concentrations, less major principal stress orientation and a greater coordination number, leading to less particle rotation and velocity compared to those of clay-fouled ballast for the same degree of fouling. Graphic abstract: [Figure not available: see fulltext.]
Chen, J, Nelson, C, Wong, M, Tee, AE, Liu, PY, La, T, Fletcher, JI, Kamili, A, Mayoh, C, Bartenhagen, C, Trahair, TN, Xu, N, Jayatilleke, N, Wong, M, Peng, H, Atmadibrata, B, Cheung, BB, Lan, Q, Bryan, TM, Mestdagh, P, Vandesompele, J, Combaret, V, Boeva, V, Wang, JY, Janoueix-Lerosey, I, Cowley, MJ, MacKenzie, KL, Dolnikov, A, Li, J, Polly, P, Marshall, GM, Reddel, RR, Norris, MD, Haber, M, Fischer, M, Zhang, XD, Pickett, HA & Liu, T 2021, 'Targeted Therapy of TERT-Rearranged Neuroblastoma with BET Bromodomain Inhibitor and Proteasome Inhibitor Combination Therapy', Clinical Cancer Research, vol. 27, no. 5, pp. 1438-1451.
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Abstract Purpose: TERT gene rearrangement with transcriptional superenhancers leads to TERT overexpression and neuroblastoma. No targeted therapy is available for clinical trials in patients with TERT-rearranged neuroblastoma. Experimental Design: Anticancer agents exerting the best synergistic anticancer effects with BET bromodomain inhibitors were identified by screening an FDA-approved oncology drug library. The synergistic effects of the BET bromodomain inhibitor OTX015 and the proteasome inhibitor carfilzomib were examined by immunoblot and flow cytometry analysis. The anticancer efficacy of OTX015 and carfilzomib combination therapy was investigated in mice xenografted with TERT-rearranged neuroblastoma cell lines or patient-derived xenograft (PDX) tumor cells, and the role of TERT reduction in the anticancer efficacy was examined through rescue experiments in mice. Results: The BET bromodomain protein BRD4 promoted TERT-rearranged neuroblastoma cell proliferation through upregulating TERT expression. Screening of an approved oncology drug library identified the proteasome inhibitor carfilzomib as the agent exerting the best synergistic anticancer effects with BET bromodomain inhibitors including OTX015. OTX015 and carfilzomib synergistically reduced TERT protein expression, induced endoplasmic reticulum stress, and induced TERT-rearranged neuroblastoma cell apoptosis which was blocked by TERT overexpression and endoplasmic reticulum stress antagonists. In mice xenografted with TERT-rearranged neuroblastoma cell lines or PDX tumor cells, OTX015 and carfilzomib synergistica...
Chen, J, Wen, S, Shi, K & Yang, Y 2021, 'Highly parallelized memristive binary neural network', Neural Networks, vol. 144, pp. 565-572.
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At present, in the new hardware design work of deep learning, memristor as a non-volatile memory with computing power has become a research hotspot. The weights in the deep neural network are the floating-point number. Writing a floating-point value into a memristor will result in a loss of accuracy, and the writing process will take more time. The binarized neural network (BNN) binarizes the weights and activation values that were originally floating-point numbers to +1 and -1. This will greatly reduce the storage space consumption and time consumption of programming the resistance value of the memristor. Furthermore, this will help to simplify the programming of memristors in deep neural network circuits and speed up the inference process. This paper provides a complete solution for implementing memristive BNN. Furthermore, we improved the design of the memristor crossbar by converting the input feature map and kernel before performing the convolution operation that can ensure the sign of the input voltage of each port constant. Therefore, we do not need to determine the sign of the input voltage required by the port in advance which simplifies the process of inputting the feature map elements to each port of the crossbar in the form of voltage. At the same time, in order to ensure that the output of the current convolution layer can be directly used as the input of the next layer, we have added a corresponding processing circuit, which integrates batch-normalization and binarization operations.
Chen, J, Wu, D, Zhao, Y, Sharma, N, Blumenstein, M & Yu, S 2021, 'Fooling intrusion detection systems using adversarially autoencoder', Digital Communications and Networks, vol. 7, no. 3, pp. 453-460.
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Due to the increasing cyber-attacks, various Intrusion Detection Systems (IDSs) have been proposed to identify network anomalies. Most existing machine learning-based IDSs learn patterns from the features extracted from network traffic flows, and the deep learning-based approaches can learn data distribution features from the raw data to differentiate normal and anomalous network flows. Although having been used in the real world widely, the above methods are vulnerable to some types of attacks. In this paper, we propose a novel attack framework, Anti-Intrusion Detection AutoEncoder (AIDAE), to generate features to disable the IDS. In the proposed framework, an encoder transforms features into a latent space, and multiple decoders reconstruct the continuous and discrete features, respectively. Additionally, a generative adversarial network is used to learn the flexible prior distribution of the latent space. The correlation between continuous and discrete features can be kept by using the proposed training scheme. Experiments conducted on NSL-KDD, UNSW-NB15, and CICIDS2017 datasets show that the generated features indeed degrade the detection performance of existing IDSs dramatically.
Chen, L, Chen, L, Ge, Z, Sun, Y, Hamilton, TJ & Zhu, X 2021, 'A 90-GHz Asymmetrical Single-Pole Double-Throw Switch With >19.5-dBm 1-dB Compression Point in Transmission Mode Using 55-nm Bulk CMOS Technology', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 68, no. 11, pp. 4616-4625.
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The millimeter-wave (mm-wave) single-pole double-throw (SPDT) switch designed in bulk CMOS technology has limited power-handling capability in terms of 1-dB compression point (P1dB) inherently. This is mainly due to the low threshold voltage of the switching transistors used for shunt-connected configuration. To solve this issue, an innovative approach is presented in this work, which utilizes a unique passive ring structure. It allows a relatively strong RF signal passing through the TX branch, while the switching transistors are turned on. Thus, the fundamental limitation for P1dB due to reduced threshold voltage is overcome. To prove the presented approach is feasible in practice, a 90-GHz asymmetrical SPDT switch is designed in a standard 55-nm bulk CMOS technology. The design has achieved an insertion loss of 3.2 dB and 3.6 dB in TX and RX mode, respectively. Moreover, more than 20 dB isolation is obtained in both modes. Because of using the proposed passive ring structure, a remarkable P1dB is achieved. No gain compression is observed at all, while a 19.5 dBm input power is injected into the TX branch of the designed SPDT switch. The die area of this design is only 0.26 mm2.
Chen, L, Liu, H, Hora, J, Zhang, JA, Yeo, KS & Zhu, X 2021, 'A Monolithically Integrated Single-Input Load-Modulated Balanced Amplifier With Enhanced Efficiency at Power Back-Off', IEEE Journal of Solid-State Circuits, vol. 56, no. 5, pp. 1553-1564.
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In this article, the design of a power amplifier (PA) using a simple but effective architecture, namely, load-modulated balanced amplifier (LMBA), is presented. Using this architecture for PA design, it can achieve not only a relatively high saturated output power but also an excellent efficiency enhancement at the power back-off (PBO) region. To prove that the presented approach is feasible in practice, a PA is designed in a 1-μm gallium arsenide (GaAs) HBT process. Operating under a 5-V power supply, the PA can deliver more than 31-dBm saturated output power with 36% collector efficiency (CE) at 5 GHz. Moreover, it also achieves 1.2 and 1.23 times CE enhancement over an idealistic Class-B PA at 6- and 9-dB PBO levels, respectively. Finally, the designed PA supports 64-quadrature amplitude modulation (QAM) with 80 Msys/s at 22-dBm average output power while still maintaining an error vector magnitude (EVM) and adjacent channel power ratio (ACPR) better than -29.5 dB and -29.4 dBc, respectively.
Chen, L, Liu, Y, Yang, S & Guo, YJ 2021, 'Efficient Synthesis of Filter-and-Sum Array With Scanned Wideband Frequency-Invariant Beam Pattern and Space-Frequency Notching', IEEE Signal Processing Letters, vol. 28, pp. 384-388.
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IEEE This work generalizes the Fourier transform (FT)-based frequency-invariant beamforming (FIB) method to the synthesis of scanned frequency-invariant (FI) beam pattern with space-frequency notching for an array with non-isotropic elements. Wideband FI pattern characteristics are described by using multiple reference sub-band FI patterns that are obtained through an iterative single-frequency FT-based synthesis method. By applying fast Fourier transform (FFT) on the combination of these multiple reference sub-band FI patterns, a wideband excitation distribution can be generated. Based on this excitation distribution, we construct a new wideband excitation distribution that is conjugate-symmetric about zero frequency, so that real-valued finite-impulse-response (FIR) filter coefficients can be obtained by applying FFT on the constructed distribution. Two numerical examples are introduced to show the effectiveness and efficiency of the proposed method for synthesizing scanned FI beam patterns with complicated notching requirements.
Chen, Q, Peng, W, Yu, R, Tao, G & Nimbalkar, S 2021, 'Laboratory Investigation on Particle Breakage Characteristics of Calcareous Sands', Advances in Civil Engineering, vol. 2021, no. 1, pp. 1-8.
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Many studies have demonstrated the fragility of calcareous sands even under small stresses. This bears an adverse influence on their engineering properties. A series of laboratory tests were carried out on poor‐graded calcareous sands to investigate the crushability mechanism. Einav’s relative breakage and fractal dimension were used as the particle breakage indices. The results show that the particles broke into smaller fragments at the low‐stress level by attrition which was caused by friction and slip between particles. In contrast, particles broke in the form of crushing at the relatively higher stresses. The evolution of the particle size was reflected by the variation in Einav’s relative breakage and fractal dimension. As testing commenced, the breakage index rapidly increased. When the stress was increased to 400 kPa, the rate of increase in the breakage index was retarded. As the stress was further increased beyond 800 kPa, the rate of increase in the fractal index became much smaller. This elucidated that the well‐graded calcareous sands could resist crushing depending on the range of applied stresses. Based on the test findings, a new breakage law is proposed.
Chen, Q, Yu, R, Li, Y, Tao, G & Nimbalkar, S 2021, 'Cyclic stress-strain characteristics of calcareous sand improved by polyurethane foam adhesive', Transportation Geotechnics, vol. 31, pp. 100640-100640.
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Chen, Q, Yu, R, Tao, G, Zhang, J & Nimbalkar, S 2021, 'Shear behavior of polyurethane foam adhesive improved calcareous sand under large-scale triaxial test', Marine Georesources & Geotechnology, vol. 39, no. 12, pp. 1449-1458.
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Chen, Q-S, Peng, W, Tao, G-L & Nimbalkar, S 2021, 'Strength and Deformation Characteristics of Calcareous Sands Improved by PFA', KSCE Journal of Civil Engineering, vol. 25, no. 1, pp. 60-69.
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© 2020, Korean Society of Civil Engineers. Calcareous sand is widely distributed in the islands of the South China Sea, which could be promisingly used as the construction materials. However, particle breakage commonly occurs in calcareous sands, which may significantly influence their mechanical characteristics. To address these issues, an eco-friendly agent, i.e., polyurethane foam adhesive (PFA) is proposed to improve the engineering properties of calcareous sands, compared to the commonly used alkaline stabilizing agents (e.g., lime, cement). The objective of this work is to examine the effectiveness of using PFA in improving the strength-deformation properties of calcareous sand. A series of laboratory tests including direct shear tests, unconfined compression tests, and oedometer tests were performed on the calcareous sands improved by PFA. In addition, A scanning electron microscope (SEM) was conducted to reveal microstructural analysis of using PFA for calcareous sand. The experimental results provided insights into the shear strength, deformation modulus, as well as the micro-structural characteristics of improved calcareous sands with various PFA contents and particle size distributions.
Chen, R, Yin, H, Jiao, Y, Dissanayake, G, Wang, Y & Xiong, R 2021, 'Deep Samplable Observation Model for Global Localization and Kidnapping', IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2296-2303.
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Chen, R, Zhu, L, Wong, S, Lin, J, Yang, Y, Li, Y & He, Y 2021, 'Miniaturized full‐metal bandpass filter and multiplexer using circular spiral resonator', IET Microwaves, Antennas & Propagation, vol. 15, no. 6, pp. 606-619.
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AbstractThis article proposes a new class of miniaturized full‐metal bandpass filters (BPFs) and multiplexers using circular spiral resonators (CSRs). The proposed metal CSR is supported on the metal cavity's wall, which serves as a short‐end resonator. This CSR has obvious size reduction compared to traditional cavity resonators and owns low insertion loss, high power capacity and high selectivity. Then, three BPFs using two, three and four CSRs are designed and analysed. All the proposed filters have transmission zeroes (TZs) produced by the source‐load coupling without introducing additional coupling structure. The proposed CSR is further used to design diplexer and quadruplexer. All the proposed filters have ultra‐compact size, especially, the size of quadruplexer is only 0.23λ0 × 0.081λ0 × 0.067λ0. Finally, the fourth‐order filter and quadruplexer are fabricated and measured, the good agreement between the measured results and the simulated results validates the proposed design concepts.
Chen, R-S, Wong, S-W, Lin, J-Y, Yang, Y, Li, Y, Zhang, L, He, Y & Zhu, L 2021, 'Reconfigurable Cavity Bandpass Filters Using Fluid Dielectric', IEEE Transactions on Industrial Electronics, vol. 68, no. 9, pp. 8603-8614.
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A novel method for the development of a reconfigurable cavity bandpass filter using fluid dielectric is proposed. Dielectric material can produce an effective permittivityeff of the resonant mode when it is loaded into the cavity. Thus, a tube filled with fluid dielectric, e.g., distilled water, can achieve controlled and reversibleeff by adjusting the amount of water in the tube. The same manner of resonant frequency can be achieved as the resonant frequency is related toeff, and then frequency tuning is realized. The fluid property can realize easier and faster tuning mechanism than conventional solid dielectric. Aseff is affected by the loaded dielectric parallel to the electric field, a triple-mode resonator with resonant modes TE101, TE011, and TM110, which have orthogonal electric fields, is investigated to realize tri-band reconfiguration. Theeff, as well as the resonant frequencies, corresponding to each mode can be individually controlled by adjusting their related water posts. Then, reconfigurable single-band and tri-band bandpass filters are designed. A reconfigurable tri-band cavity filter using a triple-mode cavity resonator and fluid dielectric with individual and continuous frequency tuning is reported for the first time. Finally, the reconfigurable tri-band filter is fabricated and measured to validate the concept.
Chen, R-S, Zhu, L, Lin, J-Y, Wong, S-W, Yang, Y, Li, Y, Zhang, L & He, Y 2021, 'High-Isolation In-Band Full-Duplex Cavity-Backed Slot Antennas in a Single Resonant Cavity', IEEE Transactions on Antennas and Propagation, vol. 69, no. 11, pp. 7092-7102.
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Chen, S, Fu, A, Yu, S, Ke, H & Su, M 2021, 'DP-QIC: A differential privacy scheme based on quasi-identifier classification for big data publication', Soft Computing, vol. 25, no. 11, pp. 7325-7339.
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With the advent of the era of big data, data privacy protection has become a valuable topic in the field of data publication. Unfortunately, traditional methods of privacy protection, k-anonymity, and its extensions are not absolutely secure as an adversary with background knowledge can determine the owner of a record. The emergence of differential privacy provides a reasonable alternative for privacy security, but the existing solutions ignore the correlation between sensitive attributes and other attributes. In this paper, we propose a new differential privacy scheme based on quasi-identifier classification for big data publication (DP-QIC). It is a new data publishing scheme based on the obfuscation of attribute correlation. We innovatively present quasi-identifier classification based on sensitive attributes and the privacy ratio for evaluating the data set vulnerability. DP-QIC achieves data privacy-protecting through four steps: data collection, grouping and shuffling, generalization, merging, and noise adding, which retains the overall statistical characteristics of the data set. Moreover, the exponential mechanism and the Laplace mechanism are integrated to ensure higher flexibility and a stronger level of privacy protection, so DP-QIC can be used for privacy processing of different data groups in future development. Finally, we have compared the performance of our scheme with the other two famous schemes in the industry. Experimental results demonstrate that DP-QIC has obvious advantages in data utility, privacy protection, and processing efficiency.
Chen, S, Wang, W, Xia, B, You, X, Peng, Q, Cao, Z & Ding, W 2021, 'CDE-GAN: Cooperative Dual Evolution-Based Generative Adversarial Network', IEEE Transactions on Evolutionary Computation, vol. 25, no. 5, pp. 986-1000.
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Chen, W, Hasanipanah, M, Nikafshan Rad, H, Jahed Armaghani, D & Tahir, MM 2021, 'A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration', Engineering with Computers, vol. 37, no. 2, pp. 1455-1471.
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Chen, W-H, Cheng, C-L, Lee, K-T, Lam, SS, Ong, HC, Ok, YS, Saeidi, S, Sharma, AK & Hsieh, T-H 2021, 'Catalytic level identification of ZSM-5 on biomass pyrolysis and aromatic hydrocarbon formation', Chemosphere, vol. 271, pp. 129510-129510.
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Chen, W-H, Chiu, G-L, Chyuan Ong, H, Shiung Lam, S, Lim, S, Sik Ok, Y & E.Kwon, E 2021, 'Optimization and analysis of syngas production from methane and CO2 via Taguchi approach, response surface methodology (RSM) and analysis of variance (ANOVA)', Fuel, vol. 296, pp. 120642-120642.
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Chen, W-H, Du, J-T, Lee, K-T, Ong, HC, Park, Y-K & Huang, C-C 2021, 'Pore volume upgrade of biochar from spent coffee grounds by sodium bicarbonate during torrefaction', Chemosphere, vol. 275, pp. 129999-129999.
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A novel approach for upgrading the pore volume of biochar at low temperatures using a green additive of sodium bicarbonate (NaHCO3) is developed in this study. The biochar was produced from spent coffee grounds (SCGs) torrefied at different temperatures (200-300 °C) with different residence times (30-60 min) and NaHCO3 concentrations (0-8.3 wt%). The results reveal that the total pore volume of biochar increases with rising temperature, residence time, or NaHCO3 aqueous solution concentration, whereas the bulk density has an opposite trend. The specific surface area and total pore volume of pore-forming SCG from 300 °C torrefaction for 60 min with an 8.3 wt% NaHCO3 solution (300-TP-SCG) are 42.050 m2 g-1 and 0.1389 cm3·g-1, accounting for the improvements of 141% and 76%, respectively, compared to the parent SCG. The contact angle (126°) and water activity (0.48 aw) of 300-TP-SCG reveal that it has long storage time. The CO2 uptake capacity of 300-TP-SCG is 0.32 mmol g-1, rendering a 39% improvement relative to 300-TSCG, namely, SCG torrefied at 300 °C for 60 min. 300-TP-SCG has higher HHV (28.31 MJ·kg-1) and lower ignition temperature (252 °C). Overall, it indicates 300-TP-SCG is a potential fuel substitute for coal. This study has successfully produced mesoporous biochar at low temperatures to fulfill '3E', namely, energy (biofuel), environment (biowaste reuse solid waste), and circular economy (bioadsorbent).
Chen, W-H, Lin, B-J, Lin, Y-Y, Chu, Y-S, Ubando, AT, Show, PL, Ong, HC, Chang, J-S, Ho, S-H, Culaba, AB, Pétrissans, A & Pétrissans, M 2021, 'Progress in biomass torrefaction: Principles, applications and challenges', Progress in Energy and Combustion Science, vol. 82, pp. 100887-100887.
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The development of biofuels has been considered as an important countermeasure to abate anthropogenic CO2 emissions, suppress deteriorated atmospheric greenhouse effect, and mitigate global warming. To produce biofuels from biomass, thermochemical conversion processes are considered as the most efficient routes wherein torrefaction has the lowest global warming potential. Combustion is the easiest way to consume biomass, which can be burned alone or co-fired with coal to generate heat and power. However, solid biomass fuels are not commonly applied in the industry due to their characteristics of hygroscopic nature and high moisture content, low bulk density and calorific value, poor grindability, low compositional homogeneity, and lower resistance against biological degradation. In recently developing biomass conversion technologies, torrefaction has attracted much attention since it can effectively upgrade solid biomass and produce coal-like fuel. Torrefaction is categorized into dry and wet torrefaction; the former can further be split into non-oxidative and oxidative torrefaction. Despite numerous methods developed, non-oxidative torrefaction, normally termed torrefaction, has a higher potential for practical applications and commercialization when compared to other methods. To provide a comprehensive review of the progress in biomass torrefaction technologies, this study aims to perform an in-depth literature survey of torrefaction principles, processes, systems, and to identify a current trend in practical torrefaction development and environmental performance. Moreover, the encountered challenges and perspectives from torrefaction development are underlined. This state-of-the-art review is conducive to the production and applications of biochar for resource utilization and environmental sustainability. To date, several kinds of reactors have been developed, while there is still no obviously preferred one as they simultaneously have pros and cons. ...
Chen, W-H, Lo, H-J, Yu, K-L, Ong, H-C & Sheen, H-K 2021, 'Valorization of sorghum distillery residue to produce bioethanol for pollution mitigation and circular economy', Environmental Pollution, vol. 285, pp. 117196-117196.
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Chen, X, Cheng, B, Li, Z, Nie, X, Yu, N, Yung, M-H & Peng, X 2021, 'Experimental cryptographic verification for near-term quantum cloud computing', Science Bulletin, vol. 66, no. 1, pp. 23-28.
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An important task for quantum cloud computing is to make sure that there is a real quantum computer running, instead of classical simulation. Here we explore the applicability of a cryptographic verification scheme for verifying quantum cloud computing. We provided a theoretical extension and implemented the scheme on a 5-qubit NMR quantum processor in the laboratory and a 5-qubit and 16-qubit processors of the IBM quantum cloud. We found that the experimental results of the NMR processor can be verified by the scheme with about 1.4% error, after noise compensation by standard techniques. However, the fidelity of the IBM quantum cloud is currently too low to pass the test (about 42% error). This verification scheme shall become practical when servers claim to offer quantum-computing resources that can achieve quantum supremacy.
Chen, X, Feng, Z, Wei, Z, Zhang, P & Yuan, X 2021, 'Code-Division OFDM Joint Communication and Sensing System for 6G Machine-Type Communication', IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12093-12105.
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Chen, X, Hu, Z, Xie, H, Ngo, HH, Guo, W & Zhang, J 2021, 'Enhanced biocatalysis of phenanthrene in aqueous phase by novel CA-Ca-SBE-laccase biocatalyst: Performance and mechanism', Colloids and Surfaces A: Physicochemical and Engineering Aspects, vol. 611, pp. 125884-125884.
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Chen, X, Huo, P, Liu, J, Li, F, Yang, L, Li, X, Wei, W, Liu, Y & Ni, B-J 2021, 'Model predicted N2O production from membrane-aerated biofilm reactor is greatly affected by biofilm property settings', Chemosphere, vol. 281, pp. 130861-130861.
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Even though modeling has been frequently used to understand the autotrophic deammonification-based membrane-aerated biofilm reactor (MABR), the relationships between system-specific biofilm property settings and model predicted N2O production have yet to be clarified. To this end, this study investigated the impacts of 4 key biofilm property settings (i.e., biofilm thickness/compactness, boundary layer thickness, diffusivity of soluble components in the biofilm structure, and biofilm discretization) on one-dimensional modeling of the MABR, with the focus on its N2O production. The results showed that biofilm thickness/compactness (200-1000 μm), diffusivity of soluble components in the biofilm structure (reduction factor of diffusivity: 0.2-0.9), and biofilm discretization (12-28 grid points) significantly influenced the simulated N2O production, while boundary layer thickness (0-300 μm) only played a marginal role. In the studied ranges of biofilm property settings, distinct upper and lower bounds of N2O production factor (i.e., the percentage ratio of N2O formed to NH4+ removed, 5.5% versus 2.3%) could be predicted. In addition to the microbial community structure, the N2O production pathway contribution differentiation was also subject to changes in biofilm property settings. Therefore, biofilm properties need to be quantified experimentally or set properly to model N2O production from the MABR correctly. As a good practice for one-dimensional modeling of N2O production from biofilm-based reactors, especially the MABR performing autotrophic deammonification, the essential information about those influential biofilm property settings identified in this study should be disclosed and clearly documented, thus ensuring both the reproducibility of modeling results and the reliable applications of N2O models.
Chen, X, Lai, L, Qin, L & Lin, X 2021, 'Efficient structural node similarity computation on billion-scale graphs.', VLDB J., vol. 30, no. 3, pp. 471-493.
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Structural node similarity is widely used in analyzing complex networks. As one of the structural node similarity metrics, role similarity has the good merit of indicating automorphism (isomorphism). Existing algorithms to compute role similarity (e.g., RoleSim and NED) suffer from severe performance bottlenecks and thus cannot handle large real-world graphs. In this paper, we propose a new framework, namely StructSim, to compute nodes’ role similarity. Under this framework, we first prove that StructSim is an admissible role similarity metric based on the maximum matching. While the maximum matching is still too costly to scale, we then devise the BinCount matching that not only is efficient to compute but also guarantees the admissibility of StructSim. BinCount-based StructSim admits a precomputed index to query a single pair of node in O(klog D) time, where k is a small user-defined parameter and D is the maximum node degree. To build the index, we further devise an FM-sketch-based technique that can handle graphs with billions of edges. Extensive empirical studies show that StructSim performs much better than the existing works regarding both effectiveness and efficiency when applied to compute structural node similarities on the real-world graphs.
Chen, X, Lu, Z, Ni, W, Wang, X, Wang, F, Zhang, S & Xu, S 2021, 'Cooling-Aware Optimization of Edge Server Configuration and Edge Computation Offloading for Wirelessly Powered Devices', IEEE Transactions on Vehicular Technology, vol. 70, no. 5, pp. 5043-5056.
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Edge computing (EC) provides an effective means to cope with explosive computation demands of the Internet-of-Things (IoT). This paper presents a new cooling-aware joint optimization of the CPU configuration of the edge servers, and the schedules of wireless power transfer (WPT), offloading and computing for WPT-powered devices, so that the resource-restrained devices can have tasks accomplished in a timely and energy-efficient manner. Alternating optimization is applied to minimize the total energy consumption of WPT, EC, and cooling, while satisfying the computation deadlines of the devices. A key aspect is that semi-closed-form solutions are derived for the WPT power, offloading duration, and CPU frequency by applying the Lagrange duality method. With the solutions, the alternating optimization converges quickly and indistinguishably closely to the lower bound of the energy consumption. The semi-closed-form solutions also reveal the structure underlying the optimal solution to the problem, and can validate the result of the alternating optimization. Extensive simulations show that the proposed algorithm can save up to 90.4% the energy of existing benchmarks in our considered cases.
Chen, X, Wang, K, Lin, X, Zhang, W, Qin, L & Zhang, Y 2021, 'Efficiently Answering Reachability and Path Queries on Temporal Bipartite Graphs.', Proc. VLDB Endow., vol. 14, no. 10, pp. 1845-1858.
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Bipartite graphs are naturally used to model relationships between two different types of entities, such as people-location, authorpaper, and customer-product. When modeling real-world applications like disease outbreaks, edges are often enriched with temporal information, leading to temporal bipartite graphs. While reachability has been extensively studied on (temporal) unipartite graphs, it remains largely unexplored on temporal bipartite graphs. To fill this research gap, in this paper, we study the reachability problem on temporal bipartite graphs. Specifically, a vertex u reaches a vertex w in a temporal bipartite graph G if u and w are connected through a series of consecutive wedges with time constraints. Towards efficiently answering if a vertex can reach the other vertex, we propose an index-based method by adapting the idea of 2-hop labeling. Effective optimization strategies and parallelization techniques are devised to accelerate the index construction process. To better support real-life scenarios, we further show how the index is leveraged to efficiently answer other types of queries, e.g., singlesource reachability query and earliest-arrival path query. Extensive experiments on 16 real-world graphs demonstrate the effectiveness and efficiency of our proposed techniques.
Chen, Y, Huang, S, Zhao, L & Dissanayake, G 2021, 'Cramér–Rao Bounds and Optimal Design Metrics for Pose-Graph SLAM', IEEE Transactions on Robotics, vol. 37, no. 2, pp. 627-641.
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Two-dimensional (2-D)/3-D pose-graph simultaneous localization and mapping (SLAM) is a problem of estimating a set of poses based on noisy measurements of relative rotations and translations. This article focuses on the relation between the graphical structure of pose-graph SLAM and Fisher information matrix (FIM), Cramér–Rao lower bounds (CRLB), and its optimal design metrics (T-optimality and D-optimality). As a main contribution, based on the assumption of isotropic Langevin noise for rotation and block-isotropic Gaussian noise for translation, the FIM and CRLB are derived and shown to be closely related to the graph structure, in particular, the weighted Laplacian matrix. We also prove that total node degree and weighted number of spanning trees, as two graph connectivity metrics, are, respectively, closely related to the trace and determinant of the FIM. The discussions show that, compared with the D-optimality metric, the T-optimality metric is more easily computed but less effective. We also present upper and lower bounds for the D-optimality metric, which can be efficiently computed and are almost independent of the estimation results. The results are verified with several well-known datasets, such as Intel, KITTI, sphere, and so on.
Chen, Y, Li, C, White, S, Nonahal, M, Xu, Z-Q, Watanabe, K, Taniguchi, T, Toth, M, Tran, TT & Aharonovich, I 2021, 'Generation of High-Density Quantum Emitters in High-Quality, Exfoliated Hexagonal Boron Nitride', ACS Applied Materials & Interfaces, vol. 13, no. 39, pp. 47283-47292.
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Single-photon emitters in hexagonal boron nitride (hBN) are promising constituents for integrated quantum photonics. Specifically, engineering these emitters in large-area, high-quality, exfoliated hBN is needed for their incorporation into photonic devices and two dimensional heterostructures. Here, we report on two different routes to generate high-density quantum emitters with excellent optical properties-including high brightness and photostability. We study in detail high-temperature annealing and plasma treatments as an efficient means to generate dense emitters. We show that both an optimal oxygen flow rate and annealing temperature are required for the formation of high-density quantum emitters. In parallel, we demonstrate that the plasma treatment in various environments, followed by standard annealing is also an effective route for emission engineering. Our work provides vital information for the fabrication of quantum emitters in high-quality, exfoliated hBN flakes and paves the way toward the integration of the quantum emitters with photonic devices.
Chen, Y, Liu, J, Zhang, Z, Wen, S & Xiong, W 2021, 'MöbiusE: Knowledge Graph Embedding on Möbius Ring', Knowledge-Based Systems, vol. 227, pp. 107181-107181.
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In this work, we propose a novel Knowledge Graph Embedding (KGE) strategy, called MöbiusE, in which the entities and relations are embedded to the surface of a Möbius ring. The proposition of such a strategy is inspired by the classic TorusE, in which the addition of two arbitrary elements is subject to a modulus operation. In this sense, TorusE naturally guarantees the critical boundedness of embedding vectors in KGE. However, the nonlinear property of addition operation on Torus ring is uniquely derived by the modulus operation, which in some extent restricts the expressiveness of TorusE. As a further generalization of TorusE, MöbiusE also uses modulus operation to preserve the closeness of addition on it, but the coordinates on Möbius ring interacts with each other in the following way: any vector attaches to the surface of a Mobius ring becomes its opposite one if it moves along its parametric trace by a cycle. Hence, MöbiusE assumes much more nonlinear representativeness than that of TorusE, and in turn it generates much more precise embedding results. In our experiments, MöbiusE outperforms TorusE and other classic embedding strategies in several key indicators.
Chen, Y, Westerhausen, MT, Li, C, White, S, Bradac, C, Bendavid, A, Toth, M, Aharonovich, I & Tran, TT 2021, 'Solvent-Exfoliated Hexagonal Boron Nitride Nanoflakes for Quantum Emitters', ACS Applied Nano Materials, vol. 4, no. 10, pp. 10449-10457.
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Quantum emitters in hexagonal boron nitride (hBN) flakes have recently emerged as a promising platform for nanophotonic and quantum applications. The solvent-exfoliation process of these flakes has, however, remained largely unexplored. In this work, we demonstrate a surfactant-assisted exfoliation technique in an aqueous solution to exfoliate a variety of commercially available hBN powders into hBN nanoflakes. We show that the selection of hBN powder greatly impacts the optical properties of the resultant quantum emitters embedded in exfoliated hBN nanoflakes. We find that the sample with the best optical performance also shows the lowest impurity levels in its starting hBN powder. Our study provides further insight into quantum emitter fabrication in hBN and tailoring of their optical properties.
Chen, Y, Wu, D, Yu, Y & Gao, W 2021, 'An improved theory in the determination of aerodynamic damping for a horizontal axis wind turbine (HAWT)', Journal of Wind Engineering and Industrial Aerodynamics, vol. 213, pp. 104619-104619.
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The aerodynamic damping reflects the aero-structure interaction and is intrinsically involved in a fully coupled turbine. However, it is still of great importance to theoretically quantify the aerodynamic damping of a HAWT in some cases. The existing theory can reasonably characterize the aerodynamic damping level of a HAWT with rigid blades, minute shaft tilt and yaw angles, while certain discrepancies were observed when compared with either experimental or numerical damping results of a more realistic turbine. This study aims to provide an improved theory to incorporate more realistic conditions (i.e., blade flexibility, shaft tilt and yaw angle) in aerodynamic damping estimation. Good agreements are found between the proposed theory and numerical results with varied influential factors, upon which modification factors against the original theory are created and discussed. Finally, the aerodynamic damping of tower is determined and included in a decoupled fatigue analysis framework to demonstrate the potential application of this improved aerodynamic damping theory.
Chen, Y, Wu, D, Yu, Y & Gao, W 2021, 'Do cyclone impacts really matter for the long-term performance of an offshore wind turbine?', Renewable Energy, vol. 178, pp. 184-201.
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With the transition on planning and construction of offshore wind turbine (OWT) from North Europe to other regions like America and East Asia, challenges are proposed for the direct application of international OWT experience to these territories due to disparate natural condition like cyclones. This article is intended to evaluate the impact of cyclone on the long-term performance of an OWT to be installed in cyclone-prone regions. To have a comprehensive consideration on the aero-hydro-structural-soil interaction, an improved decoupled method is proposed and validated for an onshore wind turbine before its application to an OWT. Two cyclone models combined with two wave theories are considered in the fatigue evaluation of an OWT under different working status, and their implications on the final estimation of fatigue damage are compared and discussed. The results obtained from this study indicate that the fatigue life reduction which is caused by cyclone, for an OWT can be conspicuous for a reasonable cyclone strength and average recurrence interval. This implies that potential premature failure of an OWT tower and relevant economic losses can be encountered during its service life if the cyclone contribution to fatigue damage is ignored in the initial conceptual design.
Chen, Y, Xu, X, Li, C, Bendavid, A, Westerhausen, MT, Bradac, C, Toth, M, Aharonovich, I & Tran, TT 2021, 'Bottom‐Up Synthesis of Hexagonal Boron Nitride Nanoparticles with Intensity‐Stabilized Quantum Emitters', Small, vol. 17, no. 17, pp. 1-7.
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AbstractFluorescent nanoparticles are widely utilized in a large range of nanoscale imaging and sensing applications. While ultra‐small nanoparticles (size ≤10 nm) are highly desirable, at this size range, their photostability can be compromised due to effects such as intensity fluctuation and spectral diffusion caused by interaction with surface states. In this article, a facile, bottom‐up technique for the fabrication of sub‐10‐nm hexagonal boron nitride (hBN) nanoparticles hosting photostable bright emitters via a catalyst‐free hydrothermal reaction between boric acid and melamine is demonstrated. A simple stabilization protocol that significantly reduces intensity fluctuation by ≈85% and narrows the emission linewidth by ≈14% by employing a common sol–gel silica coating process is also implemented. This study advances a promising strategy for the scalable, bottom‐up synthesis of high‐quality quantum emitters in hBN nanoparticles.
Chen, Z, Wei, W & Ni, B-J 2021, 'Cost-effective catalysts for renewable hydrogen production via electrochemical water splitting: Recent advances', Current Opinion in Green and Sustainable Chemistry, vol. 27, pp. 100398-100398.
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Hydrogen, with zero-carbon footprint, high energy density, and earth abundance, is proved as a great energy carrier for a sustainable energy scheme, which is recognized as one key solution to mitigate climate change and reduce air pollution. To achieve this goal, reducing the cost of renewable hydrogen production via electrochemical water splitting is a requisite for supporting a reliable and affordable hydrogen economy. Thus, the development of cost-effective catalysts for water electrolysis is of great significance. In this review, the recent advances in low-cost electrocatalysts for water splitting are summarized, including transition metal–based catalysts and metal-free catalysts. The emphasis is put on the catalyst design strategies and the underlying structure–performance mechanisms. The challenges and perspectives in this booming field are also presented.
Chen, Z, Zheng, R, Deng, S, Wei, W, Wei, W, Ni, B-J & Chen, H 2021, 'Modular design of an efficient heterostructured FeS2/TiO2 oxygen evolution electrocatalyst via sulfidation of natural ilmenites', Journal of Materials Chemistry A, vol. 9, no. 44, pp. 25032-25041.
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Modular design of an efficient FeS2/TiO2 heterostructured OER catalyst from natural ilmenites via a sulfidation process.
Chen, Z, Zheng, R, Graś, M, Wei, W, Lota, G, Chen, H & Ni, B-J 2021, 'Tuning electronic property and surface reconstruction of amorphous iron borides via W-P co-doping for highly efficient oxygen evolution', Applied Catalysis B: Environmental, vol. 288, pp. 120037-120037.
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Designing cost-effective oxygen evolution reaction (OER) electrocatalysts is essential for sustainable water splitting. Recently, amorphous transition metal borides (TMBs) as OER pre-catalysts have acquired growing attention due to their favorable characteristics such as high conductivity, compositional and structural flexibility. Nevertheless, rational design of boride-based OER pre-catalysts remains an ongoing challenge. Herein, an efficient pre-catalyst derived from FeB with accelerated surface reconstruction and regulated intrinsic activity of evolved FeOOH is obtained by W and P co-doping. The obtained catalyst demonstrates an excellent OER activity with a low overpotential of 209 mV at a current density of 10 mA cm−2, and good stability in alkaline electrolyte, which surpasses most of boride-based OER catalysts. Specifically, the anion etching facilitates the surface reconstruction and accelerates the mass/charge transfer. Density functional theory calculations suggest W doping can enhance intrinsic catalytic activity via optimizing the adsorption free energy of reaction intermediates and improving the conductivity. Additionally, the hierarchical structure and amorphous feature also benefit the OER process. This study provides a fundamental insight into the correlation between surface structure and catalytic activity, and a powerful strategy to construct efficient OER pre-catalysts.
Chen, Z, Zheng, R, Zou, W, Wei, W, Li, J, Wei, W, Ni, B-J & Chen, H 2021, 'Integrating high-efficiency oxygen evolution catalysts featuring accelerated surface reconstruction from waste printed circuit boards via a boriding recycling strategy', Applied Catalysis B: Environmental, vol. 298, pp. 120583-120583.
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Converting electronic wastes into high-efficiency energy conversion catalysts is a win-win strategy in addressing the metal resources shortage and sustainable energy challenges. Herein, a facile boriding strategy is developed to directly convert the leachates of waste printed circuit boards into magnetic mixed metal borides (FeNiCuSnBs) for oxygen evolution reaction (OER) catalysts. Via the boriding process, a metal cation recovery rate of 99.78 %, 99.98 %, 99.96 %, and 99.49 % has been attained for Fe, Ni, Cu, and Sn, respectively. The obtained catalysts with a higher ratio of Ni and Fe show better OER performance. The optimal FNCSB-4 attains 10 mA cm−2 at a low overpotential of 199 mV, as well as good stability in alkaline solution. Remarkably, FNCSB-4 represents a record‐high activity among waste-derived OER electrocatalysts. In-depth study suggests that the superior OER performance is mainly owing to accelerated surface self-reconstruction by B/Sn co-etching under OER potential region, and the newly formed multimetal (oxy)hydroxides act as the active species for OER. Additionally, the efficient mass/charge transfer, the amorphous feature, and hierarchical structure also benefit OER. Apart from providing an insight into the correlation between surface self-reconstruction and OER activity of multimetal boride-based catalysts, this study also offers a general strategy for the high-efficiency recovery and advanced energy-driven applications of critical metals from other urban mines in a sustainable and environment-friendly approach.
Chen, Z, Zou, W, Zheng, R, Wei, W, Wei, W, Ni, B-J & Chen, H 2021, 'Synergistic recycling and conversion of spent Li-ion battery leachate into highly efficient oxygen evolution catalysts', Green Chemistry, vol. 23, no. 17, pp. 6538-6547.
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A one-pot synergetic recycling and regeneration strategy to develop highly efficient tri-metal OER electrocatalysts from spent LIB leachates is demonstrated.
Cheng, D, Liu, Y, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Zhang, S, Luo, G & Bui, XT 2021, 'Sustainable enzymatic technologies in waste animal fat and protein management', Journal of Environmental Management, vol. 284, pp. 112040-112040.
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Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Liu, Y, Liu, Y, Deng, L & Chen, Z 2021, 'Evaluation of a continuous flow microbial fuel cell for treating synthetic swine wastewater containing antibiotics', Science of The Total Environment, vol. 756, pp. 144133-144133.
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Microbial fuel cell (MFC) systems are promising technologies for wastewater treatment and renewable energy generation simultaneously. Performance of a double-chamber microbial fuel cell (MFC) to treat synthetic swine wastewater containing sulfonamide antibiotics (SMs) was evaluated in this study. The MFC was operated in continuous modes at different conditions. Results indicated that the current was successfully generated during the operation. The performance of MFC under the sequential anode-cathode operating mode is better than that under the single continuous running mode. Specifically, higher removal efficiency of chemical oxygen demand (>90%) was achieved under the sequential anode-cathode operating mode in comparison with that in the single continuous mode (>80%). Nutrients were also be removed in the MFC's cathode chamber with the maximum removal efficiency of 66.6 ± 1.4% for NH4+-N and 32.1 ± 2.8% for PO43--P. Meanwhile, SMs were partly removed in the sequential anode-cathode operating with the value in a range of 49.4%-59.4% for sulfamethoxazole, 16.8%-19.5% for sulfamethazine and 14.0%-16.3% for sulfadiazine, respectively. SMs' inhibition to remove other pollutants in both electrodes of MFC was observed after SMs exposure, suggesting that SMs exert toxic effects on the microorganisms. A positive correlation was found between the higher NH4+-N concentration used in this study and the removal efficiency of SMs in the cathode chamber. In short, although the continuous flow MFC is feasible for treating swine wastewater containing antibiotics, its removal efficiency of antibiotics requires to be further improved.
Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Nguyen, QA, Zhang, J & Liang, S 2021, 'Improving sulfonamide antibiotics removal from swine wastewater by supplying a new pomelo peel derived biochar in an anaerobic membrane bioreactor', Bioresource Technology, vol. 319, pp. 124160-124160.
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Sulfonamide antibiotics (SMs), as a class of antibiotics commonly used in swine industries, pose a serious threat to animal and human health. This study aims to evaluate the performance of an anaerobic membrane bioreactor (AnMBR) with and without supplying a new pomelo peel derived biochar to treat swine wastewater containing SMs. Results show that 0.5 g/L biochar addition could increase more than 30% of sulfadiazine (SDZ) and sulfamethazine (SMZ) removal in AnMBR. Approximately 95% of chemical oxygen demand (COD) was removed in the AnMBR at an influent organic loading rate (OLR) of 3.27 kg COD/(m3·d) while an average methane yield was 0.2 L/g CODremoved with slightly change at a small dose 0.5 g/L biochar addition. SMs inhibited the COD removal and methane production and increased membrane fouling. The addition of biochar could reduce the membrane fouling by reducing the concentration of SMP and EPS.
Cheng, EJ, Prasad, M, Yang, J, Zheng, DR, Tao, X, Mery, D, Young, KY & Lin, CT 2021, 'A novel online self-learning system with automatic object detection model for multimedia applications', Multimedia Tools and Applications, vol. 80, no. 11, pp. 16659-16681.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. This paper proposes a novel online self-learning detection system for different types of objects. It allows users to random select detection target, generating an initial detection model by selecting a small piece of image sample and continue training the detection model automatically. The proposed framework is divided into two parts: First, the initial detection model and the online reinforcement learning. The detection model is based on the proportion of users of the Haar-like features to generate feature pool, which is used to train classifiers and get positive-negative (PN) classifier model. Second, as the videos plays, the detecting model detects the new sample by Nearest Neighbor (NN) Classifier to get the PN similarity for new model. Online reinforcement learning is used to continuously update classifier, PN model and new classifier. The experiment shows the result of less detection sample with automatic online reinforcement learning is satisfactory.
Cheng, H, Liu, Y, Huang, D, Cai, B & Wang, Q 2021, 'Rebooting kernel CCA method for nonlinear quality-relevant fault detection in process industries', Process Safety and Environmental Protection, vol. 149, pp. 619-630.
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Process monitoring is essential and important strategy for ensuring process safety and product quality. However, due to the nonlinear characteristics and multiple working conditions in process industries, the traditional process monitoring method cannot be effectively applied. Therefore, we propose a novel process monitoring framework, termed as mixture enhanced kernel canonical correlation analysis framework (M-NAKCCA). The innovations and advantages of M-NAKCCA are as follows: 1). The traditional CCA method is re-boosted as a new method, M-NAKCCA, to better nonlinear fault detection. Also, a matter-element model (MEm) is assimilated into M-NAKCCA to make the information more refined. 2). To overcome the curse of dimensionality that usually occurs in the high-dimensional dataset, M-NAKCCA uses the Nyström approximation technology to compress the kernel matrix. Moreover, the T2 control chart is reconstructed and the corresponding control upper limit is re-configured to improve the method sensitivity and to better the fault detection performance. 3). The proposed M-NAKCCA framework is firstly used to monitor a wastewater treatment plant (WWTP) and chemical plant with diverse process behaviors. The experimental results showed that the M-NAKCCA framework achieved the best performance for both of case studies.
Cheng, H, Wu, J, Huang, D, Liu, Y & Wang, Q 2021, 'Robust adaptive boosted canonical correlation analysis for quality-relevant process monitoring of wastewater treatment', ISA Transactions, vol. 117, pp. 210-220.
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Quality-relevant process monitoring has attracted much attention for its ability to assist in maintaining efficient plant operation. However, when the process suffers from non-stationary and over-complex (with noise, multiplicative faults, etc.) characteristics, the traditional methods usually cannot be effectively applied. To this end, a novel method, termed as Robust adaptive boosted canonical correlation analysis (Rab-CCA), is proposed to monitor the wastewater treatment processes. First, a robust decomposition method is proposed to mitigate the defects of standard CCA by decomposing the corrupted matrix into a low-matrix and a sparse matrix. Second, to further improve the performance of the standard process monitoring method, a novel criterion function and control charts are reconstructed accordingly. Moreover, an adaptive statistical control limit is proposed that can adjust the thresholds according to the state of a system and can effectively reduce the missed alarms and false alarms simultaneously. The superiority of Rab-CCA is verified by Benchmark Simulation Model 1 (BSM1) and a real full-scale wastewater treatment plant (WWTP).
Cheng, H, Yang, G, Li, D, Li, M, Cao, Y, Fu, Q & Sun, Y 2021, 'Ultralow Icing Adhesion of a Superhydrophobic Coating Based on the Synergistic Effect of Soft and Stiff Particles', Langmuir, vol. 37, no. 41, pp. 12016-12026.
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A novel superhydrophobic coating composed of soft polydimethylsiloxane microspheres and stiff SiO2 nanoparticles was developed and prepared. This superhydrophobic coating showed excellent superhydrophobicity with a large water contact angle of 171.3° and also exhibited good anti-icing performance and ultralow icing adhesion of 1.53 kPa. Furthermore, the superhydrophobic coating displayed good icing/deicing cycle stability, in which the icing adhesion was still less than 10.0 kPa after 25 cycles. This excellent comprehensive performance is attributed to stress-localization between ice and the surface, resulting from the synergistic effect of soft and stiff particles. This work thus opens a new avenue to simultaneously optimize the anti-icing and icephobic performance of a superhydrophobic surface for various applications.
Cheng, T, Lu, DD-C & Siwakoti, YP 2021, 'A MOSFET SPICE Model With Integrated Electro-Thermal Averaged Modeling, Aging, and Lifetime Estimation.', IEEE Access, vol. 9, pp. 5545-5554.
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Lifetime estimation of power semiconductor devices have been widely investigated to improve the reliability and reduce the cost of maintenance of power converters. However in most reported work, the aging effect is not considered in the lifetime evaluation process due to the omission or limitation of thermal cycle counting method. Additionally, the electrical/thermal simulation and lifetime estimation are usually implemented in different simulators/platforms, for the same reason. Thus, to tackle these problems, a concise but comprehensive MOSFET model that enables electro-thermal modeling, aging and lifetime estimation on LTspice® circuit simulator is proposed in this paper. The idea comes from the fact that, MOSFET on-state resistance R_{ds,on} is not only temperature dependent, but also widely accepted as the device failure precursor. In other words, as it carries critical information about instantaneous temperature and aging progress. Hence, co-simulation can be achieved by constructing electrical, thermal, and aging and lifetime sub-modules exclusively first, and using R_{ds,on} , to build linkages among them. Averaged modeling technique is adopted due to the ease of establishing links among these three sub-modules, and fast simulation speed as compared to a switched converter model. Behavioral models are employed to realize the thermal cycles counting, stress accumulation and degradation evaluation. This paper demonstrates that it is possible to use a single simulation software to monitor performances of devices and circuits, and their lifetime estimation simultaneously. High-stress thermal cycling and long-term random mission profiles are applied to verify the correctness of the model and to mimic a 10-year load respectively. An accelerated aging trend can be observed in the long-term mission profile simulation, which is in agreement with the theory. Facilitated by the employment of averaged circuits, the proposed method is a good simulation/analy...
Cheng, X, Wang, H, Hua, J, Xu, G & Sui, Y 2021, 'DeepWukong', ACM Transactions on Software Engineering and Methodology, vol. 30, no. 3, pp. 1-33.
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Static bug detection has shown its effectiveness in detecting well-defined memory errors, e.g., memory leaks, buffer overflows, and null dereference. However, modern software systems have a wide variety of vulnerabilities. These vulnerabilities are extremely complicated with sophisticated programming logic, and these bugs are often caused by different bad programming practices, challenging existing bug detection solutions. It is hard and labor-intensive to develop precise and efficient static analysis solutions for different types of vulnerabilities, particularly for those that may not have a clear specification as the traditional well-defined vulnerabilities. This article presents D eep W ukong , a new deep-learning-based embedding approach to static detection of software vulnerabilities for C/C++ programs. Our approach makes a new attempt by leveraging advanced recent graph neural networks to embed code fragments in a compact and low-dimensional representation, producing a new code representation that preserves high-level programming logic (in the form of control- and data-flows) together with the natural language information of a program. Our evaluation studies the top 10 most common C/C++ vulnerabilities during the past 3 years. We have conducted our experiments using 105,428 real-world programs by comparing our approach with four well-known traditional static vulnerability detectors and three state-of-the-art deep-learning-based approaches. The experimental results demonstrate the effectiveness of our research and have shed light on the promising direction of combining program analysis with deep learning techniques to address the general static code analysis challenges.
Cheng, X, Zhang, C, Qian, Y, Aloqaily, M & Xiao, Y 2021, 'Editorial: deep learning for 5G IoT systems', International Journal of Machine Learning and Cybernetics, vol. 12, no. 11, pp. 3049-3051.
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Cherukuri, SK, Balachandran, PK, Kaniganti, KR, Buddi, MK, Butti, D, Devakirubakaran, S, Babu, TS & Alhelou, HH 2021, 'Power Enhancement in Partial Shaded Photovoltaic System Using Spiral Pattern Array Configuration Scheme', IEEE Access, vol. 9, pp. 123103-123116.
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Chetty, K, Xie, S, Song, Y, McCarthy, T, Garbe, U, Li, X & Jiang, G 2021, 'Self-healing bioconcrete based on non-axenic granules: A potential solution for concrete wastewater infrastructure', Journal of Water Process Engineering, vol. 42, pp. 102139-102139.
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This paper summarized the metabolic pathways, mechanisms, and performance of various microbial induced calcite precipitation (MICP) microorganisms as self-healing agents for the development of self-healing bioconcrete. Despite the remarkable progress achieved to date, the high costs involved in the cultivation and encapsulation of the axenic cultures have led to a shift of research focus towards the use of non-axenic microbial cultures. They have superior benefits as self-healing agents in terms of easy cultivation and low cost. Granular sludge was widely used in wastewater treatment. Considering the high need for concrete-based wastewater infrastructure, granular sludge cultivated using wastewater has the potential to develop bioconcrete for sewer systems and wastewater treatment plants. To achieve large-scale application, future research should enhance the understanding of the long-term performances and develop systematic and comparable evaluation methods.
Cheung, BB, Kleynhans, A, Mittra, R, Kim, PY, Holien, JK, Nagy, Z, Ciampa, OC, Seneviratne, JA, Mayoh, C, Raipuria, M, Gadde, S, Massudi, H, Wong, IPL, Tan, O, Gong, A, Suryano, A, Diakiw, SM, Liu, B, Arndt, GM, Liu, T, Kumar, N, Sangfelt, O, Zhu, S, Norris, MD, Haber, M, Carter, DR, Parker, MW & Marshall, GM 2021, 'A novel combination therapy targeting ubiquitin-specific protease 5 in MYCN-driven neuroblastoma', Oncogene, vol. 40, no. 13, pp. 2367-2381.
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AbstractHistone deacetylase (HDAC) inhibitors are effective in MYCN-driven cancers, because of a unique need for HDAC recruitment by the MYCN oncogenic signal. However, HDAC inhibitors are much more effective in combination with other anti-cancer agents. To identify novel compounds which act synergistically with HDAC inhibitor, such as suberanoyl hydroxamic acid (SAHA), we performed a cell-based, high-throughput drug screen of 10,560 small molecule compounds from a drug-like diversity library and identified a small molecule compound (SE486-11) which synergistically enhanced the cytotoxic effects of SAHA. Effects of drug combinations on cell viability, proliferation, apoptosis and colony forming were assessed in a panel of neuroblastoma cell lines. Treatment with SAHA and SE486-11 increased MYCN ubiquitination and degradation, and markedly inhibited tumorigenesis in neuroblastoma xenografts, and, MYCN transgenic zebrafish and mice. The combination reduced ubiquitin-specific protease 5 (USP5) levels and increased unanchored polyubiquitin chains. Overexpression of USP5 rescued neuroblastoma cells from the cytopathic effects of the combination and reduced unanchored polyubiquitin, suggesting USP5 is a therapeutic target of the combination. SAHA and SE486-11 directly bound to USP5 and the drug combination exhibited a 100-fold higher binding to USP5 than individual drugs alone in microscale thermophoresis assays. MYCN bound to the USP5 promoter and induced USP5 gene expression suggesting that USP5 and MYCN expression created a forward positive feedback loop in neuroblastoma cells. Thus, USP5 acts as an oncogenic cofactor with MYCN in neuroblastoma and the novel combination of HDAC inhibitor with SE486-11 represents a novel therapeutic approach for the treatment of MYCN-driven neuroblastoma.
Chi, H, Liu, F, Yang, W, Lan, L, Liu, T, Han, B, Cheung, WK & Kwok, JT 2021, 'TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation', Advances in Neural Information Processing Systems, vol. 25, pp. 20970-20982.
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In few-shot domain adaptation (FDA), classifiers for the target domain are trained with accessible labeled data in the source domain (SD) and few labeled data in the target domain (TD). However, data usually contain private information in the current era, e.g., data distributed on personal phones. Thus, the private data will be leaked if we directly access data in SD to train a target-domain classifier (required by FDA methods). In this paper, to prevent privacy leakage in SD, we consider a very challenging problem setting, where the classifier for the TD has to be trained using few labeled target data and a well-trained SD classifier, named few-shot hypothesis adaptation (FHA). In FHA, we cannot access data in SD, as a result, the private information in SD will be protected well. To this end, we propose a targetoriented hypothesis adaptation network (TOHAN) to solve the FHA problem, where we generate highly-compatible unlabeled data (i.e., an intermediate domain) to help train a target-domain classifier. TOHAN maintains two deep networks simultaneously, in which one focuses on learning an intermediate domain and the other takes care of the intermediate-to-target distributional adaptation and the target-risk minimization. Experimental results show that TOHAN outperforms competitive baselines significantly.
Chiang, YK, Quan, L, Peng, Y, Sepehrirahnama, S, Oberst, S, Alù, A & Powell, DA 2021, 'Scalable Metagrating for Efficient Ultrasonic Focusing', Physical Review Applied, vol. 16, no. 6, pp. 1-9.
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Acoustic metalenses have been pursued over the past decades due to their pivotal role in a wide variety of applications. Recent research efforts have demonstrated that, at ultrasonic regimes, acoustic levitation can be realized with standing waves, which are created by the interference between incoming and reflected focused waves. However, the conventional gradient-metasurface approach to focus ultrasonic waves is complex, leading to poor scalability. In this work, we propose a design principle for ultrasonic metalenses, based on metagratings - arrays of discrete scatters with coarser features than gradient metasurfaces. We achieve beam focusing by locally controlling the excitation of a single diffraction order with the use of metagratings, with geometry adiabatically varying over the lens aperture. We show that our metalens can effectively focus impinging ultrasonic waves to a focal point with a full width at half maximum of 0.364 of the wavelength. The focusing performance of the metalens is demonstrated experimentally, validating our proposed approach. This metagrating approach to focusing can be adopted for different operating frequencies by scaling the size of the structure, which has coarse features suitable for high-frequency designs, with potential applications ranging from biomedical science to nondestructive testing.
Chiniforush, AA, Gharehchaei, M, Akbar Nezhad, A, Castel, A, Moghaddam, F, Keyte, L, Hocking, D & Foster, S 2021, 'Minimising risk of early-age thermal cracking and delayed ettringite formation in concrete – A hybrid numerical simulation and genetic algorithm mix optimisation approach', Construction and Building Materials, vol. 299, pp. 124280-124280.
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Chinnaraj, S, Palani, V, Yadav, S, Arumugam, M, Sivakumar, M, Maluventhen, V & Singh, M 2021, 'Green synthesis of silver nanoparticle using goniothalamus wightii on graphene oxide nanocomposite for effective voltammetric determination of metronidazole', Sensing and Bio-Sensing Research, vol. 32, pp. 100425-100425.
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Graphene oxide (GO) has piqued the interest of both academia and industry owing to its polar and two-dimensional (2D) layered structure. Antibiotic concentrations can be detected with advanced GO composites to reduce the risk of bacterial resistance, which can be done with electrochemical sensors. Herein, we have developed an eco-friendly synthesis approach, one-pot strategy towards Goniothalamus wightii biomass-derived solution preparation of Ag nanoparticle-decorated graphene oxide (GO@AgNPs) composites. As- synthesized GO@AgNPs nanocomposites were analyzed using various analytical tools including Raman, X-ray diffraction (XRD) and field emission scanning electron microscope (FESEM). The Metronidazole (MIZ) determination was then investigated using cycle volumetric and amperometric (i-t) techniques by the GO@AgNPs composites. Prepared composites exhibit a wide-linear range of 0.09 μM to 4.594 mM, low detection limit of 69 nM and a limit of quantification detection of 786 nM. Furthermore, the practical applicability of the prepared GO@AgNPs nanocomposites were examined in pharmaceutical drug Flagyl (500 mg) with satisfactory recovery results.
Chou, KP, Prasad, M, Yang, J, Su, S-Y, Tao, X, Saxena, A, Lin, W-C & Lin, C-T 2021, 'A robust real-time facial alignment system with facial landmarks detection and rectification for multimedia applications', Multimedia Tools and Applications, vol. 80, no. 11, pp. 16635-16657.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Face detection often plays the first step in various visual applications. Large variants of facial deformations due to head movements and facial expression make it difficult to identify appropriate face region. In this paper, a robust real-time face alignment system, including facial landmarks detection and face rectification, is proposed. A facial landmarks detection model based on regression tree is utilized in the proposed system. In face rectification framework, 2-D geometrical analysis based on pitch, yaw and roll movements is designed to solve the misalignment problem in face detection. The experiments on the two datasets verify the performance significantly improved by the proposed method in the facial recognition task and outperform than those obtained by other alignment methods. Furthermore, the proposed method can achieve robust recognition results even if the amount of training images is not large.
Chowdhury, H, Chowdhury, T, Hossain, N, Chowdhury, P, Salam, B, Sait, SM & Mahlia, TMI 2021, 'Exergetic sustainability analysis of industrial furnace: a case study', Environmental Science and Pollution Research, vol. 28, no. 10, pp. 12881-12888.
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Industrial furnaces play a significant role in industrial energy consumption and production. Minimizing losses from these furnaces can contribute to industrial sustainability. Exergy being an optimization tool can reduce energy loss and emission from furnaces and contribute to environmental sustainability. Currently, no exergy-based sustainability analysis has been adopted in the literature. In this analysis, a reheater furnace that is fired by natural gas is analyzed in terms of energy and exergy utilization. To address the sustainability of the furnace, several exergy-based sustainability parameters have been used. The overall energy efficiency of the furnace is 93.40%, while exergy efficiency is only 27.37%. From sustainability analysis, it is found that 72.63% of the fuel is diminished from the furnace, and it contributes to a lower sustainability index of 1.38. Higher exergy losses from this furnace positively affect the environment, which is validated from the higher value of the environmental destruction coefficient, the environmental destruction index, and the lower value of the environmental benign index. The value of the environmental destruction coefficient is 3.65, and the value of the environmental benign index is 0.38. Recovering waste energy and optimizing auxiliary equipment will increase the value of sustainability parameters.
Chowdhury, L, Kamal, MS, Ripon, SH, Parvin, S, Hussain, OK, Ashour, A & Chowdhury, BR 2021, 'A Biological Data-Driven Mining Technique by Using Hybrid Classifiers With Rough Set', International Journal of Ambient Computing and Intelligence, vol. 12, no. 3, pp. 123-139.
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Biological data classification and analysis are significant for living organs. A biological data classification is an approach that classifies the organs into a particular group based on their features and characteristics. The objective of this paper is to establish a hybrid approach with naive Bayes, apriori algorithm, and KNN classifier that generates optimal classification rules for finding biological pattern matching. The authors create combined association rules by using naïve Bayes and apriori approach with a rough set for next sequence prediction. First, the large DNA sequence is reduced by using k-nearest approach. They apply association rules by using naïve Bayes and apriori approach for the next sequence pattern. The hybrid approach provides more accuracy than single classifier for biological sequence prediction. The optimized hybrid process needs less execution time for rule generation for massive biological data analysis. The results established that the hybrid approach generally outperforms the other association rule generation approach.
Chowdhury, MA, Shuvho, MBA, Shahid, MA, Haque, AKMM, Kashem, MA, Lam, SS, Ong, HC, Uddin, MA & Mofijur, M 2021, 'Prospect of biobased antiviral face mask to limit the coronavirus outbreak', Environmental Research, vol. 192, pp. 110294-110294.
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The rapid spread of COVID-19 has led to nationwide lockdowns in many countries. The COVID-19 pandemic has played serious havoc on economic activities throughout the world. Researchers are immensely curious about how to give the best protection to people before a vaccine becomes available. The coronavirus spreads principally through saliva droplets. Thus, it would be a great opportunity if the virus spread could be controlled at an early stage. The face mask can limit virus spread from both inside and outside the mask. This is the first study that has endeavoured to explore the design and fabrication of an antiviral face mask using licorice root extract, which has antimicrobial properties due to glycyrrhetinic acid (GA) and glycyrrhizin (GL). An electrospinning process was utilized to fabricate nanofibrous membrane and virus deactivation mechanisms discussed. The nanofiber mask material was characterized by SEM and airflow rate testing. SEM results indicated that the nanofibers from electrospinning are about 15-30 μm in diameter with random porosity and orientation which have the potential to capture and kill the virus. Theoretical estimation signifies that an 85 L/min rate of airflow through the face mask is possible which ensures good breathability over an extensive range of pressure drops and pore sizes. Finally, it can be concluded that licorice root membrane may be used to produce a biobased face mask to control COVID-19 spread.
Choy, S-M, Cheng, E, Wilkinson, RH, Burnett, I & Austin, MW 2021, 'Quality of Experience Comparison of Stereoscopic 3D Videos in Different Projection Devices: Flat Screen, Panoramic Screen and Virtual Reality Headset', IEEE Access, vol. 9, pp. 9584-9594.
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Chu, L, Shi, J & Braun, R 2021, 'The Impacts of Material Uncertainty in Electro-Migration of SAC Solder Electronic Packaging by Monte Carlo-Based Stochastic Finite-Element Model', IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 11, no. 11, pp. 1864-1876.
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Electro-migration (EM) induced by electric current under the conditions of multi-physical fields in integrated circuits and electronic packaging is a crucial factor that affects reliability and safety of the entire system. The uncertainty quantification and propagation in the EM process are the challenging issues that deserve more attention. In this paper, the uncertainties in the related material parameters of Sn-Ag-Cu (SAC) solder and copper conductors are taken into consideration based on the stochastic finite element model. The corners and edges of the contact surface in SAC solder are the most dynamic and active places with the maximum concentration gradient. This reaches a satisfied agreement with the experimental results and parallel numerical investigations in the literatures. The extreme values of the concentration and its gradient in each time step are computed and recorded. The accuracy and convergence of results are confirmed by the comparison of different period durations and time step scales with discrete time points. Furthermore, the probability density distribution, mean and variance of the extreme values in different time steps are recorded and compared. Based on the huge database provided by the Monte Carlo based stochastic finite element model (MC-SFEM), the correlations between the material parameters and the concentration as well as the concentration gradients are analyzed. The proposed MC-SFEM is a feasible and effective model for the comprehensive analysis of EM with the potential in uncertainty and reliability analysis.
Chu, L, Zhou, P, Shi, J & Braun, R 2021, 'Sensitivity Analysis for Geometrical Parameters of BGA in Flip-Chip Packaging Under Random Shear Stress and Thermal Temperature', IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 11, no. 5, pp. 765-777.
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The ball grid array (BGA) is a type of popular and competitive package in electronic flip-chip packaging due to its feasibility in high density integrated circuits and convenience in the product design process. However, the effects of geometrical parameters on the product reliability and safety under complicated operating situations are not clear. In this article, an independent solder ball and four typical BGA cases are compared and analyzed based on the finite element (FE) method. The coupled random shear stress and thermal temperature are simulated in the FE models by the Latin hypercube sampling (LHS) method. According to the sensitivity analysis, the edges of the solder ball are the most dangerous places, which has the qualitative agreement with the experimental results. The complete grid array in the first BGA case with homogeneous stress and strain distribution is the most reliable and competitive design. Furthermore, the normal and Weibull distributions are not suitable to present the stochastic response of solder balls in flip-chip packaging under random coupled mechanical and thermal stress. In order to effectively improve packaging performance and reliability, the radius of the solder ball acts as the key factor, while the upper and lower height of the solder ball, as well as the pitch along the X - and Y -directions, are all feasible and potential for the geometrical optimization. However, the small scale of the solder ball causing microstress concentration points and discontinuous volumes is the essential challenge for industrial manufacturing. The work in this article provides helpful references to the industrial electronic package geometrical optimal design.
Clarke, C, Singh, M, Tawfik, SA, Xu, X, Spencer, MJS, Ramanathan, R, Reineck, P, Bansal, V & Ton-That, C 2021, 'Mono- to few-layer non-van der Waals 2D lanthanide-doped NaYF4nanosheets with upconversion luminescence', 2D Materials, vol. 8, no. 1, pp. 015005-015005.
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AbstractNaYF4is an efficient host material for lanthanide-based upconversion luminescence and has attracted immense interest for potential applications in photovoltaics, lasers and bioimaging. However, being a non-van der Waals (non-vdW) material, there have been thus far no reports on exfoliation of bulk NaYF4to nanosheets and their upconversion luminescence properties. Here, we demonstrate for the first time the fabrication of lanthanide-containing NaYF42D nanosheets using a soft liquid-phase exfoliation method and report on their optical, electronic and chemical characteristics. The nanosheets exfoliated from NaYF4:Yb,Er microcrystals consisting mainly ofβ-NaYF4become enriched inα-NaYF4post exfoliation and have a large micron-sized planar area with a preferential (100) surface orientation. X-ray absorption spectroscopy confirms that both Yb and Er doping ions are retained in the exfoliated nanosheets. Through centrifugation, NaYF42D nanosheets are successfully obtained with thicknesses ranging from a monolayer to tens of layers. Optical analysis of individual nanosheets shows that they exhibit both optical down-conversion and upconversion properties, albeit with reduced emission intensities compared with the parent microparticles. Further exploration of their electronic structure by density functional theory (DFT) calculations and photoelectron spectroscopy reveals the formation of surface F atom defects and a shrinkage of the electronic bandgap in ultrathin nanosheets. Our findings will trigger further interest in non-vdW 2D upconversion nanomaterials.
Clegg, SR & Burdon, S 2021, 'Exploring creativity and innovation in broadcasting', Human Relations, vol. 74, no. 6, pp. 791-813.
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We consider the emergence of design innovations in process, emerging around the form of polyarchy. This is done by using a case study of innovation conducted by a production organization’s project that was embedded in and hosted by a bureaucratic public institution, the Australian Broadcasting Corporation (ABC). The research reported here was part of a larger project comparing the BBC and ABC’s use of different modes of organization. It focused mainly on the organization designed to deliver a six-part television series, The Code. The innovative process of Scribe, the organization in question, in producing the story is a good example of idea work being instituted in a polyarchic design process. Scribe represents a new organizational design characterized by a polyarchic structure, which is soft and decentralized, with strict and relatively insuperable social and symbolic boundaries. This results in a project-based organization to coordinate collective innovation that is curated by making the writer also the creative director or showrunner. The research contributes further to exploring organizational idea work, through prioritizing creativity and innovation by an explicit positioning of a product and collaborative generative idea work.
Cong Nguyen, N, Thi Nguyen, H, Cong Duong, H, Chen, S-S, Quang Le, H, Cong Duong, C, Thuy Trang, L, Chen, C-K, Dan Nguyen, P, Thanh Bui, X, Guo, W & Hao Ngo, H 2021, 'A breakthrough dynamic-osmotic membrane bioreactor/nanofiltration hybrid system for real municipal wastewater treatment and reuse', Bioresource Technology, vol. 342, pp. 125930-125930.
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Cong, HP, Perry, S, Cheng, E, Trinh, VA & Hoang, XV 2021, 'ỔN ĐỊNH CHẤT LƯỢNG ẢNH LIGHT FIELD DỰA TRÊN BỘ MÃ HÓA VIDEO PHÂN TÁN THẾ HỆ MỚI (Consistent Quality Control for Light Field image based on distributed video coding)', Tạp chí Khoa học Công nghệ thông tin và Truyền thông (Journal of Science and Technology on Information and Communications), vol. 1, no. 3.
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Distributed video coding (DVC) is a promising encoding solution for low complexity video applications such as wireless sensor networks or video surveillance systems. The requirement for image quality stability is one of the important issues in advanced display systems. However, most recent distributed video encoding solutions, which are developed based on H.264/AVC or H.265/HEVC standards, cannot provide video with stable quality. In this paper, we propose a distributed video encoding solution with quantization matrices (QMs) applied to Light Field data, ensuring stability in display quality and improving compression performance. In the proposed DVC solution, the latest video coding standard, Video Versatile Coding - VVC is selected appropriately to compress Key frames. Meanwhile, to achieve stability in video quality, quantization parameters (QPs) choose to compress key frames and quantization matrices (QMs) choose to compress Wyner-Ziv frames (WZ) will be explained in detail in the article. The test results show that the proposed distributed video encoding solution with integrated VVC codec (D-VVC) is significantly superior to other related DVC encoding solutions, especially distributed encoders. DISCOVER and recent DVC-HEVC solution, in terms of compression performance while providing stability for better picture quality for decoded video.
Consoli, NC, Tonini de Araújo, M, Tonatto Ferrazzo, S, de Lima Rodrigues, V & Gravina da Rocha, C 2021, 'Increasing density and cement content in stabilization of expansive soils: Conflicting or complementary procedures for reducing swelling?', Canadian Geotechnical Journal, vol. 58, no. 6, pp. 866-878.
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The present study makes three contributions to the literature of expansive soils: (i) it proposes equations to predict soil swelling based on dry density and cement content, (ii) it checks the developed general equation by predicting the swelling of different expansive soils from the literature, and (iii) it designs experiments that investigate factors that have a significant influence on swelling. An experimental programme was carried out to analyse the expansion of bentonite–kaolin–cement blends. Different proportions of bentonite–kaolin, cement content, dry density, and moisture content were evaluated. A unique relation of the cement/porosity index was obtained for cement-stabilized expansive soils’ swelling; this index has been used before to portray strength, stiffness, and loss of mass of stabilized soils and is now shown to be applicable to describe swelling of expansive soils treated with Portland cement. In the present research, cement content and dry density are seen as conflicting parameters regarding the swelling of expansive soils, because increasing the amount of Portland cement reduces swelling and increasing the density (through compaction) causes higher expansion. A general swelling model is proposed and successfully checked with data from the literature; it is able to predict the swelling of expansive soils with different densities, expansive mineral, moisture content, and cement content.
Coronado, FC, Merigó, JM & Cancino, CA 2021, 'Business and management research in Latin America: A country-level bibliometric analysis', Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 1865-1878.
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Bibliometrics is a scientific discipline that studies quantitatively the bibliographic material of a particular topic. This study analyzes management research published by Latin American countries between 1990 and 2019. The work uses the Web of Science database and provides several country-level bibliometric indicators including the total number of publications and citations, and the h-index. The results indicate that Brazil, Chile and Mexico have constantly led the region’s scientific publications. The temporal evolution shows a significant increase on the number of publications during the last years that seems to continue in the future. The results also show that operations research and finance are the most significant topics in the region.
Correll, P, Feyer, A-M, Phan, P-T, Drake, B, Jammal, W, Irvine, K, Power, A, Muir, S, Ferdousi, S, Moubarak, S, Oytam, Y, Linden, J & Fisher, L 2021, 'Lumos: a statewide linkage programme in Australia integrating general practice data to guide system redesign', Integrated Healthcare Journal, vol. 3, no. 1, pp. e000074-e000074.
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ObjectiveWith ageing of the Australian population, more people are living longer and experiencing chronic or complex health conditions. The challenge is to have information that supports the integration of services across the continuum of settings and providers, to deliver person-centred, seamless, efficient and effective healthcare. However, in Australia, data are typically siloed within health settings, precluding a comprehensive view of patient journeys. Here, we describe the establishment of the Lumos programme—the first statewide linked data asset across primary care and other settings in Australia and evaluate its representativeness to the census population.Methods and analysisRecords extracted from general practices throughout New South Wales (NSW), Australia’s most populous state, were linked to patient records from acute and other settings. Innovative privacy and security technologies were employed to facilitate ongoing and regular updates. The marginal demographic distributions of the Lumos cohort were compared with the NSW census population by calculating multiple measures of representation to evaluate its generalisability.ResultsThe first Lumos programme data extraction linked 1.3 million patients’ general practice records to other NSW health system data. This represented 16% of the NSW population. The demographic distribution of patients in Lumos was >95% aligned to that of the NSW population in the calculated measures of representativeness.ConclusionThe Lumos programme delivers an enduring, regularly updated data resource, providing unique insights about statewide, cross-setting healthcare utilisation. General practice patients represented in the Lumos data asset are representative of the NSW population overall. Lumos ...
Cortes, CAT, Chen, H-T, Sturnieks, DL, Garcia, J, Lord, SR & Lin, C-T 2021, 'Evaluating Balance Recovery Techniques for Users Wearing Head-Mounted Display in VR', IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 1, pp. 204-215.
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Room-scale 3D position tracking allows users to explore the virtual environment by physically walking. However, since the eyesight of the user is blocked by a Head-Mounted Display (HMD), the user might lose her balance because of bumping into real-world obstacles or shifting the body weight onto virtual objects that are inexistent in the real-world. This paper investigates assistive fall recovery methods under the assumption that the onset of the fall is given. Our experiment simulated the forward loss-of-balance with a tether-release protocol. A magnetic lock attached to a counter-weight was released while the subject was in a static leaning posture and engaged in a secondary 3D object selection task. The experiment uses a two by two design that examines two assistive techniques, i.e. video-see-through and auditory warning, at two different timing, i.e. fall onset and 500ms prior to onset. The data from 17 subjects show that the video-see-through triggered 500ms before the onset of fall can effectively help users recover from falls. Surprisingly, the video-see-through at the fall onset has a significant negative impact on the fall recovery providing similar results to the baseline condition (no intervention).
Cowled, CJL, Crews, K & Gover, D 2021, 'Influence of loading protocol on the structural performance of timber-framed shear walls', Construction and Building Materials, vol. 288, pp. 123103-123103.
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Cui, Q, Zhu, Z, Ni, W, Tao, X & Zhang, P 2021, 'Edge-Intelligence-Empowered, Unified Authentication and Trust Evaluation for Heterogeneous Beyond 5G Systems', IEEE Wireless Communications, vol. 28, no. 2, pp. 78-85.
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Cullen, M, Zhao, S, Ji, J & Qiu, X 2021, 'Classification of transfer modes in gas metal arc welding using acoustic signal analysis', The International Journal of Advanced Manufacturing Technology, vol. 115, no. 9-10, pp. 3089-3104.
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Gas metal arc welding (GMAW) is a welding process in which an electric arc is formed between a wire electrode and a metal workpiece alongside a shielding gas to protect the arc from contaminants. There are several ways in which the molten electrode droplet can be transferred to the weld pool known as metal transfer modes. Identifying the metal transfer mode automatically is essential to monitor and control the welding process, especially in automated processes employed in modern Industry 4.0 manufacturing lines. However, limited research on this topic has been found in literature. This paper explores the automatic classification of metal transfer modes in GMAW based on machine learning techniques with various signals from the welding process, including acoustics, current, voltage and gas flow rate signals. Time and frequency domain features are first extracted from these signals and are used in a support vector machine classifier to detect the metal transfer modes. A feature selection algorithm is proposed to improve the prediction rate from 80 to 99% when all four signals are utilised. When only the non-intrusive acoustic signal is used, the prediction rates with and without the proposed feature selection algorithm are approximately 96% and 81%, respectively. The high prediction rate demonstrates the feasibility and promising accuracy of the acoustic signal–based classification method for future smart welding technology with real-time adaptive feedback control of the welding process.
Cuzmar, RH, Pereda, J & Aguilera, RP 2021, 'Phase-Shifted Model Predictive Control to Achieve Power Balance of CHB Converters for Large-Scale Photovoltaic Integration', IEEE Transactions on Industrial Electronics, vol. 68, no. 10, pp. 9619-9629.
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Cascaded H-bridge (CHB) converters are attractive candidates for next generation photovoltaic (PV) inverters. CHB converters present a reduced voltage stress per power switch and a high modularity. Therefore, the plant can be divided in several PV strings that can be connected to each H-bridge cell. However, due to variability on solar irradiance conditions, each PV string may present different maximum available power levels, which difficult the overall converter operation. To address this issue, this article presents a model predictive control (MPC) strategy, which works along with a phase-shifted pulsewidth modulation (PS-PWM) stage; hence, its name phase-shifted MPC (PS-MPC). The novelty of this proposal is the way both interbridge and interphase power imbalance are directly considered into the optimal control problem by a suitable system reference design. Thus, the interphase imbalance power is tackled by enforcing the converter to operate with a proper zero-sequence voltage component. Then, by exploiting the PS-PWM working principle, PS-MPC is able to handle each H-bridge cell independently. This allows the predictive controller to also deal with an interbridge power imbalance using the same control structure. Experimental results on a 3-kW prototype are provided to verify the effectiveness of the proposed PS-MPC strategy.
D’Ambrosio, U & Shannon, AG 2021, 'The IJMEST Editorial Board over the decades: a personal retrospective perspective', International Journal of Mathematical Education in Science and Technology, vol. 52, no. 2, pp. 324-329.
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Dabbaghi, F, Dehestani, M, Yousefpour, H, Rasekh, H & Navaratnam, S 2021, 'Residual compressive stress–strain relationship of lightweight aggregate concrete after exposure to elevated temperatures', Construction and Building Materials, vol. 298, pp. 123890-123890.
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Dai, M, Su, Z, Li, R & Yu, S 2021, 'A Software-Defined-Networking-Enabled Approach for Edge-Cloud Computing in the Internet of Things', IEEE Network, vol. 35, no. 5, pp. 66-73.
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The proliferation of smart devices has led to a huge amount of data streaming in the Internet of Things (IoT). However, the resource-limited devices cannot satisfy the demands of computing-in-tensive but delay-sensitive applications. The data delivery among devices may be tampered with by malicious users. These pose new challenges to provide secure and intelligent services in IoT. Blockchain and reinforcement learning (RL) are promising techniques for establishing a secure environment and intelligent resource management. In this article, we introduce a novel software defined networking (SDN)-enabled architecture for edge-cloud orchestrated computing to support secure and intelligent services in IoT. We first introduce the SDN-enabled architecture by integrating cloud computing, edge computing, and IoT networks. Then we provide several applications of SDN-enabled architecture in edge-cloud orchestrated computing. Next, we propose the blockchain and RL envisioned solutions to implement secure and intelligent services in IoT. Moreover, a case study of blockchain- and RL-enabled secure and intelligent computing offloading is presented to validate its effectiveness. We finally provide our conclusion and discuss several promising research directions.
Dai, Y, Weng, J, Yang, A, Yu, S & Deng, RH 2021, 'A Lightweight and Privacy-Preserving Answer Collection Scheme for Mobile Crowdsourcing', KSII Transactions on Internet and Information Systems, vol. 15, no. 8, pp. 2827-2848.
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Mobile Crowdsourcing (MCS) has become an emerging paradigm evolved from crowdsourcing by employing advanced features of mobile devices such as smartphones to perform more complicated, especially spatial tasks. One of the key procedures in MCS is to collect answers from mobile users (workers), which may face several security issues. First, authentication is required to ensure that answers are from authorized workers. In addition, MCS tasks are usually location-dependent, so the collected answers could disclose workers' location privacy, which may discourage workers to participate in the tasks. Finally, the overhead occurred by authentication and privacy protection should be minimized since mobile devices are resource-constrained. Considering all the above concerns, in this paper, we propose a lightweight and privacy-preserving answer collection scheme for MCS. In the proposed scheme, we achieve anonymous authentication based on traceable ring signature, which provides authentication, anonymity, as well as traceability by enabling malicious workers tracing. In order to balance user location privacy and data availability, we propose a new concept named current location privacy, which means the location of the worker cannot be disclosed to anyone until a specified time. Since the leakage of current location will seriously threaten workers' personal safety, causing such as absence or presence disclosure attacks, it is necessary to pay attention to the current location privacy of workers in MCS. We encrypt the collected answers based on timed-release encryption, ensuring the secure transmission and high availability of data, as well as preserving the current location privacy of workers. Finally, we analyze the security and performance of the proposed scheme. The experimental results show that the computation costs of a worker depend on the number of ring signature members, which indicates the flexibility for a worker to choose an appropriate size of the group...
Daly, L, Lee, MR, Darling, JR, McCarrol, I, Yang, L, Cairney, J, Forman, LV, Bland, PA, Benedix, GK, Fougerouse, D, Rickard, WDA, Saxey, DW, Reddy, SM, Smith, W & Bagot, PAJ 2021, 'Developing Atom Probe Tomography of Phyllosilicates in Preparation for Extra‐Terrestrial Sample Return', Geostandards and Geoanalytical Research, vol. 45, no. 3, pp. 427-441.
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Hydrous phyllosilicate minerals, including the serpentine subgroup, are likely to be major constituents of material that will be bought back to Earth by missions to Mars and to primitive asteroids Ryugu and Bennu. Small quantities (< 60 g) of micrometre‐sized, internally heterogeneous material will be available for study, requiring minimally destructive techniques. Many conventional methods are unsuitable for phyllosilicates as they are typically finely crystalline and electron beam‐sensitive resulting in amorphisation and dehydration. New tools will be required for nanoscale characterisation of these precious extra‐terrestrial samples. Here we test the effectiveness of atom probe tomography (APT) for this purpose. Using lizardite from the Ronda peridotite, Spain, as a terrestrial analogue, we outline an effective analytical protocol to extract nanoscale chemical and structural measurements of phyllosilicates. The potential of APT is demonstrated by the unexpected finding that the Ronda lizardite contains SiO‐rich nanophases, consistent with opaline silica that formed as a by‐product of the serpentinisation of olivine. Our new APT approach unlocks previously unobservable nanominerals and nanostructures within phyllosilicates owing to resolution limitations of more established imaging techniques. APT will provide unique insights into the processes and products of water/rock interaction on Earth, Mars and primitive asteroids.
Dang, B-T, Bui, X-T, Itayama, T, Ngo, HH, Jahng, D, Lin, C, Chen, S-S, Lin, K-YA, Nguyen, T-T, Nguyen, DD & Saunders, T 2021, 'Microbial community response to ciprofloxacin toxicity in sponge membrane bioreactor', Science of The Total Environment, vol. 773, pp. 145041-145041.
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Dang, CC, Dang, LC, Khabbaz, H & Sheng, D 2021, 'Numerical study on deformation characteristics of fibre-reinforced load-transfer platform and columns-supported embankments', Canadian Geotechnical Journal, vol. 58, no. 3, pp. 328-350.
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In this investigation, a ground-modification technique utilising a fibre-reinforced load-transfer platform (FRLTP) and columns-supported (CS) embankment constructed on multi-layered soft soils is proposed and investigated. After validating the proposed model with published data in the literature, numerical analysis was firstly conducted on the two-dimensional finite element model of a CS embankment without or with FRLTP to examine the influence of the FRLTP inclusion into the CS embankment system. Secondly, an extensive parametric study was performed to further investigate the effects of the FRLTP essential parameters — including platform thickness, shear strength, and tensile strength properties — and deformation modulus on the embankment performance during the construction and post-construction stages. Additionally, the influence of the embankment design parameters, such as column spacing, column length, and diameter, was examined. The numerical results reveal that the FRLTP inclusion can be effective in enhancing the CS embankment behaviour. It is also found that when increasing the platform thickness, the shear strength properties of FRLTP play a significant role in improving the overall performance of a column embankment with FRLTP.
Dang, KB, Nguyen, TT, Ngo, HH, Burkhard, B, Müller, F, Dang, VB, Nguyen, H, Ngo, VL & Pham, TPN 2021, 'Integrated methods and scenarios for assessment of sand dunes ecosystem services', Journal of Environmental Management, vol. 289, pp. 112485-112485.
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Dang, LC, Khabbaz, H & Ni, B-J 2021, 'Improving engineering characteristics of expansive soils using industry waste as a sustainable application for reuse of bagasse ash', Transportation Geotechnics, vol. 31, pp. 100637-100637.
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Bagasse ash (BA) is an abundant industrial waste of the sugar-cane refining industry, and its improper disposal can result in a detrimental impact on the environment. In this investigation, BA is considered to assess the possible advantages of its pozzolanic component as a novel sustainable waste application for stabilisation of expansive soils. The engineering characteristics of expansive soils were investigated through an array of laboratory experiments on treated and untreated soil specimens mixed with various contents of additive and cured for different times. A comprehensive investigation of the microstructure evolution of soils after treatment was also undertaken using Fourier transform infrared and scanning electron microscopy techniques. The results revealed that addition of BA, lime, and in particular, combined BA-lime (BAL) remarkably improved the maximum strength (815%), the bearing capacity (9.2 times), the compressibility (83%), and the 100% swell properties of stabilised soils due to rich amorphous silica properties of BA waste that promoted higher pozzolanic reactivities of BAL-soil-mixtures and therefore, enhanced the engineering characteristics of treated soils. The findings showed that a proper combination of bagasse ash waste and lime, as a stabilising additive, can effectively enhance the engineering properties of expansive soil while addressing the environmental impact of BA waste disposal. The industrial waste (BA) can be reused as a cost-effective and green construction material for the benefit of sustainable development of civil infrastructure.
Dang, Z, Li, L, Ni, W, Liu, R, Peng, H & Yang, Y 2021, 'How does rumor spreading affect people inside and outside an institution', Information Sciences, vol. 574, pp. 377-393.
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In this paper, we study the propagation characteristics of rumors in an institution and develop the rumor dissemination model inside the institution. Different from the traditional model, a person in the proposed model can make a judgement about the message and decide to propagate it or refute it, and most people inside the institution can refute the rumor spreaders and propagate the genuine messages to uninformed people when they have confirmed the message is a rumor. Then, we split all the people into two institutions (inside and outside). Since the rumors and genuine messages from the institution can have a non-negligible impact on people outside the institution, we put forward a new double-institution rumor propagation model, and the model considers the impact of messages on the inside and outside of the institution simultaneously. Based on the two proposed models, the basic reproduction numbers are obtained respectively, and the local and global stability of the rumor-free equilibrium points are discussed separately. We numerically simulate the propagation of rumors in small-world networks. The simulation is carried out to verify the validity of the proposed model, and our model is closer to the reality than traditional models.
Daniel, J & Merigó, JM 2021, 'Developing a new multidimensional model for selecting strategic plans in balanced scorecard', Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 1817-1826.
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The main motivation of this research is to develop an innovative multidimensional model through multi attribute decision making (MADM) methods for strategic plans selection process in the Balanced Scorecard (BSC). The current study adopted MADM analytical methods including AHP, ELECTRE, BORDA, TOPSIS and SAW to rank the initiatives / strategic plans in BSC. Then the results of those methods were compared against each other in order to find a robust model for selecting strategic plans. The correlation coefficient between methods indicated that multidimensional and ELECTRE methods with 0.944 are the best performing and AHP with negative correlation (–0.455) is the worst performing method for selecting strategic plans in BSC. The high correlation demonstrates that the model can be a useful and effective tool to finding the critical aspects of evaluation criteria as well as the gaps to improve company performance for achieving desired level. Developing multidimensional model is the core model for the selection of strategic plans. This study addresses the problem and issues of group decision making process for selecting strategic plans in BSC. It has numerous contributions that particularly includes; 1) Determination of the explicit criteria sub-criteria and criteria to improve ranking strategic plans in BSC, 2) Adopting MADM analytical methods including AHP, ELECTRE, BORDA, TOPSIS and SAW for the selection of strategic plans decision problem in BSC, 3) Developing multidimensional model to address the selection of strategic plans problems in BSC. The proposed model will provide an approach to facilitate strategic plans decision problem in BSC.
Dansana, D, Kumar, R, Parida, A, Sharma, R, Das Adhikari, J, Van Le, H, Thai Pham, B, Kant Singh, K & Pradhan, B 2021, 'Using Susceptible-Exposed-Infectious-Recovered Model to Forecast Coronavirus Outbreak', Computers, Materials & Continua, vol. 67, no. 2, pp. 1595-1612.
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Dao, KP, De Cocker, K, Tong, HL, Kocaballi, AB, Chow, C & Laranjo, L 2021, 'Smartphone-Delivered Ecological Momentary Interventions Based on Ecological Momentary Assessments to Promote Health Behaviors: Systematic Review and Adapted Checklist for Reporting Ecological Momentary Assessment and Intervention Studies', JMIR mHealth and uHealth, vol. 9, no. 11, pp. e22890-e22890.
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Background Healthy behaviors are crucial for maintaining a person’s health and well-being. The effects of health behavior interventions are mediated by individual and contextual factors that vary over time. Recently emerging smartphone-based ecological momentary interventions (EMIs) can use real-time user reports (ecological momentary assessments [EMAs]) to trigger appropriate support when needed in daily life. Objective This systematic review aims to assess the characteristics of smartphone-delivered EMIs using self-reported EMAs in relation to their effects on health behaviors, user engagement, and user perspectives. Methods We searched MEDLINE, Embase, PsycINFO, and CINAHL in June 2019 and updated the search in March 2020. We included experimental studies that incorporated EMIs based on EMAs delivered through smartphone apps to promote health behaviors in any health domain. Studies were independently screened. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. We performed a narrative synthesis of intervention effects, user perspectives and engagement, and intervention design and characteristics. Quality appraisal was conducted for all included studies. Results We included 19 papers describing 17 unique studies and comprising 652 participants. Most studies were quasi-experimental (13/17, 76%), had small sample sizes, and great heterogeneity in intervention designs and measurements. EMIs were most popular in the mental health domain (8/17, 47%), followed by substance abuse (3/17, 18%), diet, weight loss, physi...
Dao, N-N, Na, W, Tran, A-T, Nguyen, DN & Cho, S 2021, 'Energy-Efficient Spectrum Sensing for IoT Devices', IEEE Systems Journal, vol. 15, no. 1, pp. 1077-1085.
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Darabi, H, Torabi Haghighi, A, Rahmati, O, Jalali Shahrood, A, Rouzbeh, S, Pradhan, B & Tien Bui, D 2021, 'A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation', Journal of Hydrology, vol. 603, pp. 126854-126854.
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In regions with lack of hydrological and hydraulic data, a spatial flood modeling and mapping is an opportunity for the urban authorities to predict the spatial distribution and the intensity of the flooding. It helps decision-makers to develop effective flood prevention and management plans. In this study, flood inventory data were prepared based on the historical and field surveys data by Sari municipality and regional water company of Mazandaran, Iran. The collected flood data accompanied with different variables (digital elevation model and slope have been considered as topographic variables, land use/land cover, precipitation, curve number, distance to river, distance to channel and depth to groundwater as environmental variables) were applied to novel hybridized model based on neural network and swarm intelligence-grey wolf algorithm (NN-SGW) to map flood-inundation. Several confusion matrix criteria were used for accuracy evaluation by cutoff-dependent and independent metrics (e.g., efficiency (E), positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic curve (AUC)). The accuracy of the flood inundation map produced by the NN-SGW model was compared with that of maps produced by four state-of-the-art benchmark models: random forest (RF), logistic model tree (LMT), classification and regression trees (CART), and J48 decision tree (J48DT). The NN-SGW model outperformed all benchmark models in both training (E = 90.5%, PPV = 93.7%, NPV = 87.3%, AUC = 96.3%) and validation (E = 79.4%, PPV = 85.3%, NPV = 73.5%, AUC = 88.2%). As the NN-SGW model produced the most accurate flood-inundation map, it can be employed for robust flood contingency planning. Based on the obtained results from NN-SGW model, distance from channel, distance from river, and depth to groundwater were identified as the most important variables for spatial prediction of urban flood inundation. This work can serve as a basis fo...
Dardor, D, Al Maas, M, Minier-Matar, J, Janson, A, Abdel-Wahab, A, Shon, HK & Adham, S 2021, 'Evaluation of pretreatment and membrane configuration for pressure-retarded osmosis application to produced water from the petroleum industry', Desalination, vol. 516, pp. 115219-115219.
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Das, S, Mansouri, I, Choudhury, S, Gandomi, AH & Hu, JW 2021, 'A Prediction Model for the Calculation of Effective Stiffness Ratios of Reinforced Concrete Columns', Materials, vol. 14, no. 7, pp. 1792-1792.
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Nonlinear dynamic analyses of reinforced concrete (RC) frame buildings require the use of effective stiffness of members to capture the effect of cracked section stiffness. In the design codes and practices, the effective stiffness of RC sections is given as an empirical fraction of the gross stiffness. However, a more precise estimation of the effective stiffness is important as it affects the distribution of forces and various demands and response parameters in nonlinear dynamic analyses. In this study, an evolutionary computation method called gene expression programming (GEP) was used to predict the effective stiffness ratios of RC columns. Constitutive relationships were obtained by correlating the effective stiffness ratio with the four mechanical and geometrical parameters. The model was developed using a database of 226 samples of nonlinear dynamic analysis results collected from another study by the author. Subsequent parametric and sensitivity analyses were performed and the trends of the results were confirmed. The results indicate that the GEP model provides precise estimations of the effective stiffness ratios of the RC frames.
Datz, J, Karimi, M & Marburg, S 2021, 'Effect of Uncertainty in the Balancing Weights on the Vibration Response of a High-Speed Rotor', Journal of Vibration and Acoustics, vol. 143, no. 6, pp. 1-12.
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Abstract This work investigates how uncertainties in the balancing weights are propagating into the vibration response of a high-speed rotor. Balancing data are obtained from a 166-MW gas turbine rotor in a vacuum balancing tunnel. The influence coefficient method is then implemented to characterize the rotor system by a deterministic multi-speed and multi-plane matrix. To model the uncertainties, a non-sampling probabilistic method based on the generalized polynomial chaos expansion (gPCE) is employed. The uncertain parameters including the mass and angular positions of the balancing weights are then expressed by gPCE with deterministic coefficients. Assuming predefined probability distributions of the uncertain parameters, the stochastic Galerkin projection is applied to calculate the coefficients for the input parameters. Furthermore, the vibration amplitudes of the rotor response are represented by appropriate gPCE with unknown deterministic coefficients. These unknown coefficients are determined using the stochastic collocation method by evaluating the gPCE for the system response at a set of collocation points. The effects of individual and combined uncertain parameters from a single and multiple balancing planes on the rotor vibration response are examined. Results are compared with the Monte Carlo simulations, showing excellent agreement.
Davis, A, Mahar, A, Wong, K, Barnet, M & Kao, S 2021, 'Prolonged Disease Control on Nivolumab for Primary Pulmonary NUT Carcinoma', Clinical Lung Cancer, vol. 22, no. 5, pp. e665-e667.
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Dawson, N, Williams, M-A & Rizoiu, M-A 2021, 'Skill-driven recommendations for job transition pathways.', PloS one, vol. 16, no. 8, p. e0254722.
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Job security can never be taken for granted, especially in times of rapid, widespread and unexpected social and economic change. These changes can force workers to transition to new jobs. This may be because new technologies emerge or production is moved abroad. Perhaps it is a global crisis, such as COVID-19, which shutters industries and displaces labor en masse. Regardless of the impetus, people are faced with the challenge of moving between jobs to find new work. Successful transitions typically occur when workers leverage their existing skills in the new occupation. Here, we propose a novel method to measure the similarity between occupations using their underlying skills. We then build a recommender system for identifying optimal transition pathways between occupations using job advertisements (ads) data and a longitudinal household survey. Our results show that not only can we accurately predict occupational transitions (Accuracy = 76%), but we account for the asymmetric difficulties of moving between jobs (it is easier to move in one direction than the other). We also build an early warning indicator for new technology adoption (showcasing Artificial Intelligence), a major driver of rising job transitions. By using real-time data, our systems can respond to labor demand shifts as they occur (such as those caused by COVID-19). They can be leveraged by policy-makers, educators, and job seekers who are forced to confront the often distressing challenges of finding new jobs.
De Wyn, J, Zimmerman, MW, Weichert-Leahey, N, Nunes, C, Cheung, BB, Abraham, BJ, Beckers, A, Volders, P-J, Decaesteker, B, Carter, DR, Look, AT, De Preter, K, Van Loocke, W, Marshall, GM, Durbin, AD, Speleman, F & Durinck, K 2021, 'MEIS2 Is an Adrenergic Core Regulatory Transcription Factor Involved in Early Initiation of TH-MYCN-Driven Neuroblastoma Formation', Cancers, vol. 13, no. 19, pp. 4783-4783.
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Roughly half of all high-risk neuroblastoma patients present with MYCN amplification. The molecular consequences of MYCN overexpression in this aggressive pediatric tumor have been studied for decades, but thus far, our understanding of the early initiating steps of MYCN-driven tumor formation is still enigmatic. We performed a detailed transcriptome landscaping during murine TH-MYCN-driven neuroblastoma tumor formation at different time points. The neuroblastoma dependency factor MEIS2, together with ASCL1, was identified as a candidate tumor-initiating factor and shown to be a novel core regulatory circuit member in adrenergic neuroblastomas. Of further interest, we found a KEOPS complex member (gm6890), implicated in homologous double-strand break repair and telomere maintenance, to be strongly upregulated during tumor formation, as well as the checkpoint adaptor Claspin (CLSPN) and three chromosome 17q loci CBX2, GJC1 and LIMD2. Finally, cross-species master regulator analysis identified FOXM1, together with additional hubs controlling transcriptome profiles of MYCN-driven neuroblastoma. In conclusion, time-resolved transcriptome analysis of early hyperplastic lesions and full-blown MYCN-driven neuroblastomas yielded novel components implicated in both tumor initiation and maintenance, providing putative novel drug targets for MYCN-driven neuroblastoma.
Deepanraj, B, Senthilkumar, N, Ranjitha, J, Jayaraj, S & Ong, HC 2021, 'Biogas from food waste through anaerobic digestion: optimization with response surface methodology', Biomass Conversion and Biorefinery, vol. 11, no. 2, pp. 227-239.
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Dehghanbanadaki, A, Khari, M, Amiri, ST & Armaghani, DJ 2021, 'Estimation of ultimate bearing capacity of driven piles in c-φ soil using MLP-GWO and ANFIS-GWO models: a comparative study', Soft Computing, vol. 25, no. 5, pp. 4103-4119.
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Deliri, S, Varesi, K, Siwakoti, YP & Blaabjerg, F 2021, 'A boost type switched‐capacitor multi‐level inverter for renewable energy sources with Self‐Voltage balancing of capacitors', International Journal of Energy Research, vol. 45, no. 10, pp. 15217-15230.
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Deliri, S, Varesi, K, Siwakoti, YP & Blaabjerg, F 2021, 'Generalized diamond‐type single DC‐source switched‐capacitor based multilevel inverter with step‐up and natural voltage balancing capabilities', IET Power Electronics, vol. 14, no. 6, pp. 1208-1218.
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AbstractThis paper proposes a diamond‐shaped high step‐up switched‐capacitor based basic multilevel inverter topology. The basic switched‐capacitor (SC) stage consists of 2 active switches, 2 diodes, and 2 capacitors. Using a single DC source with the unfolding circuit (10 switches, 5 capacitors, and 5 diodes) results in the production of 17 voltage‐steps at the output with the gain of up to 8 times of the input voltage. By extending the diamond‐shaped switched‐capacitor stages, higher voltage levels and voltage gains can be possible. The suggested topology employs two half‐bridges (instead of a full‐bridge) to produce positive, zero, and negative steps, which reduces the Voltage Stress (VS) on two output switches and consequently reduces Total Voltage Stress (TVS). In addition, the natural voltage balancing of capacitors eliminates the need to an additional control circuitry and consequently reduces the total converter size, complexity, and cost. In addition, modularity, scalability, low voltage ripple on capacitors, low total voltage stress, high power quality, and capability of supplying low/medium power factor (R‐L) loads are some of the merits of the proposed topology. The low Cost Function (CF) obtained in the comparison section as well as experimental results verifies the advantages of the proposed topology.
Deng, J, Chen, X, Fan, Z, Jiang, R, Song, X & Tsang, IW 2021, 'The Pulse of Urban Transport: Exploring the Co-evolving Pattern for Spatio-temporal Forecasting', ACM Transactions on Knowledge Discovery from Data, vol. 15, no. 6, pp. 1-25.
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Transportation demand forecasting is a topic of large practical value. However, the model that fits the demand of one transportation by only considering the historical data of its own could be vulnerable since random fluctuations could easily impact the modeling. On the other hand, common factors like time and region attribute, drive the evolution demand of different transportation, leading to a co-evolving intrinsic property between different kinds of transportation. In this work, we focus on exploring the co-evolution between different modes of transport, e.g., taxi demand and shared-bike demand. Two significant challenges impede the discovery of the co-evolving pattern: (1) diversity of the co-evolving correlation, which varies from region to region and time to time. (2) Multi-modal data fusion. Taxi demand and shared-bike demand are time-series data, which have different representations with the external factors. Moreover, the distribution of taxi demand and bike demand are not identical. To overcome these challenges, we propose a novel method, known as co-evolving spatial temporal neural network (CEST). CEST learns a multi-view demand representation for each mode of transport, extracts the co-evolving pattern, then predicts the demand for the target transportation based on multi-scale representation, which includes fine-scale demand information and coarse-scale pattern information. We conduct extensive experiments to validate the superiority of our model over the state-of-art models.
Deng, S, Ji, J, Wen, G & Xu, H 2021, 'A comparative study of the dynamics of a three-disk dynamo system with and without time delay', Applied Mathematics and Computation, vol. 399, pp. 126016-126016.
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The disk dynamo plays an important role in studying the geodynamo and much research works have been devoted to the understanding of dynamo dynamics. This paper further investigates an extended disk dynamo system having three coupled conducting disks and incorporates the interaction-induced time delay in the dynamic governing equations. By carrying out a comparative analysis, the dynamic behaviors of the coupled three-disk dynamo system with and without time delay are studied to explore novel and complex nonlinear dynamic phenomena in the coupled delayed dynamo system. It is found that the double Hopf bifurcations can be induced in the time-delayed dynamo system. Three different topological structures of the unfolding are obtained under different time delays. Accordingly, it is shown that the novel dynamic behaviors, including quasi-periodic torus, three-dimensional torus and the coexistence of multiple attractors, can appear in the time-delayed dynamo system. Furthermore, by performing the continuation analysis on the periodic orbit generated from the Hopf bifurcation of equilibrium, some new coexistence patterns, e.g., the coexistence of periodic orbits and chaos, the coexistence of quasi-periodic orbits and chaos, are observed in the dynamo system with time delay. Based on the obtained results, it is believed that the inclusion of time delay in the modelling of the three-disk dynamo system is necessary and meaningful for developing an in-depth understanding of dynamo dynamics. Finally, the results of theoretical analyses are verified by the numerical simulations.
Deng, ZX, Tao, JW, Zhang, W, Mu, HJ, Wu, HJ, Wang, YB, Xu, XX & Zheng, W 2021, 'Effect of protein adsorption on electrospun hemoglobin/gelatin-MWCNTs microbelts modified electrode: Toward electrochemical measurement of hydrogen peroxide', Materials Chemistry and Physics, vol. 257, pp. 123827-123827.
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The on-line electrochemical analysis is one of powerful strategies in analytical chemistry and pathophysiology. To achieve high sensitivity and long-term stability of electrochemical biosensor, the bottleneck challenge is the spontaneous proteins adsorption onto the electrode surface within the biological fluids or in vivo environments. In this work, a hemoglobin/gelatin-multiwalled carbon nanotubes microbelts modified electrode (Hb/gelatin-MWCNTs/GC electrode) was successfully fabricated via one-step electrospinning process. The results of atomic force microscopy (AFM), scanning electron microscopy (SEM) and water contact angle test confirmed the electrospun Hb/gelatin-MWCNTs microbelts possessed smooth and hydrophilic surfaces. Furthermore, the electrospun Hb/gelatin-MWCNTs/GC electrode after protein adsorption displayed an excellent electrocatalytic sensitivity toward the reduction of hydrogen peroxide (H2O2). Moreover, the Hb/gelatin-MWCNTs/GC electrode presented very high biological affinity to H2O2 (Kmapp=503.4 ± 2.8 μmol L−1) after 360 min protein adsorption compared to that of the electrode before protein adsorption (Kmapp=298.1 ± 3.1 μmol L−1). The microbelts constructed H2O2 biosensor showed high selectivity, stability and reproducibility after protein adsorption. Therefore, this work provided the proof of the concept that the electrospun Hb/gelatin-MWCNTs/GC electrode displayed excellent sensing performance to H2O2 after protein adsorption, which could enable the implantable electrochemical biosensor for the on-line analysis.
Deshpande, NM, Gite, S, Pradhan, B, Kotecha, K & Alamri, A 2021, 'Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia', Mathematical Biosciences and Engineering, vol. 19, no. 2, pp. 1970-2001.
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<abstract> <p>The diagnosis of leukemia involves the detection of the abnormal characteristics of blood cells by a trained pathologist. Currently, this is done manually by observing the morphological characteristics of white blood cells in the microscopic images. Though there are some equipment- based and chemical-based tests available, the use and adaptation of the automated computer vision-based system is still an issue. There are certain software frameworks available in the literature; however, they are still not being adopted commercially. So there is a need for an automated and software- based framework for the detection of leukemia. In software-based detection, segmentation is the first critical stage that outputs the region of interest for further accurate diagnosis. Therefore, this paper explores an efficient and hybrid segmentation that proposes a more efficient and effective system for leukemia diagnosis. A very popular publicly available database, the acute lymphoblastic leukemia image database (ALL-IDB), is used in this research. First, the images are pre-processed and segmentation is done using Multilevel thresholding with Otsu and Kapur methods. To further optimize the segmentation performance, the Learning enthusiasm-based teaching-learning-based optimization (LebTLBO) algorithm is employed. Different metrics are used for measuring the system performance. A comparative analysis of the proposed methodology is done with existing benchmarks methods. The proposed approach has proven to be better than earlier techniques with measuring parameters of PSNR and Similarity index. The result shows a significant improvement in the performance measures with optimizing threshold algorithms and the LebTLBO technique.</p> </abstract>
Devda, V, Chaudhary, K, Varjani, S, Pathak, B, Patel, AK, Singhania, RR, Taherzadeh, MJ, Ngo, HH, Wong, JWC, Guo, W & Chaturvedi, P 2021, 'Recovery of resources from industrial wastewater employing electrochemical technologies: status, advancements and perspectives', Bioengineered, vol. 12, no. 1, pp. 4697-4718.
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Deveci, O & Shannon, AG 2021, 'Some aspects of Neyman triangles and Delannoy arrays', Mathematica Montisnigri, vol. 50, pp. 36-43.
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This note considers some number theoretic properties of the orthonormal Neyman polynomials which are related to Delannoy numbers and certain complex Delannoy numbers.
Deveci, Ö & Shannon, AG 2021, 'A note on balanced incomplete block designs and projective geometry', International Journal of Mathematical Education in Science and Technology, vol. 52, no. 5, pp. 807-813.
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© 2020 Informa UK Limited, trading as Taylor & Francis Group. This note outlines some connections between projective geometry and some designs used in clinical trials in the health sciences. The connections are not immediately obvious but they widen the scope for enrichment work at both the senior high school level and for capstone subjects at the undergraduate level.
Deveci, Ö & Shannon, AG 2021, 'Matrix manipulations for properties of Fibonacci p-numbers and their generalizations', Annals of the Alexandru Ioan Cuza University - Mathematics, vol. 67, no. 1, pp. 85-95.
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In this paper, we define the Fibonacci-Fibonacci p-sequence and then we discuss the connection of the Fibonacci-Fibonacci p-sequence with Fibonacci and Fibonacci p-sequences. We also provide a new Binet formula and a new combinatorial representation of Fibonacci p-numbers by the aid of the nth power of the generating matrix the Fibonacci-Fibonacci p-sequence. We furthermore develop relationships between the Fibonacci-Fibonacci p-numbers and their permanent, determinant and sums of certain matrices.
Deveci, Ö & Shannon, AG 2021, 'The complex-typek-Fibonacci sequences and their applications', Communications in Algebra, vol. 49, no. 3, pp. 1352-1367.
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© 2020 Taylor & Francis Group, LLC. In this article, we define the complex-type k-Fibonacci numbers and then give the relationships between the k-step Fibonacci numbers and the complex-type k-Fibonacci numbers. Also, we obtain miscellaneous properties of the complex-type k-Fibonacci numbers such as the Binet formulas, the combinatorial, permanental, determinantal representations and the sums. In addition, we study the complex-type k-Fibonacci sequence modulo m and then we give some results concerning the periods and the ranks of the complex-type k-Fibonacci sequences for any k and m which are related the periods of the k-step Fibonacci sequences modulo m. Furthermore, we extend the complex-type k-Fibonacci sequences to groups. Finally, we obtain the periods of the complex-type 2-Fibonacci sequences in the dihedral group (Formula presented.) with respect to the generating pairs (x, y) and (y, x).
Di, X, Wang, D, Zhou, J, Zhang, L, Stenzel, MH, Su, QP & Jin, D 2021, 'Quantitatively Monitoring In Situ Mitochondrial Thermal Dynamics by Upconversion Nanoparticles', Nano Letters, vol. 21, no. 4, pp. 1651-1658.
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Temperature dynamics reflect the physiological conditions of cells and organisms. Mitochondria regulate the temperature dynamics in living cells as they oxidize the respiratory substrates and synthesize ATP, with heat being released as a byproduct of active metabolism. Here, we report an upconversion nanoparticle-based thermometer that allows the in situ thermal dynamics monitoring of mitochondria in living cells. We demonstrate that the upconversion nanothermometers can efficiently target mitochondria, and the temperature-responsive feature is independent of probe concentration and medium conditions. The relative sensing sensitivity of 3.2% K-1 in HeLa cells allows us to measure the mitochondrial temperature difference through the stimulations of high glucose, lipid, Ca2+ shock, and the inhibitor of oxidative phosphorylation. Moreover, cells display distinct response time and thermodynamic profiles under different stimulations, which highlight the potential applications of this thermometer to study in situ vital processes related to mitochondrial metabolism pathways and interactions between organelles.
Diao, K, Sun, X, Lei, G, Bramerdorfer, G, Guo, Y & Zhu, J 2021, 'Robust Design Optimization of Switched Reluctance Motor Drive Systems Based on System-Level Sequential Taguchi Method', IEEE Transactions on Energy Conversion, vol. 36, no. 4, pp. 3199-3207.
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Diao, K, Sun, X, Lei, G, Bramerdorfer, G, Guo, Y & Zhu, J 2021, 'System-Level Robust Design Optimization of a Switched Reluctance Motor Drive System Considering Multiple Driving Cycles', IEEE Transactions on Energy Conversion, vol. 36, no. 1, pp. 348-357.
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In this article, a novel system-level robust design optimization method is presented to improve the performance of switched reluctance motor (SRM) drive systems under multiple operating conditions. Based on typical driving cycles of electric vehicles (EVs), five typical driving modes of the SRM are determined. The optimization objectives in each driving mode are established. The significant parameters of the motor and controller of each driving mode are selected as the optimization variables by using the sensitivity analysis. In order to simplify the optimization process, correlation analysis is performed to determine the coherence of the objective functions of all driving modes. Then, a sequential Taguchi method is applied to find an optimal design which is less sensitive to the noise factors. To verify the effectiveness of the proposed method, an SRM drive system applied in EVs with a 12/10 SRM and angle position control method is investigated. It is found that the proposed method can significantly reduce the torque ripple and improve the comprehensive performance. Finally, a 12/10 SRM is prototyped and tested to validate the simulation results.
Diao, K, Sun, X, Lei, G, Guo, Y & Zhu, J 2021, 'Multimode Optimization of Switched Reluctance Machines in Hybrid Electric Vehicles', IEEE Transactions on Energy Conversion, vol. 36, no. 3, pp. 2217-2226.
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IEEE The belt-driven starter/generator (BSG), as a cost-effective solution, has been widely employed in hybrid electric vehicles (HEVs) to improve the stability and reduce the fuel consumption of the vehicles. It can provide more than 10% reduction in CO2. Electrical machine is the heart of the BSG system, which is functioned both as motor and generator. In order to optimize both aspects of motor and generator simultaneously, this paper presents a new multimode optimization method for the switched reluctance machines. First, the general multimode concept and optimization method are presented. The switched reluctance motor and the switched reluctance generator are the two operation modes. The optimization models are established based on motor and generator functions. Sensitivity analysis, surrogate models and genetic algorithms are employed to improve the efficiency of the multimode optimization. Then, a design example of a segmented-rotor switched reluctance machine (SSRM) is investigated. Seven design variables and four driving modes are considered in the multiobjective optimization model. The Kriging model is employed to approximate the finite element model (FEM) in the optimization. Finally, the optimization results are depicted, and an optimal solution is selected. The comparison between the initial and optimal designs shows that the proposed method can improve the foremost performance of the SSRM under all driving modes.
Dickson-Deane, C 2021, 'Influencing the design outcome', Educational Technology Research and Development, vol. 69, no. 1, pp. 269-271.
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Dickson-Deane, C 2021, 'Moving practical learning online', Educational Technology Research and Development, vol. 69, no. 1, pp. 235-237.
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Dickson-Deane, C & Edwards, M 2021, 'Transcribing accounting lectures: Enhancing the pedagogical practice by acknowledging student behaviour', Journal of Accounting Education, vol. 54, pp. 100709-100709.
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Dikshit, A & Pradhan, B 2021, 'Explainable AI in drought forecasting', Machine Learning with Applications, vol. 6, pp. 100192-100192.
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Dikshit, A & Pradhan, B 2021, 'Interpretable and explainable AI (XAI) model for spatial drought prediction', Science of The Total Environment, vol. 801, pp. 149797-149797.
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Accurate prediction of any type of natural hazard is a challenging task. Of all the various hazards, drought prediction is challenging as it lacks a universal definition and is getting adverse with climate change impacting drought events both spatially and temporally. The problem becomes more complex as drought occurrence is dependent on a multitude of factors ranging from hydro-meteorological to climatic variables. A paradigm shift happened in this field when it was found that the inclusion of climatic variables in the data-driven prediction model improves the accuracy. However, this understanding has been primarily using statistical metrics used to measure the model accuracy. The present work tries to explore this finding using an explainable artificial intelligence (XAI) model. The explainable deep learning model development and comparative analysis were performed using known understandings drawn from physical-based models. The work also tries to explore how the model achieves specific results at different spatio-temporal intervals, enabling us to understand the local interactions among the predictors for different drought conditions and drought periods. The drought index used in the study is Standard Precipitation Index (SPI) at 12 month scales applied for five different regions in New South Wales, Australia, with the explainable algorithm being SHapley Additive exPlanations (SHAP). The conclusions drawn from SHAP plots depict the importance of climatic variables at a monthly scale and varying ranges of annual scale. We observe that the results obtained from SHAP align with the physical model interpretations, thus suggesting the need to add climatic variables as predictors in the prediction model.
Dikshit, A, Pradhan, B & Alamri, AM 2021, 'Long lead time drought forecasting using lagged climate variables and a stacked long short-term memory model', Science of The Total Environment, vol. 755, pp. 142638-142638.
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Dikshit, A, Pradhan, B & Alamri, AM 2021, 'Pathways and challenges of the application of artificial intelligence to geohazards modelling', Gondwana Research, vol. 100, pp. 290-301.
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© 2020 International Association for Gondwana Research The application of artificial intelligence (AI) and machine learning in geohazard modelling has been rapidly growing in recent years, a trend that is observed in several research and application areas thanks to recent advances in AI. As a result, the increasing dependence on data driven studies has made its practical applications towards geohazards (landslides, debris flows, earthquakes, droughts, floods, glacier studies) an interesting prospect. These aforementioned geohazards were responsible for roughly 80% of the economic loss in the past two decades caused by all natural hazards. The present study analyses the various domains of geohazards which have benefited from classical machine learning approaches and highlights the future course of direction in this field. The emergence of deep learning has fulfilled several gaps in: i) classification; ii) seasonal forecasting as well as forecasting at longer lead times; iii) temporal based change detection. Apart from the usual challenges of dataset availability, climate change and anthropogenic activities, this review paper emphasizes that the future studies should focus on consecutive events along with integration of physical models. The recent catastrophe in Japan and Australia makes a compelling argument to focus towards consecutive events. The availability of higher temporal resolution and multi-hazard dataset will prove to be essential, but the key would be to integrate it with physical models which would improve our understanding of the mechanism involved both in single and consecutive hazard scenario. Geohazards would eventually be a data problem, like geosciences, and therefore it is essential to develop models that would be capable of handling large voluminous data. The future works should also revolve towards interpretable models with the hope of providing a reasonable explanation of the results, thereby achieving the ultimate goal of Explainable AI.
Ding, A, Song, R, Cui, H, Cao, H, Ngo, HH, Chang, H, Nan, J, Li, G & Ma, J 2021, 'Presence of powdered activated carbon/zeolite layer on the performances of gravity-driven membrane (GDM) system for drinking water treatment: Ammonia removal and flux stabilization', Science of The Total Environment, vol. 799, pp. 149415-149415.
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Ding, A, Zhang, R, Ngo, HH, He, X, Ma, J, Nan, J & Li, G 2021, 'Life cycle assessment of sewage sludge treatment and disposal based on nutrient and energy recovery: A review', Science of The Total Environment, vol. 769, pp. 144451-144451.
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With the acceleration of urbanization, the production of urban sludge is increasing rapidly. To minimize resource input and waste output, it is crucial to execute analyses of environmental impact and assessments of sustainability on different technical strategies involving sludge disposal based on Life Cycle Assessment (LCA), which is a great potential mean of environmental management adopted internationally in the 21st century. This review aims to compare the environmental sustainability of existing sludge management schemes with a purpose of nutrient recovery and energy saving, respectively, and also to include the substitution benefits of alternative sludge products. Simultaneously, LCA research regarding the emerging sludge management technologies and sludge recycling (cement, adsorbent, bricks) is analyzed. Additionally, the key aspects of the LCA process are worth noting in the context of the current limitations reviewed here. It is worth emphasizing that no technical remediation method can reduce all environmental damage simultaneously, and these schemes are typically more applicable to the assumed local conditions. Future LCA research should pay more attention to the toxic effects of different sludge treatment methods, evaluate the technical ways of adding pretreatment technology to the 'front end' of the sludge treatment process, and further explore how to markedly reduce environmental damage in order to maximize energy and nutrient recovery from the LCA perspective.
Ding, L, Moloudi, R & Warkiani, ME 2021, 'Bioreactor-Based Adherent Cells Harvesting from Microcarriers with 3D Printed Inertial Microfluidics', pp. 257-266.
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Ding, L, Radfar, P, Rezaei, M & Warkiani, ME 2021, 'An easy-to-operate method for single-cell isolation and retrieval using a microfluidic static droplet array', Microchimica Acta, vol. 188, no. 8, pp. 1-11.
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In-depth study of cellular heterogeneity of rare cells (e.g. circulating tumour cells (CTCs) and circulating foetal cells (CFCs)) is greatly needed in disease management but has never been completely explored due to the current technological limitations. We have developed a retrieval method for single-cell detection using a static droplet array (SDA) device through liquid segmentation with almost no sample loss. We explored the potential of using SDA for low sample input and retrieving the cells of interest using everyday laboratory equipment for downstream molecular analysis. This single-cell isolation and retrieval method is low-cost, rapid and provides a solution to the remaining challenge for single rare cell detection. The entire process takes less than 15 min, is easy to fabricate and allows for on-chip analysis of cells in nanolitre droplets and retrieval of desired droplets. To validate the applicability of our device and method, we mimicked detection of single CTCs by isolating and retrieving single cells and perform real-time PCR on their mRNA contents.
Ding, L, Zhou, J, Fu, Q, Bao, G, Liu, Y & Jin, D 2021, 'Triplet Fusion Upconversion with Oxygen Resistance in Aqueous Media', Analytical Chemistry, vol. 93, no. 10, pp. 4641-4646.
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Triplet fusion upconversion (also called triplet-triplet annihilation, TTA) arouses much attention due to its potential in the fields of biological imaging, optogenetics, and light harvesting. However, oxygen quenching remains a challenge ahead, restricting its applications in aqueous media. Previous efforts to realize aqueous TTA with oxygen resistance have been focused on core-shell structures and self-assembly, but tedious processes and complicated chemical modification are required. Here, we report a direct and efficient strategy to realize aqueous TTA by controlling the ionic equilibrium of the TTA dyad. We find that the ionized organic dyad in physiological buffers and electrolyte-based media shows a natural aerotolerance without any complicated structure engineering. In particular, the upconversion intensity of this aqueous TTA in Tris buffer under an air-saturated condition is more than twice that under the deaerated condition. We further demonstrate the TTA system for potential applications in pH and temperature sensing with reversible and sensitive performance. We anticipate this facile approach will inspire the development of practical aqueous TTA and broad applications in biological science.
Ding, W, Jin, W, Zhou, X, Yang, Q, Chen, C & Wang, Q 2021, 'Role of extracellular polymeric substances in anaerobic granular sludge: Assessing dewaterability during Fe(II)-peroxydisulfate conditioning and granulation processes', Journal of Cleaner Production, vol. 286, pp. 124968-124968.
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© 2020 Elsevier Ltd In this study, Fe(II) activated peroxydisulfate (PDS) conditioning and sludge granulation were conducted to investigate the dewaterability of anaerobic granular sludge (AGS). After Fe(II)-PDS conditioning, the dewaterability of three AGS from different sources was enhanced. The specific resistance to filtration (SRF) reduction rates were achieved (98.30% ± 0.19%, 99.51% ± 0.17% and 96.47% ± 1.25%, respectively) under the optimal Fe(II) and PDS additions; And the optimal reductions of capillary suction time (CST) were 93.49% ± 2.49%, 95.33% ± 0.02% and 88.04% ± 2.95%, respectively. The mechanism of improving AGS dewaterability by Fe(II)-PDS conditioning was proposed. The radical SO4⋅−/OH⋅ destroyed the structure of extracellular polymeric substances (EPS) layers and microbial cells, resulting in the bound water released from AGS. Thereafter, the generated Fe(III) facilitated the sludge re-flocculation and decreased the electrostatic repulsion. During a 132-day granulation, the CST value showed a positive correlation with protein (S-EPS), polysaccharide and zeta potential, and a negative correlation with protein (LB-EPS), protein (TB-EPS), particle size and VSS. Collectively, the protein was the primary component in AGS and showed a strong correlation with dewaterability. The variations of protein in TB-EPS during the conditioning and the granulation were consistent with the changes of sludge dewaterability.
Ding, W, Ming, Y, Wang, Y-K & Lin, C-T 2021, 'Memory augmented convolutional neural network and its application in bioimages', Neurocomputing, vol. 466, pp. 128-138.
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The long short-term memory (LSTM) network underpins many achievements and breakthroughs especially in natural language processing fields. Essentially, it is endowed with certain memory capabilities to boost its performance. Currently, the volume and speed of big data generation are increasing exponentially, and such data require efficient models to acquire memory augmented knowledge. In this paper, we propose a memory augmented convolutional neural network (MACNN) with utilizing self-organizing maps (SOM) as the memory module. First, we depict the potential challenge about just applying solely a convolutional neural network (CNN) so as to highlight the advantage of augmenting SOM memory for better network generalization. Then, we dissert a corresponding network architecture incorporating memory to instantiate the distributed knowledge representation machanism, which tactically combines both SOM and CNN. Each component of the input vector is connected with a neuron in a two-dimensional lattice. Finally, we test the proposed network on various datasets and the experimental results reveal that MACNN can achieve competitive performance, especially for bioimages datasets. Meanwhile, we further illustrate the learned representations to interpret the SOM behavior and to comprehend the achieved results, which indicates that the proposed memory-incorporating model can exhibit the better performance.
Ding, W, Nguyen, MD, Salih Mohammed, A, Armaghani, DJ, Hasanipanah, M, Bui, LV & Pham, BT 2021, 'A new development of ANFIS-Based Henry gas solubility optimization technique for prediction of soil shear strength', Transportation Geotechnics, vol. 29, pp. 100579-100579.
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Ding, W, Pal, NR, Lin, C-T, Cheung, Y-M, Cao, Z & Luo, W 2021, 'Guest Editorial Special Issue on Emerging Computational Intelligence Techniques for Decision Making With Big Data in Uncertain Environments', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 5, no. 1, pp. 2-5.
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Ding, W, Pedrycz, W, Triguero, I, Cao, Z & Lin, C-T 2021, 'Multigranulation Supertrust Model for Attribute Reduction', IEEE Transactions on Fuzzy Systems, vol. 29, no. 6, pp. 1395-1408.
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IEEE As big data often contains a significant amount of uncertain, unstructured and imprecise data that are structurally complex and incomplete, traditional attribute reduction methods are less effective when applied to large-scale incomplete information systems to extract knowledge. Multigranular computing provides a powerful tool for use in big data analysis conducted at different levels of information granularity. In this paper, we present a novel multigranulation super-trust fuzzy-rough set-based attribute reduction (MSFAR) algorithm to support the formation of hierarchies of information granules of higher types and higher orders, which addresses newly emerging data mining problems in big data analysis. First, a multigranulation super-trust model based on the valued tolerance relation is constructed to identify the fuzzy similarity of the changing knowledge granularity with multimodality attributes. Second, an ensemble consensus compensatory scheme is adopted to calculate the multigranular trust degree based on the reputation at different granularities to create reasonable subproblems with different granulation levels. Third, an equilibrium method of multigranular-coevolution is employed to ensure a wide range of balancing of exploration and exploitation and can classify super elitists’ preferences and detect noncooperative behaviors with a global convergence ability and high search accuracy. The experimental results demonstrate that the MSFAR algorithm achieves a high performance in addressing uncertain and fuzzy attribute reduction problems with a large number of multigranularity variables.
Ding, W, Triguero, I & Lin, C-T 2021, 'Coevolutionary Fuzzy Attribute Order Reduction With Complete Attribute-Value Space Tree', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 5, no. 1, pp. 130-142.
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Ding, X, Chen, L, Zhou, P, Xu, Z, Wen, S, Lui, JCS & Jin, H 2021, 'Dynamic online convex optimization with long-term constraints via virtual queue', Information Sciences, vol. 577, pp. 140-161.
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In this paper, we investigate the online convex optimization (OCO) with long-term constraints which is widely used in various resource allocations and recommendation systems. Different from the most existing works, our work adopts a dynamic benchmark to analyze the optimization performance since the dynamic benchmark is more common than the static benchmark in practical applications. Moreover, compared with many constrained OCO works ignoring the Slater condition, we study the effect of the Slater condition on the constraint violation bounds and obtain the better performance of the constraint violations when the Slater condition holds. More importantly, we propose a novel iterative optimization algorithm based on the virtual queues to achieve sublinear regret and constraint violations. Finally, we apply our dynamic OCO model to a resource allocation problem in cloud computing and the results of the experiments validate the effectiveness of our algorithm.
Ding, X, Jiang, T, Zhong, Y, Huang, Y & Li, Z 2021, 'Wi-Fi-Based Location-Independent Human Activity Recognition via Meta Learning', Sensors, vol. 21, no. 8, pp. 2654-2654.
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Wi-Fi-based device-free human activity recognition has recently become a vital underpinning for various emerging applications, ranging from the Internet of Things (IoT) to Human–Computer Interaction (HCI). Although this technology has been successfully demonstrated for location-dependent sensing, it relies on sufficient data samples for large-scale sensing, which is enormously labor-intensive and time-consuming. However, in real-world applications, location-independent sensing is crucial and indispensable. Therefore, how to alleviate adverse effects on recognition accuracy caused by location variations with the limited dataset is still an open question. To address this concern, we present a location-independent human activity recognition system based on Wi-Fi named WiLiMetaSensing. Specifically, we first leverage a Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) feature representation method to focus on location-independent characteristics. Then, in order to well transfer the model across different positions with limited data samples, a metric learning-based activity recognition method is proposed. Consequently, not only the generalization ability but also the transferable capability of the model would be significantly promoted. To fully validate the feasibility of the presented approach, extensive experiments have been conducted in an office with 24 testing locations. The evaluation results demonstrate that our method can achieve more than 90% in location-independent human activity recognition accuracy. More importantly, it can adapt well to the data samples with a small number of subcarriers and a low sampling rate.
Do, HT, Bach, NV, Nguyen, LV, Tran, HT & Nguyen, MT 2021, 'A design of higher-level control based genetic algorithms for wastewater treatment plants', Engineering Science and Technology, an International Journal, vol. 24, no. 4, pp. 872-878.
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Do, NTT, Lin, C-T & Gramann, K 2021, 'Human brain dynamics in active spatial navigation', Scientific Reports, vol. 11, no. 1, pp. 1-12.
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Do, T-TN, Jung, T-P & Lin, C-T 2021, 'Retrosplenial Segregation Reflects the Navigation Load During Ambulatory Movement', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, no. 99, pp. 488-496.
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Spatial navigation is a complex cognitive process based on vestibular, proprioceptive, and visualcues that are integrated and processed by an extensive network of brain areas. The retrosplenial complex (RSC) is an integral part of coordination and translation between spatial reference frames. Previous studies have demonstrated that the RSC is active during a spatial navigation tasks. The specifics of RSC activity under various navigation loads, however, are still not characterized. This study investigated the local information processed by the RSC under various navigation load conditions manipulated by the number of turns in the physical navigation setup. The results showed that the local information processed via the RSC, which was reflected by the segregation network, was higher when the number of turns increased, suggesting that RSC activity is associated with the navigation task load. The present findings shed light on how the brain processes spatial information in a physical navigation task.
Do, T-TN, Lin, C-T & Gramann, K 2021, 'Human brain dynamics in active spatial navigation', Scientific Reports, vol. 11, no. 1, p. 13036.
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AbstractSpatial navigation is a complex cognitive process based on multiple senses that are integrated and processed by a wide network of brain areas. Previous studies have revealed the retrosplenial complex (RSC) to be modulated in a task-related manner during navigation. However, these studies restricted participants’ movement to stationary setups, which might have impacted heading computations due to the absence of vestibular and proprioceptive inputs. Here, we present evidence of human RSC theta oscillation (4–8 Hz) in an active spatial navigation task where participants actively ambulated from one location to several other points while the position of a landmark and the starting location were updated. The results revealed theta power in the RSC to be pronounced during heading changes but not during translational movements, indicating that physical rotations induce human RSC theta activity. This finding provides a potential evidence of head-direction computation in RSC in healthy humans during active spatial navigation.
Do, T-TN, Wang, Y-K & Lin, C-T 2021, 'Increase in Brain Effective Connectivity in Multitasking but not in a High-Fatigue State', IEEE Transactions on Cognitive and Developmental Systems, vol. 13, no. 3, pp. 566-574.
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IEEE Multitasking has become omnipresent in daily activities, and increased brain connectivity under high workload conditions has been reported. Moreover, the effect of fatigue on neural activity has been shown in participants performing cognitive tasks, but the effect of fatigue on different cognitive workload conditions is unclear. In this study, we investigated the effect of fatigue on changes in effective connectivity (EC) across the brain network under distinctive workload conditions. There were 133 electroencephalography (EEG) datasets collected from sixteen participants over a five-month study in which high-risk, reduced, and normal states of real-world fatigue were identified through a daily sampling system. The participants were required to perform a lane-keeping task (LKT) with/without multimodal dynamic attention-shifting (DAS) tasks. The results show that the EC magnitude is positively correlated with the increased workload in normal and reduced states. However, low EC was discovered in the high-risk state under high workload condition. To the best of our knowledge, this investigation is the first EEG-based longitudinal study of real-world fatigue under multitasking conditions. These results could be beneficial for real-life applications, and adaptive models are essential for monitoring important brain patterns under varying workload demands and fatigue states.
Doan, S & Fatahi, B 2021, 'Green’s function analytical solution for free strain consolidation of soft soil improved by stone columns subjected to time-dependent loading', Computers and Geotechnics, vol. 136, pp. 103941-103941.
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This paper proposes an analytical solution in terms of Green's function formulations for axisymmetric consolidation of a stone column improved soft soil deposit subjected to time-dependent loading under free strain condition. The mathematical derivations incorporate the pore water flows in radial and vertical directions in stone column and soft soil synchronously. The capabilities of the proposed analytical solution are evaluated via worked examples investigating the influences of three common time-dependent external surcharges (namely step, ramp and sinusoidal loadings) on consolidation response of the composite ground. The examples show that a faster increase of load from an initial surcharge to an expected loading might generate more significant excess pore water pressure to be dissipated during the early stages of consolidation, but the dissipation rate in soft soil would speed up significantly afterwards. The column and soil settlements along with the differential settlement between them also proceed faster corresponding to the acceleration of loading – unloading processes. Finally, the proposed analytical solution is employed to evaluate the excess pore water pressure dissipation rate at an investigation point in soft clay of a case history foundation. The calculation results exhibit a reasonable agreement with field measurement data when various constant values of stress concentration ratio are substituted into the solution to reflect the increase of stress concentration ratio with consolidation time in real practice.
Dogan, A, Akay, M, Barua, PD, Baygin, M, Dogan, S, Tuncer, T, Dogru, AH & Acharya, UR 2021, 'PrimePatNet87: Prime pattern and tunable q-factor wavelet transform techniques for automated accurate EEG emotion recognition', Computers in Biology and Medicine, vol. 138, pp. 104867-104867.
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Dolmark, T, Sohaib, O, Beydoun, G & Wu, K 2021, 'The Effect of Individual’s Technological Belief and Usage on Their Absorptive Capacity towards Their Learning Behaviour in Learning Environment', Sustainability, vol. 13, no. 2, pp. 718-718.
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Absorptive capacity is a common barrier to knowledge transfer at the individual level. However, technology absorptive capacity can enhance an individual’s learning behaviour. This study investigates that technology readiness, the tools for knowledge sources, social influences, and social networks influence an individual’s absorptive capacity on an adaptation of the individual learning behaviour. A quantitative approach is used to assess the presence of a causal relationship from the constructs mentioned above. Data were collected from university students in Australia to examine the hypotheses. With 199 responses, a partial least squares structural equation modelling (PLS-SEM) approach was used for the analysis. The results generated mixed findings. Individual’s technological belief in optimism and innovation and social influences had a significantly weaker effect on individual absorptive capacity, which in turn had a significantly weaker impact on their learning behaviour.
Dombrowski, U, Deuse, J, Ortmeier, C, Henningsen, N, Wullbrandt, J & Nöhring, F 2021, 'Auswahlsystematik für Methoden und Werkzeuge Ganzheitlicher Produktionssysteme 4.0', Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 116, no. 6, pp. 398-402.
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Abstract Das produzierende Gewerbe steht aktuell vor der Herausforderung, bestehende Produktionssysteme mit Industrie-4.0-Lösungen zu erweitern, um deren Potenziale zu nutzen und somit die Wettbewerbsfähigkeit zu sichern. Es fehlen insbesondere kleinen und mittleren Unternehmen (KMU) jedoch die Transparenz über die Wirkung und die Auswahl von Industrie-4.0-Lösungen. Im Rahmen des Forschungsprojekts „Ganzheitliche Produktionssysteme 4.0“ (GaProSys 4.0) wurde diese Problematik aufgegriffen. In diesem Beitrag werden die Vorgehensweise zur Entwicklung von GaProSys-4.0-Methoden anhand des elektronischen Shopfloor Managements aufgezeigt und die im Rahmen des Forschungsprojektes erarbeitete Auswahlsystematik präsentiert.
Dominici, L, Fleck, R, Gill, RL, Pettit, TJ, Irga, PJ, Comino, E & Torpy, FR 2021, 'Analysis of lighting conditions of indoor living walls: Effects on CO2 removal', Journal of Building Engineering, vol. 44, pp. 102961-102961.
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Vertical greening systems, or living walls, are becoming increasingly used indoors for improving the sustainability of buildings, including for the mitigation of excess CO2 levels, derived from human respiration. However, light provision within indoor environments is often insufficient for the efficient functioning of many plant species, leading to low photosynthetic CO2 removal rates, and the need for supplementary light sources. In this study, we investigated the performance of supplementary lighting employed for indoor living wall systems, and whether optimised lighting conditions could lead to improved CO2 removal. In situ trials with several medium-large indoor living walls were performed to sample the lighting scenarios currently employed. We concluded that the majority of plants in existing systems were exposed to suboptimal lighting and will have a net-zero CO2 removal efficiency. Sealed chamber experiments using two common living wall plant species were conducted to explore the effect of varying lighting conditions on CO2 removal efficiency. Comparisons on optimal and “best case” in situ conditions were carried out, showing that CO2 removal efficiency was significantly correlated with both leaf and stem angles, which suggest phototropism may influence in situ CO2 removal. After a ten-day experimental period, the highest CO2 removal efficiency for both test plant species was observed at 200 μmol m−2 s−1 light flux density (~10500 lux) at 15° from the vertical growing surface. Our results indicate that most current lighting systems are inadequate for healthy plant photosynthesis and CO2 removal, and that modified lighting systems could improve this performance. The estimation of the CO2 removal ability of a 5 m2 passive living wall decreases from an ACH of 0.21 h−1, achieved in an optimal light exposure condition, to only 0.03 h−1 when plants are exposed to sub-optimal conditions. To reduce maintenance costs, technical guidelines for indoor livin...
Dong, M, Yao, L, Wang, X, Benatallah, B, Zhang, S & Sheng, QZ 2021, 'Gradient Boosted Neural Decision Forest', IEEE Transactions on Services Computing, vol. PP, no. 99, pp. 1-1.
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Tree-based models and deep neural networks are two schools of effective classification methods in machine learning. While tree-based models are robust irrespective of data domain, deep neural networks have advantages in handling high-dimensional data. Adding a differentiable neural decision forest to the neural network can generally help exploit the benefits of both models. Therefore, traditional decision trees diverge into a bagging version (i.e., random forest) and a boosting version (i.e., gradient boost decision tree). In this work, we aim to harness the advantages of both bagging and boosting by applying gradient boost to a neural decision forest. We propose a gradient boost that can learn the residual using a neural decision forest, considering the residual as a part of the final prediction. Besides, we design a structure for learning the parameters of neural decision forest and gradient boost module in contiguous steps, which is extendable to incorporate multiple gradient-boosting modules in an end-to-end manner. Our extensive experiments on several public datasets demonstrate the competitive performance and efficiency of our model against a series of baseline methods in solving various machine learning tasks.
Dong, S, Zhao, Y, Li, JJ & Xing, D 2021, 'Global Research Trends in Revision Total Knee Arthroplasty: A Bibliometric and Visualized Study', Indian Journal of Orthopaedics, vol. 55, no. 5, pp. 1335-1347.
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Background: Revision total knee arthroplasty (TKA) is a longstanding area of research interest in orthopedics due to its increasing global demand and associated technical challenges. The present study aims to analyze and present the current state of research and trends in this active field. Methods: Articles on revision TKA published from inception to 2018 were retrieved from Web of Science. Bibliometric analysis was conducted using the metadata of the included articles. Visualized analysis was conducted using VOSviewer software to reveal global trends in revision TKA research, through analyses of bibliographic coupling, co-authorship, co-citation and co-occurrence. Results: A total of 6027 articles were included. The number of publications and relative research interest in the field of revision TKA displayed strong upward growth over the time period examined. The USA had the highest number of citations for publications in this field, as well as the highest H-index. Studies in the field could be categorized into five clusters: prosthesis design, periprosthetic fracture, periprosthetic joint infection, risk factors for revision TKA, and survivorship of implants. Studies focused on infection and risk factors for revision TKA are likely to become the most popular research topics in the field. Conclusion: Global trends over the past few years suggest that the field of revision TKA research will continue to grow and lead to increasing rates of publication output over the coming years. Future developments in the field will likely include more preventative and etiological studies relating to revision TKA.
Dong, W, Guo, Y, Sun, Z, Tao, Z & Li, W 2021, 'Development of piezoresistive cement-based sensor using recycled waste glass cullets coated with carbon nanotubes', Journal of Cleaner Production, vol. 314, pp. 127968-127968.
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Different from widely exploring the application of waste glass to replace natural aggregate or cement powder, this study firstly utilized waste glass cullets coated with carbon nanotubes (CNTs) as conductive fillers to develop novel self-sensing cement-based sensors. The coating efficiency of CNTs and self-sensing properties were also investigated in terms of workability, water absorption, mechanical properties, electrical resistivity and microstructure. The results show that CNTs are attached to the surfaces of waste glass particles, especially the small-size waste glass particles with high roughness. Workability decreased significantly with the increased waste glass. Cementitious mortar with sand replaced by CNTs-coated waste glass exhibited the highest flowability when the replacement ratio was 25%. Moreover, the water impermeability continuously increased with the content of waste glass. The compressive strength was higher than that of the control mortar, which reached the highest with 50% waste glass content. Additionally, an excellent piezoresistivity was achieved for cement-based sensors with CNTs-coated waste glass particles for the self-monitoring of stress magnitude and failure. The CNTs are uniformly distributed well in the cement matrix by attaching the surfaces of waste glass particles, thus the conductive passages are formed in cement-based sensors for structural health monitoring.
Dong, W, Li, W & Tao, Z 2021, 'A comprehensive review on performance of cementitious and geopolymeric concretes with recycled waste glass as powder, sand or cullet', Resources, Conservation and Recycling, vol. 172, pp. 105664-105664.
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Recycling waste glass for developing cementitious and geopolymeric concretes as sustainable construction materials have recently attracted increasing attentions for the construction industry. There are many previous studies on the effects of waste glass used as powder, sand or cullet based on the various sizes on the fresh and mechanical properties of concrete. However, there are few studies conducted on the durability performance of waste glass concrete. In this paper, in addition to a brief review on the fresh and mechanical properties and microstructure, the durability performance of concrete with waste glass is comprehensively reviewed under various environmental actions, including chemical attacks, chloride transport, high temperature, freeze-thaw cycles, carbonation, efflorescence, abrasion, alkali-silica reaction (ASR), and practical applications. It was found that the type, size and replacement ratio of waste glass significantly affect concrete durability. Compared to the glass cullet, the fine glass powder can usually improve the long-term durability, because the enhanced pozzolanic reactivity can reduce the ASR expansion due to the densified microstructure and reduced porosity. On the other hand, other factors such as mineral additives, mixing and curing methods also potentially affect the durability. Finally, some research perspectives and challenges of concrete with recycled waste glass are also presented and discussed. Considering the potential applications of waste glass concrete, this comprehensive review will provide an insight into an in-depth understanding of the production and performance for promising application.
Dong, W, Li, W, Shen, L, Zhang, S & Vessalas, K 2021, 'Integrated self-sensing and self-healing cementitious composite with microencapsulation of nano-carbon black and slaked lime', Materials Letters, vol. 282, pp. 128834-128834.
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In this paper, multifunctional cementitious composites with integrated self-sensing and self-healing properties are developed using microencapsulation of nano-carbon black (NCB) to enclose slaked lime (SL). The results show that the cracks healing efficiency is strongly improved with NCB enclosed SL. With SL, the self-sensing capacity of NCB-cementitious composite exhibits higher and more stable piezoresistivity before or after self-healing. The NCB enclosing SL particles not only achieve excellent piezoresistivity, but also preserve SL from initial hydration. Furthermore, the remained SL can be released for further reactions in the cracks. The results provide a promising integrated multifunctional self-sensing and self-healing cementitious composite for structural health monitoring application.
Dong, W, Li, W, Vessalas, K, He, X, Sun, Z & Sheng, D 2021, 'Piezoresistivity deterioration of smart graphene nanoplate/cement-based sensors subjected to sulphuric acid attack', Composites Communications, vol. 23, pp. 100563-100563.
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Smart cement-based sensors with self-sensing capacity have been explored for structural health monitoring (SHM) with the intrinsic piezoresistive performance. However, few studies had studied the piezoresistivity degradation of cement-based sensors after exposure to the aggressive environments, especially under sulphate acid attacks. In this study, graphene nanoplate (GNP)/cementitious composites were immersed in sulphuric acid solutions (concentrations of 0, 1%, 2%, and 3%) for 90 and 180 days. Then surface appearance, weight loss, mechanical properties, piezoresistivity and microstructure were investigated and compared before and after sulphuric acid immersion. The results show that after acid immersion, the surface deterioration and mass loss were increased, and the compressive strength was significantly decreased. As for the intact GNP/cementitious composite, the piezoresistivity exhibited excellent linearity and repeatability, demonstrating the great potential to act as intelligent cement-based sensors for SHM. After 90 and 180 days of acid immersion, the piezoresistivity was sensitive to the initial low load initially but then turned less sensitive to the later high load. The highly corroded GNP/cementitious composites exhibited porous microstructures associated with the low compressive strength. The fractional changes to resistivity (FCR) under the low load could be attributed to the compressed pores and voids filled with erosion products that would form conductive passages. In contrast, with the increase of applied load, the intact cement matrix became much denser, which in turn constrained the further development of conductive passages in the GNP/cementitious composites.
Dong, W, Li, W, Wang, K & Shah, SP 2021, 'Physicochemical and Piezoresistive properties of smart cementitious composites with graphene nanoplates and graphite plates', Construction and Building Materials, vol. 286, pp. 122943-122943.
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Graphene nanoplate (GNP) and graphite plate (GP) are promising functional nanofillers for smart self-sensing cementitious composites. The effects of GNP and GP on physicochemical, mechanical and piezoresistive properties of cementitious composite were investigated in this paper. The results show that cement hydration was accelerated with the increased amounts of GNP and GP because of nucleation effect. The electrical resistivity of GNP-cementitious composites was always lower than the counterpart with GP with the same concentration. On the other hand, percolation occurred for the GNP/cementitious composites at the dosages from 2 to 3% (by weight), while it never happens for the GP/cementitious composites. Moreover, the GNP/cementitious composites reached the maximum mechanical strength when the GNP content was 1.0%, while for the GP/cementitious composites, only minor strength improvement was obtained with a dosage of 0.5% GP. As for the piezoresistivity, the cementitious composites with GNP exhibited higher fractional changes of resistivity. Irreversible resistivity happened for 2–3% GP/cementitious composites subjected to cyclic compression, due to the poor and loose microstructures. The outcomes are expected to provide an insight into the application of GNP/cementitious and GP/cement composites as cement-based sensors for the future structural health monitoring.
Dong, W, Li, W, Zhu, X, Sheng, D & Shah, SP 2021, 'Multifunctional cementitious composites with integrated self-sensing and hydrophobic capacities toward smart structural health monitoring', Cement and Concrete Composites, vol. 118, pp. 103962-103962.
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In this study, multifunctional cementitious composites with integrated self-sensing and hydrophobicity capacities were developed and investigated using conductive graphene nanoplate (GNP) and silicone hydrophobic powder (SHP). The mechanical properties, permeability, water contact angle, microstructure and piezoresistivity were studied and compared under different contents of GNP and SHP. The highest compressive and flexural strengths with 1% SHP and 2% GNP reached 62.6 MPa and 8.9 MPa, respectively. The water absorption significantly was decreased with the content of SHP, but was minorly affected by GNP. The water contact angle firstly increased but then decreased with the dosages of GNP and SHP. SHP and GNP could reduce the microscale pores and enhance the density of microstructures. The piezoresistivity under compression firstly exhibited low gauge factor, but then gradually increased to a constant value under high-stress magnitude. Moreover, compared to the conventional cement-based sensors, this piezoresistive cementitious composites containing SHP and GNP as novel cement-based sensors are less sensitive to water content and humidity. The outcomes can provide an insight into promoting the application of multifunctional cement-based sensors toward structural health monitoring under various ambient conditions.
Donnelly, S & Tran, N 2021, 'Commandeering the mammalian Ago2 miRNA network: a newly discovered mechanism of helminth immunomodulation', Trends in Parasitology, vol. 37, no. 12, pp. 1031-1033.
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MicroRNAs (miRNAs) are a class of noncoding RNAs that contribute to a broad range of biological processes through post-transcriptional regulation of gene expression. Helminths exploit this system to target mammalian gene expression, to modulate the host immune response. Recent discoveries have shed new light on the mechanisms involved.
Doosti, M, Kumar, N, Delavar, M & Kashefi, E 2021, 'Client-server Identification Protocols with Quantum PUF', ACM Transactions on Quantum Computing, vol. 2, no. 3, pp. 1-40.
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Recently, major progress has been made towards the realisation of quantum internet to enable a broad range of classically intractable applications. These applications such as delegated quantum computation require running a secure identification protocol between a low-resource and a high-resource party to provide secure communication. In this work, we propose two identification protocols based on the emerging hardware-secure solutions, the quantum Physical Unclonable Functions (qPUFs). The first protocol allows a low-resource party to prove its identity to a high-resource party and in the second protocol, it is vice versa. Unlike existing identification protocols based on Quantum Read-out PUFs that rely on the security against a specific family of attacks, our protocols provide provable exponential security against any Quantum Polynomial-Time adversary with resource-efficient parties. We provide a comprehensive comparison between the two proposed protocols in terms of resources such as quantum memory and computing ability required in both parties as well as the communication overhead between them.
Dorji, U, Tenzin, U, Dorji, P, Pathak, N, Johir, MAH, Volpin, F, Dorji, C, Chernicharo, CAL, Tijing, L, Shon, H & Phuntsho, S 2021, 'Exploring shredded waste PET bottles as a biofilter media for improved on-site sanitation', Process Safety and Environmental Protection, vol. 148, pp. 370-381.
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This study explores an improved alternative on-site treatment for unsewered urban Bhutan. The system combines up-flow anaerobic sludge blanket for blackwater treatment and anaerobic biofilter for a mixture of up-flow anaerobic sludge blanket effluent and greywater. Shredded waste plastic bottles are used as novel biofilter media that provides a large surface area for attached growth while addressing waste plastic problems. A bench-scale up-flow anaerobic sludge blanket (operated at hydraulic retention time or HRT of 1–10 days) and anaerobic biofilter (HRT of 0.25–3 days) study were conducted for 188 days. At 2-d HRT, up-flow anaerobic sludge blanket removed 70–80 % of chemical oxygen demand (COD) while anaerobic biofilter achieved 90–98 % COD removal at eight-hour HRT. Combined up-flow anaerobic sludge blanket and anaerobic biofilter achieved final effluent with COD less than 50 mg/L and turbidity of less than 3 NTU that meets the discharge standard of Bhutan. The study shows that shredded waste plastic bottles can be an effective biofilter support medium for low-cost on-site treatment while helping address waste plastic problems.
Douglas, ANJ, Morgan, AL, Rogers, EIE, Irga, PJ & Torpy, FR 2021, 'Evaluating and comparing the green wall retrofit suitability across major Australian cities', Journal of Environmental Management, vol. 298, pp. 113417-113417.
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Urban densification continues to present a unique set of economic and environmental challenges. A growing shortage of green space and infrastructure is intrinsically linked with urban growth and development. With this comes the loss of ecosystem services such as urban heat island effects, reduction of air quality and biodiversity loss. Vertical greenery systems (VGS) offer an adaptive solution to space-constrained areas that are characteristic of dense urban areas, and can potentially improve the sustainability of cities. However, in order to promote VGS uptake, methods are required to enable systematic appraisal of whether existing walls can be retrofitted with VGS. Further, feasibility studies that quantify the potential for retrofit suitability of VGS across entire urban areas are lacking. This study established an evaluation tool for green wall constructability in urban areas and validated the assessment tool by determining the quantity of walls in five major Australian cities that could potentially have VGS incorporated into the existing infrastructure. Each wall was analysed using an exclusionary set of criteria that evaluated and ranked a wall based on its suitability to VGS implementation. Sydney and Brisbane recorded the greatest proportional length of walls suitable for VGS, with 33.74% and 34.12% respectively. Conversely, Perth's urban centre was the least feasible site in which to incorporate VGS, with over 97% of surveyed walls excluded, mainly due to the prevalence of <1 m high fence lines and glazed shopfronts. This study aimed to evaluate feasibility assessments of green wall retrofitability in highly urbanised areas with the intention of creating an analytical method that is accessible to all. This method, coupled with the promising number of feasible walls found in this study, emphasises the need for more government policy and incentives encouraging green wall uptake and could play a pivotal role in the expansion of green infrastructur...
Dragicevic, T & Vinnikov, D 2021, 'Guest Editorial Special Issue on Topology, Modeling, Control, and Reliability of Bidirectional DC/DC Converters in DC Microgrids', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 2, pp. 1188-1191.
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Du, G, Huang, N, Zhao, Y, Lei, G & Zhu, J 2021, 'Comprehensive Sensitivity Analysis and Multiphysics Optimization of the Rotor for a High Speed Permanent Magnet Machine', IEEE Transactions on Energy Conversion, vol. 36, no. 1, pp. 358-367.
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To improve the reliability of high speed permanent magnet machines (HSPMMs) under multiphysics constraints, including the electromagnetic properties, losses, rotor stress, rotor dynamics, and temperature, the rotor of an HSPMM is optimized to achieve low loss and temperature in this paper. To assess the impact of each rotor design parameter on multiphysics performance, a comprehensive sensitivity analysis of the rotor parameters on multiphysics performance is first implemented. On this basis, a multiphysics optimization process for HSPMM rotor is proposed to obtain the optimal design parameters. A comparison of the multiphysics performances of the initial and optimized design schemes shows that the optimized scheme can achieve much lower rotor loss and temperature. The optimization scheme is verified by comprehensive experimental tests on a 400 kW, 10 000 rpm HSPMM prototype.
Du, H, Gao, H & Jia, W 2021, 'Joint Frequency and DOA Estimation with Automatic Pairing Using the Rayleigh–Ritz Theorem', Computers, Materials & Continua, vol. 67, no. 3, pp. 3907-3919.
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This paper presents a novel scheme for joint frequency and direction of arrival (DOA) estimation, that pairs frequencies and DOAs automatically without additional computations. First, when the property of the Kronecker product is used in the received array signal of the multiple-delay output model, the frequency-angle steering vector can be reconstructed as the product of the frequency steering vector and the angle steering vector. The frequency of the incoming signal is then obtained by searching for the minimal eigenvalue among the smallest eigenvalues that depend on the frequency parameters but are irrelevant to the DOAs. Subsequently, the DOA related to the selected frequency is acquired through some operations on the minimal eigenvector according to the Rayleigh–Ritz theorem, which realizes the natural pairing of frequencies and DOAs. Furthermore, the proposed method can not only distinguish multiple sources, but also effectively deal with other arrays. The effectiveness and superiority of the proposed algorithm are further analyzed by simulations.
Du, H, Wang, S & Huo, H 2021, 'XFinder: Detecting Unknown Anomalies in Distributed Machine Learning Scenario', Frontiers in Computer Science, vol. 3, pp. 1-13.
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In recent years, the emergence of distributed machine learning has enabled deep learning models to ensure data security and privacy while training efficiently. Anomaly detection for network traffic in distributed machine learning scenarios is of great significance for network security. Although deep neural networks have made remarkable achievements in anomaly detection for network traffic, they mainly focus on closed sets, that is, assuming that all anomalies are known. However, in a real network environment, unknown abnormalities are fatal risks faced by the system because they have no labels and occur before the known anomalies. In this study, we design and implement XFinder, a dynamic unknown traffic anomaly detection framework in distributed machine learning. XFinder adopts an online mode to detect unknown anomalies in real-time. XFinder detects unknown anomalies by the unknowns detector, transfers the unknown anomalies to the prior knowledge base by the network updater, and adopts the online mode to report new anomalies in real-time. The experimental results show that the average accuracy of the unknown anomaly detection of our model is increased by 27% and the average F1-Score is improved by 20%. Compared with the offline mode, XFinder’s detection time is reduced by an average of approximately 33% on three datasets, and can better meet the network requirement.
Du, J, Dong, P, Sugumaran, V & Castro‐Lacouture, D 2021, 'Dynamic decision support framework for production scheduling using a combined genetic algorithm and multiagent model', Expert Systems, vol. 38, no. 1.
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AbstractDue to the dynamic nature, complexity, and interactivity of production scheduling in an actual business environment, suitable combined and hybrid methods are necessary. This paper takes prefabricated concrete components as an example and develops the dynamic decision support framework based on a genetic algorithm and multiagent system (MAS) to optimize and simulate the production scheduling. First, a multiobjective genetic algorithm is integrated into the MAS for preliminary optimization and a series of near‐optimal solutions are obtained. Subsequently, considering the resource constraints and uncertainties, the MAS is used to simulate complex real‐world production environments. Considering the different types of uncertainty factors, the paper proposes the corresponding dynamic scheduling method and uses MAS to generate the optimal production schedule. Finally, a practical prefabricated construction case is used to validate the proposed model. The results show that the model can effectively address the occurrence of uncertain events and can provide dynamic decision support for production scheduling.
Du, M, Liu, X, Wang, D, Yang, Q, Duan, A, Chen, H, Liu, Y, Wang, Q & Ni, B-J 2021, 'Understanding the fate and impact of capsaicin in anaerobic co-digestion of food waste and waste activated sludge', Water Research, vol. 188, pp. 116539-116539.
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Anaerobic co-digestion is an attractive option to treat food waste and waste activated sludge, which is increasingly applied in real-world situations. As an active component in Capsicum species being substantially present in food waste in many areas, capsaicin has been recently demonstrated to inhibit the anaerobic co-digestion. However, the interaction between capsaicin and anaerobic co-digestion are still poorly understood. This work therefore aims to deeply understand the fate and impact of capsaicin in the anaerobic co-digestion. Experiment results showed that capsaicin was completely degraded in anaerobic co-digestion by hydroxylation, O-demethylation, dehydrogenation and doubly oxidization, respectively. Although methane was proven to be produced from capsaicin degradation, the increase in capsaicin concentration resulted in decrease in methane yield from the anaerobic co-digestion. With an increase of capsaicin from 2 ± 0.7 to 68 ± 4 mg/g volatile solids (VS), the maximal methane yield decreased from 274.6 ± 9.7 to 188.9 ± 8.4 mL/g VS. The mechanic investigations demonstrated that the presence of capsaicin induced apoptosis, probably by either altering key kinases or decreasing the intracellular NAD+/NADH ratio, which led to significant inhibitions to hydrolysis, acidogenesis, and methanogenesis, especially acetotrophic methanogenesis. Illumina Miseq sequencing analysis exhibited that capsaicin promoted the populations of complex organic degradation microbes such as Escherichia-Shigella and Fonticella but decreased the numbers of anaerobes relevant to hydrolysis, acidogenesis, and methanogenesis such as Bacteroide and Methanobacterium.
Du, Y, Hsieh, M-H, Liu, T, Tao, D & Liu, N 2021, 'Quantum noise protects quantum classifiers against adversaries', Physical Review Research, vol. 3, no. 2, p. 023153.
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Noise in quantum information processing is often viewed as a disruptive and difficult-to-avoid feature, especially in near-term quantum technologies. However, noise has often played beneficial roles, from enhancing weak signals in stochastic resonance to protecting the privacy of data in differential privacy. It is then natural to ask: Can we harness the power of quantum noise that is beneficial to quantum computing? An important current direction for quantum computing is its application to machine learning, such as classification problems. One outstanding problem in machine learning for classification is its sensitivity to adversarial examples. These are small, undetectable perturbations from the original data where the perturbed data is completely misclassified in otherwise extremely accurate classifiers. They can also be considered as worst-case perturbations by unknown noise sources. We show that by taking advantage of depolarization noise in quantum circuits for classification, a robustness bound against adversaries can be derived where the robustness improves with increasing noise. This robustness property is intimately connected with an important security concept called differential privacy, which can be extended to quantum differential privacy. For the protection of quantum data, this quantum protocol can be used against the most general adversaries. Furthermore, we show how the robustness in the classical case can be sensitive to the details of the classification model, but in the quantum case the details of the classification model are absent, thus also providing a potential quantum advantage for classical data. This opens the opportunity to explore other ways in which quantum noise can be used in our favor, as well as identifying other ways quantum algorithms can be helpful in a way which is distinct from quantum speedups.
Du, Y, Hsieh, M-H, Liu, T, You, S & Tao, D 2021, 'Learnability of Quantum Neural Networks', PRX Quantum, vol. 2, no. 4, p. 040337.
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Quantum neural network (QNN), or equivalently, the parameterized quantum circuit (PQC) with a gradient-based classical optimizer, has been broadly applied to many experimental proposals for noisy intermediate-scale quantum (NISQ) devices. However, the learning capability of QNN remains largely unknown due to the nonconvex optimization landscape, the measurement error, and the unavoidable gate noise introduced by NISQ machines. In this study, we theoretically explore the learnability of QNN in the view of the trainability and generalization. Particularly, we derive the convergence performance of QNN under the NISQ setting, and identify classes of computationally hard concepts that can be efficiently learned by QNN. Our results demonstrate that large gate noise, few quantum measurements, and deep circuit depth will lead to poor convergence rates of QNN towards the empirical risk minimization. Moreover, we prove that any concept class, which is efficiently learnable by a quantum statistical query (QSQ) learning model, can also be efficiently learned by PQCs. Since the QSQ learning model can tackle certain problems such as parity learning with a runtime speedup, our result suggests that PQCs established on NISQ devices will retain the quantum advantage measured by generalization ability. Our work provides theoretical guidance for developing advanced QNNs and opens up avenues for exploring quantum advantages beyond hybrid quantum-classical learning protocols in the NISQ era.
Duan, L, Gao, T, Ni, W & Wang, W 2021, 'A hybrid intelligent service recommendation by latent semantics and explicit ratings', International Journal of Intelligent Systems, vol. 36, no. 12, pp. 7867-7894.
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User rating of a service is the explicit behavior of users expressing their preference for the service. Most exciting recommendation methods focus on predicting user-service ratings according to users' historical rating behaviors. However, the behavior of users invoking services is implicit feedback. By analyzing the services called by users, mining their potential semantic representations can also help model users' hidden interests. To this end, how to integrate the implicit feedback and explicit rating of users to provide users with better recommendation experience is a problem to be addressed for service recommendation. In this paper, we propose a novel latent semantic integrated explicit rating (LSIER) scheme to recommend services to users. The LSIER scheme is designed by integrating the probabilistic matrix factorization (PMF) model and the probabilistic latent semantic index (PLSI) model. consists of the two stages: (1) the PMF model is used to generate a user feature matrix and a service feature matrix, and the two feature matrices are updated to complete the missing service score records of the users, and (2) the PLSI model is used to train users access records, where an expectation maximization algorithm is applied to derive the model parameters to realize unsupervised soft clustering of services. When the user gives explicit or implicit feedback to the service, the LSIER scheme can identify the current interest probability distribution of the user according to the category to which the called service belongs, and provide the user with a list of service recommendations with scores. The performance of the proposed LSIER scheme is evaluated using the Netflix data set and the Movielens data set. Experiments show that the scheme can achieve better recommendation accuracy and recall rate than existing methods.
Duong, HC, Cao, HT, Hoang, NB & Nghiem, LD 2021, 'Reverse osmosis treatment of condensate from ammonium nitrate production: Insights into membrane performance', Journal of Environmental Chemical Engineering, vol. 9, no. 6, pp. 106457-106457.
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Ammonium nitrate is an important fertilizer and industrial explosive. The production of ammonium nitrate entails the generation of a large volume of condensate laden with nitrogen that must be treated before environment discharge. Results in this study show that through appropriate membrane selection, over 90% rejection of ammonium nitrate can be achieved by reverse osmosis (RO) filtration. Using RO (which is highly compact and efficient) to enrich ammonium nitrate in the condensate would significantly reduce the size of the evaporation separator for ammonia recovery. The results also highlight the importance of membrane selection for this application. Results reported here suggest that a low pressure RO membrane (e.g. ESPA2) is more suitable for the dilute condensate while a high pressure RO membrane (e.g. SW30) is recommended for the concentrated condensate to ensure adequate ammonia and nitrate rejection. Ammonia and nitrate rejections were dependent on key operating parameters including applied pressure (or water flux), temperature, feed solution pH, and initial ammonium nitrate concentration in the condensate. The impact of operating conditions on ammonia and nitrate rejections was more profound for low pressure (thus high flux) than high pressure RO membrane. An extended filtration experiment shows no evidence of membrane fouling. Results from this study are useful to the integration of a compact RO system to ammonium nitrate manufacturing for pollution prevention and improving product yield.
Duong, HC, Tran, LTT, Vu, MT, Nguyen, D, Tran, NTV & Nghiem, LD 2021, 'A new perspective on small-scale treatment systems for arsenic affected groundwater', Environmental Technology & Innovation, vol. 23, pp. 101780-101780.
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This work provides a new perspective on small-scale treatment systems to remove arsenic from groundwater for potable applications in low-income communities. Data corroborated from the literature highlight a significant challenge to providing potable water in a financially sustainable manner in arsenic affected areas. Analysis of the literature also reveals notable deficiency in the current practice, especially the overfocus on household-scale treatment systems for arsenic affected groundwater without adequate maintenance, monitoring, and a systematic cost–benefit analysis. Accurate and reliable analysis of arsenic in water samples at relevant health guideline values is costly and technologically demanding for low-income communities. Significant discrepancy in the performance of household-scale treatment systems can be attributed to the lack of maintenance and systematic monitoring. Moreover, data on the maintenance and compliance monitoring cost of small-scale arsenic treatment systems are very limited in the literature, and the available data show an exponential increase in maintenance cost per treatment capacity unit as the treatment size decreases. On the other hand, significant opportunities exist to increase performance reliability and reduce water treatment cost by taking advantage of the current digital transformation of the water sector. The analysis in this work suggests the need to reframe current practice towards commune-scale treatment systems as an interim step before centralised water supply is available.
Durinck, K, Zimmerman, M, Weichert-Leahey, N, Dewyn, J, Van Loocke, W, Nunes, C, Beckers, A, Decaesteker, B, Volders, P-J, Van Neste, C, Cheung, B, Carter, D, Look, TA, Marshall, G, De Preter, K, Durbin, A & Speleman, F 2021, 'Abstract 2481: Time-resolved transcriptome analysis of murine TH-MYCN driven neuroblastoma identifies MEIS2 as early initiating factor and novel core gene regulatory circuitry constituent', Cancer Research, vol. 81, no. 13_Supplement, pp. 2481-2481.
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Abstract Introduction: Neuroblastoma (NB) is a pediatric malignancy arising from peripheral neuronal sympathoblasts and exhibiting remarkable clinical and genetic heterogeneity. Patients older than 18 months have a poor prognosis with tumors presenting with highly recurrent segmental copy number alterations and MYCN amplification in half of these high-risk cases. The mechanism by which MYCN contributes to the development of neuroblastoma is unresolved and direct targeting of this key oncogene is not currently possible. Experimental Procedures: Our discovery efforts focused on identifying cooperating interactors and vulnerabilities in the MYCN regulatory network. MYCN-driven NBs can be modeled in mice with morphologic and genomic features that recapitulate human MYCN amplified NBs. Thus, this model serves as a valid tool for cross-species genomic analysis. Using this model, we performed a time-resolved analysis of the dynamic transcriptional changes of protein coding genes during murine TH-MYCN driven neuroblastoma development, focusing on timepoints representing tumor initiation and early tumor growth. We triangulated expression changes of key genes with publicly available exome-wide CRISPR-cas9 knockout analyses on a panel of human neuroblastoma cell lines and patient survival data. This unique data resource uncovered the relevance of MEIS2 as putative early cooperating initiating factor for neuroblastoma. Analysis of the genome-wide binding profile of MEIS2 in MYCN-amplified NB cell lines showed a striking overlap with enhancer-driven gene expression in regions of open chromatin, providing evidence that MEIS2 is a novel member of the adrenergic neuroblastoma core-regulatory circuitry. CRISPR-Cas9 mediated deletion of MEIS2 in animal models suppresses establishment of neuroblastoma tumors, indicating its putative requirement for tumor initiation. MEIS2, as a member of the C...
Dutta, S & Gandomi, AH 2021, 'Erratum for “Bilevel Data-Driven Modeling Framework for High-Dimensional Structural Optimization under Uncertainty Problems” by Subhrajit Dutta and Amir H. Gandomi', Journal of Structural Engineering, vol. 147, no. 7.
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The authors have identified two typographical errors in Fig. 1 and Table 4 of the original paper. In Fig. 1, an error exists in the height and base width of the 244-member steel transmission tower, while in Table 4 an error exists in the thicknesses of the optimum angle sections. It must be noted that these typographical errors are inconsequential to the published results and do not alter the conclusions of the published paper. The corrected Fig. 1 and Table 4 are provided herein. (Table Presented).
Dzaklo, CK, Rujikiatkamjorn, C, Indraratna, B & Kelly, R 2021, 'Cyclic behaviour of compacted black soil-coal wash matrix', Engineering Geology, vol. 294, pp. 106385-106385.
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Eager, D, Chapman, C, Qi, Y, Ishac, K & Hossain, MI 2021, 'Additional Criteria for Playground Impact Attenuating Sand', Applied Sciences, vol. 11, no. 19, pp. 8805-8805.
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Falls within children’s playgrounds result in long bone and serious injuries. To lower the likelihood and severity of injury, impact attenuating surfaces (IAS) are installed within the impact area (fall zone). There are three primary IAS materials used, namely: granulated rubber products, wood fibre products, and sand. There is a deficiency with existing IAS test methods in that they do not take account of sand degradation over time. When children use the playground, sand degradation can occur when sand produces fines and smaller particles with low sphericity and angular which fill the voids between the sand particles. These fines and smaller particles tend to bind the sand and lower its impact attenuating performance. This paper proposes an additional IAS test to eliminate sands that degrade above an established threshold rate after installation due to normal usage. IAS degradation properties of fifteen IAS sands were tested including sand particle shape, sand particle distribution, percentage fines and sand particle degradation. This accelerated ageing test method is applicable only to sands and not rubber or wood fibre IAS products. The best IAS sands were sourced from quarries located on rivers that had eroded volcanic outcrops. These sands were shown to degrade the least and had little to no fines, and their particle shape was rounded to well-rounded. The most reliable source for good quality IAS sands on these rivers was on specific bends. The sand mined at these locations consistently had a tight particle size distribution.
Eager, D, Halkon, B, Zhou, S, Walker, P, Covey, K & Braiden, S 2021, 'Greyhound Racing Track Lure Systems—Acoustical Measurements within and Adjacent to the Starting Boxes', Technologies, vol. 9, no. 4, pp. 74-74.
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This study investigates and compares the acoustic signatures of a traditional wire-cable-pulled lure system and two alternative battery-operated lure systems jointly developed by Covey Associates Pty. Ltd. and Steriline Pty. Ltd. to eliminate the hazardous steel-wire cable and make the sport of greyhound racing safer for greyhounds, participants and spectators. The acoustical measurements of these three lure systems were conducted at the Murray Bridge greyhound racing track. The lure sounds were measured by the high-frequency Brüel & Kjær (B&K) Type 4191 microphones for the 395 m and 455 m starts at two positions: within the starting box and on the track adjacent to the starting boxes. The measurements capture the sounds that the greyhounds hear before and after the opening of the starting box gate. The frequency-domain analysis and sound quality analysis were conducted to compare the lure sounds. It was found when the battery-lure was installed with all nylon rollers, it presented less sound energy and lower frequency than the traditional wire-cable-pulled lure. When two of the nylon rollers were replaced with steel rollers, the battery-operated lure emitted a louder and higher frequency sound than the traditional wire-cable-pulled lure. The different acoustic characteristics of these lure systems suggest future research is warranted on the reaction of greyhounds to different lure sounds, particularly their excitement level within the starting box as the lure approaches. This initial research also suggests some greyhounds may not clearly hear the battery-operated lure with all nylon rollers approaching the starting boxes and the timing of these greyhounds to jump may be delayed, particularly during high wind conditions.
Eager, D, Hossain, I, Ishac, K & Robins, S 2021, 'Analysis of Racing Greyhound Path Following Dynamics Using a Tracking System', Animals, vol. 11, no. 9, pp. 2687-2687.
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The University of Technology Sydney (UTS) has been working closely with the Australasian greyhound industry for more than 5 years to reduce greyhound race-related injuries. During this period, UTS has developed and deployed several different techniques including inertial measurement units, drones, high-frame-rate cameras, track geometric surveys, paw print analysis, track soil spring-force analysis, track maintenance data, race injury data, race computer simulation and modelling to assist in this task. During the period where the UTS recommendations have been adopted, the injury rate has dropped significantly. This has been achieved by animal welfare interventions that lower racing congestion, and lower transient forces and jerk rates the greyhounds experience during a race. This study investigated the use of a greyhound location tracing system where small and lightweight signal emitting devices were placed inside a pocket in the jackets of racing greyhounds. The system deployed an enhanced version of a player tracking system currently used to track the motion of human athletes. Greyhounds gallop at speeds of almost 20 m/s and are known to change their heading direction to exceed a yaw rate of 0.4 rad/s. The high magnitudes of velocity, acceleration and jerk posed significant technical challenges, as the greyhounds pushed the human tracking system beyond its original design limits. Clean race data gathered over a six-month period were analysed and presented for a typical 2-turn greyhound racing track. The data confirmed that on average, greyhounds ran along a path that resulted in the least energy wastage, which includes smooth non-linear paths that resemble easement curves at the transition between the straights to the semi-circular bends. This study also verified that the maximum jerk levels greyhounds experienced while racing were lower than the jerk levels that had been predicted with simulations and modelling for the track path. Furthermore...
Easttom, C, Bianchi, L, Valeriani, D, Nam, CS, Hossaini, A, Zapala, D, Roman-Gonzalez, A, Singh, AK, Antonietti, A, Sahonero-Alvarez, G & Balachandran, P 2021, 'A Functional Model for Unifying Brain Computer Interface Terminology', IEEE Open Journal of Engineering in Medicine and Biology, vol. 2, no. 99, pp. 91-96.
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Easttom, C, Bianchi, L, Valeriani, D, Nam, CS, Hossaini, A, Zapała, D, Roman-Gonzalez, A, Singh, AK, Antonietti, A, Sahonero-Alvarez, G & Balachandran, P 2021, 'A functional BCI model by the P2731 working group: control interface', Brain-Computer Interfaces, vol. 8, no. 4, pp. 154-160.
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In order to facilitate communication and collaboration between researchers, Brain–computer interfaces (BCI) require a generally applicable functional model as well as a common vocabulary. The IEEE P2731 working group is in the process of developing such a functional model and a lexicon of BCI terminology. Such a functional model has multiple aspects including the control interface, physiology, transducers, etc. This current paper focuses on the control interface aspects of that model. Having a generally applicable control interface model will facilitate interdisciplinar y research and communication. The control interface is a critical part of the functional model and is described in this current paper. The control interface presented intentionally is intentionally kept general in order to be widely applicable. Some details are specific to a particular application and are thus left to those applications. It does contain the encoder (which also contains a decoder), with a feedback submodule.
Ebrahimi, A, Sivakumar, M, McLauchlan, C, Ansari, A & Vishwanathan, AS 2021, 'A critical review of the symbiotic relationship between constructed wetland and microbial fuel cell for enhancing pollutant removal and energy generation', Journal of Environmental Chemical Engineering, vol. 9, no. 1, pp. 105011-105011.
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Echaroj, S, Pannucharoenwong, N, Ong, HC & Rattanadecho, P 2021, 'Production of bio-fuel from alcohothermal liquefaction of rice straw over sulfated-graphene oxide', Energy Reports, vol. 7, pp. 744-752.
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Ejegwa, PA, Wen, S, Feng, Y, Zhang, W & Chen, J 2021, 'Some new Pythagorean fuzzy correlation techniques via statistical viewpoint with applications to decision-making problems', Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9873-9886.
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Pythagorean fuzzy set is a reliable technique for soft computing because of its ability to curb indeterminate data when compare to intuitionistic fuzzy set. Among the several measuring tools in Pythagorean fuzzy environment, correlation coefficient is very vital since it has the capacity to measure interdependency and interrelationship between any two arbitrary Pythagorean fuzzy sets (PFSs). In Pythagorean fuzzy correlation coefficient, some techniques of calculating correlation coefficient of PFSs (CCPFSs) via statistical perspective have been proposed, however, with some limitations namely; (i) failure to incorporate all parameters of PFSs which lead to information loss, (ii) imprecise results, and (iii) less performance indexes. Sequel, this paper introduces some new statistical techniques of computing CCPFSs by using Pythagorean fuzzy variance and covariance which resolve the limitations with better performance indexes. The new techniques incorporate the three parameters of PFSs and defined within the range [-1, 1] to show the power of correlation between the PFSs and to indicate whether the PFSs under consideration are negatively or positively related. The validity of the new statistical techniques of computing CCPFSs is tested by considering some numerical examples, wherein the new techniques show superior performance indexes in contrast to the similar existing ones. To demonstrate the applicability of the new statistical techniques of computing CCPFSs, some multi-criteria decision-making problems (MCDM) involving medical diagnosis and pattern recognition problems are determined via the new techniques.
Ejeian, M, Grant, A, Shon, HK & Razmjou, A 2021, 'Is lithium brine water?', Desalination, vol. 518, pp. 115169-115169.
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With the development of light and rechargeable batteries for electric vehicles, global demand for lithium has increased considerably in recent years. This has drawn more attention to how lithium is produced, especially on primary extraction operations such as those at the Salar de Atacama in Northern Chile. There are concerns that brine extraction at the Atacama could irreversibly damage the basin's complex hydrological system. However, differing opinions over the definition of water have frustrated basic action measures for minimizing impacts of operations like these. Some lithium industry stakeholders have historically described brine as a mineral, while others emphasize that brine is also a type of water in a complex network of different water resources. In this communication, we show that brines are undeniably a type of water. We support this position by investigating brine's water molecular structure using molecular dynamics simulations and comparing Gibbs formation energy of the brine using thermodynamic principles. Molecular dynamics show that the structure of water molecules in brine is similar to the structure of molecules in pure water at a pressure of 1.2 atm. The analysis of Gibbs formation energy shows that more than 99% of the brine's formation energy is directly from water, not dissolved minerals.
Ekanayake, D, Loganathan, P, Johir, MAH, Kandasamy, J & Vigneswaran, S 2021, 'Enhanced Removal of Nutrients, Heavy Metals, and PAH from Synthetic Stormwater by Incorporating Different Adsorbents into a Filter Media', Water, Air, & Soil Pollution, vol. 232, no. 3.
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Stormwater harvesting and reuse is an attractive option to lower the demand placed on other sources of water supply. However, it contains a wide range of pollutants that need to be removed before it can be reused or even discharged to the waterways and receiving waters. An experimental protocol to estimate the efficiency of a soil-based-filter medium for the treatment of stormwater pollutants from 1 to 3 years rainfall experienced in the field was developed using a laboratory column-set-up over short-term duration. The filter removed substantial amounts of PO -P and NH -N for up to 8 h at a flow velocity of 100 mm/h which is a 1-year time-equivalent of rainfall at a locality in Sydney, Australia. An addition of 10% zeolite to the soil-based filter extended the column saturation period to 24 h. The breakthrough data for PO -P and NH -N were satisfactorily described by the Thomas model. The majority of the nine heavy metals tested were removed by more than 50% for up to 4 h in the soil-based filter. This level of removal increased to 16 h when 10% zeolite was added to the filter. The column with the soil-based filter + 10% zeolite had higher affinity for Pb, Cu, Zn, and As than Ni, with Pb having the highest percentage removal. Soil-based filter + 10% zeolite removed considerable amounts of 3 polycyclic aromatic hydrocarbons (PAHs) (30–50%), while soil-based filter + 10% zeolite + 0.3% granular activated carbon removed 65 to > 99% of the PAHs at 24-h operation. 4 4 4 4
Ekanayake, UGM, Barclay, M, Seo, DH, Park, MJ, MacLeod, J, O'Mullane, AP, Motta, N, Shon, HK & Ostrikov, KK 2021, 'Utilization of plasma in water desalination and purification', Desalination, vol. 500, pp. 114903-114903.
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© 2020 Elsevier B.V. Supplying fresh drinking water to the world population is a persistent global challenge. Therefore, effective and efficient desalination processes are becoming increasingly important. Oceans account for most of the water on Earth and the presence of salts and other contaminants in seawater prevents them from being used as a source of drinking water. Owing to this challenge, non-thermal plasma can be utilized in order to enhance the existing desalination processes via membrane or material modification while it can also be used as a direct tool for seawater desalination leading to significant process improvements. A direct non-thermal plasma-based desalination process is a new emerging area of research and recent efforts have shown its promise with many unexplored mechanisms, providing benefits that conventional desalination processes cannot offer. Here we critically review the use of plasma technologies in water desalination including membrane modification by plasma for pressure, thermal, photothermal processes and direct plasma-based desalination process. We also address the use of plasmas in water purification. Finally, the existing challenges and future prospects are outlined.
Elazezy, M, Schwentesius, S, Stegat, L, Wikman, H, Werner, S, Mansour, WY, Failla, AV, Peine, S, Müller, V, Thiery, JP, Ebrahimi Warkiani, M, Pantel, K & Joosse, SA 2021, 'Emerging Insights into Keratin 16 Expression during Metastatic Progression of Breast Cancer', Cancers, vol. 13, no. 15, pp. 3869-3869.
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Keratins are the main identification markers of circulating tumor cells (CTCs); however, whether their deregulation is associated with the metastatic process is largely unknown. Previously we have shown by in silico analysis that keratin 16 (KRT16) mRNA upregulation might be associated with more aggressive cancer. Therefore, in this study, we investigated the biological role and the clinical relevance of K16 in metastatic breast cancer. By performing RT-qPCR, western blot, and immunocytochemistry, we investigated the expression patterns of K16 in metastatic breast cancer cell lines and evaluated the clinical relevance of K16 expression in CTCs of 20 metastatic breast cancer patients. High K16 protein expression was associated with an intermediate mesenchymal phenotype. Functional studies showed that K16 has a regulatory effect on EMT and overexpression of K16 significantly enhanced cell motility (p < 0.001). In metastatic breast cancer patients, 64.7% of the detected CTCs expressed K16, which was associated with shorter relapse-free survival (p = 0.0042). Our findings imply that K16 is a metastasis-associated protein that promotes EMT and acts as a positive regulator of cellular motility. Furthermore, determining K16 status in CTCs provides prognostic information that helps to identify patients whose tumors are more prone to metastasize.
Elgharabawy, A, Prasad, M & Lin, C-T 2021, 'Subgroup Preference Neural Network', Sensors, vol. 21, no. 18, pp. 6104-6104.
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Subgroup label ranking aims to rank groups of labels using a single ranking model, is a new problem faced in preference learning. This paper introduces the Subgroup Preference Neural Network (SGPNN) that combines multiple networks have different activation function, learning rate, and output layer into one artificial neural network (ANN) to discover the hidden relation between the subgroups’ multi-labels. The SGPNN is a feedforward (FF), partially connected network that has a single middle layer and uses stairstep (SS) multi-valued activation function to enhance the prediction’s probability and accelerate the ranking convergence. The novel structure of the proposed SGPNN consists of a multi-activation function neuron (MAFN) in the middle layer to rank each subgroup independently. The SGPNN uses gradient ascent to maximize the Spearman ranking correlation between the groups of labels. Each label is represented by an output neuron that has a single SS function. The proposed SGPNN using conjoint dataset outperforms the other label ranking methods which uses each dataset individually. The proposed SGPNN achieves an average accuracy of 91.4% using the conjoint dataset compared to supervised clustering, decision tree, multilayer perceptron label ranking and label ranking forests that achieve an average accuracy of 60%, 84.8%, 69.2% and 73%, respectively, using the individual dataset.
El-Haddad, BA, Youssef, AM, Pourghasemi, HR, Pradhan, B, El-Shater, A-H & El-Khashab, MH 2021, 'Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt', Natural Hazards, vol. 105, no. 1, pp. 83-114.
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Floods represent catastrophic environmental hazards that have a significant impact on the environment and human life and their activities. Environmental and water management in many countries require modeling of flood susceptibility to help in reducing the damages and impact of floods. The objective of the current work is to employ four data mining/machine learning models to generate flood susceptibility maps, namely boosted regression tree (BRT), functional data analysis (FDA), general linear model (GLM), and multivariate discriminant analysis (MDA). This study was done in Wadi Qena Basin in Egypt. Flood inundated locations were determined and extracted from the interpretation of different datasets, including high-resolution satellite images (sentinel-2 and Astro digital) (after flood events), historical records, and intensive field works. In total, 342 flood inundated locations were mapped using ArcGIS 10.5, which separated into two groups; training (has 239 flood locations represents 70%) and validating (has 103 flood locations represents 30%), respectively. Nine themes of flood-influencing factors were prepared, including slope-angle, slope length, altitude, distance from main wadis, landuse/landcover, lithological units, curvature, slope-aspect, and topographic wetness index. The relationships between the flood-influencing factors and the flood inventory map were evaluated using the mentioned models (BRT, FDA, GLM, and MDA). The results were compared with flood inundating locations (validating flood sites), which were not used in constructing the models. The accuracy of the models was calculated through the success (training data) and prediction (validation data) rate curves according to the receiver operating characteristics (ROC) and the area under the curve (AUC). The results showed that the AUC for success and prediction rates are 0.783, 0.958, 0.816, 0.821 and 0.812, 0.856, 0.862, 0.769 for BRT, FDA, GLM, and MDA models, respectively. Subseque...
El-Magd, SAA, Pradhan, B & Alamri, A 2021, 'Machine learning algorithm for flash flood prediction mapping in Wadi El-Laqeita and surroundings, Central Eastern Desert, Egypt', Arabian Journal of Geosciences, vol. 14, no. 4.
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Elman, SJ, Chapman, A & Flammia, ST 2021, 'Free Fermions Behind the Disguise', Communications in Mathematical Physics, vol. 388, no. 2, pp. 969-1003.
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Emami, Y, Wei, B, Li, K, Ni, W & Tovar, E 2021, 'Joint Communication Scheduling and Velocity Control in Multi-UAV-Assisted Sensor Networks: A Deep Reinforcement Learning Approach', IEEE Transactions on Vehicular Technology, vol. 70, no. 10, pp. 10986-10998.
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Recently, Unmanned Aerial Vehicle (UAV) swarm has been increasingly studied to collect data from ground sensors in remote and hostile areas. A key challenge is the joint design of the velocities and data collection schedules of the UAVs, as inadequate velocities and schedules would lead to failed transmissions and buffer overflows of sensors and, in turn, significant packet losses. In this paper, we optimize jointly the velocity controls and data collection schedules of multiple UAVs to minimize data losses, adapting to the battery levels, queue lengths and channel conditions of the ground sensors, and the trajectories of the UAVs. In the absence of the up-to-date knowledge of the ground sensors' states, a Multi-UAV Deep Reinforcement Learning based Scheduling Algorithm (MADRL-SA) is proposed to allow the UAVs to asymptotically minimize the data loss of the system under the outdated knowledge of the network states at individual UAVs. Numerical results demonstrate that the proposed MADRL-SA reduces the packet loss by up to 54% and 46% in the considered simulation setting, as compared to an existing DRL solution with single-UAV and non-learning greedy heuristic, respectively.
Enfrin, M, Wang, J, Merenda, A, Dumée, LF & Lee, J 2021, 'Mitigation of membrane fouling by nano/microplastics via surface chemistry control', Journal of Membrane Science, vol. 633, pp. 119379-119379.
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Nano/microplastic materials fouling across filtration membranes can impact the performance of filtration systems, which constitutes a critical challenge for water facilities operation. In this study, plasma surface modifications aiming at reducing nano/microplastic materials adsorption on ultrafiltration membranes were investigated. Hydrophilic acrylic acid and cyclopropylamine plasma coatings caused a water flux decline of less than 8% after 6 h of crossflow filtration. Both hydrophilic coatings reduced the percentage of nano/microplastics adsorbed on the membranes by more than 60%. On the contrary, the hydrophobic hexamethyldisiloxane layer had no impact on the cumulative percentage of adsorbed nano/microplastics compared to that of the pristine poly(sulfone) membranes, which culminated at 40%, resulting in a water flux decline of 40% upon filtration for both membranes. The extended Derjaguin–Landau–Verwey–Overbeek (XDLVO) theory was then applied to the system particle-membrane, which identified polar forces as the predominant intermolecular interactions contributing to membrane fouling. Tuning the hydrophilicity of the membranes was, therefore, a more efficient strategy to reduce nano/microplastic materials adsorption during filtration than tailoring the surface charge of the membranes, showing potential for complex water matrices remediation.
Es, HA, Cox, TR, Sarafraz-Yazdi, E, Thiery, JP & Warkiani, ME 2021, 'Pirfenidone Reduces Epithelial–Mesenchymal Transition and Spheroid Formation in Breast Carcinoma through Targeting Cancer-Associated Fibroblasts (CAFs)', Cancers, vol. 13, no. 20, pp. 5118-5118.
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The aim of this study was to assess the effects of pirfenidone (PFD) on promoting epithelial–mesenchymal-transition (EMT) and stemness features in breast carcinoma cells through targeting cancer-associated-fibroblasts (CAFs). Using The Cancer Genome Atlas (TCGA) database, we analyzed the association between stromal index, EMT, and stemness-related genes across 1084 breast cancer patients, identifying positive correlation between YAP1, EMT, and stemness genes in samples with a high-stromal index. We monitored carcinoma cell invasion and spheroid formation co-cultured with CAFs in a 3D microfluidic device, followed by exposing carcinoma cells, spheroids, and CAFs with PFD. We depicted a positive association between the high-stromal index and the expression of EMT and stemness genes. High YAP1 expression in samples correlated with more advanced EMT status and stromal index. Additionally, we found that CAFs promoted spheroid formation and induced the expression of YAP1, VIM, and CD44 in spheroids. Treatment with PFD reduced carcinoma cell migration and decreased the expression of these genes at the protein level. The cytokine profiling showed significant depletion of various EMT- and stemness-regulated cytokines, particularly IL8, CCL17, and TNF-beta. These data highlight the potential application of PFD on inhibiting EMT and stemness in carcinoma cells through the targeting of critical cytokines.
Es, HA, Mahdizadeh, H, Asl, AAH & Totonchi, M 2021, 'Genomic alterations and possible druggable mutations in carcinoma of unknown primary (CUP)', Scientific Reports, vol. 11, no. 1.
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AbstractCarcinoma of Unknown Primary (CUP) is a heterogeneous and metastatic disease where the primary site of origin is undetectable. Currently, chemotherapy is the only state-of-art treatment option for CUP patients. The molecular profiling of the tumour, particularly mutation detection, offers a new treatment approach for CUP in a personalized fashion using targeted agents. We analyzed the mutation and copy number alterations profile of 1709 CUP samples deposited in the AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE) cohort and explored potentially druggable mutations. We identified 52 significant mutated genes (SMGs) among CUP samples, in which 13 (25%) of SMGs were potentially targetable with either drugs are approved for the know primary tumour or undergoing clinical trials. The most variants detected were TP53 (43%), KRAS (19.90%), KMT2D (12.60%), and CDKN2A (10.30%). Additionally, using pan-cancer analysis, we found similar variants of TERT promoter in CUP and NSCLC samples, suggesting that these mutations may serve as a diagnostic marker for identifying the primary tumour in CUP. Taken together, the mutation profiling analysis of the CUP tumours may open a new way of identifying druggable targets and consequently administrating appropriate treatment in a personalized manner.
Eslahi, H, Hamilton, TJ & Khandelwal, S 2021, 'Small signal model and analog performance analysis of negative capacitance FETs', Solid-State Electronics, vol. 186, pp. 108161-108161.
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Esmaili, N, Buchlak, QD, Piccardi, M, Kruger, B & Girosi, F 2021, 'Multichannel mixture models for time-series analysis and classification of engagement with multiple health services: An application to psychology and physiotherapy utilization patterns after traffic accidents', Artificial Intelligence in Medicine, vol. 111, pp. 101997-101997.
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Background
Motor vehicle accidents (MVA) represent a significant burden on health systems globally. Tens of thousands of people are injured in Australia every year and may experience significant disability. Associated economic costs are substantial. There is little literature on the health service utilization patterns of MVA patients. To fill this gap, this study has been designed to investigate temporal patterns of psychology and physiotherapy service utilization following transport-related injuries.
Method
De-identified compensation data was provided by the Australian Transport Accident Commission. Utilization of physiotherapy and psychology services was analysed. The datasets contained 788 psychology and 3115 physiotherapy claimants and 22,522 and 118,453 episodes of service utilization, respectively. 582 claimants used both services, and their data were preprocessed to generate multidimensional time series. Time series clustering was applied using a mixture of hidden Markov models to identify the main distinct patterns of service utilization. Combinations of hidden states and clusters were evaluated and optimized using the Bayesian information criterion and interpretability. Cluster membership was further investigated using static covariates and multinomial logistic regression, and classified using high-performing classifiers (extreme gradient boosting machine, random forest and support vector machine) with 5-fold cross-validation.
Results
Four clusters of claimants were obtained from the clustering of the time series of service utilization. Service volumes and costs increased progressively from clusters 1 to 4. Membership of cluster 1 was positively associated with nerve damage and negatively associated with severe ABI and spinal injuries. Cluster 3 was positively associated with severe ABI, brain/head injury and psychiatric injury. Cluster 4 was positively associated with internal injuries. The classifiers were capable of cla...
Espinoza-Audelo, LF, León-Castro, E, Mellado-Cid, C, Merigó, JM & Blanco-Mesa, F 2021, 'Uncertain induced prioritized aggregation operators in the analysis of the imports and exports', Journal of Multiple-Valued Logic and Soft Computing, vol. 36, no. 6, pp. 543-568.
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Interval numbers are widely used in many fields to provide information about different scenarios. This paper presents several new uncertain average formulations using the ordered weighted average, prioritized, probabilistic and induced operators. First, the work introduces the uncertain prioritized induced probabilistic ordered weighted average (UPIPOWA) operator that its main applicability is in complex group decision making problems. Also, a wide range of special cases and extensions using quasi-arithmetic means are presented, such is the case of the quasi-arithmetic UPIPOWA (QUPIPOWA) operator. The study analyzes the applicability of this new approach in economic variables, specifically are imports and exports. Particularly, the paper focuses on measuring the imports and exports for Latin America for 2017.
Esselle, KP 2021, 'Distinguished Lecturer Program News And New Appointments [Distinguished Lecturers]', IEEE Antennas and Propagation Magazine, vol. 63, no. 3, pp. 138-141.
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Eyni, H, Ghorbani, S, Nazari, H, Hajialyani, M, Razavi Bazaz, S, Mohaqiq, M, Ebrahimi Warkiani, M & Sutherland, DS 2021, 'Advanced bioengineering of male germ stem cells to preserve fertility', Journal of Tissue Engineering, vol. 12, pp. 204173142110605-204173142110605.
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In modern life, several factors such as genetics, exposure to toxins, and aging have resulted in significant levels of male infertility, estimated to be approximately 18% worldwide. In response, substantial progress has been made to improve in vitro fertilization treatments (e.g. microsurgical testicular sperm extraction (m-TESE), intra-cytoplasmic sperm injection (ICSI), and round spermatid injection (ROSI)). Mimicking the structure of testicular natural extracellular matrices (ECM) outside of the body is one clear route toward complete in vitro spermatogenesis and male fertility preservation. Here, a new wave of technological innovations is underway applying regenerative medicine strategies to cell-tissue culture on natural or synthetic scaffolds supplemented with bioactive factors. The emergence of advanced bioengineered systems suggests new hope for male fertility preservation through development of functional male germ cells. To date, few studies aimed at in vitro spermatogenesis have resulted in relevant numbers of mature gametes. However, a substantial body of knowledge on conditions that are required to maintain and mature male germ cells in vitro is now in place. This review focuses on advanced bioengineering methods such as microfluidic systems, bio-fabricated scaffolds, and 3D organ culture applied to the germline for fertility preservation through in vitro spermatogenesis.
Fadillah, G, Fatimah, I, Sahroni, I, Musawwa, MM, Mahlia, TMI & Muraza, O 2021, 'Recent Progress in Low-Cost Catalysts for Pyrolysis of Plastic Waste to Fuels', Catalysts, vol. 11, no. 7, pp. 837-837.
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The catalytic and thermal decomposition of plastic waste to fuels over low-cost catalysts like zeolite, clay, and bimetallic material is highlighted. In this paper, several relevant studies are examined, specifically the effects of each type of catalyst used on the characteristics and product distribution of the produced products. The type of catalyst plays an important role in the decomposition of plastic waste and the characteristics of the oil yields and quality. In addition, the quality and yield of the oil products depend on several factors such as (i) the operating temperature, (ii) the ratio of plastic waste and catalyst, and (iii) the type of reactor. The development of low-cost catalysts is revisited for designing better and effective materials for plastic solid waste (PSW) conversion to oil/bio-oil products.
Fahmideh, M, Grundy, JC, Beydoun, G, Zowghi, D, Susilo, W & Mougouei, D 2021, 'A Model-Driven Approach to Reengineering Processes in Cloud Computing.', CoRR, vol. abs/2109.11896.
Fahmideh, M, Low, G & Beydoun, G 2021, 'Conceptualising Cloud Migration Lifecycle.', CoRR, vol. abs/2109.01757.
Faisal, SN, Amjadipour, M, Izzo, K, Singer, JA, Bendavid, A, Lin, C-T & Iacopi, F 2021, 'Non-invasive on-skin sensors for brain machine interfaces with epitaxial graphene', Journal of Neural Engineering, vol. 18, no. 6, pp. 066035-066035.
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Abstract Objective. Brain–machine interfaces are key components for the development of hands-free, brain-controlled devices. Electroencephalogram (EEG) electrodes are particularly attractive for harvesting the neural signals in a non-invasive fashion. Approach. Here, we explore the use of epitaxial graphene (EG) grown on silicon carbide on silicon for detecting the EEG signals with high sensitivity. Main results and significance. This dry and non-invasive approach exhibits a markedly improved skin contact impedance when benchmarked to commercial dry electrodes, as well as superior robustness, allowing prolonged and repeated use also in a highly saline environment. In addition, we report the newly observed phenomenon of surface conditioning of the EG electrodes. The prolonged contact of the EG with the skin electrolytes functionalize the grain boundaries of the graphene, leading to the formation of a thin surface film of water through physisorption and consequently reducing its contact impedance more than three-fold. This effect is primed in highly saline environments, and could be also further tailored as pre-conditioning to enhance the performance and reliability of the EG sensors.
Fan, J, Li, J, Wang, J, Wei, Z & Hsieh, M-H 2021, 'Asymmetric Quantum Concatenated and Tensor Product Codes With Large Z-Distances', IEEE Transactions on Communications, vol. 69, no. 6, pp. 3971-3983.
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In this paper, we present a new construction of asymmetric quantum codes (AQCs) by combining classical concatenated codes (CCs) with tensor product codes (TPCs), called asymmetric quantum concatenated and tensor product codes (AQCTPCs) which have the following three advantages. First, only the outer codes in AQCTPCs need to satisfy the orthogonal constraint in quantum codes, and any classical linear code can be used for the inner, which makes AQCTPCs very easy to construct. Second, most AQCTPCs are highly degenerate, which means they can correct many more errors than their classical TPC counterparts. Consequently, we construct several families of AQCs with better parameters than known results in the literature. Third, AQCTPCs can be efficiently decoded although they are degenerate, provided that the inner and outer codes are efficiently decodable. In particular, we significantly reduce the inner decoding complexity of TPCs from Ω(n2an1)(a>1) to O(n2) by considering error degeneracy, where n1 and n2 are the block length of the inner code and the outer code, respectively. Furthermore, we generalize our concatenation scheme by using the generalized CCs and TPCs correspondingly.
Fan, L, Weijie, Y, Jinhong, Y, Andrew, ZJ, Zesong, F & Jianming, Z 2021, 'Radar-communication Spectrum Sharing and Integration: Overview and Prospect', Journal of Radars, vol. 10, no. 3, pp. 467-484.
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The need of extra wireless spectrum is on the rise, given the rapid development of global wireless communication industry. To this end, Radar and Communication Spectrum Sharing (RCSS) has gained considerable attentions recently from both industry and academia. In particular, RCSS aims not only at enabling the spectral cohabitation of radar and communication systems, but also at designing a novel joint system that is capable of both functionalities. In this paper, a systematic overview of RCSS by focusing on the two main research directions are provided, i.e., Radar-Communication Coexistence (RCC) and Dual-Functional Radar-Communication (DFRC). We commence by discussing the coexistence examples of radar and communication at various frequency bands, and then elaborate on the practical application scenarios of the DFRC techniques. As a further step, the state-of-the-art approaches of both RCC and DFRC are reviewed. Finally we conclude the paper by identifying a number of open problems in the research area of RCSS.
Fan, Q, Bao, G, Ge, D, Wang, K, Sun, M, Liu, T, Liu, J, Zhang, Z, Xu, X, Xu, X, He, B, Rao, J & Zheng, Y 2021, 'Effective easing of the side effects of copper intrauterine devices using ultra-fine-grained Cu-0.4Mg alloy', Acta Biomaterialia, vol. 128, pp. 523-539.
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Copper intrauterine device is one of the most adopted contraceptive methods with high effectiveness (over 99 %), low cost, spontaneous reversibility and long-lasting usage. However, the side effects induced from the initial burst release of copper ions (Cu2+) hinder the continuation of the Cu-IUD made of Coarse-Grained Copper (CG Cu). We proposed to tailor the bio-corrosion behaviors of better control of Cu2+ release via the addition of bioactive Mg into the Ultra-Fine Grained (UFG) Bulk Cu. Thus, UFG bulk Cu with 0.4 wt.% Mg was produced via equal-channel angular pressing. The microstructures of the UFG Cu-0.4Mg was observed using electron backscatter diffraction and transmission electron microscopy techniques. The in vitro long-term corrosion behaviors in simulated uterine fluid, cytotoxicity to four cell lines, in vivo biocompatibility and contraceptive efficacy were all studied on CG Cu, UFG Cu and UFG Cu-0.4Mg materials. The results demonstrate that both the ultrafine grains and the addition of bioactive Mg into Cu contribute to the suppression of the burst release of Cu2+ in the initial stage and the maintenance of high level Cu2+ in long-term release. Moreover, the UFG Cu-0.4Mg also exhibited much improved cell and tissue biocompatibility from both the in vitro and in vivo evaluations. Therefore, the contraceptive efficacy of UFG Cu-0.4Mg is still maintained as high as the CG Cu and UFG Cu while the side effects are significantly eased, suggesting the high potential of the UFG Cu-0.4Mg alloy as a new upgrading or alternative material for Cu-IUD. STATEMENT OF SIGNIFICANCE: The side effects from burst release of Cu2+ at the initial implantation stage of Cu-containing intrauterine devices (Cu-IUD) is one of the main drawbacks of these devices. In this work, an ultra-fine-grained Cu (UFG Cu) alloyed with a low amount of bioactive Mg was used for a Cu-IUD. The UFG Cu-0.4Mg alloy exhibited suppressed burst release of Cu2+ at initial implantation, while...
Fan, W, Xiao, F, Chen, X, Cui, L & Yu, S 2021, 'Efficient Virtual Network Embedding of Cloud-Based Data Center Networks into Optical Networks', IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 11, pp. 2793-2808.
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The demand for data center bandwidth has exploded due to the continuous development of cloud computing, causing the use of network resources close to saturation. Optical network has become an encouraging technology for many burgeoning networks and parallel/distributed computing applications because of its huge bandwidth. This article focuses on efficient embedding of data centers into optical networks, which aims to reduce complexity of the network topology by using the parallel transmission characteristics of optical fiber. We first present a novel virtual network embedding (VNE) mathematical model used for optical data center networks. Then we derive a priority of location VNE algorithm according to node proximity sensing and path comprehensive evaluation. Furthermore, we propose routing and wavelength assignment for DCNs into optical networks, and identify the lower bound of the required number of wavelengths. Extensive evaluations show that the proposed embedding algorithm can reduce the average waiting time of virtual network requests by 20 percent, increase the request acceptance rate and revenue-overhead ratio by 13 percent, as compared to the latest VNE algorithm.
Fan, X, Xiang, C, Chen, C, Yang, P, Gong, L, Song, X, Nanda, P & He, X 2021, 'BuildSenSys: Reusing Building Sensing Data for Traffic Prediction With Cross-Domain Learning', IEEE Transactions on Mobile Computing, vol. 20, no. 6, pp. 2154-2171.
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With the rapid development of smart cities, smart buildings are generating a massive amount of building sensing data by
the equipped sensors. Indeed, building sensing data provides a promising way to enrich a series of data-demanding and
cost-expensive urban mobile applications. In this paper, as a preliminary exploration, we study how to reuse building sensing data to
predict traffic volume on nearby roads. Compared with existing studies, reusing building sensing data has considerable merits of
cost-efficiency and high-reliability. Nevertheless, it is non-trivial to achieve accurate prediction on such cross-domain data with two
major challenges. First, relationships between building sensing data and traffic data are not unknown as prior, and the spatio-temporal
complexities impose more difficulties to uncover the underlying reasons behind the above relationships. Second, it is even more
daunting to accurately predict traffic volume with dynamic building-traffic correlations, which are cross-domain, non-linear, and
time-varying. To address the above challenges, we design and implement BuildSenSys, a first-of-its-kind system for nearby traffic
volume prediction by reusing building sensing data. Our work consists of two parts, i.e., Correlation Analysis and Cross-domain
Learning. First, we conduct a comprehensive building-traffic analysis based on multi-source datasets, disclosing how and why building
sensing data is correlated with nearby traffic volume. Second, we propose a novel recurrent neural network for traffic volume prediction
based on cross-domain learning with two attention mechanisms. Specifically, a cross-domain attention mechanism captures the
building-traffic correlations and adaptively extracts the most relevant building sensing data at each predicting step. Then, a temporal
attention mechanism is employed to model the temporal dependencies of data across historical time intervals. The extensive
experimental studies demonstrate that BuildSenSys outp...
Fan, Z, Holmes, DW, Sauret, E, Islam, MS, Saha, SC, Ristovski, Z & Gu, Y 2021, 'A multiscale modeling method incorporating spatial coupling and temporal coupling into transient simulations of the human airways', International Journal for Numerical Methods in Fluids, vol. 93, no. 9, pp. 2905-2920.
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AbstractIn this article, a novel multiscale modeling method is proposed for transient computational fluid dynamics (CFD) simulations of the human airways. The developed method is the first attempt to incorporate spatial coupling and temporal coupling into transient human airway simulations, aiming to improve the flexibility and the efficiency of these simulations. In this method, domain decomposition was used to separate the complex airway model into different scaled domains. Each scaled domain could adopt a suitable mesh and timestep, as necessary: the coarse mesh and large timestep were employed in the macro regions to reduce the computational cost, while the fine mesh and small timestep were used in micro regions to maintain the simulation accuracy. The radial point interpolation method was used to couple data between the coarse mesh and the fine mesh. The continuous micro solution–intermittent temporal coupling method was applied to bridge different timesteps. The developed method was benchmarked using a well‐studied four‐generation symmetric airway model under realistic normal breath conditions. The accuracy and efficiency of the method were verified separately in the inhalation phase and the exhalation phase. Similar airflow behavior to previous studies was observed from the multiscale airway model. The developed multiscale method has the potential to improve the flexibility and efficiency of transient human airway simulations without sacrificing accuracy.
Fang, C, Liu, C, Wang, Z, Sun, Y, Ni, W, Li, P & Guo, S 2021, 'Cache-Assisted Content Delivery in Wireless Networks: A New Game Theoretic Model', IEEE Systems Journal, vol. 15, no. 2, pp. 2653-2664.
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With the rapid increase of mobile data traffic, efficient content delivery in wireless networks is increasingly important to provide broadcast and multicast services. To reduce network cost and enrich end-user quality of experience, Internet service providers (ISPs) and content providers (CPs) are expected to collaborate to improve content delivery services. However, the influence of content popularity has been generally overlooked, and profit split problem between ISPs and CPs has not been studied. We investigate the novel economic behaviors between ISPs and CPs in the presence of edge caches, and formulate the profit split problem as a centralized model, which can achieve optimal content caching and maximal network benefits. A Stackelberg game is designed to obtain its distributed win-win solution, where a feasible backward induction is proposed and the influence of edge caches and content popularity is analyzed. Simulation results show that the performance of our proposed Stackelberg game model is comparable to the centralized alternative and much better than existing ISP-CP cooperative schemes not considering cache deployment in the network.
Fang, C, Liu, W, Zhang, P, Rajabzadeh, S, Kato, N, Sasaki, Y, Shon, HK & Matsuyama, H 2021, 'Hollow fiber membranes with hierarchical spherulite surface structure developed by thermally induced phase separation using triple-orifice spinneret for membrane distillation', Journal of Membrane Science, vol. 618, pp. 118586-118586.
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© 2020 Elsevier B.V. Polyvinylidene fluoride (PVDF) hollow fiber membranes were developed by the thermally induced phase separation (TIPS) process using a triple-orifice spinneret with solvent co-extrusion at the outermost channel for applications in membrane distillation (MD). The polymer surface concentration during membrane preparation was controlled by exploiting the interfacial interactions of the diluent and polymer at the extruded solvent surface. The membrane surface was controlled from a dense to a porous structure with a large pore size and a high porosity, which considerably enhanced the membrane water vapor permeability to 13.5 L m−2 h−1. Furthermore, the solvent co-extrusion was responsible for the formation of surface spherulites with different shapes, such as contacted spherulites, isolated spherulites, and isolated spherulites with humps. The spherulites with humps constructed a novel hierarchical structure, which created a superhydrophobic surface that conferred upon the PVDF membrane a remarkable wetting resistance in the MD process toward low-surface-tension saline water. More significantly, all the unique structures were achieved using the one-step membrane fabrication process of solvent co-extrusion without additional processes and materials. Thus, this work provides a new, simple, and useful alternative for the preparation of hollow fiber membranes with high performances for MD desalination.
Fang, C, Liu, W, Zhang, P, Yao, M, Rajabzadeh, S, Kato, N, Kyong Shon, H & Matsuyama, H 2021, 'Controlling the inner surface pore and spherulite structures of PVDF hollow fiber membranes in thermally induced phase separation using triple-orifice spinneret for membrane distillation', Separation and Purification Technology, vol. 258, pp. 117988-117988.
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© 2020 Elsevier B.V. In this study, we controlled the inner surface structures of polyvinylidene fluoride (PVDF) hollow fiber membranes via a thermally induced phase separation process using a triple-orifice spinneret for direct-contact membrane distillation (DCMD). The coextrusion of propylene carbonate (PC) through the outermost channel of the spinneret led to porous outer surfaces with similar pore sizes and spherulitic structures for all the PVDF hollow fiber membranes. In the innermost channel, the extrusion of solvents having different compatibilities with PVDF and the diluent (PC) as the bore liquids controlled the inner surface pore sizes and spherulite structures, and the effects of these inner surface structures on the DCMD performance were investigated in detail. Increasing the compatibility of the bore liquids toward the diluent led to an increase in the inner surface pore size because of the formation of loose, isolated spherulites, which remarkably enhanced the water vapor permeability from 4 to 8.3 L m−2 h−1, while reducing the membrane hydrophobicity, liquid entry pressure, and salt rejection. When increasing the bore liquid compatibility with the polymer, the surface pore size decreased because of the tight spherulite contact, enhancing membrane salt rejection and wetting resistance. Given the significance of bore liquid compatibility with the diluent and the polymer in controlling the inner surface structures, a useful guideline is presented for selecting the appropriate bore liquids to prepare hollow fiber membranes with the desired inner surface structures for high MD performance.
Fang, F, Xu, R-Z, Huang, Y-Q, Luo, J-Y, Xie, W-M, Ni, B-J & Cao, J-S 2021, 'Exploring the feasibility of nitrous oxide reduction and polyhydroxyalkanoates production simultaneously by mixed microbial cultures', Bioresource Technology, vol. 342, pp. 126012-126012.
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Nitrous oxide (N2O), as a powerful greenhouse gas, has drawn increasing attention in recent years and different strategies for N2O reduction were explored. In this study, a novel strategy for valuable polyhydroxyalkanoates (PHA) production coupling with N2O reduction by mixed microbial cultures (MMC) using different substrates was evaluated. Results revealed that N2O was an effective electron acceptor for PHA production. The highest PHA yield (0.35 Cmmol PHA/Cmmol S) and PHA synthesis rate (227.47 mg PHA/L/h) were obtained with acetic acid as substrate. Low temperature (15℃) and pH of 8.0 were beneficial for PHA accumulation. Results of the thermogravimetric analysis showed that PHA produced with N2O as electron acceptor has better thermal stability (melting temperature of 99.4℃ and loss 5% weight temperature of 211.4℃). Our work opens up new avenues for simultaneously N2O reduction and valuable bioplastic production, which is conducive to resource recovery and climate protection.
Fang, G, Lu, H, Aboulkheyr Es, H, Wang, D, Liu, Y, Warkiani, ME, Lin, G & Jin, D 2021, 'Unidirectional intercellular communication on a microfluidic chip', Biosensors and Bioelectronics, vol. 175, pp. 112833-112833.
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Cell co-culture serves as a standard method to study intercellular communication. However, random diffusion of signal molecules during co-culture may arouse crosstalk among different types of cells and hide directive signal-target responses. Here, a microfluidic chip is proposed to study unidirectional intercellular communication by spatially controlling the flow of the signal molecules. The chip contains two separated chambers connected by two channels where the culture media flows oppositely. A zigzag signal-blocking channel is designed to study the function of a specific signal. The chip is applied to study the unidirectional communication between tumor cells and stromal cells. It shows that the expression of α-smooth muscle actin (a marker of cancer-associated fibroblast (CAF)) of both MRC-5 fibroblasts and mesenchymal stem cells can be up-regulated only by the secreta from invasive MDA-MB-231 cells, but not from non-invasive MCF-7 cells. The proliferation of the tumor cells can be improved by the stromal cells. Moreover, transforming growth factor beta 1 is found as one of the main factors for CAF transformation via the signal-blocking function. The chip achieves unidirectional cell communication along X-axis, signal concentration gradient along Y-axis and 3D cell culture along Z-axis, which provides a useful tool for cell communication studies.
Fang, G, Lu, H, Rodriguez de la Fuente, L, Law, AMK, Lin, G, Jin, D & Gallego‐Ortega, D 2021, 'Mammary Tumor Organoid Culture in Non‐Adhesive Alginate for Luminal Mechanics and High‐Throughput Drug Screening', Advanced Science, vol. 8, no. 21, pp. 1-13.
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AbstractMammary tumor organoids have become a promising in vitro model for drug screening and personalized medicine. However, the dependency on the basement membrane extract (BME) as the growth matrices limits their comprehensive application. In this work, mouse mammary tumor organoids are established by encapsulating tumor pieces in non‐adhesive alginate. High‐throughput generation of organoids in alginate microbeads is achieved utilizing microfluidic droplet technology. Tumor pieces within the alginate microbeads developed both luminal‐ and solid‐like structures and displayed a high similarity to the original fresh tumor in cellular phenotypes and lineages. The mechanical forces of the luminal organoids in the alginate capsules are analyzed with the theory of the thick‐wall pressure vessel (TWPV) model. The luminal pressure of the organoids increase with the lumen growth and can reach 2 kPa after two weeks’ culture. Finally, the mammary tumor organoids are treated with doxorubicin and latrunculin A to evaluate their application as a drug screening platform. It is found that the drug response is related to the luminal size and pressures of organoids. This high‐throughput culture for mammary tumor organoids may present a promising tool for preclinical drug target validation and personalized medicine.
Fang, J, Wu, C, Rabczuk, T, Wu, C, Sun, G & Li, Q 2021, 'Correction to: Phase field fracture in elasto-plastic solids: a length-scale insensitive model for quasi-brittle materials', Computational Mechanics, vol. 67, no. 6, pp. 1769-1770.
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The original article contained typographical errors that a number of double brackets.
Fang, L, Li, Y, Liu, Z, Yin, C, Li, M & Cao, ZJ 2021, 'A Practical Model Based on Anomaly Detection for Protecting Medical IoT Control Services Against External Attacks', IEEE Transactions on Industrial Informatics, vol. 17, no. 6, pp. 4260-4269.
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Fang, Z, Lu, J, Liu, F, Xuan, J & Zhang, G 2021, 'Open Set Domain Adaptation: Theoretical Bound and Algorithm', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 10, pp. 4309-4322.
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The aim of unsupervised domain adaptation is to leverage the knowledge in a labeled (source) domain to improve a model's learning performance with an unlabeled (target) domain--the basic strategy being to mitigate the effects of discrepancies between the two distributions. Most existing algorithms can only handle unsupervised closed set domain adaptation (UCSDA), i.e., where the source and target domains are assumed to share the same label set. In this article, we target a more challenging but realistic setting: unsupervised open set domain adaptation (UOSDA), where the target domain has unknown classes that are not found in the source domain. This is the first study to provide learning bound for open set domain adaptation, which we do by theoretically investigating the risk of the target classifier on unknown classes. The proposed learning bound has a special term, namely, open set difference, which reflects the risk of the target classifier on unknown classes. Furthermore, we present a novel and theoretically guided unsupervised algorithm for open set domain adaptation, called distribution alignment with open difference (DAOD), which is based on regularizing this open set difference bound. The experiments on several benchmark data sets show the superior performance of the proposed UOSDA method compared with the state-of-the-art methods in the literature.
Far, H & Nejadi, S 2021, 'Experimental investigation on flexural behaviour of composite PVC encased macro-synthetic fibre reinforced concrete walls', Construction and Building Materials, vol. 273, pp. 121756-121756.
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Composite PVC encased concrete walls provide substantial advantages in terms of structural strength and durability enhancement, ultraviolet radiation and pest infestation resistance, design flexibility, ease of construction and excellent resistance to impact. In this study, the effects of using macro-synthetic fibre reinforced concrete on flexural behaviour of composite PVC encased walls in comparison with composite PVC encased walls filled with conventional plain concrete and reinforced concrete have been experimentally investigated. Fifteen composite PVC encased concrete wall specimens were cast and tested using three-point bending test. Based on the load-deflection curves resulting from the three-point bending tests, flexural parameters including ultimate loads, ultimate flexural strengths, stiffness and flexural rigidity values for cracked and uncracked conditions were determined for three different cases including i) test specimens filled with plain concrete, ii) test specimens filled with macro-synthetic fibre reinforced concrete, and iii) test specimens filled with reinforced concrete. The determined parameters as well as the measured load-deflection curves for the three cases were compared and the final findings have been discussed. Based on this study, it has become apparent that using BarChip 48 macro-synthetic fibre reinforced concrete in composite PVC encased walls instead of plain concrete can lead to 43.5% flexural strength improvement and 25% stiffness enhancement at the age of 28 days. Based on the experimental measurements and theoretical comparison in this study, it has been concluded that composite PVC encased walls filled with BarChip 48 macro-synthetic fibre reinforced concrete, without steel reinforcement, are deemed suitable for sway-prevented structures such as retaining walls. If using steel reinforcement in composite PVC encased retaining walls is not an option due to high-risk of steel corrosion in harsh environment; it is hig...
Far, H & Nejadi, S 2021, 'Experimental investigation on interface shear strength of composite PVC encased macro-synthetic fibre reinforced concrete walls', Structures, vol. 34, pp. 729-737.
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Over the past decade, Polyvinyl Chloride (PVC) stay-in-place formwork has become a popular alternative for conventional formwork in concrete construction industry due to its relatively lower cost of construction and ease of assembly. The PVC panels are joined using connectors and serve as a permanent formwork into which fresh concrete is poured to form composite PVC encased concrete walls. This study has experimentally investigated the effects of using macro-synthetic fibre reinforced concrete on the interface shear strength of composite PVC encased walls in comparison with composite PVC encased walls filled with conventional plain concrete and reinforced concrete. Nine composite PVC encased concrete wall specimens were cast and tested using direct shear tests. Based on the load–deflection curves obtained from the direct shear tests, the maximum shear loads and interface shear strength values were determined for three different cases including i) test specimens filled with plain concrete, ii) test specimens filled with macro-synthetic fibre reinforced concrete, and iii) test specimens filled with reinforced concrete. The determined parameters as well as the measured load–deflection curves for the three cases were compared and the final findings have been discussed. Based on the outcomes of this study, it has become apparent that the tested composite PVC encased macro-synthetic fibre reinforced concrete wall specimens can noticeably exhibit higher interface shear strength values compared to the tested wall specimens filled with plain concrete. Since AS 3600 (2018) does not prescribe the shear plane surface coefficients for determining the interface shear strength of composite PVC encased concrete walls, in order to enable structural designers to determine the interface shear strength for those panels using AS 3600 (2018), those coefficients have been extracted from the test results for the three mentioned cases and proposed for practical applications.
Far, H, Nejadi, S & Aghayarzadeh, M 2021, 'Experimental investigation on in‐plane lateral stiffness and degree of ductility of composite PVC reinforced concrete walls', Structural Concrete, vol. 22, no. 4, pp. 2126-2137.
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AbstractThis study investigates the in‐plane lateral stiffness and ductility of composite PVC encased concrete walls subject to the lateral loads using pushover tests to determine lateral strength and ductility characteristics of composite PVC encased walls filled with plain concrete, macro‐synthetic fiber reinforced concrete (RC), and steel RC. Eighteen concrete wall specimens were cast and subjected to pushover test to determine the load‐deflection curves. Based on the capacity curves resulting from the pushover tests, the yield and maximum displacements and subsequently structural ductility and performance factors according to Australian Standard for seismic design of buildings have been determined. The determined parameters as well as the initial and effective lateral stiffness values measured from the load‐deflection curves for all three cases were compared and the final findings have been discussed. Based on the outcomes of this study, it has become apparent that the tested composite PVC encased macro‐synthetic fiber RC walls can exhibit superior performance in terms of ductility when compared to the unreinforced concrete specimens. In addition, the results indicated that the initial in‐plane lateral stiffness values of the tested composite PVC encased macro‐synthetic fiber RC walls increased by 25% compared to the tested walls filled with plain concrete. In order to enable structural designers to design composite PVC encased concrete walls, ductility factors for this type of walls have been extracted from the test results for the three mentioned cases and proposed for practical applications. It has been concluded that all the PVC encased concrete walls evaluated in this study can be categorized as fully ductile structures.
Farahmandian, S & Hoang, D 2021, 'Policy-based Interaction Model for Detection and Prediction of Cloud Security Breaches', Journal of Telecommunications and the Digital Economy, vol. 9, no. 2, pp. 92-116.
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The ever-increasing number and gravity of cyberattacks against the cloud's assets, together with the introduction of new technologies, have brought about many severe cloud security issues. The main challenge is finding effective mechanisms for constructing dynamic isolation boundaries for securing cloud assets at different cloud infrastructure levels. Our security architecture tackles these issues by introducing a policy-driven interaction model. The model is governed by cloud system security policies and constrained by cloud interacting entities' locations and levels. Security policies are used to construct security boundaries between cloud objects at their interaction level. The novel interaction model relies on its unique parameters to develop an agile detection and prediction mechanism of security threats against cloud resources. The proposed policy-based interaction model and its interaction security algorithms are developed to protect cloud resources. The model deals with external and internal interactions among entities representing diverse participating elements of different complexity levels in a cloud environment. We build a security controller and simulate various scenarios for testing the proposed interaction model and security algorithms.
Farasat, M, Thalakotuna, DN, Hu, Z & Yang, Y 2021, 'A Review on 5G Sub-6 GHz Base Station Antenna Design Challenges', Electronics, vol. 10, no. 16, pp. 2000-2000.
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Modern wireless networks such as 5G require multiband MIMO-supported Base Station Antennas. As a result, antennas have multiple ports to support a range of frequency bands leading to multiple arrays within one compact antenna enclosure. The close proximity of the arrays results in significant scattering degrading pattern performance of each band while coupling between arrays leads to degradation in return loss and port-to-port isolations. Different design techniques are adopted in the literature to overcome such challenges. This paper provides a classification of challenges in BSA design and a cohesive list of design techniques adopted in the literature to overcome such challenges.
Fardjahromi, MA, Ejeian, F, Razmjou, A, Vesey, G, Mukhopadhyay, SC, Derakhshan, A & Warkiani, ME 2021, 'Enhancing osteoregenerative potential of biphasic calcium phosphates by using bioinspired ZIF8 coating', Materials Science and Engineering: C, vol. 123, pp. 111972-111972.
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Biphasic calcium phosphate ceramics (BCPs) have been extensively used as a bone graft in dental clinics to reconstruct lost bone in the jaw and peri-implant hard tissue due to their good bone conduction and similar chemical structure to the teeth and bone. However, BCPs are not inherently osteoinductive and need additional modification and treatment to enhance their osteoinductivity. The present study aims to develop an innovative strategy to improve the osteoinductivity of BCPs using unique features of zeolitic imidazolate framework-8 (ZIF8). In this method, commercial BCPs (Osteon II) were pre-coated with a zeolitic imidazolate framework-8/polydopamine/polyethyleneimine (ZIF8/PDA/PEI) layer to form a uniform and compact thin film of ZIF8 on the surface of BCPs. The surface morphology and chemical structure of ZIF8 modified Osteon II (ZIF8-Osteon) were confirmed using various analytical techniques such as XRD, FTIR, SEM, and EDX. We evaluated the effect of ZIF8 coating on cell attachment, growth, and osteogenic differentiation of human adipose-derived mesenchymal stem cells (hADSCs). The results revealed that altering the surface chemistry and topography of Osteon II using ZIF8 can effectively promote cell attachment, proliferation, and bone regeneration in both in vitro and in vivo conditions. In conclusion, the method applied in this study is simple, low-cost, and time-efficient and can be used as a versatile approach for improving osteoinductivity and osteoconductivity of other types of alloplastic bone grafts.
Farooq, MA, Nimbalkar, S & Fatahi, B 2021, 'Three-dimensional finite element analyses of tyre derived aggregates in ballasted and ballastless tracks', Computers and Geotechnics, vol. 136, pp. 104220-104220.
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Scrap tyres are a significant source of pollution and pose a grave threat to the environment and human health. The present study aims to examine the application of Tyre Derived Aggregate (TDA) in a concrete slab track and ballasted track and compare its performance in both track forms. In this study, long-term performance of slab track and ballasted track subjected to train induced loading is demonstrated based on the three-dimensional finite element modelling. The most suitable constitutive hyperelastic model for TDA has been identified. Subsequently, the most suitable position for the location of TDA is determined for both track types. A comparative analysis between slab track and ballasted track, with and without TDA, is presented in terms of stress transfer, vibration reduction and displacement (elastic and plastic). It is shown that TDA helps in reducing up to 50% vibration levels of both track types. The influence of train speed and axle load on the vertical and horizontal displacement and stress response of both track forms is shown for a large number of load cycles. Overall, it is observed that the long-term performance of TDA is better in slab track compared to ballasted track.
Fatema, I, Kong, X & Fang, G 2021, 'Electricity demand and price forecasting model for sustainable smart grid using comprehensive long short term memory', International Journal of Sustainable Engineering, vol. 14, no. 6, pp. 1714-1732.
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Feng, L, Hu, C, Yu, J, Jiang, H & Wen, S 2021, 'Fixed-time Synchronization of Coupled Memristive Complex-valued Neural Networks', Chaos, Solitons & Fractals, vol. 148, pp. 110993-110993.
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By directly proposing discontinuous complex-valued controllers and constructing Lyapunov functions for complex-valued networks, this article is devoted to fixed-time (F-T) synchronization of coupled memristive complex-valued neural networks (MCNNs) with time-varying delays. Above all, a new theorem for F-T stability is established to reduce the conservatism of the existing work in terms of conditions and the estimate of stability time. Subsequently, under the framework of the complex-valued sign function, several simplified complex-valued F-T control laws are proposed by excluding the linear part in previous F-T design. Furthermore, without splitting the addressed coupled MCNNs into real-valued submodels in the theoretical analysis, several sets of conditions of F-T synchronization are obtained for coupled MCNNs by means of the theory of complex-variable functions and the improved F-T stability. Lastly, the developed control strategies and the results on F-T synchronization are verified by the presence of a numerical example.
Feng, S, Hao Ngo, H, Guo, W, Woong Chang, S, Duc Nguyen, D, Cheng, D, Varjani, S, Lei, Z & Liu, Y 2021, 'Roles and applications of enzymes for resistant pollutants removal in wastewater treatment', Bioresource Technology, vol. 335, pp. 125278-125278.
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Resistant pollutants like oil, grease, pharmaceuticals, pesticides, and plastics in wastewater are difficult to be degraded by traditional activated sludge methods. These pollutants are prevalent, posing a great threat to aquatic environments and organisms since they are toxic, resistant to natural biodegradation, and create other serious problems. As a high-efficiency biocatalyst, enzymes are proposed for the treatment of these resistant pollutants. This review focused on the roles and applications of enzymes in wastewater treatment. It discusses the influence of enzyme types and their sources, enzymatic processes in resistant pollutants remediation, identification and ecotoxicity assay of enzymatic transformation products, and typically employed enzymatic wastewater treatment systems. Perspectives on the major challenges and feasible future research directions of enzyme-based wastewater treatment are also proposed.
Feng, S, Shi, H, Huang, L, Shen, S, Yu, S, Peng, H & Wu, C 2021, 'Unknown hostile environment-oriented autonomous WSN deployment using a mobile robot', Journal of Network and Computer Applications, vol. 182, pp. 103053-103053.
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In this study, we consider the Internet of Things (IoT) with an autonomous deployment framework and seek optimal localizable k-coverage (OLKC) strategies to preserve the connectivity and robustness in IoT networks to assist robots during disaster recovery activities. Therefore, we define localizable k-coverage as the covered region within which a mobile robot can localize itself aided by k neighboring beacon nodes (BNs) in a wireless sensor network (WSN). To this end, we first propose the optimal localizable k-coverage WSN deployment problem (OLKWDP) and present a novel framework that preserves WSN connectivity and robustness for mobile robots. To localize a mobile robot with at least k BNs and overcome the network hole problem that can occur in unknown hostile environments, we propose a hole recovery method for the OLKC achieved by a mobile robot that knows the concurrent mapping, deployment and localization of the WSN. We then present a mapping-to-image transformation method to reveal the interactions between the WSN deployment and the network holes for the OLKC while constructing the online mapping. To solve the OLKWDP, we also develop two optimality conditions to achieve maximum coverage by the proposed OLKC in the unknown hostile environment using the minimum number of sensors. Moreover, we analyze the factors that influence the probability of success of the OLKC and the factors that influence the performance of a mobile robot when determining the WSN deployment. The simulation results illustrate that our framework outperforms the trilateration and spanning tree (TST) method in unknown hostile environment exploration and can achieve the OLKC in a WSN. In 27 simulated situations, our framework achieved average rates of nearly 100% 1-coverage, 91.34% 2-coverage and 89.00% 3-coverage.
Feng, Y, Li, S & Ying, M 2021, 'Verification of Distributed Quantum Programs.', CoRR, vol. abs/2104.14796.
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Distributed quantum systems and especially the Quantum Internet have the ever-increasing potential to fully demonstrate the power of quantum computation. This is particularly true given that developing a general-purpose quantum computer is much more difficult than connecting many small quantum devices. One major challenge of implementing distributed quantum systems is programming them and verifying their correctness. In this paper, we propose a CSP-like distributed programming language to facilitate the specification and verification of such systems. After presenting its operational and denotational semantics, we develop a Hoare-style logic for distributed quantum programs and establish its soundness and (relative) completeness with respect to both partial and total correctness. The effectiveness of the logic is demonstrated by its applications in the verification of quantum teleportation and local implementation of non-local CNOT gates, two important algorithms widely used in distributed quantum systems.
Feng, Y, Wang, Q, Wu, D, Luo, Z, Chen, X, Zhang, T & Gao, W 2021, 'Machine learning aided phase field method for fracture mechanics', International Journal of Engineering Science, vol. 169, pp. 103587-103587.
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Feng, Y, Zhang, JA, Cheng, B, He, X & Chen, J 2021, 'Magnetic Sensor-Based Multi-Vehicle Data Association', IEEE Sensors Journal, vol. 21, no. 21, pp. 24709-24719.
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Sensors have been playing an increasingly important role in smart cities. Using small roadside magnetic sensors provides a cost-efficient method for monitoring vehicle traffic. However, there are significant challenges associated with vehicle data misalignment due to the timing-offsets between sensors and missed or increased data because of vehicle lane-changing. In this paper, we propose a novel traffic information acquisition and vehicle state estimation scheme using multiple road magnetic sensors. To efficiently solve the multi-sensor registration problem in the presence of timing-offset, we develop a linear discrimination analysis method to achieve vehicle separation and classification. To handle the situation of lane-changing, we propose a data smoothing technique based on a multi-hypotheses tracker that exploits vehicle correlation. The road density effect on the probability of correct data association is investigated, with numerical and experimental results provided. The results show that our proposed scheme can effectively detect vehicles with a 95.5% accuracy rate. It also outperforms some other speed sensing methods in terms of the vehicle speed estimation accuracy.
Fernandez, E, Hossain, MJ, Mahmud, K, Nizami, MSH & Kashif, M 2021, 'A Bi-level optimization-based community energy management system for optimal energy sharing and trading among peers', Journal of Cleaner Production, vol. 279, pp. 123254-123254.
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© 2020 Elsevier Ltd The economic and environmental benefits of renewable energy have increased in significance over the past decade. Local energy markets can play a vital role in energy transition by facilitating the rapid proliferation of renewable-based energy resources, thereby increasing the renewable energy hosting capacity of the power grid. This paper proposes an energy management system for a smart locality that facilitates local energy trading involving consumers with renewable energy units, a central storage facility, and a power grid. Two optimization frameworks for sharing surplus onsite produced energy are developed here. The first framework maximizes the combined revenue of sellers and buyers, while the second, a game theoretical model, maximizes consumer utilization at the lower level and the revenue of the common storage facility at the higher level. An intensive study is carried out to investigate the benefits of energy sharing that maximizes overall revenue. The results indicate that the grid pricing scheme is a major factor that determines the revenue sharing between the central storage facility entity and the consumers. The first framework results in optimal resource allocation, while the second framework concentrates only on revenue generation. Results indicate that the energy seller profits are higher if the real-time grid prices are used and if the consumers are not charged according to their willingness to pay.
Ferreira, FGDC, Gandomi, AH & Cardoso, RTN 2021, 'Artificial Intelligence Applied to Stock Market Trading: A Review', IEEE Access, vol. 9, pp. 30898-30917.
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The application of Artificial Intelligence (AI) to financial investment is a research area that has attracted extensive research attention since the 1990s, when there was an accelerated technological development and popularization of the personal computer. Since then, countless approaches have been proposed to deal with the problem of price prediction in the stock market. This paper presents a systematic review of the literature on Artificial Intelligence applied to investments in the stock market based on a sample of 2326 papers from the Scopus website between 1995 and 2019. These papers were divided into four categories: portfolio optimization, stock market prediction using AI, financial sentiment analysis, and combinations involving two or more approaches. For each category, the initial introductory research to its state-of-the-art applications are described. In addition, an overview of the review leads to the conclusion that this research area is gaining continuous attention and the literature is becoming increasingly specific and thorough.
Ferro, R, Cordeiro, GA, Ordóñez, REC, Beydoun, G & Shukla, N 2021, 'An Optimization Tool for Production Planning: A Case Study in a Textile Industry', Applied Sciences, vol. 11, no. 18, pp. 8312-8312.
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The textile industry is an important sector of the Brazilian economy, being considered the fifth largest textile industry in the world. To support further growth and development in this sector, this document proposes a process for production analysis through the use of Discrete Event Simulation (DES) and optimization through genetic algorithms. The focus is on production planning for weaving processes and optimization to help make decisions about batch sizing and production scheduling activities. In addition, the correlations between some current technological trends and their implications for the textile industry are also highlighted. Another important contribution of this study is to detail the use of the commercial software Tecnomatix Plant Simulation 13®, to simulate and optimize a production problem by applying genetic algorithms with real production data.
Fisher, BM, Tang, KD, Warkiani, ME, Punyadeera, C & Batstone, MD 2021, 'A pilot study for presence of circulating tumour cells in adenoid cystic carcinoma', International Journal of Oral and Maxillofacial Surgery, vol. 50, no. 8, pp. 994-998.
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Adenoid cystic carcinoma (ACC) is a rare salivary gland neoplasm with a poor long-term prognosis due to multiple recurrences and distant metastatic spread. Circulating tumour cells (CTCs) are tumour cells shed from a primary, recurrent, or metastatic cancer that are detectable in the blood or lymphatics. There is no literature to date confirming the presence of CTCs in ACC. The aim of this study was to determine whether CTCs are detectable in ACC. Blood samples were collected from eight patients with histologically confirmed ACC. The TNM stage of the tumour was recorded, as well as any prior treatment. CTCs were isolated by spiral microfluidics and detected by immunofluorescence staining. Three of the eight patients recruited (32.5%) had staining consistent with the presence of CTCs. Of these three patients with detectable CTCs, one had confirmed pulmonary metastasis, one had suspected pulmonary metastasis and was awaiting confirmation, and one had local recurrence confirmed on re-resection. One patient with known isolated pulmonary metastasis had previously undergone a lung metastasectomy and did not have CTCs detected. CTCs are detectable in ACC. In this small patient sample, CTCs were found to be present in those patients with recurrent local disease and known distant metastatic disease. CTCs in ACC should be investigated further for their potential use as an adjunct in staging, prognosis, and the detection of recurrence.
Flores-Sosa, M, Avilés-Ochoa, E, Merigó, JM & Yager, RR 2021, 'Volatility GARCH models with the ordered weighted average (OWA) operators', Information Sciences, vol. 565, pp. 46-61.
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Fong, JSL, Booth, MA, Rifai, A, Fox, K & Gelmi, A 2021, 'Diamond in the Rough: Toward Improved Materials for the Bone−Implant Interface', Advanced Healthcare Materials, vol. 10, no. 14.
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AbstractThe ability of an orthopedic implant to integrate successfully with the surrounding bone tissue is imperative for optimal patient outcomes. Here, the recent advances and future prospects for diamond‐based coatings of conventional osteo‐implant materials (primarily titanium) are explored. The ability of these diamond coatings to enhance integration into existing bone, improved implant mechanical properties, facilitate surface chemical functionalization, and provide anti‐microbial properties are discussed in context of orthopedic implants. These diamond‐based materials may have the additional benefit of providing an osteo‐inductive effect, enabling better integration into existing bone via stem cell recruitment and bone regeneration. Current and timely research is highlighted to support the discussion and suggestions in further improving implant integration via an osseoinductive effect from the diamond composite materials.
Fonseka, C, Ryu, S, Choo, Y, Mullett, M, Thiruvenkatachari, R, Naidu, G & Vigneswaran, S 2021, 'Selective Recovery of Rare Earth Elements from Mine Ore by Cr-MIL Metal–Organic Frameworks', ACS Sustainable Chemistry & Engineering, vol. 9, no. 50, pp. 16896-16904.
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Rare earth elements (REEs) have become a strategic resource extensively used in renewable energy technologies and modern electronic devices. Depletion of natural REE-bearing mineral deposits has made selective recovery of REEs from alternative sources crucial in meeting the rising global demand. A chromium-based metal–organic framework was synthesized and modified with N-(phosphonomethyl)iminodiacetic acid (PMIDA) in this study to selectively recover REEs (europium, Eu) from chemically complex zinc ore leachate. The adsorbent was characterized and comprehensively examined for Eu uptake as a function of adsorbate concentration, contact time, and pH of the solution. Cr-MIL-PMIDA showed a maximum adsorption capacity of 69.14 mg/g at pH 5.5 while adsorption kinetics best fitted the pseudo-second-order model. Furthermore, Cr-MIL-PMIDA showed exceptional selectivity (88%) toward Eu over competing transitional metal ions (Na, Mg, Al, Ca, Mn, Fe, Ni, Cu, Co, and Zn) found in the dissolved mine ore. High selectivity toward REEs was attributed to the formation of coordinative complexes with grafted carboxylate, phosphonic, and residual amine functional groups. Cr-MIL-PMIDA demonstrated excellent structural stability over multiple regeneration cycles, highlighting its potential for industrial application for REE recovery.
Franzen, TMO, Seymour, N, Sadler, EM, Mauch, T, White, SV, Jackson, CA, Chhetri, R, Quici, B, Bell, ME, Callingham, JR, Dwarakanath, KS, For, B, Gaensler, BM, Hancock, PJ, Hindson, L, Hurley-Walker, N, Johnston-Hollitt, M, Kapińska, AD, Lenc, E, McKinley, B, Morgan, J, Offringa, AR, Procopio, P, Staveley-Smith, L, Wayth, RB, Wu, C & Zheng, Q 2021, 'The GLEAM 200-MHz local radio luminosity function for AGN and star-forming galaxies', Publications of the Astronomical Society of Australia, vol. 38, p. e041.
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AbstractThe GaLactic and Extragalactic All-sky Murchison Widefield Array (GLEAM) is a radio continuum survey at 76–227 MHz of the entire southern sky (Declination$<\!{+}30^{\circ}$) with an angular resolution of${\approx}2$arcmin. In this paper, we combine GLEAM data with optical spectroscopy from the 6dF Galaxy Survey to construct a sample of 1 590 local (median$z \approx 0.064$) radio sources with$S_{200\,\mathrm{MHz}} > 55$mJy across an area of${\approx}16\,700\,\mathrm{deg}^{2}$. From the optical spectra, we identify the dominant physical process responsible for the radio emission from each galaxy: 73% are fuelled by an active galactic nucleus (AGN) and 27% by star formation. We present the local radio luminosity function for AGN...
Freguia, S, Sharma, K, Benichou, O, Mulliss, M & Shon, HK 2021, 'Sustainable engineering of sewers and sewage treatment plants for scenarios with urine diversion', Journal of Hazardous Materials, vol. 415, pp. 125609-125609.
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Fu, Q, Wang, D, Li, X, Yang, Q, Xu, Q, Ni, B-J, Wang, Q & Liu, X 2021, 'Towards hydrogen production from waste activated sludge: Principles, challenges and perspectives', Renewable and Sustainable Energy Reviews, vol. 135, pp. 110283-110283.
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Hydrogen production from waste activated sludge (WAS) was widely considered and intensively investigated as a promising technology to recover energy from wastewater treatment plants. To date, no efforts have been made on either systematic summarization or critical thinking of the application niche of hydrogen production from WAS treatment. It is therefore time to evaluate whether and how to recover hydrogen in a future paradigm of WAS treatment. In this critical review, the principles and potentials, microorganisms, possible technologies, and process parameters of hydrogen generation were analyzed. Microbial electrolysis cell shows high theoretical hydrogen yield and could utilize a variety of organic compounds as substrates, which is regarded as a prospective technology for hydrogen production. However, the poor organics utilization and rapid consumptions of produced hydrogen hindered hydrogen recovery from WAS. Based on the analysis of the current state of the literatures, the opportunities and challenges of hydrogen production from WAS are rethought, the detailed knowledge gaps and perspective of hydrogen production from WAS were discussed, and the probable solutions of hydrogen recovery from WAS treatment are figured out. To guide the application and development of hydrogen recovery, a more promising avenue through rational integration of the available technologies to form a hybrid process is finally proposed. The integrated operational paradigm of WWTPs could achieve substantial technical, environmental and economic benefits. In addition, how this hybrid process works is illustrated, the challenges of this hybrid process and future efforts to be made in the future are put forward.
Fu, Z, Xu, W, Hu, R, Long, G & Jiang, J 2021, 'MHieR-encoder: Modelling the high-frequency changes across stocks', Knowledge-Based Systems, vol. 224, pp. 107092-107092.
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Gadipudi, N, Elamvazuthi, I, Lu, C-K, Paramasivam, S & Su, S 2021, 'WPO-Net: Windowed Pose Optimization Network for Monocular Visual Odometry Estimation', Sensors, vol. 21, no. 23, pp. 8155-8155.
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Visual odometry is the process of estimating incremental localization of the camera in 3-dimensional space for autonomous driving. There have been new learning-based methods which do not require camera calibration and are robust to external noise. In this work, a new method that do not require camera calibration called the “windowed pose optimization network” is proposed to estimate the 6 degrees of freedom pose of a monocular camera. The architecture of the proposed network is based on supervised learning-based methods with feature encoder and pose regressor that takes multiple consecutive two grayscale image stacks at each step for training and enforces the composite pose constraints. The KITTI dataset is used to evaluate the performance of the proposed method. The proposed method yielded rotational error of 3.12 deg/100 m, and the training time is 41.32 ms, while inference time is 7.87 ms. Experiments demonstrate the competitive performance of the proposed method to other state-of-the-art related works which shows the novelty of the proposed technique.
Gan, YY, Chen, W-H, Ong, HC, Lin, Y-Y, Sheen, H-K, Chang, J-S & Ling, TC 2021, 'Effect of wet torrefaction on pyrolysis kinetics and conversion of microalgae carbohydrates, proteins, and lipids', Energy Conversion and Management, vol. 227, pp. 113609-113609.
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Ganesan, B, Yip, J, Luximon, A, Gibbons, PJ, Chivers, A, Balasankar, SK, Tong, RK-Y, Chai, R & Al-Jumaily, A 2021, 'Infrared Thermal Imaging for Evaluation of Clubfoot After the Ponseti Casting Method—An Exploratory Study', Frontiers in Pediatrics, vol. 9, pp. 1-10.
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Background:Conservative treatment, Ponseti method, has been considered as a standard method to correct the clubfoot deformity among Orthopedic society. Although the result of conservative methods have been reported with higher success rates than surgical methods, many more problems have been reported due to improper casting, casting pressure or bracing discomfort. Nowadays, infrared thermography (IRT) is widely used as a diagnostic tool to assess musculoskeletal disorders or injuries by detecting temperature abnormalities. Similarly, the foot skin temperature evaluation can be added along with the current subjective evaluation to predict if there is any casting pressure, excessive manipulation, or overcorrections of the foot, and other bracing pressure-related complications.Purpose:The main purpose of this study was to explore the foot skin temperature changes before and after using of manipulation and weekly castings.Methods:This is an explorative study design. Infrared Thermography (IRT), E33 FLIR thermal imaging camera model, was used to collect the thermal images of the clubfoot before and after casting intervention. A total of 120 thermal images (Medial region of the foot–24, Lateral side of the foot–24, Dorsal side of the foot−24, Plantar side of the foot−24, and Heel area of the foot–24) were collected from the selected regions of the clubfoot.Results:The results of univariate statistical analysis showed that significant temperature changes in some regions of the foot after casting, especially, at the 2nd (M = 32.05°C, SD = 0.77,p= 0.05), 3rd (M = 31.61, SD = 1.11; 95% CI: 31.27–31.96;p= 0.00), and 6th week of evaluation on the lateral side of the foot (M = 31.15°C, SD = 1.59; 95% CI: 30.75–31.54,p= 0.000). There was n...
Ganguly, D, Sorrelle, N, Dominguez, A, Toombs, J, Schmidt, M, Mora, FV, Ortega, DG, Wellstein, A & Brekken, RA 2021, 'Abstract LT010: Pleiotrophin drives pro-metastatic immune niche within breast tumor microenvironment', Cancer Research, vol. 81, no. 5_Supplement, pp. LT010-LT010.
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Abstract Background: Pleiotrophin (PTN), a heparin binding cytokine important during embryonic development, is expressed at elevated levels in several cancers, including breast cancer. High PTN expression in serum and tumor tissues of breast cancer patients correlates with worse outcome. Previous studies indicate PTN contributes to angiogenesis, hematopoiesis, and inflammation; however, the function of PTN in breast cancer progression and metastasis has not been elucidated. Methods: The effect of anti-PTN therapy (mAb; 3B10) on breast tumor progression and pulmonary metastasis was evaluated in four preclinical models of breast cancer: 4T1, E0771, Met-1, MMTV-PyMT. In addition, the effect of global loss of PTN using Ptn-deficient mice was evaluated in two murine models of breast cancer. Tissues from these experiments were analyzed by RNAseq and histology for changes in angiogenesis, hematopoiesis, immune cell infiltration, and cytokine/chemokine expression. Findings: TCGA data shows high PTN expression correlates with poor survival of Stage IV breast cancer patients. Furthermore, our data indicate that: a) blocking PTN pharmacologically or genetically results in a significant reduction of lung metastasis in multiple preclinical mouse models of breast cancer, b) scRNAseq and dual FISH-IHC (fluorescence in-situ hybridization and immunohistochemistry) demonstrates that endothelial cells and select population of tumor cells express PTN in the tumor microenvironment (TME), c) inhibition of PTN results in a decrease in overall inflammation with a particular decrease in neutrophil populations at primary and secondary tumor sites, d) in a genetically engineered mouse model (GEMM) of breast cancer, MMTV-PyMT, PTN expression is enriched at the metastatic site. Conclusion: Our data suggests that PTN is important in driving ...
Gangwar, AK, Mahela, OP, Rathore, B, Khan, B, Alhelou, HH & Siano, P 2021, 'A Novel $k$-Means Clustering and Weighted $k$-NN-Regression-Based Fast Transmission Line Protection', IEEE Transactions on Industrial Informatics, vol. 17, no. 9, pp. 6034-6043.
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Gao, C, Huang, L, Yan, L, Kasal, B, Li, W, Jin, R, Wang, Y, Li, Y & Deng, P 2021, 'Compressive performance of fiber reinforced polymer encased recycled concrete with nanoparticles', Journal of Materials Research and Technology, vol. 14, pp. 2727-2738.
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Nanomaterials have been used in improving the performance of construction materials due to their compacting micro-structure effect and accelerating cement hydration reaction. Considering the brittle characteristic of fiber reinforced polymer (termed as FRP) tube encased concrete and inferior properties of recycled concrete, nanoparticles were used in FRP tube encased recycled aggregate concrete. The axial compressive performance of FRP tube used in recycled concrete treated with nanoparticles strengthening, termed as FRP-NPRC, were investigated by axial compression experiments and theoretical analysis. Five experimental variables were considered including (1) the dosages and (2) varieties of nanoparticles (i.e. 1% and 2% of nanoSiO2, 1% and 2% of nanoCaCO3), (3) replacement ratios of recycled coarse aggregates (termed as RCAs) (0%, 50%, 70% and 100%) the RCAs were mainly produced from the waste cracked bricks, (4) the number of glass FRP (GFRP) tube layers (2, 4 and 6-layer) and (5) the mixing methods of concrete. Results indicate that the combination of FRP confinement and nanoparticle modification in recycled concrete exhibited up to 76.2% increase in compressive strength and 7.62 times ductility improvement. Furthermore, a design-oriented stress–strain model on the basis of the ultimate condition analysis were executed to evaluate the stress–strain property of this strengthened component.
Gao, F, He, X & Zhang, S 2021, 'Pumping effect of rainfall-induced excess pore pressure on particle migration', Transportation Geotechnics, vol. 31, pp. 100669-100669.
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Gao, G, Yu, Y, Xie, J, Yang, J, Yang, M & Zhang, J 2021, 'Constructing multilayer locality-constrained matrix regression framework for noise robust face super-resolution', Pattern Recognition, vol. 110, pp. 107539-107539.
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Gao, H, Hussain, W, Yin, Y, Zhao, W & Iqbal, M 2021, 'Editorial: AI-based mobile multimedia computing for data-smart processing', Computer Networks, vol. 195, pp. 108197-108197.
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Gao, J, Dong, S, Li, JJ, Ge, L, Xing, D & Lin, J 2021, 'New technology‐based assistive techniques in total knee arthroplasty: A Bayesian network meta‐analysis and systematic review', The International Journal of Medical Robotics and Computer Assisted Surgery, vol. 17, no. 2, p. e2189.
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AbstractBackgroundThe radiological and clinical efficiency among robot‐assisted surgery (RAS), computer‐assisted navigation system (CAS) and conventional (CON) total knee arthroplasty (TKA) remains controversial.MethodsBayesian network meta‐analysis (NMA) and systematic review were performed to investigate radiological and clinical efficiency, respectively. The certainty of the evidence was evaluated using Grading of Recommendations, Assessment, Development and Evaluation and Confidence in the Evidence from Reviews of Qualitative tools.ResultsThirty‐four RCTs (7289 patients and 7424 knees) were included. The NMA showed that RAS‐TKA had the highest probability for mechanical axis restoration (odds ratio for RAS vs. CAS 3.79, credible interval [CrI] 1.14–20.54, very low certainty), followed by CAS‐TKA (odds ratio for CAS vs. CON 2.55, CrI 1.67–4.01, very low certainty) and then CON‐TKA, without significant differences in other radiological parameters. No differences were found in clinical outcomes after qualitative systematic review (overall low certainty).ConclusionsTechnology‐based assistive techniques (CAS and RAS) may surpass the CON‐TKA, when considering higher radiological accuracy and comparable clinical outcomes.
Gao, K, Liu, Z, Wu, C, Li, J, Liu, K, Liu, Y & Li, S 2021, 'Effect of low gas concentration in underground return tunnels on characteristics of gas explosions', Process Safety and Environmental Protection, vol. 152, pp. 679-691.
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This study numerically investigated the effect of a very low gas concentration on a gas explosion's performance numerically using OpenFOAM. The use of the Harten–Lax–van Leer–Contact (HLLC) approximation algorithm based on the density-based solver was proposed to capture the shock wave. The process variable in XiFOAM of the OpenFOAM toolbox was used for the deflagration reaction. A gas explosion test was performed, and the numerical model with OpenFOAM was validated using the testing data. Based on the numerical investigation, the influence of a very low methane concentration on the flame and shock wave propagation law of a gas explosion was analyzed. It showed that the flame initially accelerated, followed by deceleration, and then accelerated again before slowing down. An increase in the methane concentration had an enhanced effect on the maximum overpressure ratio, which increased linearly with an increase in the methane concentration from 0 vol. % to 3.0 vol. % in the return tunnels. Increasing the explosive methane volume and concentration caused a significant increase in the flame spread distance. It was also noted that increasing the methane concentration caused a linear increase in the maximum overpressure ratio, and the methane volume and concentration both had a sensitive effects on the maximum overpressure ratio and average overpressure rising rate. The results clarified how the gas explosion law was affected by a very low gas concentration and provided theoretical support for controlling gas explosion disasters.
Gao, S, Yu, S & Yao, S 2021, 'An efficient protein homology detection approach based on seq2seq model and ranking', Biotechnology & Biotechnological Equipment, vol. 35, no. 1, pp. 633-640.
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Evolutionary information is essential for the protein annotation. The number of homologs of a protein retrieved is correlated with the annotations related to the protein structure or function. With the continuous increase in the number of available sequences, fast and effective homology detection methods are particularly important. To increase the efficiency of homology detection, a novel method named CONVERT is proposed in this paper. This method regards homology detection as a translation task and presents a concept of representative protein. Representative proteins are not real proteins. A representative protein corresponds to a protein family, it contains the characteristics of the family. Our method employs the seq2seq model to establish the many-to-one relationship between proteins and representative proteins. Based on the many-to-one relationship, CONVERT converts protein sequences into fixed-length numerical representations, so as to increase the efficiency of homology detection by using numerical comparison instead of sequence alignment. For alignment results, our method adopts ranking to obtain a sorted list. We evaluate the proposed method on two benchmark datasets. The experimental results show that the performances of our method are comparable with the state-of-the-art methods. Meanwhile, our method is ultra-fast and can obtain results in hundreds of milliseconds.
Gao, X, Xu, Z, Li, Y, Zhang, L, Li, G, Nghiem, LD & Luo, W 2021, 'Bacterial dynamics for gaseous emission and humification in bio-augmented composting of kitchen waste', Science of The Total Environment, vol. 801, pp. 149640-149640.
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Gao, X, Zhang, T, Du, J, An, J, Bu, X & Guo, J 2021, 'A dual-beam lens-free slot-array antenna coupled high-T c superconducting fundamental mixer at the W-band', Superconductor Science and Technology, vol. 34, no. 12, pp. 125006-125006.
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Abstract This paper presents a W-band high-T c superconducting (HTS) Josephson-junction fundamental mixer which is coupled using a dual-beam lens-free slot-array antenna. The antenna features a uniplanar six-element slot array fed by an ungrounded coplanar waveguide line, of which each element is a long slot loaded by four rectangular loops. Highly directional radiation is therefore realized by utilizing the long slots and array synthesis to form a relatively large antenna aperture. The antenna also enables asymmetric dual-beam radiation in opposite directions, which not only reduces the RF coupling losses but greatly facilitates the quasi-optics design for the integration of the HTS mixer into a cryocooler. The electromagnetic simulations show that a coupling efficiency as high as −2.2 dB, a realized gain of 13 dB and a front-to-back ratio of 10 dB are achieved at the frequency of 84 GHz. Using this on-chip antenna, a W-band HTS fundamental mixer module is experimentally developed and characterized for different operating temperatures. The measured conversion gain is −10 dB at 20 K and −14.6 dB at 40 K, respectively. The mixer noise temperature is predicted to be around 780 K at 20 K and 1600 K at 40 K, respectively. It is also analyzed that the mixer performance can be further improved if the Josephson junction parameters were optimized.
Gao, Y, Sun, S, Zhang, X, Liu, Y, Hu, J, Huang, Z, Gao, M & Pan, H 2021, 'Amorphous Dual‐Layer Coating: Enabling High Li‐Ion Conductivity of Non‐Sintered Garnet‐Type Solid Electrolyte', Advanced Functional Materials, vol. 31, no. 15, pp. 2009692-2009692.
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AbstractGarnet‐type oxide Li6.4La3Zr1.4Ta0.6O12 (LLZTO) has attracted considerable attention as a highly promising solid state electrolyte. However, its high ionic conductivity is achievable only after high temperature sintering (≈1200 °C) to form dense pellets but with detrimental brittleness and poor contact with electrodes. Herein, a novel strategy to achieve high Li+ ion conductivity of LLZTO without sintering is demonstrated. This is realized by ball milling LLZTO together with LiBH4, which results in a LLZTO composite with unique amorphous dual coating: LiBO2 as the inner layer and LiBH4 as the outer layer. After cold pressing the LLZTO composite powders under 300 MPa to form electrolyte pellets, a high Li+ ion conductivity of 8.02 × 10–5 S cm–1 is obtained at 30 °C, which is four orders of magnitude higher than that of the non‐sintered pristine LLZTO pellets (4.17 × 10–9 S cm–1). The composite electrolyte displays an ultrahigh Li+ transference number of 0.9999 and enables symmetric Li–Li cells to be cycled for 1000 h at 60 °C and 300 h at 30 °C. The significant improvements are attributed to the continuous ionic conductive network among LLZTO particles facilitated by LiBH4 that is chemically compatible and electrochemically stable with Li metal electrode.
Gao, Y, Sun, S, Zhang, X, Liu, Y, Hu, J, Huang, Z, Gao, M & Pan, H 2021, 'Solid State Electrolytes: Amorphous Dual‐Layer Coating: Enabling High Li‐Ion Conductivity of Non‐Sintered Garnet‐Type Solid Electrolyte (Adv. Funct. Mater. 15/2021)', Advanced Functional Materials, vol. 31, no. 15, pp. 2170100-2170100.
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Gao, Y, Zhu, S, Yang, C & Wen, S 2021, 'State bounding for fuzzy memristive neural networks with bounded input disturbances', Neural Networks, vol. 134, pp. 163-172.
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This paper investigates the state bounding problem of fuzzy memristive neural networks (FMNNs) with bounded input disturbances. By using the characters of Metzler, Hurwitz and nonnegative matrices, this paper obtains the exact delay-independent and delay-dependent boundary ranges of the solution, which have less conservatism than the results in existing literatures. The validity of the results is verified by two numerical examples.
Gao, Z-K, Liu, A-A, Wang, Y, Small, M, Chang, X & Kurths, J 2021, 'IEEE Access Special Section Editorial: Big Data Learning and Discovery', IEEE Access, vol. 9, pp. 158064-158073.
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Gaur, VK, Sharma, P, Gaur, P, Varjani, S, Ngo, HH, Guo, W, Chaturvedi, P & Singhania, RR 2021, 'Sustainable mitigation of heavy metals from effluents: Toxicity and fate with recent technological advancements', Bioengineered, vol. 12, no. 1, pp. 7297-7313.
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Increase in anthropogenic activities due to rapid industrialization had caused an elevation in heavy metal contamination of aquatic and terrestrial ecosystems. These pollutants have detrimental effects on human and environmental health. The majority of these pollutants are carcinogenic, neurotoxic, and are very poisonous even at very low concentrations. Contamination caused by heavy metals has become a global concern for which the traditional treatment approaches lack in providing a cost-effective and eco-friendly solution. Therefore, the use of microorganisms and plants to reduce the free available heavy metal present in the environment has become the most acceptable method by researchers. Also, in microbial- and phyto-remediation the redox reaction shifts the valence which makes these metals less toxic. In addition to this, the use of biochar as a remediation tool has provided a sustainable solution that needs further investigations toward its implementation on a larger scale. Enzymes secreted by microbes and whole microbial cell are considered an eco-efficient biocatalyst for mitigation of heavy metals from contaminated sites. To the best of our knowledge there is very less literature available covering remediation of heavy metals aspect along with the sensors used for detection of heavy metals. Systematic management should be implemented to overcome the technical and practical limitations in the use of these bioremediation techniques. The knowledge gaps have been identified in terms of its limitation and possible future directions have been discussed.
Gavhane, RS, Kate, AM, Soudagar, MEM, Wakchaure, VD, Balgude, S, Rizwanul Fattah, IM, Nik-Ghazali, N-N, Fayaz, H, Khan, TMY, Mujtaba, MA, Kumar, R & Shahabuddin, M 2021, 'Influence of Silica Nano-Additives on Performance and Emission Characteristics of Soybean Biodiesel Fuelled Diesel Engine', Energies, vol. 14, no. 5, pp. 1489-1489.
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The present study examines the effect of silicon dioxide (SiO2) nano-additives on the performance and emission characteristics of a diesel engine fuelled with soybean biodiesel. Soybean biofuel was prepared using the transesterification process. The morphology of nano-additives was studied using scanning electron microscopy (SEM), X-ray diffraction (XRD) and energy-dispersive X-ray spectroscopy (EDS). The Ultrasonication process was used for the homogeneous blending of nano-additives with biodiesel, while surfactant was used for the stabilisation of nano-additives. The physicochemical properties of pure and blended fuel samples were measured as per ASTM standards. The performance and emissions characteristics of different fuel samples were measured at different loading conditions. It was found that the brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) increased by 3.48–6.39% and 5.81–9.88%, respectively, with the addition of SiO2 nano-additives. The carbon monoxide (CO), hydrocarbon (HC) and smoke emissions for nano-additive added blends were decreased by 1.9–17.5%, 20.56–27.5% and 10.16–23.54% compared to SBME25 fuel blends.
Gavriilaki, E, Asteris, PG, Touloumenidou, T, Koravou, E-E, Koutra, M, Papayanni, PG, Karali, V, Papalexandri, A, Varelas, C, Chatzopoulou, F, Chatzidimitriou, M, Chatzidimitriou, D, Veleni, A, Grigoriadis, S, Rapti, E, Chloros, D, Kioumis, I, Kaimakamis, E, Bitzani, M, Boumpas, D, Tsantes, A, Sotiropoulos, D, Sakellari, I, Kalantzis, IG, Parastatidis, ST, Koopialipoor, M, Cavaleri, L, Armaghani, DJ, Papadopoulou, A, Brodsky, RA, Kokoris, S & Anagnostopoulos, A 2021, 'Genetic justification of severe COVID-19 using a rigorous algorithm', Clinical Immunology, vol. 226, pp. 108726-108726.
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Ge, M, Pineda, JA, Sheng, D, Burton, GJ & Li, N 2021, 'Microstructural effects on the wetting-induced collapse in compacted loess', Computers and Geotechnics, vol. 138, pp. 104359-104359.
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This paper presents the results of an experimental study aimed at evaluating the effects of soil microstructure on volume change and wetting-induced collapse of a compacted loess from Xi'an, China. One-dimensional (1D) compression tests are combined with Mercury Intrusion Porosimetry (MIP) tests and Scanning Electron Microscopy (SEM) analysis to examine the collapse behaviour for different compaction states and applied stresses. A phenomenon of partial collapse occurs upon full saturation (wetting), whose magnitude depends on the as-compacted suction, the as-compacted microstructure and the stress level applied. Following partial collapse upon full saturation some of the initially meta-stable microstructure of the compacted soil is preserved which leads to higher compressibility in subsequent loading stages. Additional collapse tests carried out under isotropic conditions show that partial collapse upon full saturation takes place only under zero-lateral deformation (1D) conditions due to the residual (‘locked-in’) horizontal stresses maintained in the sample after compaction. Microstructural results and a simple macroscopic model for soil compaction are used to qualitatively explain the phenomenon of partial collapse observed in compacted loess.
Ge, Z, Chen, L, Gomez-Garcia, R & Zhu, X 2021, 'Millimeter-Wave Wide-Band Bandpass Filter in CMOS Technology Using a Two-Layered Highpass-Type Approach With Embedded Upper Stopband', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 5, pp. 1586-1590.
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Ge, Z, Chen, L, Yang, L, Gomez-Garcia, R & Zhu, X 2021, 'On-Chip Millimeter-Wave Integrated Absorptive Bandstop Filter in (Bi)-CMOS Technology', IEEE Electron Device Letters, vol. 42, no. 1, pp. 114-117.
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Geissinger, A, Laurell, C, Öberg, C, Sandström, C, Sick, N & Suseno, Y 2021, 'Social media analytics for knowledge acquisition of market and non-market perceptions in the sharing economy', Journal of Knowledge Management, vol. 25, no. 2, pp. 500-512.
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PurposeUsing the case of Foodora, this paper aims to assess the impact of technological innovation of an emerging actor in the sharing economy through stakeholders’ perceptions in the market and non-market domains.Design/methodology/approachUsing a methodological approach called social media analytics (SMA) to explore the case of Foodora, 3,250 user-generated contents in social media are systematically gathered, coded and analysed.FindingsThe findings indicate that, while Foodora appears to be a viable provider in the marketplace, there is mounting public concern about the working conditions of its employees. In the market domain, Foodora manages its status as an online delivery platform and provider well, but at the same time, it struggles with its position in the non-market sphere, suggesting that the firm is vulnerable to regulatory change. These insights highlight the importance of simultaneously exploring and balancing market and non-market perceptions when assessing the impact of disruptive innovation.Originality/valueThis study offers originality by providing an integrative approach to consider both the market and non-market domains. It is also novel in its use of SMA as a tool for knowledge acquisition and management to evaluate the impact of emerging technologies in the sharing economy.
Gentile, CG 2021, 'Printability, Durability, Contractility and Vascular Network Formation in 3D Bioprinted Cardiac Endothelial Cells Using Alginate–Gelatin Hydrogels', Frontiers in Bioengineering and Biotechnology, vol. 9, p. 636257.
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Ghabrial, A, Franklin, DR & Zaidi, H 2021, 'A Monte Carlo simulation study of scatter fraction and the impact of patient BMI on scatter in long axial field-of-view PET scanners', Zeitschrift für Medizinische Physik, vol. 31, no. 3, pp. 305-315.
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Ghadi, MJ, Azizivahed, A, Mishra, DK, Li, L, Zhang, J, Shafie-khah, M & Catalão, JPS 2021, 'Application of small-scale compressed air energy storage in the daily operation of an active distribution system', Energy, vol. 231, pp. 120961-120961.
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While compressed air energy storage (CAES) has many applications in the field of generation and transmission power systems based on the state-of-the-art, this paper proposes the application of small-scale CAESs (SCAESs) in form of a storage aggregator in the daily operation of an active distribution system (ADS), joining the distribution system operator (DSO) for the participation in the day-ahead (DA) wholesale market. An innovative two-agent modeling approach is formulated. The first agent is responsible for aggregating SCAES units and the profit maximization of the aggregator is based on the distribution local marginal price. The DSO as the second agent receives the DA scheduling from the independent SCAES aggregator and is thus responsible for the secure operation of the ADS, utilizing solar and dispatchable distributed generation (DG) as well as purchasing power from the wholesale market. Linear programming is used for the formulation and optimization of the SCAES aggregator, while a bi-objective optimization algorithm (with the objectives of minimum operating cost as well as minimum power loss and emissions in different scenarios) is employed for DSO scheduling. The results show that the CAES aggregator can offer a considerable impact for power loss reduction, specifically, when diesel generators are not committed in the system operation (i.e., where emission has very low values between 10,000 and 12000 kg). Additionally, the CAES aggregator could reduce the operation costs of the grid in a wide range of operations, even though for the scenario in which the CAES units are not under the control of the DSO anymore and also are scheduled to maximize their own profit. Moreover, results demonstrated that CAES units can be a significant voltage control device for a distribution grid with different objectives. Finally, some conclusions are duly drawn.
Ghalambaz, M, Mehryan, SAM, Ayoubi Ayoubloo, K, Hajjar, A, Islam, MS, Younis, O & Aly, AM 2021, 'Thermal behavior and energy storage of a suspension of nano-encapsulated phase change materials in an enclosure', Advanced Powder Technology, vol. 32, no. 6, pp. 2004-2019.
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Ghalambaz, M, Mohammed, HI, Naghizadeh, A, Islam, MS, Younis, O, Mahdi, JM, Chatroudi, IS & Talebizadehsardari, P 2021, 'Optimum Placement of Heating Tubes in a Multi-Tube Latent Heat Thermal Energy Storage', Materials, vol. 14, no. 5, pp. 1232-1232.
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Utilizing phase change materials in thermal energy storage systems is commonly considered as an alternative solution for the effective use of energy. This study presents numerical simulations of the charging process for a multitube latent heat thermal energy storage system. A thermal energy storage model, consisting of five tubes of heat transfer fluids, was investigated using Rubitherm phase change material (RT35) as the. The locations of the tubes were optimized by applying the Taguchi method. The thermal behavior of the unit was evaluated by considering the liquid fraction graphs, streamlines, and isotherm contours. The numerical model was first verified compared with existed experimental data from the literature. The outcomes revealed that based on the Taguchi method, the first row of the heat transfer fluid tubes should be located at the lowest possible area while the other tubes should be spread consistently in the enclosure. The charging rate changed by 76% when varying the locations of the tubes in the enclosure to the optimum point. The development of streamlines and free-convection flow circulation was found to impact the system design significantly. The Taguchi method could efficiently assign the optimum design of the system with few simulations. Accordingly, this approach gives the impression of the future design of energy storage systems.
Ghanbari, F, Wang, Q, Hassani, A, Wacławek, S, Rodríguez-Chueca, J & Lin, K-YA 2021, 'Electrochemical activation of peroxides for treatment of contaminated water with landfill leachate: Efficacy, toxicity and biodegradability evaluation', Chemosphere, vol. 279, pp. 130610-130610.
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Contaminated water with landfill leachate (CWLL) with high salinity and high organic content (total organic carbon (TOC) = 649 mg/L and Chemical Oxygen Demand (COD) = 1175 mg/L) is a toxic and non-biodegradable effluent. The present research aimed to assess the treatment effectiveness of CWLL by electrocoagulation (EC)/oxidant process. The ferrous ions generated during the process were employed as coagulant and catalyst for the activation of different oxidants such as peroxymonosulfate (PMS), peroxydisulfate (PDS), hydrogen peroxide (HP), and percarbonate (PC) to decrease TOC in CWLL. Removal of ammonia, color, phosphorous, and chemical oxygen demand (COD) from CWLL effluent was explored at various processes. EC/HP had the best performance (∼73%) in mineralization of organic pollutants compared to others under the condition of pH 6.8, applied current of 200 mA, oxidant dosage of 6 mM, and time of 80 min. The oxidation priority was to follow this order: EC/HP > EC/PMS > EC/PDS > EC/PC. These processes enhanced the biodegradability of CWLL based on the average oxidation state and biochemical oxygen demand (BOD)/COD ratio. SUVA254 and E2/E3 indices were also investigated on obtained effluents. The phytotoxicity evaluation was carried out based on the germination index, indicating that the electro-activated oxidant was an effective system to reduce the toxicity of polluted waters. EC/HP showed supremacy compared to others in terms of efficiency, cost, and detoxification. Therefore, the electro-activated oxidant system is a good means for removing organic pollutants from real wastewater.
Ghantous, GB & Gill, AQ 2021, 'Evaluating the DevOps Reference Architecture for Multi-cloud IoT-Applications.', SN Comput. Sci., vol. 2, pp. 123-123.
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Gharleghi, R, Wright, H, Luvio, V, Jepson, N, Luo, Z, Senthurnathan, A, Babaei, B, Prusty, BG, Ray, T & Beier, S 2021, 'A multi-objective optimization of stent geometries', Journal of Biomechanics, vol. 125, pp. 110575-110575.
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Ghasemian, R, Shamshirian, A, Heydari, K, Malekan, M, Alizadeh‐Navaei, R, Ebrahimzadeh, MA, Ebrahimi Warkiani, M, Jafarpour, H, Razavi Bazaz, S, Rezaei Shahmirzadi, A, Khodabandeh, M, Seyfari, B, Motamedzadeh, A, Dadgostar, E, Aalinezhad, M, Sedaghat, M, Razzaghi, N, Zarandi, B, Asadi, A, Yaghoubi Naei, V, Beheshti, R, Hessami, A, Azizi, S, Mohseni, AR & Shamshirian, D 2021, 'The role of vitamin D in the age of COVID‐19: A systematic review and meta‐analysis', International Journal of Clinical Practice, vol. 75, no. 11, pp. 1-16.
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BackgroundEvidence recommends that vitamin D might be a crucial supportive agent for the immune system, mainly in cytokine response regulation against COVID-19. Hence, we carried out a systematic review and meta-analysis in order to maximise the use of everything that exists about the role of vitamin D in the COVID-19.MethodsA systematic search was performed in PubMed, Scopus, Embase and Web of Science up to December 18, 2020. Studies focused on the role of vitamin D in confirmed COVID-19 patients were entered into the systematic review.ResultsTwenty-three studies containing 11 901 participants entered into the meta-analysis. The meta-analysis indicated that 41% of COVID-19 patients were suffering from vitamin D deficiency (95% CI, 29%-55%), and in 42% of patients, levels of vitamin D were insufficient (95% CI, 24%-63%). The serum 25-hydroxyvitamin D concentration was 20.3 ng/mL among all COVID-19 patients (95% CI, 12.1-19.8). The odds of getting infected with SARS-CoV-2 are 3.3 times higher among individuals with vitamin D deficiency (95% CI, 2.5-4.3). The chance of developing severe COVID-19 is about five times higher in patients with vitamin D deficiency (OR: 5.1, 95% CI, 2.6-10.3). There is no significant association between vitamin D status and higher mortality rates (OR: 1.6, 95% CI, 0.5-4.4).ConclusionThis study found that most of the COVID-19 patients were suffering from vitamin D deficiency/insufficiency. Also, there is about three times higher chance of getting infected with SARS-CoV-2 among vitamin-D-deficient individuals and about five times higher probability of developing the severe disease in vitamin-D-deficient patients. Vitamin D deficiency showed no significant association with mortality rates in this population.
Ghavidel, S, Rajabi, A, Jabbari Ghadi, M, Azizivahed, A, Li, L & Zhang, J 2021, 'Hybrid power plant bidding strategy for voltage stability improvement, electricity market profit maximization, and congestion management', IET Energy Systems Integration, vol. 3, no. 2, pp. 130-141.
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AbstractThis article models a hybrid power plant (HPP), including a compressed air energy storage (CAES) aggregator with a wind power aggregator (WPA) considering network constraints. Three objective functions are considered including electricity market profit maximization, congestion management, and voltage stability improvement. In order to accurately model the WPA, pitch control curtailment wind power levels are also added to the wind power generator models. To optimize all the mentioned objective functions, a multi‐objective Pareto front solution strategy is used. Finally, a fuzzy method is used to find the best compromise solution. The proposed approach is tested on a realistic case study based on an electricity market and wind farm located in Spain, and IEEE 57‐bus test system is used to evaluate the network constraint effects on the HPP scheduling for different objective functions.
Gheisari, S, Shariflou, S, Phu, J, Kennedy, PJ, Agar, A, Kalloniatis, M & Golzan, SM 2021, 'A combined convolutional and recurrent neural network for enhanced glaucoma detection', Scientific Reports, vol. 11, no. 1, p. 1945.
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AbstractGlaucoma, a leading cause of blindness, is a multifaceted disease with several patho-physiological features manifesting in single fundus images (e.g., optic nerve cupping) as well as fundus videos (e.g., vascular pulsatility index). Current convolutional neural networks (CNNs) developed to detect glaucoma are all based on spatial features embedded in an image. We developed a combined CNN and recurrent neural network (RNN) that not only extracts the spatial features in a fundus image but also the temporal features embedded in a fundus video (i.e., sequential images). A total of 1810 fundus images and 295 fundus videos were used to train a CNN and a combined CNN and Long Short-Term Memory RNN. The combined CNN/RNN model reached an average F-measure of 96.2% in separating glaucoma from healthy eyes. In contrast, the base CNN model reached an average F-measure of only 79.2%. This proof-of-concept study demonstrates that extracting spatial and temporal features from fundus videos using a combined CNN and RNN, can markedly enhance the accuracy of glaucoma detection.
Ghezelbash, F, Eskandari, AH, Shirazi-Adl, A, Kazempour, M, Tavakoli, J, Baghani, M & Costi, JJ 2021, 'Modeling of human intervertebral disc annulus fibrosus with complex multi-fiber networks', Acta Biomaterialia, vol. 123, pp. 208-221.
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Ghobadi, R, Altaee, A, Zhou, JL, Karbassiyazdi, E & Ganbat, N 2021, 'Effective remediation of heavy metals in contaminated soil by electrokinetic technology incorporating reactive filter media', Science of The Total Environment, vol. 794, pp. 148668-148668.
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Soil contamination is increasingly a global problem with serious implications for human health. Among different soil decontamination approaches, electrokinetic (EK) remediation is a relatively new technology for treating organic and inorganic contaminants in soil. This research aims to develop an enhanced EK treatment method incorporating a compost-based reactive filter media (RFM) with the advantages of low-cost and strong affinity for heavy metals and test and improve the treatment efficiency for multiple heavy metals in natural soil. A series of EK operations were performed to investigate the performance of EK-RFM under different operating conditions such as the electric current and voltage, processing time, and the amount of RFM. The electric current and treatment time demonstrated a significant positive impact on removing Zn, Cd and Mn ions while changing the amount of RFM had an insignificant impact on the efficiency of heavy metals removal. Overall, 51.6%–72.1% removal of Zn, Cd, and Mn was achieved at 30.00 mA of electric current and 14 days of treatment duration. The energy consumption of the EK process was 0.17 kWh kg−1. The soil organic matter adversely affected the mobilization and migration of heavy metals such as Cu and Pb during EK treatment. The results are valuable in optimizing the design of the EK-RFM system, which will extend its application to field-scale soil decontamination practices.
Ghobadi, R, Altaee, A, Zhou, JL, McLean, P, Ganbat, N & Li, D 2021, 'Enhanced copper removal from contaminated kaolinite soil by electrokinetic process using compost reactive filter media', Journal of Hazardous Materials, vol. 402, pp. 123891-123891.
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Gholampour, A, Gandomi, AH, Ozbakkaloglu, T & Xie, T 2021, 'Corrigendum to “New formulations for mechanical properties of recycled aggregate concrete using gene expression programming” [Constr. Build. Mater. 130 (2017) 122–145]', Construction and Building Materials, vol. 278, pp. 122930-122930.
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Ghosh, B, Fatahi, B, Khabbaz, H, Nguyen, HH & Kelly, R 2021, 'Field study and numerical modelling for a road embankment built on soft soil improved with concrete injected columns and geosynthetics reinforced platform', Geotextiles and Geomembranes, vol. 49, no. 3, pp. 804-824.
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Gibbons, J, Jones, CMS, Bennett, NS & Marques-Hueso, J 2021, 'Determination of the refractive index of BaY2F8:Er3+ (0.5 mol% to 30 mol%) in the 300 nm to 1800 nm range by ellipsometry; a record-breaking upconversion material', Journal of Luminescence, vol. 230, pp. 117639-117639.
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© 2020 The Author(s) This work reports the ellipsometric study of trivalent erbium (Er3+) doped monocrystalline barium yttrium fluoride (BaY2F8), which has recently been shown to be one of the best photon upconversion (UC) materials available. This spans the BaY2F8 applications in a large range of wavelengths, from ultraviolet (UV) to near-infrared (NIR). We detail the optical properties of BaY2F8: Er3+ (0.5 mol%, 10 mol%, and 30 mol%), measured via variable angle spectroscopic ellipsometry over a spectral range from 300 nm to 1800 nm, reporting for the first time the indices of refraction for BaY2F8:Er3+. The upconversion external photoluminescence quantum yield (ePLQY) of the BaY2F8:Er3+ samples have also been studied by exciting at [JG]λ=1588nmλ=1493 nm. The highest ePLQY of BaY2F8 was found for the largest dopant concentration 30 mol% Er3+ reaching the value of [JG]1.07% ± 0.12%, at an irradiance of 9.512 ×10−2W/cm23.62% ± 0.01%, at an irradiance of (6.23 ± 0.45) ×10−2 W/cm2. The refractive index (λ= 589.3 nm) was determined to be 1.4808± 0.014 for 0.5 mol%, 1.4980± 0.003 for 10 mol%, and 1.5022± 0.006 for 30 mol%. Increasing Er3+ doping concentration increased the refractive index. All samples decreased monotonically with increasing wavelength. The Brewster angle of BaY2F8:Er3+ is observed to be ≈ 56∘, whilst the Abbe number of the samples was found to be as high as 124.62. These findings provide valuable insight into the optical properties of BaY2F8:Er3+ in the wide range of frequencies that is has proven useful.
Gil Aparicio, A & Valls Miro, J 2021, 'An Efficient Stochastic Constrained Path Planner for Redundant Manipulators', Applied Sciences, vol. 11, no. 22, pp. 10636-10636.
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This brief proposes a novel stochastic method that exploits the particular kinematics of mechanisms with redundant actuation and a well-known manipulability measure to track the desired end-effector task-space motion in an efficient manner. Whilst closed-form optimal solutions to maximise manipulability along a desired trajectory have been proposed in the literature, the solvers become unfeasible in the presence of obstacles. A manageable alternative to functional motion planning is thus proposed that exploits the inherent characteristics of null-space configurations to construct a generic solution able to improve manipulability along a task-space trajectory in the presence of obstacles. The proposed Stochastic Constrained Optimization (SCO) solution remains close to optimal whilst exhibiting computational tractability, being an attractive proposition for implementation on real robots, as shown with results in challenging simulation scenarios, as well as with a real 7R Sawyer manipulator, during surface conditioning tasks.
Gill, AQ 2021, 'A Theory of Information Trilogy: Digital Ecosystem Information Exchange Architecture.', Inf., vol. 12, no. 7, pp. 283-283.
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Information sharing is a critical component of a distributed and multi‐actor digital ecosystem (DE). DE actors, individuals and organisations, require seamless, effective, efficient, and secure architecture for exchanging information. Traditional point‐to‐point and ad hoc integrations hinder the ability of DE actors to do so. The challenge is figuring out how to enable information sharing in a complex DE. This paper addresses this important research challenge and proposes the theory of information trilogy and conceptual DE information exchange architecture, which is inspired by the study of nature and flow of matter, energy, and its states in natural ecosystems. This work is a part of the large DE information framework. The scope of this paper is limited to the emerging concept of DE information exchange. The application of the DE information exchange concept is demonstrated with the help of a geospatial information sharing case study example. The results from this paper can be used by researchers and practitioners for defining the DE information exchange as appropriate to their context. This work also complements Shannon’s mathematical theory of communication.
Gill, G, Mittal, RK & Rawat, S 2021, 'Comprehensive feasibility study for application of waste tire chips in enhancing the performance of shallow foundations', Environmental Science and Pollution Research, vol. 28, no. 39, pp. 55554-55578.
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Gite, S, Pradhan, B, Alamri, A & Kotecha, K 2021, 'ADMT: Advanced Driver’s Movement Tracking System Using Spatio-Temporal Interest Points and Maneuver Anticipation Using Deep Neural Networks', IEEE Access, vol. 9, pp. 99312-99326.
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Assistive driving is a complex engineering problem and is influenced by several factors such as the sporadic nature of the quality of the environment, the response of the driver, and the standard of the roads on which the vehicle is being driven. The authors track the driver's anticipation based on his head movements using Spatio-Temporal Interest Point (STIP) extraction and enhance the anticipation of action accuracy well before using the RNN-LSTM framework. This research tackles a fundamental problem of lane change assistance by developing a novel model called Advanced Driver's Movement Tracking (ADMT). ADMT uses customized convolution-based deep learning networks by using Recurrent Convolutional Neural Network (RCNN). STIP with eye gaze extraction and RCNN performed in ADMT on brain4cars dataset for driver movement tracking. Its performance is compared with the traditional machine learning and deep learning models, namely Support Vector Machines (SVM), Hidden Markov Model (HMM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and provided an increment of almost 12% in the prediction accuracy and 44% in the anticipation time. Furthermore, ADMT systems outperformed all of the models in terms of both the accuracy of the system and the previously mentioned time of anticipation that is discussed at length in the paper. Thus it assists the driver with additional anticipation time to access the typical reaction time for better preparedness to respond to undesired future behavior. The driver is then assured of a safe and assisted driving experience with the proposed system.
Goldman, S, Bramante, J, Vrdoljak, G, Guo, W, Wang, Y, Marjanovic, O, Orlowicz, S, Di Lorenzo, R & Noestheden, M 2021, 'The analytical landscape of cannabis compliance testing', Journal of Liquid Chromatography & Related Technologies, vol. 44, no. 9-10, pp. 403-420.
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Gong, S, Guo, Z, Wen, S & Huang, T 2021, 'Finite-Time and Fixed-Time Synchronization of Coupled Memristive Neural Networks With Time Delay', IEEE Transactions on Cybernetics, vol. 51, no. 6, pp. 2944-2955.
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This article is devoted to analyzing the finite-time and fixed-time synchronization of coupled memristive neural networks with time delays. The synchronization is leaderless rather than leader-follower as the tracking targets are uncertain. By designing a proper controller and using the Lyapunov method, several sufficient conditions are obtained to achieve the finite-time and fixed-time synchronization of coupled memristive neural networks by introducing a class of special auxiliary matrices. Moreover, the settling times can be estimated for finite-time synchronization that depends on the initial values as well as fixed-time synchronization that is uniformly bounded for any initial values. Finally, two examples are presented to substantiate the effectiveness of the theoretical results.
Gong, Y, Li, Z, Zhang, J, Liu, W, Yin, Y & Zheng, Y 2021, 'Missing Value Imputation for Multi-view Urban Statistical Data via Spatial Correlation Learning', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Gong, Y, Zhang, L, Liu, R, Yu, K & Srivastava, G 2021, 'Nonlinear MIMO for Industrial Internet of Things in Cyber–Physical Systems', IEEE Transactions on Industrial Informatics, vol. 17, no. 8, pp. 5533-5541.
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Gong, Z, Zhang, C, Ba, X & Guo, Y 2021, 'Improved Deadbeat Predictive Current Control of Permanent Magnet Synchronous Motor Using a Novel Stator Current and Disturbance Observer', IEEE Access, vol. 9, pp. 142815-142826.
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Gonzales, RR, Abdel-Wahab, A, Adham, S, Han, DS, Phuntsho, S, Suwaileh, W, Hilal, N & Shon, HK 2021, 'Salinity gradient energy generation by pressure retarded osmosis: A review', Desalination, vol. 500, pp. 114841-114841.
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Pressure retarded osmosis (PRO) has gained attention due to its use as a salinity gradient energy-generating membrane process. This process can convert difference in salinity between two streams into energy as it allows water transport through a semi-permeable membrane against the application of hydraulic pressure. This review provides a comprehensive look at the history and latest developments in preparation of membranes and modules for the PRO process, as well as the various applications of PRO. This review also explored the influence of feed characteristics and pretreatment strategies on water permeation and power generation during PRO operation. The current status and technological advancements of PRO as a process were reviewed, revealing how PRO can be operated as a stand-alone process or in integration with other hybrid processes. Despite the recent advancements in material and process development for PRO, membrane performance, wide-scale implementation, and commercialization efforts still leave much to be desired. Recognizing the current challenges facing the PRO technology, the advancements in PRO membrane and module development, and the various applications of the process, this review also draws out the future direction of PRO research and generation of osmotic salinity gradient energy as a viable energy source.
Gonzales, RR, Abdel-Wahab, A, Han, DS, Matsuyama, H, Phuntsho, S & Shon, HK 2021, 'Control of the antagonistic effects of heat-assisted chlorine oxidative degradation on pressure retarded osmosis thin film composite membrane surface', Journal of Membrane Science, vol. 636, pp. 119567-119567.
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During pressure retarded osmosis (PRO) operation, thin film composite (TFC) membranes are continuously exposed to chemicals present in the stream that can deteriorate the membrane's selective layer with exposure time. Following this observation, TFC membranes are placed in controlled oxidative degradation conditions using aqueous NaOCl solutions. Active chlorine, along with heat, can thin out the dense layer and, when controlled and optimized, can tune the membrane surface properties and separation efficiency as desirable for specific applications. The chlorine oxidative degradation is optimized in terms of chlorine exposure (a factor of both exposure time and chemical dosage), solution pH, and the subsequent heating time. After the chemical modification process, the membrane surface properties were characterized and the PRO performance as well as the osmotic energy harvesting capability were determined. The modified membranes exhibited different levels of polyamide degradation and increase in water permeability, which came along with decrease in selectivity. Optimization of the chlorine oxidative degradation using response surface methodology was performed to maximize the water permeability and extractable osmotic power while keeping salt rejection satisfactory. After performing chlorine oxidation at the following optimized conditions: 3025 ppm Cl2·h, pH 10.72, and 3 min heating time, initial non-pressure retarded water flux of 73.2 L m−2 h−1, specific reverse solute flux of 1.17 g L−1, and power density of 18.71 W m−2 (corresponding to water flux of 56.1 L m-2 h-1) at 12 bar were obtained using 0.6 M NaCl as draw and deionized water as feed.
Gonzales, RR, Zhang, L, Guan, K, Park, MJ, Phuntsho, S, Abdel-Wahab, A, Matsuyama, H & Shon, HK 2021, 'Aliphatic polyketone-based thin film composite membrane with mussel-inspired polydopamine intermediate layer for high performance osmotic power generation', Desalination, vol. 516, pp. 115222-115222.
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Gonzales, RR, Zhang, L, Sasaki, Y, Kushida, W, Matsuyama, H & Shon, HK 2021, 'Facile development of comprehensively fouling-resistant reduced polyketone-based thin film composite forward osmosis membrane for treatment of oily wastewater', Journal of Membrane Science, vol. 626, pp. 119185-119185.
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© 2021 Forward osmosis (FO) has proven to be a suitable process for treatment of problematic oily wastewater, due to its relatively higher water recovery rate and lower energy requirement, as opposed to pressure-driven membrane processes. Despite the lower membrane fouling propensity during FO operation, the development of comprehensively fouling-resistant membranes is further desired in FO as a suitable oily wastewater treatment process. In this current work, reduced aliphatic polyketone (rPK)-based thin film composite (TFC) membranes were developed. Reduction conditions using NaBH4 were tested, and the suitability of reduction was evaluated with membrane morphology, water wettability, and resistance to oil. The resultant rPK-TFC membrane, whose substrate was reduced with 0.5% (w/w) NaBH4 for 10 min, exhibited 37.8 L m−2 h−1 water flux in PRO mode. Using a foulant solution containing 1% (v/v) soybean oil, and 100 ppm humic acid, sodium alginate, and bovine serum albumin, the resultant rPK-TFC membrane maintained an outstanding 95% average flux recovery ratio, while the pristine PK-TFC membrane achieved an average flux recovery ratio of 67%. The results indicate that reduction of aliphatic polyketone is a facile method to develop membranes with outstanding water permeability and fouling resistance.
Gooch, LJ, Masia, MJ & Stewart, MG 2021, 'Application of stochastic numerical analyses in the assessment of spatially variable unreinforced masonry walls subjected to in-plane shear loading', Engineering Structures, vol. 235, pp. 112095-112095.
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This paper develops a modelling strategy for the finite element analysis of perforated (arched) unreinforced masonry walls subjected to in-plane shear loading. An experimental baseline was used to facilitate an accurate calibration and assessment of the chosen modelling strategy. This study provides the procedure and the results relevant to a stochastic assessment of unreinforced masonry shear walls. These results may be used in future studies of the reliability of these structures and may be applied in the calibration of reliability-based design practices. Utilising a two-dimensional micro-modelling approach, the capacity of a monotonic loading scheme to capture the envelope of a cyclically applied load was examined. It was found that, while the elastic stiffness of the laboratory specimens was overestimated by the finite element models, the peak load and global response was accurately recreated by the monotonically loaded models. Once the applicability of this procedure had been established, a series of spatially variable stochastic finite element analyses were created by considering the stochastic properties of key material parameters. These analyses were able to estimate the mean load resistance of the experimentally tested walls with a greater accuracy than a deterministic model. Furthermore, these analyses produced an accurate estimate of the variability of shear capacity of and the observed damage to the laboratory specimens. Due to the fact that the tested walls failed almost exclusively in a rocking mode, a failure mechanism highly dependent upon the structures’ geometry, the variability of the peak strength was minimal. However, the observed damage and presence of some sliding and stepped cracking indicates that the proposed methodology is likely to capture more variable and unstable failure modes in shear walls with a smaller height-to-length ratio or those more highly confined.
Goodswen, SJ, Barratt, JLN, Kennedy, PJ, Kaufer, A, Calarco, L & Ellis, JT 2021, 'Machine learning and applications in microbiology', FEMS Microbiology Reviews, vol. 45, no. 5, p. fuab015.
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ABSTRACT To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution.
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2021, 'Applying Machine Learning to Predict the Exportome of Bovine and Canine Babesia Species That Cause Babesiosis', Pathogens, vol. 10, no. 6, pp. 660-660.
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Babesia infection of red blood cells can cause a severe disease called babesiosis in susceptible hosts. Bovine babesiosis causes global economic loss to the beef and dairy cattle industries, and canine babesiosis is considered a clinically significant disease. Potential therapeutic targets against bovine and canine babesiosis include members of the exportome, i.e., those proteins exported from the parasite into the host red blood cell. We developed three machine learning-derived methods (two novel and one adapted) to predict for every known Babesia bovis, Babesia bigemina, and Babesia canis protein the probability of being an exportome member. Two well-studied apicomplexan-related species, Plasmodium falciparum and Toxoplasma gondii, with extensive experimental evidence on their exportome or excreted/secreted proteins were used as important benchmarks for the three methods. Based on 10-fold cross validation and multiple train–validation–test splits of training data, we expect that over 90% of the predicted probabilities accurately provide a secretory or non-secretory indicator. Only laboratory testing can verify that predicted high exportome membership probabilities are creditable exportome indicators. However, the presented methods at least provide those proteins most worthy of laboratory validation and will ultimately save time and money.
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2021, 'Computational Antigen Discovery for Eukaryotic Pathogens Using Vacceed', vol. 2183, pp. 29-42.
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© Springer Science+Business Media, LLC, part of Springer Nature 2021. Bioinformatics programs have been developed that exploit informative signals encoded within protein sequences to predict protein characteristics. Unfortunately, there is no program as yet that can predict whether a protein will induce a protective immune response to a pathogen. Nonetheless, predicting those pathogen proteins most likely from those least likely to induce an immune response is feasible when collectively using predicted protein characteristics. Vacceed is a computational pipeline that manages different standalone bioinformatics programs to predict various protein characteristics, which offer supporting evidence on whether a protein is secreted or membrane -associated. A set of machine learning algorithms predicts the most likely pathogen proteins to induce an immune response given the supporting evidence. This chapter provides step by step descriptions of how to configure and operate Vacceed for a eukaryotic pathogen of the user’s choice.
Gour, G & Tomamichel, M 2021, 'Entropy and Relative Entropy From Information-Theoretic Principles', IEEE Transactions on Information Theory, vol. 67, no. 10, pp. 6313-6327.
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We introduce an axiomatic approach to entropies and relative entropies that relies only on minimal information-theoretic axioms, namely monotonicity under mixing and data-processing as well as additivity for product distributions. We find that these axioms induce sufficient structure to establish continuity in the interior of the probability simplex and meaningful upper and lower bounds, e.g., we find that every relative entropy satisfying these axioms must lie between the Rényi divergences of order 0 and infty . We further show simple conditions for positive definiteness of such relative entropies and a characterisation in terms of a variant of relative trumping. Our main result is a one-to-one correspondence between entropies and relative entropies.
Graś, M, Kolanowski, Ł, Chen, Z, Lota, K, Jurak, K, Ryl, J, Ni, B-J & Lota, G 2021, 'Partial inhibition of borohydride hydrolysis using porous activated carbon as an effective method to improve the electrocatalytic activity of the DBFC anode', Sustainable Energy & Fuels, vol. 5, no. 17, pp. 4401-4413.
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Utilization of activated carbons from coffee waste in the complex borohydride electrooxidation process has great potential in increasing the efficiency of an anode based on the AB5-hydrogen storage alloy, as well as in proper management of waste.
Gravina da Rocha, C, Marin, EJB, Quiñónez Samaniego, RA & Consoli, NC 2021, 'Decision-Making Model for Soil Stabilization: Minimizing Cost and Environmental Impacts', Journal of Materials in Civil Engineering, vol. 33, no. 2, pp. 06020024-06020024.
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Gu, M, Gu, Y, Luo, W, Xu, G, Yang, Z, Zhou, J & Qu, W 2021, 'From text to graph: a general transition-based AMR parsing using neural network', Neural Computing and Applications, vol. 33, no. 11, pp. 6009-6025.
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© 2020, Springer-Verlag London Ltd., part of Springer Nature. Semantic understanding is an essential research issue for many applications, such as social network analysis, collective intelligence and content computing, which tells the inner meaning of language form. Recently, Abstract Meaning Representation (AMR) is attracted by many researchers for its semantic representation ability on an entire sentence. However, due to the non-projectivity and reentrancy properties of AMR graphs, they lose some important semantic information in parsing from sentences. In this paper, we propose a general AMR parsing model which utilizes a two-stack-based transition algorithm for both Chinese and English datasets. It can incrementally parse sentences to AMR graphs in linear time. Experimental results demonstrate that it is superior in recovering reentrancy and handling arcs while is competitive with other transition-based neural network models on both English and Chinese datasets.
Gu, P, Han, Y, Gao, W, Xu, G & Wu, J 2021, 'Enhancing session-based social recommendation through item graph embedding and contextual friendship modeling', Neurocomputing, vol. 419, pp. 190-202.
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Gu, X, Cao, Z, Jolfaei, A, Xu, P, Wu, D, Jung, T-P & Lin, C-T 2021, 'EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications', IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 5, pp. 1645-1666.
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Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.
Guan, J, Wang, Q & Ying, M 2021, 'quantum walks.', Quantum Inf. Comput., vol. 21, pp. 395-408.
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Guan, L, Abbasi, A & Ryan, MJ 2021, 'A simulation-based risk interdependency network model for project risk assessment', Decision Support Systems, vol. 148, pp. 113602-113602.
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Guan, L, Abbasi, A & Ryan, MJ 2021, 'Modelling risk interdependencies to support decision making in project risk management: Analytical and simulation-based methods', Project Governance and Controls Annual Review, vol. 4, no. 1, pp. 24-44.
Guan, W, Song, X, Gan, T, Lin, J, Chang, X & Nie, L 2021, 'Cooperation Learning From Multiple Social Networks: Consistent and Complementary Perspectives', IEEE Transactions on Cybernetics, vol. 51, no. 9, pp. 4501-4514.
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GWI survey1 has highlighted the flourishing use of multiple social networks: the average number of social media accounts per Internet user is 5.54, and among them, 2.82 are being used actively. Indeed, users tend to express their views in more than one social media site. Hence, merging social signals of the same user across different social networks together, if available, can facilitate the downstream analyses. Previous work has paid little attention on modeling the cooperation among the following factors when fusing data from multiple social networks: 1) as data from different sources characterizes the characteristics of the same social user, the source consistency merits our attention; 2) due to their different functional emphases, some aspects of the same user captured by different social networks can be just complementary and results in the source complementarity; and 3) different sources can contribute differently to the user characterization and hence lead to the different source confidence. Toward this end, we propose a novel unified model, which co-regularizes source consistency, complementarity, and confidence to boost the learning performance with multiple social networks. In addition, we derived its theoretical solution and verified the model with the real-world application of user interest inference. Extensive experiments over several state-of-the-art competitors have justified the superiority of our model.1http://tinyurl.com/zk6kgc9.
Gudigar, A, Nayak, S, Samanth, J, Raghavendra, U, A J, A, Barua, PD, Hasan, MN, Ciaccio, EJ, Tan, R-S & Rajendra Acharya, U 2021, 'Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization', International Journal of Environmental Research and Public Health, vol. 18, no. 19, pp. 10003-10003.
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Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and—when chronic—calcium components. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) performed during invasive coronary angiography are reference standards for characterizing the atherosclerotic plaque. Fine image spatial resolution attainable with contemporary coronary computed tomographic angiography (CCTA) has enabled noninvasive plaque assessment, including identifying features associated with vulnerable plaques known to presage acute coronary events. Manual interpretation of IVUS, IVOCT and CCTA images demands scarce physician expertise and high time cost. This has motivated recent research into and development of artificial intelligence (AI)-assisted methods for image processing, feature extraction, plaque identification and characterization. We performed parallel searches of the medical and technical literature from 1995 to 2021 focusing respectively on human plaque characterization using various imaging modalities and the use of AI-assisted computer aided diagnosis (CAD) to detect and classify atherosclerotic plaques, including their composition and the presence of high-risk features denoting vulnerable plaques. A total of 122 publications were selected for evaluation and the analysis was summarized in terms of data sources, methods—machine versus deep learning—and performance metrics. Trends in AI-assisted plaque characterization are detailed and prospective research challenges discussed. Future directions for the development of accurate and efficient CAD systems to characterize plaque noninvasively using CCTA are proposed.
Gudigar, A, Raghavendra, U, Nayak, S, Ooi, CP, Chan, WY, Gangavarapu, MR, Dharmik, C, Samanth, J, Kadri, NA, Hasikin, K, Barua, PD, Chakraborty, S, Ciaccio, EJ & Acharya, UR 2021, 'Role of Artificial Intelligence in COVID-19 Detection', Sensors, vol. 21, no. 23, pp. 8045-8045.
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The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.
Guertler, MR & Sick, N 2021, 'Exploring the enabling effects of project management for SMEs in adopting open innovation – A framework for partner search and selection in open innovation projects', International Journal of Project Management, vol. 39, no. 2, pp. 102-114.
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© 2020 Open Innovation (OI) facilitates a multitude of innovation opportunities through allowing access to a broad variety of external partners, expertise and knowledge. Although OI has been established in academia and the corporate world, implementation by SMEs remains a formidable challenge, especially concerning the identification and selection of suitable OI partners. Given methodical support for such an endeavour is currently lacking, this article investigates how project management can support OI projects. Based on evidence from an exploratory multi-case study with four SMEs, this article develops a Situational Open Innovation framework that provides methodical support for SMEs in leveraging the complementarities between OI and project management towards effective partner search and selection. The findings illustrate how sensing capabilities for OI opportunities can benefit from systematic problem and stakeholder analyses as they allow for identifying and focussing on the most relevant innovation tasks and partners.
Guff, T, McMahon, NA, Sanders, YR & Gilchrist, A 2021, 'A resource theory of quantum measurements', Journal of Physics A: Mathematical and Theoretical, vol. 54, no. 22, pp. 225301-225301.
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Abstract Resource theories are broad frameworks that capture how useful objects are in performing specific tasks. In this paper we devise a formal resource theory quantum measurements, focusing on the ability of a measurement to acquire information. The objects of the theory are equivalence classes of positive operator-valued measures, and the free transformations are changes to a measurement device that can only deteriorate its ability to report information about a physical system. We show that catalysis and purification, protocols that are possible in other resource theories, are impossible in our resource theory for quantum measurements. Standard measures of information gain are shown to be resource monotones, and the resource theory is applied to the task of quantum state discrimination.
Gul, M, Zulkifli, NWM, Kalam, MA, Masjuki, HH, Mujtaba, MA, Yousuf, S, Bashir, MN, Ahmed, W, Yusoff, MNAM, Noor, S, Ahmad, R & Hassan, MT 2021, 'RSM and Artificial Neural Networking based production optimization of sustainable Cotton bio-lubricant and evaluation of its lubricity & tribological properties', Energy Reports, vol. 7, pp. 830-839.
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Gunatilake, A, Piyathilaka, L, Tran, A, Vishwanathan, VK, Thiyagarajan, K & Kodagoda, S 2021, 'Stereo Vision Combined With Laser Profiling for Mapping of Pipeline Internal Defects', IEEE Sensors Journal, vol. 21, no. 10, pp. 11926-11934.
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Underground potable water pipes are essential infrastructure assets for any country. A significant proportion of those assets are deteriorating due to pipe corrosion which results in premature failure of pipes causing enormous disruptions to the public and loss to the economy. To address such adverse effects, the water utilities in Australia exploit advanced pipelining technologies with a motive of extending the service life of their pipe assets. However, the linings are prone to defects due to improper liner application and unfavorable environmental conditions during the liner curing phase. To monitor the imperfections of the pipe linings, in this article, we propose a mobile robotic sensing system that can scan, detect, locate and measure pipeline internal defects by generating three-dimensional RGB-Depth maps using stereo camera vision combined with infrared laser profiling unit. The system does not require complex calibration procedures and it utilizes orientation correction to provide accurate real-time RGB-D maps. The defects are identified and color mapped for easier visualization. The robotic sensing system was extensively tested in laboratory conditions followed by field deployments in buried water pipes in Sydney, Australia. The experimental results show that the RGB-D maps were generated with millimeter (mm) level accuracy with demonstrated liner defect quantification.
Gunawan, Y, Putra, N, Hakim, II, Agustina, D & Mahlia, TMI 2021, 'Withering of tea leaves using heat pipe heat exchanger by utilizing low-temperature geothermal energy', International Journal of Low-Carbon Technologies, vol. 16, no. 1, pp. 146-155.
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Abstract The volume of Indonesian tea exports to the European Union (EU) decreased by 43% in 2014 because of the EU setting a maximum residue limit of anthraquinone (AQ) for tea as 0.02 mg/kg. The content of AQ in tea leaves increases when there is incomplete combustion in the combustion of firewood for the energy source of withering and drying of tea leaves. This study aims to develop and test a new concept for the direct use of low-temperature geothermal energy with a heat pipe heat exchanger (HPHE) for the withering of tea leaves as a solution for energy sources free from AQ. The geothermal fluid simulators use water, which is heated by heater and flowed by a pump. The HPHE used consists of 42 heat pipes and 181 fins. The heat pipe used has a length of 700 mm with an outer diameter of 10 mm. Each fin is made of aluminum with a thickness of 0.105 mm and a size of 76 × 345 mm2. The results show that the effectiveness of the HPHE varies from 66% to 79.59%. For 100 g of fresh tea leaves, the heating energy produced ranges from 15.21 W to 45.07 W, meaning it can wither tea leaves from 80% (w.b.) to 54% (w.b.) in a variety of 11 h 56 min to only 49.6 min. The Page mathematical model is the best model to represent the behavior of the tea leaves with this HPHE system.
Guo, H, Nguyen, H, Bui, X-N & Armaghani, DJ 2021, 'A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET', Engineering with Computers, vol. 37, no. 1, pp. 421-435.
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Guo, H, Wang, Y, Tian, L, Wei, W, Zhu, T & Liu, Y 2021, 'Insight into the enhancing short-chain fatty acids (SCFAs) production from waste activated sludge via polyoxometalates pretreatment: Mechanisms and implications', Science of The Total Environment, vol. 800, pp. 149392-149392.
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Polyoxometalates (POMs), a versatile and environmentally-friendly inorganic material, have been extensively studied and applied in chemical catalytic oxidation and biological nutrients removal processes. However, little is known about effects of POMs pretreatment on anaerobic sludge fermentation. This study thereby filled such knowledge gap and provided insights into the underlying mechanisms. Results demonstrated the maximal short-chain fatty acids (SCFAs) production increased by 6.18 times with POMs rising from 0 to 0.05 g/g TSS. Mechanistic investigations revealed that the oxidation stress of POMs as well as reactive oxygen species (ROS) activated by POMs were responsible for the disintegration of waste activated sludge (WAS). More importantly, POMs pretreatment improved the biodegradability of organics released, providing more biodegradable substrates for SCFAs generation. Furthermore, the inhibition of POMs to SCFAs producers was less severe than that to SCFAs consumers, leading to SCFAs accumulation. Microbial community analysis exhibited that increased the population of hydrolysis (i.e., Longilinea) and SCFAs generation microbes (i.e., Acinetobacter and Fusibacter). Further evaluation showed that the POMs-based technology is economically and environmentally attractive for the pretreatment of WAS. Finally, a 'closed-loop' concept of the reutilization of renewable POMs may provide an important implication of WAS management in the future.
Guo, H, Wang, Y, Tian, L, Wei, W, Zhu, T & Liu, Y 2021, 'Unveiling the mechanisms of a novel polyoxometalates (POMs)-based pretreatment technology for enhancing methane production from waste activated sludge', Bioresource Technology, vol. 342, pp. 125934-125934.
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Guo, H, Zhou, J, Koopialipoor, M, Jahed Armaghani, D & Tahir, MM 2021, 'Deep neural network and whale optimization algorithm to assess flyrock induced by blasting', Engineering with Computers, vol. 37, no. 1, pp. 173-186.
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Guo, J, Gong, Z & Cao, L 2021, 'dhCM: Dynamic and Hierarchical Event Categorization and Discovery for Social Media Stream', ACM Transactions on Intelligent Systems and Technology, vol. 12, no. 5, pp. 1-25.
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The online event discovery in social media based documents is useful, such as for disaster recognition and intervention. However, the diverse events incrementally identified from social media streams remain accumulated, ad hoc, and unstructured. They cannot assist users in digesting the tremendous amount of information and finding their interested events. Further, most of the existing work is challenged by jointly identifying incremental events and dynamically organizing them in an adaptive hierarchy. To address these problems, this article proposes d ynamic and h ierarchical C ategorization M odeling (dhCM) for social media stream. Instead of manually dividing the timeframe, a multimodal event miner exploits a density estimation technique to continuously capture the temporal influence between documents and incrementally identify online events in textual, temporal, and spatial spaces. At the same time, an adaptive categorization hierarchy is formed to automatically organize the documents into proper categories at multiple levels of granularities. In a nonparametric manner, dhCM accommodates the increasing complexity of data streams with automatically growing the categorization hierarchy over adaptive growth. A sequential Monte Carlo algorithm is used for the online inference of the dhCM parameters. Extensive experiments show that dhCM outperforms the state-of-the-art models in terms of term coherence, category abstraction and specialization, hierarchical affinity, and event categorization and discovery accuracy.
Guo, K & Guo, Y 2021, 'Design Optimization of Linear-Rotary Motion Permanent Magnet Generator With E-Shaped Stator', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-5.
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A linear-rotary motion permanent magnet (PM) generator is investigated with six axial modular E-shaped stator sections arranged circumferentially, which can meet the requirement of wave and tidal energies generation. The PM pole is magnetized in radial direction, and the adjacent PM poles with opposite magnetized directions are interlaced by half mover pole pitch both in circumferential and axial directions, which are located on the mover surface. The rotational and rectilinear motions are achieved by one magnetic circuit structure according to the transverse flux principle and electromagnetic induction principle. The optimization design and electromagnetic properties of the proposed motor are calculated by 3-D finite element simulation. Then the optimal structural parameters are obtained. The back electromotive force (back EMF) and harmonics, the amplitudes of the cogging torque and detent force are decreased than those of the initial topology. Since three phases of the nine phase windings generate same initial phase angle of back EMF whether it works in rotational or rectilinear motion, the traditional three phase energy storage system can be used to realize the energy storage of the nine phase windings, which reduces the difficulty of electrical energy storage. The variation of the amplitude of back EMF is hardly affected with different mover positions, which is conducive to improve the efficiency of marine energy power generation.
Guo, K & Guo, Y 2021, 'Electromagnetic Characteristic Analysis of BFSLRM', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-6.
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Flux concentrated structure is adopted in the design of bidirectional flux switching linear-rotary motor, which not only brings high torque/thrust density, but also increases stator iron material magnetic saturation. In order to decrease the magnetic maturation level of the motor, the stator pole width, stator pole axial length, permanent magnet (PM) width, PM axial length and stator yoke height are selected as the analysis variables, which are closely related to the two magnetic saturation regions of the stator section based on the initial analysis. The expressions of flux density in the two magnetically saturated regions of the stator core related with the selected five structure variables are derived by numerical fitting method based on the initial simulation result calculated by finite element method. Then the optimization structure variable values are achieved, a prototype and its test experiment are carried out, which verifies that the torque and thrust densities are improved and the numerical fitting method is efficient and accurate for magnetic saturation calculation.
Guo, W, Ngo, HH, Surampalli, RY & Zhang, TC 2021, 'Preface', Sustainable Resource Management: Technologies for Recovery and Reuse of Energy and Waste Materials, pp. xix-xx.
Guo, YJ, Ansari, M & Fonseca, NJG 2021, 'Circuit Type Multiple Beamforming Networks for Antenna Arrays in 5G and 6G Terrestrial and Non-Terrestrial Networks', IEEE Journal of Microwaves, vol. 1, no. 3, pp. 704-722.
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Guo, YJ, Ansari, M, Ziolkowski, RW & Fonseca, NJG 2021, 'Quasi-Optical Multi-Beam Antenna Technologies for B5G and 6G mmWave and THz Networks: A Review', IEEE Open Journal of Antennas and Propagation, vol. 2, pp. 807-830.
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Multi-beam antennas are critical components in future terrestrial and non-terrestrial wireless communications networks. The multiple beams produced by these antennas will enable dynamic interconnection of various terrestrial, airborne and space-borne network nodes. As the operating frequency increases to the high millimeter wave (mmWave) and terahertz (THz) bands for beyond 5G (B5G) and sixth-generation (6G) systems, quasi-optical techniques are expected to become dominant in the design of high gain multi-beam antennas. This paper presents a timely overview of the mainstream quasi-optical techniques employed in current and future multi-beam antennas. Their operating principles and design techniques along with those of various quasi-optical beamformers are presented. These include both conventional and advanced lens and reflector based configurations to realize high gain multiple beams at low cost and in small form factors. New research challenges and industry trends in the field, such as planar lenses based on transformation optics and metasurface-based transmitarrays, are discussed to foster further innovations in the microwave and antenna research community.
Gupta, A, Agrawal, RK, Kirar, JS, Andreu-Perez, J, Ding, W-P, Lin, C-T & Prasad, M 2021, 'On the Utility of Power Spectral Techniques With Feature Selection Techniques for Effective Mental Task Classification in Noninvasive BCI', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 5, pp. 3080-3092.
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Gupta, BB, Yadav, K, Razzak, I, Psannis, K, Castiglione, A & Chang, X 2021, 'A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment', Computer Communications, vol. 175, pp. 47-57.
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In recent times, we can see a massive increase in the number of devices that are being connected to the internet. These devices include but are not limited to smartphones, IoT, and cloud networks. In comparison to other possible cyber-attacks, these days, hackers are targeting these devices with phishing attacks since it exploits human vulnerabilities rather than system vulnerabilities. In a phishing attack, an online user is deceived by a seemingly trusted entity to give their personal data, i.e., login credentials or credit card details. When this private information is leaked to the hackers, this information becomes the source of other sophisticated attacks. In recent times many researchers have proposed the machine learning-based approach to solve phishing attacks; however, they have used a large number of features to develop reliable phishing detection techniques. A large number of features requires large processing powers to detect phishing, which makes it very much unsuitable for resource constrained devices. To address this issue, we have developed a phishing detection approach that only needs nine lexical features for effectively detecting phishing attacks. We used ISCXURL-2016 dataset for our experimental purpose, where 11964 instances of legitimate and phishing URLs are used. We have tested our approach against different machine learning classifiers and have obtained the highest accuracy of 99.57% with the Random forest algorithm.
Gupta, D, Choudhury, A, Gupta, U, Singh, P & Prasad, M 2021, 'Computational approach to clinical diagnosis of diabetes disease: a comparative study', Multimedia Tools and Applications, vol. 80, no. 20, pp. 30091-30116.
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© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature. Diabetes is one of the most prevalent non-communicable diseases and is the 6th leading cause of death worldwide. It’s a chronic metabolic disorder which has no cure, however, it is a highly treatable condition, if diagnosed and managed on time to avoid its complications. This paper explores and compares various machine learning (ML) approaches that can help in determining the risk of diabetes at an early stage and aid in improving the medical diagnosis of diabetes. The paper considers two real-world datasets one is a diabetic clinical dataset (DCA) collected from a medical practitioner in the state of Assam, India during the year 2017–2018 and other is public PIMA Indian diabetic dataset. To analyze the various machine learning techniques on DCA and PIMA Indian diabetic datasets for the classification of diabetic and non-diabetic patients, different classifiers like perceptron, Gaussian process, linear discriminant analysis, quadratic discriminant analysis, statistical gradient descent, ridge regression classifier, support vector machines, k-nearest neighbors, decision tree, naïve Bayes, logistic regression, random forest and ELM for multiquadric, RBF, sigmoid activation functions are used. The results of numerical experiments suggested that logistic regression yields better performance in comparison to the other techniques.
Gupta, S, Kashani, A, Mahmood, AH & Han, T 2021, 'Carbon sequestration in cementitious composites using biochar and fly ash – Effect on mechanical and durability properties', Construction and Building Materials, vol. 291, pp. 123363-123363.
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Gyamfi, BA, Adebayo, TS, Bekun, FV, Agyekum, EB, Kumar, NM, Alhelou, HH & Al-Hinai, A 2021, 'Beyond environmental Kuznets curve and policy implications to promote sustainable development in Mediterranean', Energy Reports, vol. 7, pp. 6119-6129.
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Haering, M, Bano, M, Zowghi, D, Kearney, M & Maalej, W 2021, 'Automating the Evaluation of Education Apps With App Store Data.', IEEE Trans. Learn. Technol., vol. 14, no. 1, pp. 16-27.
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Hafiz, M, Alfahel, R, Hawari, AH, Hassan, MK & Altaee, A 2021, 'A Hybrid NF-FO-RO Process for the Supply of Irrigation Water from Treated Wastewater: Simulation Study', Membranes, vol. 11, no. 3, pp. 191-191.
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Municipal treated wastewater could be considered as a water source for food crop irrigation purposes. Enhancing the quality of treated wastewater to meet irrigation standards has become a necessary practice. Nanofiltration (NF) was used in the first stage to produce permeate at relatively low energy consumption. In the second stage, two membrane combinations were tested for additional water extraction from the brine generated by the NF process. The simulation results showed that using a hybrid forward osmosis (FO)–reverse osmosis (RO) system is more efficient than using the RO process alone for the further extraction of water from the brine generated by the NF process. The total specific energy consumption can be reduced by 27% after using FO as an intermediate process between NF and RO. In addition, the final permeate water quality produced using the hybrid FO-RO system was within the allowable standards for food crops irrigation.
Hafiz, M, Hawari, AH, Alfahel, R, Hassan, MK & Altaee, A 2021, 'Comparison of Nanofiltration with Reverse Osmosis in Reclaiming Tertiary Treated Municipal Wastewater for Irrigation Purposes', Membranes, vol. 11, no. 1, pp. 32-32.
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This study compares the performance of nanofiltration (NF) and reverse osmosis (RO) for the reclamation of ultrafiltered municipal wastewater for irrigation of food crops. RO and NF technologies were evaluated at different applied pressures; the performance of each technology was evaluated in terms of water flux, recovery rate, specific energy consumption and quality of permeate. It was found that the permeate from the reverse osmosis (RO) process complied with Food and Agriculture Organization (FAO) standards at pressures applied between 10 and 18 bar. At an applied pressure of 20 bar, the permeate quality did not comply with irrigation water standards in terms of chloride, sodium and calcium concentration. It was found that nanofiltration process was not suitable for the reclamation of wastewater as the concentration of chloride, sodium and calcium exceeded the allowable limits at all applied pressures. In the reverse osmosis process, the highest recovery rate was 36%, which was achieved at a pressure of 16 bar. The specific energy consumption at this applied pressure was 0.56 kWh/m3. The lowest specific energy of 0.46 kWh/m3 was achieved at an applied pressure of 12 bar with a water recovery rate of 32.7%.
Halat, DM, Snyder, RL, Sundararaman, S, Choo, Y, Gao, KW, Hoffman, ZJ, Abel, BA, Grundy, LS, Galluzzo, MD, Gordon, MP, Celik, H, Urban, JJ, Prendergast, D, Coates, GW, Balsara, NP & Reimer, JA 2021, 'Modifying Li+ and Anion Diffusivities in Polyacetal Electrolytes: A Pulsed-Field-Gradient NMR Study of Ion Self-Diffusion', Chemistry of Materials, vol. 33, no. 13, pp. 4915-4926.
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Hamilton, T 2021, 'The best of both worlds', Nature Machine Intelligence, vol. 3, no. 3, pp. 194-195.
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Han, B, Tsang, IW, Xiao, X, Chen, L, Fung, S-F & Yu, CP 2021, 'Privacy-Preserving Stochastic Gradual Learning', IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 8, pp. 3129-3140.
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It is challenging for stochastic optimization to handle large-scale sensitive data safely. Duchi et al. recently proposed a private sampling strategy to solve privacy leakage in stochastic optimization. However, this strategy leads to a degeneration in robustness, since this strategy is equal to noise injection on each gradient, which adversely affects updates of the primal variable. To address this challenge, we introduce a robust stochastic optimization under the framework of local privacy, which is called Privacy-pREserving StochasTIc Gradual lEarning (PRESTIGE). PRESTIGE bridges private updates of the primal variable (by private sampling) with gradual curriculum learning (CL). The noise injection leads to similar issue from label noise, but the robust learning process of CL can combat with label noise. Thus, PRESTIGE yields 'private but robust' updates of the primal variable on the curriculum, that is, a reordered label sequence provided by CL. In theory, we reveal the convergence rate and maximum complexity of PRESTIGE. Empirical results on six datasets show that PRESTIGE achieves a good tradeoff between privacy preservation and robustness over baselines.
Han, C, Wang, X, Peng, J, Xia, Q, Chou, S, Cheng, G, Huang, Z & Li, W 2021, 'Recent Progress on Two-Dimensional Carbon Materials for Emerging Post-Lithium (Na+, K+, Zn2+) Hybrid Supercapacitors', Polymers, vol. 13, no. 13, pp. 2137-2137.
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The hybrid ion capacitor (HIC) is a hybrid electrochemical energy storage device that combines the intercalation mechanism of a lithium-ion battery anode with the double-layer mechanism of the cathode. Thus, an HIC combines the high energy density of batteries and the high power density of supercapacitors, thus bridging the gap between batteries and supercapacitors. Two-dimensional (2D) carbon materials (graphite, graphene, carbon nanosheets) are promising candidates for hybrid capacitors owing to their unique physical and chemical properties, including their enormous specific surface areas, abundance of active sites (surface and functional groups), and large interlayer spacing. So far, there has been no review focusing on the 2D carbon-based materials for the emerging post-lithium hybrid capacitors. This concept review considers the role of 2D carbon in hybrid capacitors and the recent progress in the application of 2D carbon materials for post-Li (Na+, K+, Zn2+) hybrid capacitors. Moreover, their challenges and trends in their future development are discussed.
Han, DS, Solayman, KMD, Shon, HK & Abdel-Wahab, A 2021, 'Pyrite (FeS2)-supported ultrafiltration system for removal of mercury (II) from water', Emergent Materials, vol. 4, no. 5, pp. 1441-1453.
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AbstractThis study investigated the Hg(II) removal efficiencies of the reactive adsorbent membrane (RAM) hybrid filtration process, a removal process that produces stable final residuals. The reaction mechanism between Hg(II) and pyrite and the rejection of the solids over time were characterized with respect to flux decline, pH change, and Hg and Fe concentration in permeate water. Effects of the presence of anions (Cl−, SO42−, NO3−) or humic acid (HA) on the rejection of the Hg(II)-contacted pyrite were studied. The presence of both HA and Hg(II) increased the rate of flux decline due to the formation of irreversible gel-like compact cake layers as shown in the experimental data and modeling related to the flux decline and the SEM images. Stability experiments of the final residuals retained on the membrane using a thiosulfate solution (Na2S2O3) show that the Hg(II)-laden solids were very stable due to little or no detection of Hg(II) in the permeate water. Experiment on the possibility of continuously removing Hg(II) by reusing the Hg/pyrite-laden membrane shows that almost all Hg(II) was adsorbed onto the pyrite surface regardless of the presence of salts or HA, and the Hg(II)-contacted pyrite residuals were completely rejected by the DE/UF system. Therefore, a membrane filter containing pyrite-Hg(II) could provide another reactive cake layer capable of further removal of Hg(II) without post-chemical treatment for reuse.
Han, R, Diao, J, Kumar, S, Lyalin, A, Taketsugu, T, Casillas, G, Richardson, C, Liu, F, Yoon, CW, Liu, H, Sun, X & Huang, Z 2021, 'Boron nitride for enhanced oxidative dehydrogenation of ethylbenzene', Journal of Energy Chemistry, vol. 57, pp. 477-484.
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Han, S, Hu, C, Yu, J, Jiang, H & Wen, S 2021, 'Stabilization of inertial Cohen-Grossberg neural networks with generalized delays: A direct analysis approach', Chaos, Solitons & Fractals, vol. 142, pp. 110432-110432.
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The paper is mainly devoted to the stabilization problem of Cohen-Grossberg type inertial neural networks (INNs) with generalized delays by developing a direct analysis approach to replace the previous transformations of reduced order. Above all, a generalized form of time delays is developed to unify discrete constant delays, discrete variable delays and proportional delays. In stabilization analysis, in the absence of variable substitutions, a direct method is proposed by constructing Lyapunov functionals and designing control schemes for the addressed second-order Cohen-Grossberg INNs to achieve asymptotical or adaptive stabilization. The obtained criteria are simpler and more easily verified in applications compared with the related existing results. At last, three specified examples are provided to verify the theoretical results.
Han, Y, Wu, A, Zhu, L & Yang, Y 2021, 'Visual commonsense reasoning with directional visual connections', Frontiers of Information Technology & Electronic Engineering, vol. 22, no. 5, pp. 625-637.
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To boost research into cognition-level visual understanding, i.e., making an accurate inference based on a thorough understanding of visual details, visual commonsense reasoning (VCR) has been proposed. Compared with traditional visual question answering which requires models to select correct answers, VCR requires models to select not only the correct answers, but also the correct rationales. Recent research into human cognition has indicated that brain function or cognition can be considered as a global and dynamic integration of local neuron connectivity, which is helpful in solving specific cognition tasks. Inspired by this idea, we propose a directional connective network to achieve VCR by dynamically reorganizing the visual neuron connectivity that is contextualized using the meaning of questions and answers and leveraging the directional information to enhance the reasoning ability. Specifically, we first develop a GraphVLAD module to capture visual neuron connectivity to fully model visual content correlations. Then, a contextualization process is proposed to fuse sentence representations with visual neuron representations. Finally, based on the output of contextualized connectivity, we propose directional connectivity to infer answers and rationales, which includes a ReasonVLAD module. Experimental results on the VCR dataset and visualization analysis demonstrate the effectiveness of our method.
Han, Z, Jiang, J, Xu, J, Zhang, P, Zhao, X, Wen, D & Dou, Y 2021, 'A high-throughput scalable BNN accelerator with fully pipelined architecture', CCF Transactions on High Performance Computing, vol. 3, no. 1, pp. 17-30.
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Han, Z, Xu, C, Xiong, Z, Zhao, G & Yu, S 2021, 'On-Demand Dynamic Controller Placement in Software Defined Satellite-Terrestrial Networking', IEEE Transactions on Network and Service Management, vol. 18, no. 3, pp. 2915-2928.
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Software defined satellite-terrestrial networking has been identified as a promising approach to support the diversity of network services. As the fundamental issue to improve the flexibility of network management, the controller placement problem has been attracted increasing attentions for the integration of satellite and terrestrial networking. However, the impact of the dynamic coverage demands on the controller placement have not been well investigated in existed works, which makes them fail to adjust the coverage dynamically according to the actual demands, and leads to an obvious increase of networking response latency to the terminals. Aiming to address this issue, we propose a novel on-demand dynamic controller placement scheme, which can optimize the placement of controllers to improve networking response latency while meeting the dynamic coverage demands. Firstly, to optimize the number of controllers and meet the dynamic coverage demands, we define the coverage redundancy and propose the redundancy-based satellite subnet division method to establish the reliable satellite subnets. Secondly, we quantify the networking response latency of the distributed satellite subnet, and build an optimization mathematical model to optimize the number and location of controllers. Then, we formulate the controller placement problem into the capacitated facility location problem and build the mathematical model for it. Moreover, the on-demand dynamic approximation algorithm is proposed to obtain the approximation solution. Finally, the simulation results demonstrate that the proposed algorithm can effectively optimize the network latency compared with related algorithms.
Hannan, MA, Al-Shetwi, AQ, Ker, PJ, Begum, RA, Mansor, M, Rahman, SA, Dong, ZY, Tiong, SK, Mahlia, TMI & Muttaqi, KM 2021, 'Impact of renewable energy utilization and artificial intelligence in achieving sustainable development goals', Energy Reports, vol. 7, pp. 5359-5373.
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Hannan, MA, How, DNT, Lipu, MSH, Mansor, M, Ker, PJ, Dong, ZY, Sahari, KSM, Tiong, SK, Muttaqi, KM, Mahlia, TMI & Blaabjerg, F 2021, 'Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model', Scientific Reports, vol. 11, no. 1, p. 19541.
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AbstractAccurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.
Hannan, MA, Wali, SB, Ker, PJ, Rahman, MSA, Mansor, M, Ramachandaramurthy, VK, Muttaqi, KM, Mahlia, TMI & Dong, ZY 2021, 'Battery energy-storage system: A review of technologies, optimization objectives, constraints, approaches, and outstanding issues', Journal of Energy Storage, vol. 42, pp. 103023-103023.
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Due to urbanization and the rapid growth of population, carbon emission is increasing, which leads to climate change and global warming. With an increased level of fossil fuel burning and scarcity of fossil fuel, the power industry is moving to alternative energy resources such as photovoltaic power (PV), wind power (WP), and battery energy-storage systems (BESS), among others. BESS has some advantages over conventional energy sources, which include fast and steady response, adaptability, controllability, environmental friendliness, and geographical independence, and it is considered as a potential solution to the global warming problem. This paper provides a comprehensive review of the battery energy-storage system concerning optimal sizing objectives, the system constraint, various optimization models, and approaches along with their advantages and weakness. Furthermore, for better understanding, the optimization objectives and methods have been classified into different categories. This paper also provides a detailed discussion on the BESS applications and explores the shortages of existing optimal BESS sizing algorithms to identify the gaps for future research. The issues and challenges are also highlighted to provide a clear idea to the researchers in the field of BESS. Overall, this paper conveys some significant recommendations that would be useful to the researchers and policymakers to structure a productive, powerful, efficient, and robust battery energy-storage system toward a future with a sustainable environment.
Hao, D, Chen, Z-G, Figiela, M, Stepniak, I, Wei, W & Ni, B-J 2021, 'Emerging alternative for artificial ammonia synthesis through catalytic nitrate reduction', Journal of Materials Science & Technology, vol. 77, pp. 163-168.
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Artificial catalytic synthesis of ammonia has become a hot research frontier in recent years. It is regarded as a promising approach that may replace the Haber-Bosch process and reduce global carbon dioxide emission. However, it is extremely difficult for the cleavage of nitrogen molecules under ambient conditions. Thus the ammonia yield rate is still low and the study is still limited in lab scale. If nitrites or nitrates are used as nitrogen sources, rather than nitrogen gas, the catalytic efficiency can be significantly improved, and the residual nitrate and nitrite contaminations in water systems can be efficiently eliminated and converted to energy sources at the same time. It is an emerging alternative for artificial ammonia synthesis, while there is not enough focus on the reduction of nitrate and nitrite. Herein, we systematically compared the differences between the reduction of nitrogen and nitrates, as well as listed the challenges in this area. The total conversion rate and energy efficiency of catalytic nitrate reduction are much higher than nitrogen gas reduction due to the much higher solubility and better converting pathway, which might be further enhanced by employing catalysts improvement strategies. Further, we also proposed suitable materials as well as a few future researches needs that may help boost the development of artificial ammonia synthesis using nitrate.
Hao, D, Huang, Q, Wei, W, Bai, X & Ni, B-J 2021, 'A reusable, separation-free and biodegradable calcium alginate/g-C3N4 microsphere for sustainable photocatalytic wastewater treatment', Journal of Cleaner Production, vol. 314, pp. 128033-128033.
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Graphitic carbon nitride (g-C3N4) is a metal-free photocatalyst with the advantages of facile preparation, low cost and good photocatalytic performance. However, as an extensively studied powder catalyst, it is though difficult to be recycled and reused which requires attention urgently. In this study, we report a facile preparation of g-C3N4 hydrogel microspheres with the cross-link reactions between sodium alginate and calcium ions, which can be simply removed from liquid for reuse. The hydroxyl of calcium alginate enabled to boost the adsorption of organic pollutants as well as to boost the transfer and separation of photogenerated charge carriers. The g-C3N4 hydrogel microspheres showed remarkable performance in the control of organic pollutant contamination. The sample 25%-SACN had the best photocatalytic activity, which can remove 80.94% MB in 42 h. The total removal is 1.77 times as that of 0%-SACN. Meanwhile, it had good cycle stability and the catalytic performance did not decrease after 5-time usage. The used g-C3N4 hydrogel microspheres were also demonstrated to be biodegraded anaerobically to produce methane for energy recovery and recycling. The results and outcome of this paper might bring new inspiration for the study of easily reusable and sustainable photocatalysts for wastewater treatment.
Hao, D, Liu, Y, Gao, S, Arandiyan, H, Bai, X, Kong, Q, Wei, W, Shen, PK & Ni, B-J 2021, 'Emerging artificial nitrogen cycle processes through novel electrochemical and photochemical synthesis', Materials Today, vol. 46, pp. 212-233.
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The nitrogen cycle is an important part of the global biogeochemical cycle, while the human activities have already caused a severe imbalance of the global nitrogen cycle. In this review, we proposed a new generation of artificial nitrogen cycle via electrochemical and photocatalytic reactions. In details, the N2 from the air, NO3−/NO2− containing wastewater, nitrogen oxides from vehicle emission are all able to be utilized as a nitrogen source for the synthesis of NH3 under ambient conditions. The oxidation of NH3, N2 and nitrogen oxides can all achieve the aim of obtaining NO3−. Hydrazine can also be synthesized electrochemical and photochemical reactions. Utilizing electrochemical and photocatalytic processes enables to eliminate the hazardous of nitrogen-containing organic chemicals, and some inorganic nitrogen polluted wastewater. More importantly, coupling N-based reaction with other reaction like CO2 reduction enables to synthesize some high-value chemicals such as urea. Then we highlighted some recent achievements in these reactions and proposed some future potential developing directions. The results and funding of this work may help us develop highly efficient catalysts and strategies for the artificial nitrogen cycle, repairing the broken nitrogen cycle balance.
Hao, D, Ren, J, Wang, Y, Arandiyan, H, Garbrecht, M, Bai, X, Shon, HK, Wei, W & Ni, B-J 2021, 'A Green Synthesis of Ru Modified g-C 3 N 4 Nanosheets for Enhanced Photocatalytic Ammonia Synthesis', Energy Material Advances, vol. 2021, pp. 1-12.
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Nitrate is a crucial environmental pollutant, and its risk on ecosystem keeps increasing. Photocatalytic conversion of nitrate to ammonia can simultaneously achieve the commercialization of environmental hazards and recovery of valuable ammonia, which is green and sustainable for the planet. However, due to the thermodynamic and kinetic energy barriers, photocatalytic nitrate reduction usually involves a higher selectivity of the formation of nitrogen that largely limits the ammonia synthesis activity. In this work, we reported a green and facile synthesis of novel metallic ruthenium particle modified graphitic carbon nitride photocatalysts. Compare with bulk graphitic carbon nitride, the optimal sample had 2.93-fold photocatalytic nitrate reduction to ammonia activity (2.627 mg/h/g cat ), and the NH 3 selectivity increased from 50.77% to 77.9%. According to the experimental and calculated results, the enhanced photocatalytic performance is attributed to the stronger light absorption, nitrate adsorption, and lower energy barrier for the generation of ammonia. This work may provide a facile way to prepare metal modified photocatalysts to achieve highly efficient nitrate reduction to ammonia.
Hao, S, Shi, C, Cao, L, Niu, Z & Guo, P 2021, 'Learning deep relevance couplings for ad-hoc document retrieval', Expert Systems with Applications, vol. 183, pp. 115335-115335.
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Haq, S, Seah, TH, Chao, KC & Rujikiatkamjorn, C 2021, 'A numerical approach to cyclic consolidation of saturated clays', Geotechnical Engineering, vol. 51, no. 4, pp. 52-60.
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A finite-difference numerical code is written in MATLAB to predict excess pore pressures and settlements under stepped/square wave cyclic loads. The numerical code is developed by approximating the Terzaghi's 1D consolidation equation under time-dependent loading using the Crank Nicolson scheme. A method of applying the stepped/square wave cyclic loads is proposed. The code considers the nonlinear inelastic stress ~ strain relationship and can be used for both homogeneous and heterogeneous layers. The code is validated by comparing the results with analytical, experimental, and field monitoring data in the literature. A good agreement of the results shows that the code is well developed and can be used in predicting the settlements in practice. The analyses show that the maximum steady-state degree of consolidation calculated based on settlement and the maximum steady-state average degree of consolidation calculated based on dissipation of excess pore pressures decrease as the time period decreases. Below a specific time period, both remain unchanged. For a specific time period, both increase as the percentage of loaded portion in a cycle increases. Besides, the maximum steady-state degree of consolidation based on settlement, for a specific time period, increases with an increase in stress levels, which is due to the nonlinear stress ~ strain behavior.
Harandizadeh, H & Armaghani, DJ 2021, 'Prediction of air-overpressure induced by blasting using an ANFIS-PNN model optimized by GA', Applied Soft Computing, vol. 99, pp. 106904-106904.
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Harandizadeh, H, Armaghani, DJ, Asteris, PG & Gandomi, AH 2021, 'TBM performance prediction developing a hybrid ANFIS-PNN predictive model optimized by imperialism competitive algorithm', Neural Computing and Applications, vol. 33, no. 23, pp. 16149-16179.
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A reliable and accurate prediction of the tunnel boring machine (TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects. This research aims to develop a novel hybrid intelligent system, i.e., adaptive neuro-fuzzy inference system (ANFIS)-polynomial neural network (PNN) optimized by the imperialism competitive algorithm (ICA), ANFIS-PNN-ICA for prediction of TBM performance. In fact, the role of ICA in this hybrid system is to optimize the membership functions obtained by ANFIS-PNN model for receiving a higher level of performance prediction. Based on previously published works, seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength, rock mass weathering, the uniaxial compressive strength, revolution per minute and thrust force were set as inputs to predict TBM performance. Together with the ANFIS-PNN-ICA model, two single model of PNN and ANFIS were also constructed for comparison purposes. These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of several performance indices, e.g., correlation coefficient (R). R values of (0.9642, 0.9654 and 1), (0.9482, 0.9671 and 0.9778) and (0.9652, 0.9642, 0.9898) were obtained for training, testing and all datasets of PNN, ANFIS and ANFIS-PNN-ICA models, respectively. These results revealed that the greater prediction capacity can be provided by the ANFIS-PNN-ICA predictive model compared to ANFIS and PNN models and this hybrid intelligent model can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction.
Harandizadeh, H, Jahed Armaghani, D & Khari, M 2021, 'A new development of ANFIS–GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets', Engineering with Computers, vol. 37, no. 1, pp. 685-700.
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Harari, PA, Banapurmath, NR, Yaliwal, VS, Khan, TMY, Badruddin, IA, Kamangar, S & Mahlia, TMI 2021, 'Effect of Injection Timing and Injection Duration of Manifold Injected Fuels in Reactivity Controlled Compression Ignition Engine Operated with Renewable Fuels', Energies, vol. 14, no. 15, pp. 4621-4621.
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In the current work, an effort is made to study the influence of injection timing (IT) and injection duration (ID) of manifold injected fuels (MIF) in the reactivity controlled compression ignition (RCCI) engine. Compressed natural gas (CNG) and compressed biogas (CBG) are used as the MIF along with diesel and blends of Thevetia Peruviana methyl ester (TPME) are used as the direct injected fuels (DIF). The ITs of the MIF that were studied includes 45° ATDC, 50° ATDC, and 55° ATDC. Also, present study includes impact of various IDs of the MIF such as 3, 6, and 9 ms on RCCI mode of combustion. The complete experimental work is conducted at 75% of rated power. The results show that among the different ITs studied, the D+CNG mixture exhibits higher brake thermal efficiency (BTE), about 29.32% is observed at 50° ATDC IT, which is about 1.77, 3.58, 5.56, 7.51, and 8.54% higher than D+CBG, B20+CNG, B20+CBG, B100+CNG, and B100+CBG fuel combinations. The highest BTE, about 30.25%, is found for the D+CNG fuel combination at 6 ms ID, which is about 1.69, 3.48, 5.32%, 7.24, and 9.16% higher as compared with the D+CBG, B20+CNG, B20+CBG, B100+CNG, and B100+CBG fuel combinations. At all ITs and IDs, higher emissions of nitric oxide (NOx) along with lower emissions of smoke, carbon monoxide (CO), and hydrocarbon (HC) are found for D+CNG mixture as related to other fuel mixtures. At all ITs and IDs, D+CNG gives higher In-cylinder pressure (ICP) and heat release rate (HRR) as compared with other fuel combinations.
Hardwick, J, Taylor, J, Mehta, M, Satija, S, Paudel, KR, Hansbro, PM, Chellappan, DK, Bebawy, M & Dua, K 2021, 'Targeting Cancer using Curcumin Encapsulated Vesicular Drug Delivery Systems', Current Pharmaceutical Design, vol. 27, no. 1, pp. 2-14.
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Curcumin is a major curcuminoid present in turmeric. The compound is attributed to various therapeuticproperties, which include anti-oxidant, anti-inflammatory, anti-bacterial, anti-malarial, and neuroprotection.Due to its therapeutic potential, curcumin has been employed for centuries in treating different ailments. Curcuminhas been investigated lately as a novel therapeutic agent in the treatment of cancer. However, the mechanismsby which curcumin exerts its cytotoxic effects on malignant cells are still not fully understood. One of themain limiting factors in the clinical use of curcumin is its poor bioavailability and rapid elimination. Advancementsin drug delivery systems such as nanoparticle-based vesicular drug delivery platforms have improved severalparameters, namely, drug bioavailability, solubility, stability, and controlled release properties. The use ofcurcumin-encapsulated niosomes to improve the physical and pharmacokinetic properties of curcumin is one suchapproach. This review provides an up-to-date summary of nanoparticle-based vesicular drug carriers and theirtherapeutic applications. Specifically, we focus on niosomes as novel drug delivery formulations and their potentialin improving the delivery of challenging small molecules, including curcumin. Overall, the applications ofsuch carriers will provide a new direction for novel pharmaceutical drug delivery, as well as for biotechnology,nutraceutical, and functional food industries.
Harley, W, Yoshie, H & Gentile, C 2021, 'Three-Dimensional Bioprinting for Tissue Engineering and Regenerative Medicine in Down Under: 2020 Australian Workshop Summary', ASAIO Journal, vol. 67, no. 4, pp. 363-369.
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Hasan, ASMM, Tuhin, RA, Ullah, M, Sakib, TH, Thollander, P & Trianni, A 2021, 'A comprehensive investigation of energy management practices within energy intensive industries in Bangladesh', Energy, vol. 232, pp. 120932-120932.
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Industrial energy efficiency is acknowledged as a cost-effective mean contributing to sustainable development and industrial competitiveness. Implementing energy management practices becomes even more imperative for developing countries, considering their energy usage trends and economic development forecasts. Based on the circumstances, an empirical investigation is conducted on energy efficiency and management practices, as well as barriers and drivers to energy efficiency in the energy-intensive industries of Bangladesh. The study finds that majority of the companies barely implement the energy management practices. Energy audits represent the mostly implemented energy management practice at the industries, though a comprehensive approach on a detailed level is still lacking. In addition, this study finds that the number of dedicated and specialised energy professionals employed in the industries is yet negligible. The cumulated results show that energy efficiency is mostly disrupted due to inadequate support from preeminent administration and bureaucratic intricacy. Energy blueprint cost-saving due to less use of energy and rules and regulations were distinctively signified as most imperative drivers for energy efficiency. On the other hand, lack of information is found to be the most significant barrier to consult energy service companies. Analysis of the country's energy usage and supply-demand relationship points towards insufficient energy efficiency measures and energy management practices in the country. The study also finds that energy efficiency could be improved by 8%–10% through the practice of energy management. Our findings, besides pointing out specific issues to be tackled in the specific context of investigation, pave the way for further research over industrial energy efficiency in developing countries.
Hasan, M, Zhao, J, Jia, F, Wu, H, Ahmad, F, Huang, Z, Wei, D, Ma, L & Jiang, Z 2021, 'Optimisation of sintering parameters for bonding nanocrystalline cemented tungsten carbide powder and solid high strength steel', Composite Interfaces, vol. 28, no. 5, pp. 477-492.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. In this study we examined the effects of compaction pressure for bonding nanocrystalline cemented tungsten carbide (WC-10Co) and high-strength steel (AISI4340) and successfully fabricated a bilayered composite of ceramic and steel. The obtained results were compared with our previous studies, and then the optimised sintering conditions were suggested. The compaction pressure examined varied from 120–200 MPa at 1150°C for 20 min. The study shows that the change in experimental parameters has significant effects on both the sintering properties of nanocrystalline WC-10Co powders and their bonding with AISI4340 steel. The microstructure reveals a successful metallurgical bonding between ceramic and steel. Bonding temperature determines, to a great extent, the diffusion processes across the bonding interface and has found to be the most influential variable compared to sintering time and compaction pressure. The obtained average maximum bonding strength of the bimetal composite is 226 MPa, which is higher than that of previous studies.
Hasan, MH, Mahlia, TMI, Mofijur, M, Rizwanul Fattah, IM, Handayani, F, Ong, HC & Silitonga, AS 2021, 'A Comprehensive Review on the Recent Development of Ammonia as a Renewable Energy Carrier', Energies, vol. 14, no. 13, pp. 3732-3732.
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Global energy sources are being transformed from hydrocarbon-based energy sources to renewable and carbon-free energy sources such as wind, solar and hydrogen. The biggest challenge with hydrogen as a renewable energy carrier is the storage and delivery system’s complexity. Therefore, other media such as ammonia for indirect storage are now being considered. Research has shown that at reasonable pressures, ammonia is easily contained as a liquid. In this form, energy density is approximately half of that of gasoline and ten times more than batteries. Ammonia can provide effective storage of renewable energy through its existing storage and distribution network. In this article, we aimed to analyse the previous studies and the current research on the preparation of ammonia as a next-generation renewable energy carrier. The study focuses on technical advances emerging in ammonia synthesis technologies, such as photocatalysis, electrocatalysis and plasmacatalysis. Ammonia is now also strongly regarded as fuel in the transport, industrial and power sectors and is relatively more versatile in reducing CO2 emissions. Therefore, the utilisation of ammonia as a renewable energy carrier plays a significant role in reducing GHG emissions. Finally, the simplicity of ammonia processing, transport and use makes it an appealing choice for the link between the development of renewable energy and demand.
Hasanpour, S, Forouzesh, M, Siwakoti, Y & Blaabjerg, F 2021, 'A New High-Gain, High-Efficiency SEPIC-Based DC–DC Converter for Renewable Energy Applications', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 2, no. 4, pp. 567-578.
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Hasanpour, S, Forouzesh, M, Siwakoti, Y & Blaabjerg, F 2021, 'A Novel Full Soft-Switching High-Gain DC/DC Converter Based on Three-Winding Coupled-Inductor', IEEE Transactions on Power Electronics, vol. 36, no. 11, pp. 12656-12669.
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In this article, a new nonisolated full soft-switching step-up dc/dc converter is introduced with a continuous input current for renewable energy applications. The use of a three-winding coupled-inductor (TWCI) along with a voltage multiplier, enables the proposed converter to enhance the voltage gain with lower turns ratios and duty cycles. Also, a lossless regenerative passive clamp circuit is employed to limit the voltage stress across the power switch. In addition to zero current switching performance at the turn-on instant of the power switch, the turn-off current value is also alleviated by adopting a quasi-resonance operation between the leakage inductor of the TWCI and middle capacitors. Moreover, the current of all diodes reaches zero with a slow slew rate, which leads to the elimination of the reverse recovery problem in the converter. Soft-switching of the power switch and all the diodes in the proposed converter significantly reduces the switching power dissipations. Therefore, the presented converter can provide a high voltage gain ratio with high efficiency. Steady-state analysis, comprehensive comparisons with other related converters, and design considerations are discussed in detail. Finally, a 160 W prototype with 200 V output voltage is demonstrated to justify the theoretical analysis.
Hasanpour, S, Siwakoti, Y & Blaabjerg, F 2021, 'Analysis of a New Soft-Switched Step-Up Trans-Inverse DC/DC Converter Based on Three-Winding Coupled-Inductor', IEEE Transactions on Power Electronics, pp. 1-1.
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Hasanpour, S, Siwakoti, YP, Mostaan, A & Blaabjerg, F 2021, 'New Semiquadratic High Step-Up DC/DC Converter for Renewable Energy Applications', IEEE Transactions on Power Electronics, vol. 36, no. 1, pp. 433-446.
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© 1986-2012 IEEE. In this article, a new semiquadratic high step-up coupled-inductor dc/dc converter (SQHSUCI) with continuous input current and low voltage stress on semiconductor components is presented. The proposed structure employs a coupled-inductor (CI) and two power switches with simultaneous operation to achieve an extremely high voltage conversion ratio in a semiquadratic form. The voltage stress across the main power switch is clamped by two regenerative clamp capacitors. Here, the switching losses of both MOSFETs have been reduced by applying quasi-resonance operation of the circuit created by the leakage inductance of the CI along with the balancing and clamp capacitors. Therefore, by considering the high gain conversion ratio along with low voltage stress on components, the magnetic and semiconductors losses of the SQHSUCI are reduced significantly. Also, the energy stored in the leakage inductance of CI is recycled to the output capacitor. These features make the proposed SQHSUCI more suitable for industrial applications. The operation principle, steady state, and also comparisons with other related converters in continuous conduction mode (CCM) are discussed in detail. Finally, experimental results of a prototype with 20 V input and 200 W-200 V output at 50 kHz switching frequency, verify the theoretical advantages of the proposed strategy.
Hasib, KM, Towhid, NA & Islam, MR 2021, 'HSDLM', International Journal of Cloud Applications and Computing, vol. 11, no. 4, pp. 1-13.
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Imbalanced data presents many difficulties, as the majority of learners will be prejudice against the majority class, and in severe cases, may fully disregard the minority class. Over the last few decades, class inequality has been extensively researched using traditional machine learning techniques. However, there is relatively little analytical research in the field of deep learning with class inequality. In this article, the authors classify the imbalanced data with the combination of both sampling method and deep learning method. They propose a novel sampling-based deep learning method (HSDLM) to address the class imbalance problem. They preprocess the data with label encoding and remove the noisy data with the under-sampling technique edited nearest neighbor (ENN) algorithm. They also balance the data using the over-sampling technique SMOTE and apply parallelly three types of long short-term memory networks, which is a deep learning classifier. The experimental findings indicate that HSDLM is a promising and fruitful solution to working with strongly imbalanced datasets.
Hasib, SSB & Al-Kilidar, H 2021, 'Identifying Project Delay Factors in the Australian Construction Industry', International Journal of Structural and Construction Engineering, vol. 171, no. 3, pp. 135-141.
Hassan, M, Hossain, MJ & Shah, R 2021, 'DC Fault Identification in Multiterminal HVDC Systems Based on Reactor Voltage Gradient', IEEE Access, vol. 9, pp. 115855-115867.
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Hassan, M, Hossain, MJ & Shah, R 2021, 'Impact of Meshed HVDC Grid Operation and Control on the Dynamics of AC/DC Systems', IEEE Systems Journal, vol. 15, no. 4, pp. 5209-5220.
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IEEE The efficacy of long-distance and bulk power transmission largely depends on the efficient control and reliable operation of a multiterminal high-voltage direct current (MT-HVdc) grid, more precisely, a meshed HVdc grid. The capability of enduring the dc grid fault eventually enhances the reliability and improves the dynamic performance of the grid. This article investigates the operation and control of an AC/multiterminal dc (MTDC) system with bipolar topology incorporating the dc grid protection schemes. Based on the scale of a circuit breaker's operating time, the performance of three different protection strategies is compared and analyzed using DIgSILENT PowerFactory. Simulation results explicitly reveal that the dynamic performance of the MTDC grid significantly deteriorates with the slow functioning of the protection schemes, followed by a dc grid fault. Besides, prolonged recovery time causes a substantial loss of power infeed and affects the ac/dc grid's stability. Finally, to assess the frailty of the MTDC grid, a transient energy stability index is proposed considering the voltage variation in the prestate and poststate fault clearing interval. Relevant case studies are performed on the MTDC grid using an analytical approach and nonlinear simulation studies to validate the effectiveness of the proposed index.
Hassani, S, Mousavi, M & Gandomi, AH 2021, 'Structural Health Monitoring in Composite Structures: A Comprehensive Review', Sensors, vol. 22, no. 1, pp. 153-153.
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This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented.
Hastings, C 2021, 'A critical realist methodology in empirical research: foundations, process, and payoffs', Journal of Critical Realism, vol. 20, no. 5, pp. 458-473.
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This article describes and evaluates the application of an explicitly critical realist methodology to a quantitative doctoral research project on the causes of family homelessness in Australia. It is offered as an example of a critical realist approach to empirical research, in the hope that it will provide ideas and motivation to other scholars seeking a critical realist foundation to their research practice. The paper demonstrates the role of critical realism in informing and defining the analytical and theoretical approach I took. It shows how the philosophy influenced the foundations and practical development of my work. It describes the process of moving from empirical data to theoretical models by stepping through and describing each stage. Finally, I offer an assessment of how critical realism changed, enabled, improved, and liberated my project; that is, what advantage critical realism offered to explaining a complex social phenomenon and crisis in contemporary Australia.
Hastings, C 2021, 'Homelessness and critical realism: a search for richer explanations', Housing Studies, vol. 36, no. 5, pp. 737-757.
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Hayat, T, Afzal, MU, Ahmed, F, Zhang, S, Esselle, KP & Vardaxoglou, J 2021, 'The Use of a Pair of 3D-Printed Near Field Superstructures to Steer an Antenna Beam in Elevation and Azimuth', IEEE Access, vol. 9, pp. 153995-154010.
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The paper presents a method to design beam-steering antennas using a pair of 3D printed perforated dielectric structures (PDSs) placed in the near-field region of a base antenna, which has a fixed beam. Detailed designs and quantitative comparison of two beam-steering antenna systems are presented. One antenna system has a conical horn antenna and the other uses a resonant-cavity antenna (RCA) as the base antenna. In both cases, the first PDS transforms the phase distribution of the aperture near field and hence tilts the antenna beam to an offset angle. The second PDS, placed above the first, introduces an additional linear progression to the phase of the near field. The two PDSs are rotated independently to steer the beam in both azimuth and elevation. The PDSs have been 3D-printed using acrylonitrile butadiene styrene (ABS) filaments. Each prototype was fabricated in about 16 hours, weighs 300 grams, and costs approximately 5.5 US Dollars. The measured results show that, at the operating frequency of 11 GHz, the RCA-based system has a peak gain of 17.7 dBi compared to the 16.6 dBi gain obtained with the horn-based system. In a fixed E-plane, the variation in the aperture near-field phase of the horn antenna (115°) is much less than that of the RCA (360°). This reduces the efforts required for phase correction and hence led to the former having a larger 3dB measured gain bandwidth of 1.2 GHz compared with the 0.7 GHz bandwidth of the latter, but at the cost of 35.6% increase in the total height of the antenna system.
Hazrat, MA, Rasul, MG, Khan, MMK, Mofijur, M, Ahmed, SF, Ong, HC, Vo, D-VN & Show, PL 2021, 'Techniques to improve the stability of biodiesel: a review', Environmental Chemistry Letters, vol. 19, no. 3, pp. 2209-2236.
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He, D, Xiao, J, Wang, D, Liu, X, Fu, Q, Li, Y, Du, M, Yang, Q, Liu, Y, Wang, Q, Ni, B-J, Song, K, Cai, Z, Ye, J & Yu, H 2021, 'Digestion liquid based alkaline pretreatment of waste activated sludge promotes methane production from anaerobic digestion', Water Research, vol. 199, pp. 117198-117198.
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This work proved an efficient method to significantly increase methane production from anaerobic digestion of WAS. This method is to reflux proper of digestion liquid into waste activated sludge pretreatment unit (pH 9.5 for 24 h). The yield of maximum methane improved between 174.2 ± 7.3 and 282.5 ± 14.1 mL/g VSS with the reflux ratio of digestion liquid increasing from 0% to 20%. It was observed that the biodegradable organics in the digestion liquid did not affect the biological processes related to anaerobic digestion but increased methane production through reutilization. The ammonium in the digestion liquid was the main contributor to the increase in methane production via promoting sludge solubilization, but refractory organics were the major inhibitors to anaerobic digestion. It should be emphasized that the metal ions present in the digestion liquid were beneficial rather than harmful to the biological processes in the anaerobic digestion, which may be connected with the fact that certain metal ions were involved in the expression and activation of key enzymes. In addition, it was found that anaerobes in digestion liquid were another potential contributor to the enhanced anaerobic digestion.
He, D, Xiao, J, Wang, D, Liu, X, Li, Y, Fu, Q, Li, C, Yang, Q, Liu, Y & Ni, B-J 2021, 'Understanding and regulating the impact of tetracycline to the anaerobic fermentation of waste activated sludge', Journal of Cleaner Production, vol. 313, pp. 127929-127929.
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Tetracycline (TC), a widely used antibiotic, was enriched in waste activated sludge (WAS) at significant levels. However, the TC impact on WAS anaerobic fermentation are still poorly understood. This work aims to analyze the effect of TC to the WAS anaerobic fermentation by investigating the differences of sludge properties, short-chain fatty acids (SCFAs) production and microbial community abundance. The results showed that the environmental level of TC had no effect on the SCFAs production, but with the further increase of the content of TC to 60 mg/kg TSS, the maximum SCFAs yield decreased from 125.1 ± 3.2 to 90.8 ± 1.7 mg COD/g VSS. Mechanism exploration indicated that TC had no significant effect on solubilization, hydrolysis and homoacetogenesis processes, but severely inhibited acidogenesis, acetogenesis and methanogenesis processes. Microbial analysis showed that the presence of TC reduced the diversity of microbial communities and the abundance of functional microorganisms relevant to SCFAs production and complex organic degradation, such as Proteiniclasticun and Novosphingobium. This negative effect was persistent because only a small amount of TC can be degraded in the anaerobic fermentation process. Hence, CaO2 was proposed and studied as a regulation strategy that can reduce the toxicity of TC on anaerobic fermentation.
He, F, Huang, X, Wang, X, Qiu, S, Jiang, F & Ling, SH 2021, 'A neuron image segmentation method based Deep Boltzmann Machine and CV model', Computerized Medical Imaging and Graphics, vol. 89, pp. 101871-101871.
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He, M, Zhang, X, Huang, J, Li, J, Yan, C, Kim, J, Chen, Y, Yang, L, Cairney, JM, Zhang, Y, Chen, S, Kim, J, Green, MA & Hao, X 2021, 'High Efficiency Cu2ZnSn(S,Se)4 Solar Cells with Shallow LiZn Acceptor Defects Enabled by Solution‐Based Li Post‐Deposition Treatment', Advanced Energy Materials, vol. 11, no. 13, pp. 2003783-2003783.
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AbstractLithium incorporation in kesterite Cu2ZnSn(S,Se)4 (CZTSSe) materials has been experimentally proven effective in improving electronic quality for application in photovoltaic devices. Herein, a feasible and effective solution‐based lithium post‐deposition treatment (PDT), enabling further efficiency improvement on the high‐performance baseline is reported and the dominant mechanism for this improvement is proposed. In this way, lithium is uniformly incorporated into grain interiors (GIs) without segregation at grain boundaries (GBs), which can occupy the Zn sites with a high solubility in the CZTSSe matrix, producing high density of LiZn antisites with shallower acceptor levels than the intrinsic dominant defect (CuZn antisites). As a result, CZTSSe absorber with better p‐type doping is obtained, leading to a pronounced enhancement in fill factor and a corresponding gain in open‐circuit voltage and short‐circuit current and consequently a significant efficiency boost from 9.3% to 10.7%. This work provides a feasible alternative alkali‐PDT treatment for chalcogenide semiconductors and promotes a better understanding of the mechanism of Li incorporation in kesterite materials.
He, X & Qiao, Y 2021, 'On the Baer–Lovász–Tutte construction of groups from graphs: Isomorphism types and homomorphism notions', European Journal of Combinatorics, vol. 98, pp. 103404-103404.
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Let p be an odd prime. From a simple undirected graph G, through the classical procedures of Baer (1938), Tutte (1947) and Lovász (1989), there is a p-group PG of class 2 and exponent p that is naturally associated with G. Our first result is to show that this construction of groups from graphs respects isomorphism types. That is, given two graphs G and H, G and H are isomorphic as graphs if and only if PG and PH are isomorphic as groups. Our second contribution is a new homomorphism notion for graphs. Based on this notion, a category of graphs can be defined, and the Baer–Lovász–Tutte construction naturally leads to a functor from this category of graphs to the category of groups.
He, X, Deng, L, Yang, Y & Feng, B 2021, 'Multifunctional ultrathin reflective metasurface via polarization-decoupled phase for arbitrary circularly or elliptically polarized waves', Optics Express, vol. 29, no. 8, pp. 12736-12736.
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Metasurface offers a promising platform in the design of multifunctional devices owing to its unique ability for EMWs manipulation. However, wave-manipulation capabilities for metasurfaces face challenges in manipulating orthogonal EMWs with arbitrary circularly or elliptically polarized EMWs in the microwave region. Herein, single-layer reflective metasurfaces are proposed for independent manipulation of an arbitrary set of orthogonal circularly or elliptically polarized EMWs via polarization-decoupled phase. Taking advantage of single-layer anisotropic meta-atoms, the proposed metasurface can act as a tandem phase modulator, which introduces polarization-decoupled phase profiles for arbitrary circularly and elliptically polarized EMWs based on the Jones matrix. In this way, the proposed metasurface can distinguish a set of orthogonal EMWs with circular or elliptical polarization states and impose arbitrary phase profiles on them independently and simultaneously. For proof-of-concept, bifunctional metasurfaces operating in the microwave region are presented for independent manipulation of three different sets of orthogonal circularly or elliptically polarized EMWs. They create dual independent channels associated with a pair of orthogonal polarization states, performing functions including polarization beam splitting and orbital angular momentum (OAM) multiplexing. Measured and simulated results show a good agreement, confirming that the proposed single-layer reflective metasurfaces are efficient devices that enable meta-devices to independently control arbitrary circular and elliptical polarized EMWs, achieving arbitrary functionalities.
He, X, Wang, F, Li, W & Sheng, D 2021, 'Efficient reliability analysis considering uncertainty in random field parameters: Trained neural networks as surrogate models', Computers and Geotechnics, vol. 136, pp. 104212-104212.
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This paper presents an efficient reliability analysis framework, by using trained artificial neural networks (ANNs) as surrogate models, for geotechnical problems where the random field parameters like the mean and standard deviation are themselves uncertain. Random field theory has been extensively used to model soil uncertainty and spatial variability. However, due to limited availability of data, random field parameters can rarely be estimated accurately, often estimated in confidence intervals (uncertain parameters). Monte Carlo based reliability analysis is computationally extremely demanding because the function to map outcomes of random fields to structural response can only be calculated via numerical simulations. The authors have used trained ANNs as surrogate models in reliability analysis. However, these ANNs are specific for random fields with deterministic parameters. This paper presents a new framework in which trained ANN models are for random fields with variable parameters. A key component is the design of experiments – generating representative outcomes. In the prediction of the bearing capacity for strip footings, the efficiency and accuracy of this framework are successfully demonstrated. This framework is also efficient in reliability sensitivity studies. One main finding is that ignoring random field parameter uncertainty could lead to underestimated failure probability and hence unsafe design.
He, X, Yang, Y, Deng, L, Li, S & Feng, B 2021, '3D Printed Sub-Terahertz All-Dielectric Lens for Arbitrary Manipulation of Quasi-Nondiffractive Orbital Angular Momentum Waves', ACS Applied Materials & Interfaces, vol. 13, no. 17, pp. 20770-20778.
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Terahertz (THz) vortex waves carrying orbital angular momentum (OAM) hold great potential in dealing with the capacity crunch in wireless high-speed communication systems. Nevertheless, it is quite a challenge for the widespread applications of OAM in the THz regime due to the beam divergence and stringent alignment requirement. To address this issue, an all-dielectric lens (ADL) is proposed for the arbitrary manipulation of quasi-nondiffractive THz OAM waves (QTOWs). On the basis of the concept of the optical conical lens and the multivorticity metasurface, the beam number, the topological charge (TC), and the deflection angle as well as the nondiffractive depth of the generated THz OAM waves are controllable. For proof-of-concept, two ADLs are 3D printed to create single and dual deflected QTOWs, respectively. Remarkably, measured by a THz imaging camera, the desired QTOWs with high mode purity are observed in predesigned directions with a nondiffractive depth predefined theoretically. The proposed designs and experiments, for the first time, verified that the QTOWs could be achieved with a nondiffractive range of 55.58λg (λg = wavelength at 140 GHz) and large deflection angles of 30° and 45°.
He, Y, Wang, K, Zhang, W, Lin, X & Zhang, Y 2021, 'Exploring cohesive subgraphs with vertex engagement and tie strength in bipartite graphs', Information Sciences, vol. 572, pp. 277-296.
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We propose a novel cohesive subgraph model called τ-strengthened (α,β)-core (denoted as (α,β)τ-core), which is the first to consider both tie strength and vertex engagement on bipartite graphs. An edge is a strong tie if contained in at least τ butterflies (2×2-bicliques). (α,β)τ-core requires each vertex on the upper or lower level to have at least α or β strong ties, given strength level τ. To retrieve the vertices of (α,β)τ-core optimally, we construct index Iα,β,τ to store all (α,β)τ-cores. Effective optimization techniques are proposed to improve index construction. To make our idea practical on large graphs, we propose 2D-indexes Iα,β,Iβ,τ, and Iα,τ that selectively store the vertices of (α,β)τ-core for some α,β, and τ. The 2D-indexes are more space-efficient and require less construction time, each of which can support (α,β)τ-core queries. As query efficiency depends on input parameters and the choice of 2D-index, we propose a learning-based hybrid computation paradigm by training a feed-forward neural network to predict the optimal choice of 2D-index that minimizes the query time. Extensive experiments show that (1) (α,β)τ-core is an effective model capturing unique and important cohesive subgraphs; (2) the proposed techniques significantly improve the efficiency of index construction and query processing.
He, Z, Armaghani, DJ, Masoumnezhad, M, Khandelwal, M, Zhou, J & Murlidhar, BR 2021, 'A Combination of Expert-Based System and Advanced Decision-Tree Algorithms to Predict Air-Overpressure Resulting from Quarry Blasting', Natural Resources Research, vol. 30, no. 2, pp. 1889-1903.
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Hembram, TK, Saha, S, Pradhan, B, Abdul Maulud, KN & Alamri, AM 2021, 'Robustness analysis of machine learning classifiers in predicting spatial gully erosion susceptibility with altered training samples', Geomatics, Natural Hazards and Risk, vol. 12, no. 1, pp. 794-828.
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Henke, T & Deuse, J 2021, 'Application of heuristics for packing problems to optimise throughput time in fixed position assembly islands', International Journal of Productivity and Quality Management, vol. 1, no. 1, pp. 1-1.
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Herath, S, Sadeghi Rad, H, Radfar, P, Ladwa, R, Warkiani, M, O’Byrne, K & Kulasinghe, A 2021, 'The Role of Circulating Biomarkers in Lung Cancer', Frontiers in Oncology, vol. 11, p. 801269.
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Lung cancer is the leading cause of cancer morbidity and mortality worldwide and early diagnosis is crucial for the management and treatment of this disease. Non-invasive means of determining tumour information is an appealing diagnostic approach for lung cancers as often accessing and removing tumour tissue can be a limiting factor. In recent years, liquid biopsies have been developed to explore potential circulating tumour biomarkers which are considered reliable surrogates for understanding tumour biology in a non-invasive manner. Most common components assessed in liquid biopsy include circulating tumour cells (CTCs), cell-free DNA (cfDNA), circulating tumour DNA (ctDNA), microRNA and exosomes. This review explores the clinical use of circulating tumour biomarkers found in liquid biopsy for screening, early diagnosis and prognostication of lung cancer patients.
Heravi, FS, Zakrzewski, M, Aboulkheyr Estarabadi, H, Vickery, K & Hu, H 2021, 'Evaluation of Host Immune Response in Diabetic Foot Infection Tissues Using an RNA Sequencing-Based Approach', Frontiers in Microbiology, vol. 12.
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The normal continuity of skin tissue can be affected by invading pathogens and lead to a series of complicated physiological events. Using an RNA sequencing-based approach, we have captured a metatranscriptomic landscape from diabetic foot infections (DFIs). The hierarchical clustering of the top 2,000 genes showed the expression of four main clusters in DFIs (A, B, C, and D). Clusters A and D were enriched in genes mainly involved in the recruitment of inflammatory cells and immune responses and clusters B and C were enriched in genes related to skin cell development and wound healing processes such as extracellular structure organization and blood vessel development. Differential expression analysis showed more than 500 differentially expressed genes (DEGs) between samples with a low number of virulence factors and samples with a high number of virulence factors. Up-regulated and down-regulated genes were mainly involved in adaptive/native immune responses and transport of mature mRNAs, respectively. Our results demonstrated the importance of inflammatory cytokines of adaptive/native immunity in the progression of DFIs and provided a useful groundwork for capturing gene snapshots in DFIs. In addition, we have provided a general introduction to the challenges and opportunities of RNA sequencing technology in the evaluation of DFIs. Pathways identified in this study such as immune chemokines, Rho GTPases, and corresponding effectors might be important therapeutic targets in the management of DFIs.
Hesamian, MH, Jia, W, He, X, Wang, Q & Kennedy, PJ 2021, 'Synthetic CT images for semi-sequential detection and segmentation of lung nodules', Applied Intelligence, vol. 51, no. 3, pp. 1616-1628.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Accurately detecting and segmenting lung nodules from CT images play a critical role in the earlier diagnosis of lung cancer and thus have attracted much interest from the research community. However, due to the irregular shapes of nodules, and the low-intensity contrast between the nodules and other lung areas, precisely segmenting nodules from lung CT images is a very challenging task. In this paper, we propose a highly effective and robust solution to this problem by innovatively utilizing the changes of nodule shapes over continuous slices (inter-slice changes) and develop a deep learning based end-to-end system. Different from the existing 2.5D or 3D methods that attempt to explore the inter-slice features, we propose to create a novel synthetic image to depict the unique changing pattern of nodules between slices in distinctive colour patterns. Based on the new synthetic images, we then adopt the deep learning based image segmentation techniques and develop a modified U-Net architecture to learn the unique color patterns formed by nodules. With our proposed approach, the detection and segmentation of nodules can be achieved simultaneously with an accuracy significantly higher than the state of the arts by 10% without introducing high computation cost. By taking advantage of inter-slice information and form the proposed synthetic image, the task of lung nodule segmentation is done more accurately and effectively.
Hewa, TM, Hu, Y, Liyanage, M, Kanhare, SS & Ylianttila, M 2021, 'Survey on Blockchain-Based Smart Contracts: Technical Aspects and Future Research', IEEE Access, vol. 9, pp. 87643-87662.
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Hickey, BA, Chalmers, T, Newton, P, Lin, C-T, Sibbritt, D, McLachlan, CS, Clifton-Bligh, R, Morley, J & Lal, S 2021, 'Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review', Sensors, vol. 21, no. 10, pp. 3461-3461.
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Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.
Hieu, NQ, Hoang, DT, Niyato, D & Kim, DI 2021, 'Optimal Power Allocation for Rate Splitting Communications With Deep Reinforcement Learning', IEEE Wireless Communications Letters, vol. 10, no. 12, pp. 2820-2823.
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This letter introduces a novel framework to optimize the power allocation for users in a Rate Splitting Multiple Access (RSMA) network. In the network, messages intended for users are split into different parts that are a single common part and respective private parts. This mechanism enables RSMA to flexibly manage interference and thus enhance energy and spectral efficiency. Although possessing outstanding advantages, optimizing power allocation in RSMA is very challenging under the uncertainty of the communication channel and the transmitter has limited knowledge of the channel information. To solve the problem, we first develop a Markov Decision Process framework to model the dynamic of the communication channel. The deep reinforcement algorithm is then proposed to find the optimal power allocation policy for the transmitter without requiring any prior information of the channel. The simulation results show that the proposed scheme can outperform baseline schemes in terms of average sum-rate under different power and QoS requirements.
Hill, M & Tran, N 2021, 'Global miRNA to miRNA Interactions: Impacts for miR-21', Trends in Cell Biology, vol. 31, no. 1, pp. 3-5.
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miRNAs inherently alter the cellular environment by regulating target genes. miRNAs may also regulate other miRNAs, with far-reaching influence on miRNA and mRNA expression. We explore this realm of small RNA regulation with a focus on the role of the oncogenic miR-21 and its impact on other miRNA species.
Hill, M & Tran, N 2021, 'miRNA interplay: mechanisms and consequences in cancer', Disease Models & Mechanisms, vol. 14, no. 4, pp. 1-9.
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ABSTRACT Canonically, microRNAs (miRNAs) control mRNA expression. However, studies have shown that miRNAs are also capable of targeting non-coding RNAs, including long non-coding RNAs and miRNAs. The latter, termed a miRNA:miRNA interaction, is a form of self-regulation. In this Review, we discuss the three main modes of miRNA:miRNA regulation: direct, indirect and global interactions, and their implications in cancer biology. We also discuss the cell-type-specific nature of miRNA:miRNA interactions, current experimental approaches and bioinformatic techniques, and how these strategies are not sufficient for the identification of novel miRNA:miRNA interactions. The self-regulation of miRNAs and their impact on gene regulation has yet to be fully understood. Investigating this hidden world of miRNA self-regulation will assist in discovering novel regulatory mechanisms associated with disease pathways.
Hinge, G, Surampalli, RY, Goyal, MK, Gupta, BB & Chang, X 2021, 'Soil carbon and its associate resilience using big data analytics: For food Security and environmental management', Technological Forecasting and Social Change, vol. 169, pp. 120823-120823.
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Soils are a binding site for carbon storage. Climatic variables, namely precipitation, and temperature are regarded as the primary factors controlling soil organic carbon (SOC) storage; however, no consensus has been made about the magnitude and direction that changes in climatic variables may have on SOC. Based on copula theory, the present study investigates the soil carbon dynamics and the likelihood of SOC occurrence under varying climatic conditions across India's 14 agro-climatic zones. Results demonstrate the possibility of occurrence of SOC under both low and high temperature/precipitation conditions. It was found that the SOC of agro-climatic zones situated in semi-arid and arid regions are more sensitive to changes in climatic variables compared to that of the others. We then quantify the soil resilience of the agro-climatic zones based on the amount of SOC content. Results showed that only 1/3 of India's agro-climatic zones were resilient during the study period (1985–2005). Thus, the study's findings facilitate the identification of India's most sensitive agro-climatic zone for soil carbon management and climate-related policy. It stresses the need for big data assimilation to identify site-specific management practices that can facilitate soil health and improve the country's soil resilient capacity for food security and environmental management.
Ho, LV, Nguyen, DH, Mousavi, M, De Roeck, G, Bui-Tien, T, Gandomi, AH & Wahab, MA 2021, 'A hybrid computational intelligence approach for structural damage detection using marine predator algorithm and feedforward neural networks', Computers & Structures, vol. 252, pp. 106568-106568.
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Finite element (FE) based structural health monitoring (SHM) algorithms seek to update structural damage indices through solving an optimisation problem in which the difference between the response of the real structure and a corresponding FE model to some excitation force is minimised. These techniques, therefore, exploit advanced optimisation algorithms to alleviate errors stemming from the lack of information or the use of highly noisy measured responses. This study proposes an effective approach for damage detection by using a recently developed novel swarm intelligence algorithm, i.e. the marine predator algorithm (MPA). In the proposed approach, optimal foraging strategy and marine memory are employed to improve the learning ability of feedforward neural networks. After training, the hybrid feedforward neural networks and marine predator algorithm, MPAFNN, produces the best combination of connection weights and biases. These weights and biases then are re-input to the networks for prediction. Firstly, the classification capability of the proposed algorithm is investigated in comparison with some well-known optimization algorithms such as particle swarm optimization (PSO), gravitational search algorithm (GSA), hybrid particle swarm optimization-gravitational search algorithm (PSOGSA), and grey wolf optimizer (GWO) via four classification benchmark problems. The superior and stable performance of MPAFNN proves its effectiveness. Then, the proposed method is applied for damage identification of three numerical models, i.e. a simply supported beam, a two-span continuous beam, and a laboratory free-free beam by using modal flexibility indices. The obtained results reveal the feasibility of the proposed approach in damage identification not only for different structures with single damage and multiple damage, but also considering noise effect.
Hoang, AT, Nizetic, S, Ong, HC, Chong, CT, Atabani, AE & Pham, VV 2021, 'Acid-based lignocellulosic biomass biorefinery for bioenergy production: Advantages, application constraints, and perspectives', Journal of Environmental Management, vol. 296, pp. 113194-113194.
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The production of chemicals and fuels from renewable biomass with the primary aim of reducing carbon footprints has recently become one of the central points of interest. The use of lignocellulosic biomass for energy production is believed to meet the main criteria of maximizing the available global energy source and minimizing pollutant emissions. However, before usage in bioenergy production, lignocellulosic biomass needs to undergo several processes, among which biomass pretreatment plays an important role in the yield, productivity, and quality of the products. Acid-based pretreatment, one of the existing methods applied for lignocellulosic biomass pretreatment, has several advantages, such as short operating time and high efficiency. A thorough analysis of the characteristics of acid-based biomass pretreatment is presented in this review. The environmental concerns and future challenges involved in using acid pretreatment methods are discussed in detail to achieve clean and sustainable bioenergy production. The application of acid to biomass pretreatment is considered an effective process for biorefineries that aim to optimize the production of desired products while minimizing the by-products.
Hoang, AT, Nižetić, S, Ong, HC, Mofijur, M, Ahmed, SF, Ashok, B, Bui, VTV & Chau, MQ 2021, 'Insight into the recent advances of microwave pretreatment technologies for the conversion of lignocellulosic biomass into sustainable biofuel', Chemosphere, vol. 281, pp. 130878-130878.
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The utilization of renewable lignocellulosic biomasses for bioenergy synthesis is believed to facilitate competitive commercialization and realize affordable clean energy sources in the future. Among the pathways for biomass pretreatment methods that enhance the efficiency of the whole biofuel production process, the combined microwave irradiation and physicochemical approach is found to provide many economic and environmental benefits. Several studies on microwave-based pretreatment technologies for biomass conversion have been conducted in recent years. Although some reviews are available, most did not comprehensively analyze microwave-physicochemical pretreatment techniques for biomass conversion. The study of these techniques is crucial for sustainable biofuel generation. Therefore, the biomass pretreatment process that combines the physicochemical method with microwave-assisted irradiation is reviewed in this paper. The effects of this pretreatment process on lignocellulosic structure and the ratio of achieved components were also discussed in detail. Pretreatment processes for biomass conversion were substantially affected by temperature, irradiation time, initial feedstock components, catalyst loading, and microwave power. Consequently, neoteric technologies utilizing high efficiency-based green and sustainable solutions should receive further focus. In addition, methodologies for quantifying and evaluating effects and relevant trade-offs should be develop to facilitate the take-off of the biofuel industry with clean and sustainable goals.
Hoang, AT, Ong, HC, Fattah, IMR, Chong, CT, Cheng, CK, Sakthivel, R & Ok, YS 2021, 'Progress on the lignocellulosic biomass pyrolysis for biofuel production toward environmental sustainability', Fuel Processing Technology, vol. 223, pp. 106997-106997.
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The increasing energy demand and diminishing fossil fuel sources have called for the exploration of new energy sources. To satisfy growing global energy demand and accomplish sustainable energy development goals, biomass plays an essential role in present and future energy. Pyrolysis holds considerable potential approaches among biomass conversion technologies. This study presents a critical review of the effect of the key pyrolysis parameters from lignocellulosic biomass to product distribution. The lignocellulosic biomass composition and pyrolysis conversion behavior of every single component was discussed in detail. On top of that, CO2-based benefits, economic assessment, and technical orientation for biofuel production from biomass are included. The carbon and hydrogen content of biomass is critical for producing high-quality bio-oil. When compared to other energy crops and agricultural residues, pyrolysis of clean wood and polar demonstrated the best bio-oil production. The increased cellulose and hemicellulose content aiding in the synthesis of bio-oil, but the high concentration of lignin results in more biochar. The article delves into product upgrading via several routes such as physical, chemical, and catalytic. From the review, considering factors such as pyrolysis technologies, energy demand, and bio-oil yields, greenhouse potential benefits needs to be evaluated, and this needs to be done on an individual basis. In terms of production cost, the current cost of biomass feedstock can range between $50 to $97/ton, which is approximately 30−54% of the liquid fuel production cost. A wide range of studies covering different aspects of biomass pyrolysis technology, from reactor configuration and the heating source to single and poly-step pyrolysis processes, have been carried out in the search for solutions in optimizing the current technologies. Thus, expanding and improving awareness of the lignocellulosic biomass in the pyrolysis technology wou...
Hoang, AT, Sandro Nižetić, Olcer, AI, Ong, HC, Chen, W-H, Chong, CT, Thomas, S, Bandh, SA & Nguyen, XP 2021, 'Impacts of COVID-19 pandemic on the global energy system and the shift progress to renewable energy: Opportunities, challenges, and policy implications', Energy Policy, vol. 154, pp. 112322-112322.
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Being declared a global emergency, the COVID-19 pandemic has taken many lives, threatened livelihoods and businesses around the world. The energy industry, in particular, has experienced tremendous pressure resulting from the pandemic. In response to such a challenge, the development of sustainable resources and renewable energy infrastructure has demonstrated its potential as a promising and effective strategy. To sufficiently address the effect of COVID-19 on renewable energy development strategies, short-term policy priorities should be identified, while mid-term and long-term action plans should be formulated in achieving the well-defined renewable energy targets and progress towards a more sustainable energy future. In this review, opportunities, challenges, and significant impacts of the COVID-19 pandemic on current and future sustainable energy strategies were analyzed in detail; while drawing from experiences in identifying reasonable behaviors, orientating appropriate actions, and policy implications on the sustainable energy trajectory were also mentioned. Indeed, the question is that whether the COVID-19 pandemic will kill us or provide us with a precious lesson on future sustainable energy development.
Hoang, DK, Le, NM, Vo‐Thi, UP, Nguyen, HG, Ho‐Pham, LT & Nguyen, TV 2021, 'Mechanography assessment of fall risk in older adults: the Vietnam Osteoporosis Study', Journal of Cachexia, Sarcopenia and Muscle, vol. 12, no. 5, pp. 1161-1167.
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AbstractBackgroundJumping mechanography is a technology for quantitatively assessing muscular function and balance in older adults. This study sought to define the association between jumping mechanography parameters and fall risk in Vietnamese individuals.MethodsThe study involved 375 women and 244 men aged 50 years and older, who were recruited from the general population in Ho Chi Minh City (Vietnam). The individuals had been followed for 2 years. At baseline, Esslinger Fitness index (EFI), jumping power, force, velocity of lower limbs, and the ability to maintain balance were measured by a Leonardo Mechanograph Ground Reaction Force system (Novotec Medical, Pforxheim, Germany). The incidence of falls during the follow‐up period was ascertained from self‐report. Logistic regression analysis was used to analyse the association between jumping mechanography parameters and fall risk.ResultsThe average age of participants at baseline was 56.7 years (SD 5.85). During the 2 year follow‐up, 92 falls were reported, making the incidence of fall at ~15% [95% confidence interval (CI), 12.1 to 18.2]. The incidence of fall increased with advancing age, and women had a higher incidence than men (17.6% vs. 10.7%; P = 0.024). In univariate analysis, maximal velocity [odds ratio (OR) 0.65; 95% CI, 0.52 to 0.82], maximal force (OR 0.83; 95% CI, 0.65 to 1.04), and maximal power (OR 0.68; 95% CI, 0.52 to 0.88) were each significantly associated with fall risk. EFI was not significantly associated with fall risk (OR 1.09; 95% CI, 0.86 to 1.39). However, in a multiple logistic regression model, greater maximum velocity was associated with lower odds of fall (OR 0.38; 95% CI, 0.16 to 0.92).Conclusions<...
Hoang, H-G, Lin, C, Chiang, C-F, Bui, X-T, Lukkhasorn, W, Bui, T-P-T, Tran, H-T, Vo, T-D-H, Le, V-G & Nghiem, LD 2021, 'The Individual and Synergistic Indexes for Assessments of Heavy Metal Contamination in Global Rivers and Risk: a Review', Current Pollution Reports, vol. 7, no. 3, pp. 247-262.
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Hoang, LM, Zhang, JA, Nguyen, DN, Huang, X, Kekirigoda, A & Hui, K-P 2021, 'Suppression of Multiple Spatially Correlated Jammers', IEEE Transactions on Vehicular Technology, vol. 70, no. 10, pp. 10489-10500.
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Effective suppression of inadvertent or deliberate jamming signals is crucial to ensure reliable wireless communication. However, as demonstrated in this paper, when the transmitted jamming signals are highly correlated, and especially when the correlation coefficient varies, nullifying the jamming signals can be challenging. Unlike existing techniques that often assume uncorrelated jamming signals or non-zero but constant correlation, we analyze the impact of the non-zero and varying correlations between transmitted jamming signals on the suppression of the jamming signals. Specifically, we observe that by varying the correlation coefficients between transmitted jamming signals, jammers can 'virtually change' the jamming channels hence their nullspace, even when these channels do not physically change. This makes most jamming suppression techniques that rely on steering receiving beams towards the nullspace of jamming channels no longer applicable. To tackle the problem, we develop techniques to effectively track the jamming nullspace and correspondingly update receiving beams. Monte Carlo simulations show that our proposed techniques can suppress/nullify jamming signals for all considered scenarios with non-zero and varying correlation coefficients amongst transmitted jamming signals.
Hoang, PM, Tuan, HD, Son, TT & Poor, HV 2021, 'Qualitative HD Image and Video Recovery via High-Order Tensor Augmentation and Completion', IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 3, pp. 688-701.
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IEEE This paper presents a new framework for severely distorted image and video recovery via tensor augmentation and completion. By considering a task of representing a matrix by a high-order-n tensor as that of encoding the matrix two-dimension (2D) indices (i, j) by n-digit words i1i2… in, we then develop a new high order tensor augmentation to cast a third order tensor of color images or video sequences containing missing pixels into a higher order tensor, which likes the ket augmentation of quantum physics, is capable of capturing all correlations and entanglements between entries of the original third order tensor. Accordingly, the resultant high-order tensor is completed by our previously developed parallel matrix factorization via tensor train. Simulations are provided to show the clear advantages of our approach to enhance important metrics of the visual quality such as relative square error and structural similarity index in image and video processing that help to achieve high recovery rates even for high-definition images and videos with 95% missing pixels.
Hoang, TM, Duong, TQ, Tuan, HD, Lambotharan, S & Hanzo, L 2021, 'Physical Layer Security: Detection of Active Eavesdropping Attacks by Support Vector Machines', IEEE Access, vol. 9, pp. 31595-31607.
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This article presents a framework for converting wireless signals into structured datasets, which can be fed into machine learning algorithms for the detection of active eavesdropping attacks at the physical layer. More specifically, a wireless communication system, which consists of an access point (AP), K legitimate users and an active eavesdropper, is considered. To detect the eavesdropper who breaks into the system during the authentication phase, we first build structured datasets based on different features and then apply sophisticated support vector machine (SVM) classifiers to those structured datasets. To be more specific, we first process the signals received by the AP and then define a pair of statistical features based on the post-processing of the signals. By arranging for the AP to simulate the entire process of transmission and the process of constructing features, we form the so-called artificial training data (ATD). By training SVM classifiers on the ATD, we classify the received signals associated with eavesdropping attacks and nonattacks, thereby detecting the presence of the eavesdropper. Two SVM classifiers are considered, including a classic twin-class SVM (TC-SVM) and a single-class SVM (SC-SVM). While the TC-SVM is preferred in the case of having perfect channel state information (CSI) of all channels, the SC-SVM is preferred in the realistic scenario when we have only the CSI of legitimate users. We also evaluate the accuracy of the trained models depending on the choice of kernel functions, the choice of features and on the eavesdropper's power. Our numerical results show that careful parameter-tuning is required for exceeding an eavesdropper detection probability of 95%.
Hobbie, JG, Gandomi, AH & Rahimi, I 2021, 'A Comparison of Constraint Handling Techniques on NSGA-II', Archives of Computational Methods in Engineering, vol. 28, no. 5, pp. 3475-3490.
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Almost all real-world and engineering problems involve multi-objective optimization of some sort that is often constrained. To solve these constrained multi-objective optimization problems, constrained multi-objective optimization evolutionary algorithms (CMOEAs) are enlisted. These CMOEAs require specific constraint handling techniques. This study aims to address and test the most successful constraint handling techniques, seven different penalty constraint techniques, as applied to the Non-dominated Sorting Genetic Algorithm II (NSGA-II). In this paper, NSGA-II is chosen because of its high popularity amongst evolutionary algorithms. Inverted Generational Distance and Hypervolume are the main metrics that are discussed to compare the constraint handling techniques. NSGA-II is applied on 13 constrained multi-objective problems known as CF1-CF10, C1-DTLZ1, C2-DTLZ2, and C3-DTLZ4. The result of IGD and HV values are compared and the feasibility proportions of each combination on each problem are shown. The results of simulation present interesting findings that have been presented at the end of paper as discussion and conclusion.
Hofer, OJ, McKinlay, CJD, Tran, T & Crowther, CA 2021, 'Antenatal corticosteroids, maternal body mass index and infant morbidity within the ASTEROID trial', Australian and New Zealand Journal of Obstetrics and Gynaecology, vol. 61, no. 3, pp. 380-385.
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BackgroundAntenatal corticosteroids (ACSs) administered to women before preterm birth improve neonatal health. Proportionately more women are obese or overweight in current obstetric populations than those who were included in the original trials of ACSs, and it remains uncertain if higher doses are required for such women.AimOur aim was to assess the association between maternal body mass index (BMI) and infant morbidity after the administration of ACSs.MethodsIn the secondary analysis of the ASTEROID trial cohort, women at risk of preterm birth at <34 weeks’ gestation were randomised to betamethasone or dexamethasone. Infant outcomes were compared according to whether women were of normal weight (BMI < 25 kg/m2), overweight (BMI 25–29.9 kg/m2) or obese (BMI ≥ 30 kg/m2).ResultsOf 982 women with a singleton pregnancy and BMI data, 519 (52.9%) were of normal size, 241 (24.5%) were overweight and 222 (22.6%) were obese. Compared with infants born to women of normal weight, there was little or no difference in respiratory distress syndrome in infants born to women who were overweight (odds ratio (OR) = 0.92, 95% confidence interval (CI) 0.57, 1.49) or obese (OR = 1.44, 95% CI 0.90, 2.31). Similarly, there were no significant differences between infants born to women in the three BMI groups for other morbidities, including bronchopulmonary dysplasia, mechanical ventilation, intraventricular haemorrhage, retinopathy of prematurity, patent ductus arteriosus, necrotising enterocolitis, perinatal death or combined serious morbidity.ConclusionsMaternal body size is not associated with infant morbidity aft...
Ho-Le, TP, Tran, HTT, Center, JR, Eisman, JA, Nguyen, HT & Nguyen, TV 2021, 'Assessing the clinical utility of genetic profiling in fracture risk prediction: a decision curve analysis', Osteoporosis International, vol. 32, no. 2, pp. 271-280.
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Using decision curve analysis on 2188 women and 1324 men, we found that an osteogenomic profile constructed from 62 genetic variants improved the clinical net benefit of fracture risk prediction over and above that of clinical risk factors and BMD.
Introduction
Genetic profiling is a promising tool for assessing fracture risk. This study sought to use the decision curve analysis (DCA), a novel approach to determine the impact of genetic profiling on fracture risk prediction.
Methods
The study involved 2188 women and 1324 men, aged 60 years and above, who were followed for up to 23 years. Bone mineral density (BMD) and clinical risk factors were obtained at baseline. The incidence of fracture and mortality were recorded. A weighted individual genetic risk score (GRS) was constructed from 62 BMD-associated genetic variants. Four models were considered: CRF (clinical risk factors); CRF + GRS; Garvan model (GFRC) including CRF and femoral neck BMD; and GFRC + GRS. The DCA was used to evaluate the clinical net benefit of predictive models at a range of clinically reasonable risk thresholds.
Results
In both women and men, the full model GFRC + GRS achieved the highest net benefits. For 10-year risk threshold > 18% for women and > 15% for men, the GRS provided net benefit above those of the CRF models. At 20% risk threshold, adding the GRS could help to avoid 1 additional treatment per 81 women or 1 per 24 men compared with the Garvan model. At lower risk thresholds, there was no significant difference between the four models.
Conclusions
The addition of genetic profiling into the clinical risk factors can improve the net clinical benefit at higher risk thresholds of fracture. Although the contribution of genetic profiling was modest in the presence of BMD + CRF, it appeared to be able to replace BMD for fracture prediction.
Ho-Le, TP, Tran, TS, Bliuc, D, Pham, HM, Frost, SA, Center, JR, Eisman, JA & Nguyen, TV 2021, 'Epidemiological transition to mortality and refracture following an initial fracture', eLife, vol. 10, pp. 1-15.
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This study sought to redefine the concept of fracture risk that includes refracture and mortality, and to transform the risk into 'skeletal age'. We analysed data obtained from 3521 women and men aged 60 years and older, whose fracture incidence, mortality, and bone mineral density (BMD) have been monitored since 1989. During the 20-year follow-up period, among 632 women and 184 men with a first incident fracture, the risk of sustaining a second fracture was higher in women (36%) than in men (22%), but mortality risk was higher in men (41%) than in women (25%). The increased risk of mortality was not only present with an initial fracture, but was accelerated with refractures. Key predictors of post-fracture mortality were male gender (hazard ratio [HR] 2.4; 95% CI, 1.79–3.21), advancing age (HR 1.67; 1.53–1.83), and lower femoral neck BMD (HR 1.16; 1.01–1.33). A 70-year-old man with a fracture is predicted to have a skeletal age of 75. These results were incorporated into a prediction model to aid patient-doctor discussion about fracture vulnerability and treatment decisions.
Holmes, NP, Chambon, S, Holmes, A, Xu, X, Hirakawa, K, Deniau, E, Lartigau-Dagron, C & Bousquet, A 2021, 'Organic semiconductor colloids: From the knowledge acquired in photovoltaics to the generation of solar hydrogen fuel', Current Opinion in Colloid & Interface Science, vol. 56, pp. 101511-101511.
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Homaira, M & Hassan, R 2021, 'Prediction of Agricultural Emissions in Malaysia Using Machine Learning Algorithms', International Journal on Perceptive and Cognitive Computing, vol. 7, no. 1, pp. 33-40.
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Hong, G-J, Li, D-L, Pare, S, Saxena, A, Prasad, M & Lin, C-T 2021, 'Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection', Applied Sciences, vol. 11, no. 23, pp. 11268-11268.
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A new online multi-class learning algorithm is proposed with three main characteristics. First, in order to make the feature pool fitter for the pattern pool, the adaptive feature pool is proposed to dynamically combine the three general features, Haar-like, Histogram of Oriented Gradient (HOG), and Local Binary Patterns (LBP). Second, the external model is integrated into the proposed model without re-training to enhance the efficacy of the model. Third, a new multi-class learning and updating mechanism are proposed that help to find unsuitable decisions and adjust them automatically. The performance of the proposed model is validated with multi-class detection and online learning system. The proposed model achieves a better score than other non-deep learning algorithms used in public pedestrian and multi-class databases. The multi-class databases contain data for pedestrians, faces, vehicles, motorcycles, bicycles, and aircraft.
Hong, J-H, Kang, S, Sa, JK, Park, G, Oh, YT, Kim, TH, Yin, J, Kim, SS, D’Angelo, F, Koo, H, You, Y, Park, S, Kwon, HJ, Kim, CI, Ryu, H, Lin, W, Park, EJ, Kim, Y-J, Park, M-J, Kim, H, Kim, M-S, Chung, S, Park, C-K, Park, S-H, Kang, YH, Kim, JH, Saya, H, Nakano, I, Gwak, H-S, Yoo, H, Lee, J, Hur, E-M, Shi, B, Nam, D-H, Iavarone, A, Lee, S-H & Park, JB 2021, 'Modulation of Nogo receptor 1 expression orchestrates myelin-associated infiltration of glioblastoma', Brain, vol. 144, no. 2, pp. 636-654.
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Abstract As the clinical failure of glioblastoma treatment is attributed by multiple components, including myelin-associated infiltration, assessment of the molecular mechanisms underlying such process and identification of the infiltrating cells have been the primary objectives in glioblastoma research. Here, we adopted radiogenomic analysis to screen for functionally relevant genes that orchestrate the process of glioma cell infiltration through myelin and promote glioblastoma aggressiveness. The receptor of the Nogo ligand (NgR1) was selected as the top candidate through Differentially Expressed Genes (DEG) and Gene Ontology (GO) enrichment analysis. Gain and loss of function studies on NgR1 elucidated its underlying molecular importance in suppressing myelin-associated infiltration in vitro and in vivo. The migratory ability of glioblastoma cells on myelin is reversibly modulated by NgR1 during differentiation and dedifferentiation process through deubiquitinating activity of USP1, which inhibits the degradation of ID1 to downregulate NgR1 expression. Furthermore, pimozide, a well-known antipsychotic drug, upregulates NgR1 by post-translational targeting of USP1, which sensitizes glioma stem cells to myelin inhibition and suppresses myelin-associated infiltration in vivo. In primary human glioblastoma, downregulation of NgR1 expression is associated with highly infiltrative characteristics and poor survival. Together, our findings reveal that loss of NgR1 drives myelin-associated infiltration of glioblastoma and suggest that novel therapeutic strategies aimed at reactivating expression of NgR1 will improve the clinical outcome of glioblastoma patients.
Hoque, MA-A, Pradhan, B, Ahmed, N & Sohel, MSI 2021, 'Agricultural drought risk assessment of Northern New South Wales, Australia using geospatial techniques', Science of The Total Environment, vol. 756, pp. 143600-143600.
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Hoque, MA-A, Pradhan, B, Ahmed, N, Ahmed, B & Alamri, AM 2021, 'Cyclone vulnerability assessment of the western coast of Bangladesh', Geomatics, Natural Hazards and Risk, vol. 12, no. 1, pp. 198-221.
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Coastal Bangladesh is one of the hotspots of tropical cyclone’s landfall in South Asia. A spatial vulnerability assessment is required to formulate disaster risk reduction strategies. This study develops a comprehensive tropical cyclone vulnerability mapping approach by applying Fuzzy Analytical Hierarchy Process (FAHP) and geospatial techniques and examines the spatial distribution of tropical cyclone vulnerability in the western coastal region of Bangladesh. We have selected 18 spatial criteria under the physical, social, and mitigation capacity categories as the components of vulnerability. Results indicate that the southern and south-eastern peripheral areas exhibit higher vulnerability to tropical cyclones since these areas comprise low elevation, gentle slope, closeness to the sea, a high number of historical cyclone tracks, vulnerable land cover classes (settlements and crops land), and poor socio-economic structures. These areas cover most of the Barguna, Khulna, Bagerhat, Jhalokati, and southern parts of Satkhira, and Pirojpur districts. The existing mitigation capacity measures, for example, the construction of cyclone shelters, embankments, road networks, and effective warning systems in these areas are not adequate levels. The findings would be useful for policymakers and local authorities in formulating appropriate cyclone risk mitigation plans in coastal Bangladesh.
Horry, MJ, Chakraborty, S, Pradhan, B, Fallahpoor, M, Chegeni, H & Paul, M 2021, 'Factors determining generalization in deep learning models for scoring COVID-CT images', Mathematical Biosciences and Engineering, vol. 18, no. 6, pp. 9264-9293.
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<abstract> <p>The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focused on the diagnosis of COVID-19 from medical images. However, these models have found limited, if any, clinical application due in part to unproven generalization to data sets beyond their source training corpus. This study investigates the generalizability of deep learning models using publicly available COVID-19 Computed Tomography data through cross dataset validation. The predictive ability of these models for COVID-19 severity is assessed using an independent dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. We show that under certain conditions, deep learning models can generalize well to an external dataset with F1 scores up to 86%. The best performing model shows predictive accuracy of between 75% and 96% for lung involvement scoring against an external expertly stratified dataset. From these results we identify key factors promoting deep learning generalization, being primarily the uniform acquisition of training images, and secondly diversity in CT slice position.</p> </abstract>
Hoseini, SA, Fallahpour, A, Wong, KY, Mahdiyar, A, Saberi, M & Durdyev, S 2021, 'Sustainable Supplier Selection in Construction Industry through Hybrid Fuzzy-Based Approaches', Sustainability, vol. 13, no. 3, pp. 1413-1413.
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Due to increase in the public and stakeholders’ awareness regarding economic, environmental, and social issues, the construction industry tends to follow the sustainability policies and practices in supply chain management. Hence, one of the most crucial aspects for a construction company in this regard is sustainable supplier selection, and, to this end, an accurate and reliable model is required. In this paper a hybrid fuzzy best-worst method and fuzzy inference system model is developed for sustainable supplier selection. In the first phase of this study, after determining 19 criteria in three main aspects, the final weight of each aspect and criterion is obtained using fuzzy best-worst method approach. In the second phase, the most sustainable supplier is selected by running the weighted fuzzy inference system both in aspect and criterion level, providing more accurate results compared to the use of other available models. Finally, two different tests are employed to validate the results and evaluate the robustness of the proposed model. The novel developed model enables the decision-maker to simulate the decision-making process, reduce the calculations loads, consider a large number of criteria in decision making, and resolve the inherited uncertainties in experts’ responses.
Hossain Lipu, MS, Hannan, MA, Karim, TF, Hussain, A, Saad, MHM, Ayob, A, Miah, MS & Indra Mahlia, TM 2021, 'Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook', Journal of Cleaner Production, vol. 292, pp. 126044-126044.
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Globally, the research on battery technology in electric vehicle applications is advancing tremendously to address the carbon emissions and global warming issues. The effectiveness of electric vehicles depends on the accurate assessment of key parameters as well as proper functionality and diagnosis of the battery storage system. However, poor monitoring and safety strategies of the battery storage system can lead to critical issues such as battery overcharging, over-discharging, overheating, cell unbalancing, thermal runaway, and fire hazards. To address these concerns, an effective battery management system plays a crucial role in enhancing battery performance including precise monitoring, charging-discharging control, heat management, battery safety, and protection. The goal of this paper is to deliver a comprehensive review of different intelligent approaches and control schemes of the battery management system in electric vehicle applications. In line with that, the review evaluates the intelligent algorithms in battery state estimation concerning their features, structure, configuration, accuracy, advantages, and disadvantages. Moreover, the review explores the various controllers in battery heating, cooling, equalization, and protection highlighting categories, characteristics, targets, achievements, benefits, and shortcomings. The key issues and challenges in terms of computation complexity, execution problems along with various internal and external factors are identified. Finally, future opportunities and directions are delivered to design an efficient intelligent algorithm and controller toward the development of an advanced battery management system for future sustainable electric vehicle applications.
Hossain, I, Zhou, S, Ishac, K, Lind, E, Sharwood, L & Eager, D 2021, 'A Measurement of ‘Walking-the-Wall’ Dynamics: An Observational Study Using Accelerometry and Sensors to Quantify Risk Associated with Vertical Wall Impact Attenuation in Trampoline Parks', Sensors, vol. 21, no. 21, pp. 7337-7337.
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This study illustrates the application of a tri-axial accelerometer and gyroscope sensor device on a trampolinist performing the walking-the-wall manoeuvre on a high-performance trampoline to determine the performer dynamic conditions. This research found that rigid vertical walls would allow the trampolinist to obtain greater control and retain spatial awareness at greater levels than what is achievable on non-rigid vertical walls. With a non-rigid padded wall, the reaction force from the wall can be considered a variable force that is not constrained, and would not always provide the feedback that the trampolinist needs to maintain the balance with each climb up the wall and fall from height. This research postulates that unattenuated vertical walls are safer than attenuated vertical walls for walking-the-wall manoeuvres within trampoline park facilities. This is because non-rigid walls would provide higher g-force reaction feedback from the wall, which would reduce the trampolinist’s control and stability. This was verified by measuring g-force on a horizontal rigid surface versus a non-rigid surface, where the g-force feedback was 27% higher for the non-rigid surface. Control and stability are both critical while performing the complex walking-the-wall manoeuvre. The trampolinist experienced a very high peak g-force, with a maximum g-force of approximately 11.5 g at the bottom of the jump cycle. It was concluded that applying impact attenuation padding to vertical walls used for walking-the-wall and similar activities would increase the likelihood of injury; therefore, padding of these vertical surfaces is not recommended.
Hossain, N, Hoong, LL, Barua, P, Soudagar, MEM & Mahlia, TMI 2021, 'The effect of enzymatic hydrolysis of pretreated wastepaper for bioethanol production', Korean Journal of Chemical Engineering, vol. 38, no. 12, pp. 2493-2499.
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Enzymatic hydrolysis of waste biomass for bioethanol production is considered a decades old traditional, inexpensive, and energy-effective approach. In this study, waste office paper was pretreated with diluted sulfuric acid (H2SO4) and hydrolyzed with one of the most available and cost-effective enzymes, cellulase derived from Trichoderma reesei, under submerged static condition. Three different pretreatment approaches—cut into 2 cm2, blended with distilled water, and pretreated with diluted H2SO4—have been implemented, and pretreatment with diluted H2SO4 was the most effective. Hydrolysis with different concentrations—0.5 M, 1.0 M, 1.5 M, 2.0 M of H2SO4—was performed. The maximum glucose content was obtained at 2.0 M H2SO4 at 90 min reaction time, and glucose yield was 0.11 g glucose/g wastepaper. The cut paper, wet-blended, and acid-treated wastepaper was hydrolyzed with cellulase enzyme for 2, 4, and 5 consecutive days with 5 mg, 10 mg, 15 mg, and 20 mg enzyme loadings. The maximum glucose content obtained was 9.75 g/l from acid-treated wastepaper, after 5 days of enzymatic hydrolysis with 20 mg enzyme loading and a glucose yield of a 0.5 g glucose/g wastepaper. The wastepaper hydrolysate was further fermented for 6, 8, and 10 hours continuously with Saccharomyces cerevisiae (yeast), and at 10 hours of fermentation, the maximum glucose consumption was 0.18 g by yeast. Further, HPLC analysis of the fermented medium presented a strong peak of bioethanol content at 16.12 min. The distillation of bioethanol by rotary evaporator presented 0.79 ml bioethanol/fermented solution, which indicated the conversion efficiency of 79%.
Hossain, N, Mahlia, TMI, Miskat, MI, Chowdhury, T, Barua, P, Chowdhury, H, Nizamuddin, S, Ahmad, NB, Zaharin, NAB, Mazari, SA & Soudagar, MEM 2021, 'Bioethanol production from forest residues and life cycle cost analysis of bioethanol-gasoline blend on transportation sector', Journal of Environmental Chemical Engineering, vol. 9, no. 4, pp. 105542-105542.
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Hossain, SI, Luo, Z, Deplazes, E & Saha, SC 2021, 'Shape matters—the interaction of gold nanoparticles with model lung surfactant monolayers', Journal of The Royal Society Interface, vol. 18, no. 183.
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The lung surfactant monolayer (LSM) forms the main biological barrier for any inhaled particles to enter our bloodstream, including gold nanoparticles (AuNPs) present as air pollutants and under investigation for use in biomedical applications. Understanding the interaction of AuNPs with lung surfactant can assist in understanding how AuNPs enter our lungs. In this study, we use coarse-grained molecular dynamics simulations to investigate the effect of four different shape D AuNPs (spherical, box, icosahedron and rod) on the structure and dynamics of a model LSM, with a particular focus on differences resulting from the shape of the AuNP. Monolayer-AuNP systems were simulated in two different states: the compressed state and the expanded state, representing inhalation and exhalation conditions, respectively. Our results indicate that the compressed state is more affected by the presence of the AuNPs than the expanded state. Our results show that in the compressed state, the AuNPs prevent the monolayer from reaching the close to zero surface tension required for normal exhalation. In the compressed state, all four nanoparticles (NPs) reduce the lipid order parameters and cause a thinning of the monolayer where the particles drag surfactant molecules into the water phase. Comparing the different properties shows no trend concerning which shape has the biggest effect on the monolayer, as shape-dependent effects vary among the different properties. Insights from this study might assist future work of how AuNP shapes affect the LSM during inhalation or exhalation conditions.
Hossain, SM, Park, H, Kang, H-J, Mun, JS, Tijing, L, Rhee, I, Kim, J-H, Jun, Y-S & Shon, HK 2021, 'Facile synthesis and characterization of anatase TiO2/g-CN composites for enhanced photoactivity under UV–visible spectrum', Chemosphere, vol. 262, pp. 128004-128004.
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© 2020 Elsevier Ltd For the purpose of atmospheric NO removal, anatase TiO2/g-CN photocatalytic composites were prepared by using a facile template-free calcination route in atmospheric conditions. Considerably fiscal NP400 and laboratory-grade melamine were used as the precursor of the composites. Additionally, samples were prepared with different wt. ratios of TiO2 and melamine by using two distinct calcination temperatures (550 °C/600 °C). The morphological attributes of the composites were assessed with X-ray diffraction, scanning and transmission electron microscopy, infrared spectroscopy, and X-ray photoelectron spectroscopy. Additionally, the optical traits were evaluated and compared using UV–visible diffuse reflectance spectroscopy and photoluminescence analysis. Finally, the photodegradation potentials for atmospheric NO by using the as-prepared composites were assessed under both UV and visible light irradiation. All the composites showed superior NO oxidation compared to NP400 and bulk g-CN. For the composites prepared by using the calcination temperature of 550 °C, the maximum NO removal was observed when the NP400 to melamine ratio was 1:2, irrespective of the utilized light irradiation type. Whereas for increased calcination temperature (600 °C), the maximum NO removal was observed at the precursor mix ratio of 1:3 (NP400:melamine). Successfully narrowed energy bandgaps were perceived in the as-prepared composites. Moreover, a subsequent drop in NO2 generation during NO oxidation was observed under both UV and visible light irradiation. Interestingly, higher calcination temperature during the synthesis of the catalysts has shown a significant drop in NO2 generation during the photodegradation of NO.
Hossain, SM, Park, H, Kang, H-J, Mun, JS, Tijing, L, Rhee, I, Kim, J-H, Jun, Y-S & Shon, HK 2021, 'Synthesis and NOx removal performance of anatase S–TiO2/g-CN heterojunction formed from dye wastewater sludge', Chemosphere, vol. 275, pp. 130020-130020.
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In this study, sludges generated from Ti-based flocculation of dye wastewater were used to retrieve photoactive titania (S-TiO2). It was heterojunctioned with graphitic carbon nitride (g-CN) to augment photoactivity under UV/visible light irradiance. Later the as-prepared samples were utilized to remove nitrogen oxides (NOx) in the atmospheric condition through photocatalysis. Heterojunction between S-TiO2 and g-CN was prepared through facile calcination (@550 °C) of S-TiO2 and melamine mix. Advanced sample characterization was carried out and documented extensively. Successful heterojunction was confirmed from the assessment of morphological and optical attributes of the samples. Finally, the prepared samples' level of photoactivity was assessed through photooxidation of NOx under both UV and visible light irradiance. Enhanced photoactivity was observed in the prepared samples irrespective of the light types. After 1 h of UV/visible light-based photooxidation, the best sample STC4 was found to remove 15.18% and 9.16% of atmospheric NO, respectively. In STC4, the mixing ratio of S-TiO2, to melamine was maintained as 1:3. Moreover, the optical bandgap of STC4 was found as 2.65 eV, where for S-TiO2, it was 2.83 eV. Hence, the restrained rate of photogenerated charge recombination and tailored energy bandgap of the as-prepared samples were the primary factors for enhancing photoactivity.
Hosseini, MR, Jupp, J, Papadonikolaki, E, Mumford, T, Joske, W & Nikmehr, B 2021, 'Position paper: digital engineering and building information modelling in Australia', Smart and Sustainable Built Environment, vol. 10, no. 3, pp. 331-344.
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PurposeThis position paper urges a drive towards clarity in the key definitions, terminologies and habits of speech associated with digital engineering and building information modelling (BIM). The ultimate goal of the paper is to facilitate the move towards arriving at an ideal definition for both concepts.Design/methodology/approachThis paper takes the “explanation building” review approach in providing prescriptive guidelines to researchers and industry practitioners. The aim of the review is to draw upon existing studies to identify, describe and find application of principles in a real-world context.FindingsThe paper highlights the definitional challenges surrounding digital engineering and BIM in Australia, to evoke a debate on BIM and digital engineering boundaries, how and why these two concepts may be linked, and how they relate to emerging concepts.Originality/valueThis is the first scholarly attempt to clarify the definition of digital engineering and address the confusion between the concepts of BIM and digital engineering.
Hosseini, SAH, Rahmani, O, Hayati, H & Jahanshir, A 2021, 'Surface effect on forced vibration of DNS by viscoelastic layer under a moving load', Coupled Systems Mechanics, vol. 10, no. 4, pp. 333-350.
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The surface effect for a forced vibration of a double-nanobeam-system (DNS) coupled by a viscoelastic layer under a moving constant load is studied in this paper. The viscoelastic layer that couples the nanobeams to each other, is modelled as spring-damper system. The Euler- Bernoulli theory and a simply supported boundary condition are considered for both nanobeams. By using the analytical solution, the dynamic displacement is obtained by considering the surface elasticity and residual tension effect on each nanobeams. Furthermore, the several significant parameters such as the velocity of the moving load, spring constant, damping coefficient and also the surface effect have been studied using some plots and examples. Finally, by observing the diagrams it was concluded that as the length of the beams reduces, the surface effect has a considerable effect on each of nanobeams especially at Nano scale, where it was not achieved by classic theories.
Hosseini, SM, Kalhori, H & Al-Jumaily, A 2021, 'Active vibration control in human forearm model using paired piezoelectric sensor and actuator', Journal of Vibration and Control, vol. 27, no. 19-20, pp. 2231-2242.
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An active vibration control system to monitor and suppress the human forearm tremor is proposed in this article. The forearm is modelled as a uniform flexible continuous beam supported by a pin joint and a rotational spring at one end, whereas the other end is free. The beam is covered with a layer of piezoelectric sensor on its top surface and a layer of piezoelectric actuator on its bottom surface to form a control system, through which a closed-loop active control paradigm is implemented for tremor suppression. The governing equation of motion is derived using the Hamilton principle as well as the Galerkin procedure, leading to a second-order ordinary differential equation in time. The vibration response of the structure to an external harmonic excitation, analogous to tremor, is obtained analytically, enabling parametric study of the control system for tremor reduction. Using the obtained analytical expression, the effects of various parameters such as the control gain, the piezoelectric coefficient and the dielectric constant on the vibration response are studied. The results indicated that the proposed active vibration control system is an effective tool for active vibration control. Increasing the control gain of the control system as well as the magnitude of the piezoelectric constant decreased the amplitude of vibration, whereas the dielectric constant of the piezoelectric material did not show to have a significant effect on the beam vibration. The obtained results will pave the way for further experimental exploration to take and fabricate the most appropriate piezoelectric material and to design an effective active vibration control system for tremor suppression in people with Parkinson’s disease.
Hosseinzadeh, A, Najafpoor, AA, Navaei, AA, Zhou, JL, Altaee, A, Ramezanian, N, Dehghan, A, Bao, T & Yazdani, M 2021, 'Improving Formaldehyde Removal from Water and Wastewater by Fenton, Photo-Fenton and Ozonation/Fenton Processes through Optimization and Modeling', Water, vol. 13, no. 19, pp. 2754-2754.
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This study aimed to assess, optimize and model the efficiencies of Fenton, photo-Fenton and ozonation/Fenton processes in formaldehyde elimination from water and wastewater using the response surface methodology (RSM) and artificial neural network (ANN). A sensitivity analysis was used to determine the importance of the independent variables. The influences of different variables, including H2O2 concentration, initial formaldehyde concentration, Fe dosage, pH, contact time, UV and ozonation, on formaldehyde removal efficiency were studied. The optimized Fenton process demonstrated 75% formaldehyde removal from water. The best performance with 80% formaldehyde removal from wastewater was achieved using the combined ozonation/Fenton process. The developed ANN model demonstrated better adequacy and goodness of fit with a R2 of 0.9454 than the RSM model with a R2 of 0. 9186. The sensitivity analysis showed pH as the most important factor (31%) affecting the Fenton process, followed by the H2O2 concentration (23%), Fe dosage (21%), contact time (14%) and formaldehyde concentration (12%). The findings demonstrated that these treatment processes and models are important tools for formaldehyde elimination from wastewater.
Hosseinzadeh, A, Zhou, JL, Navidpour, AH & Altaee, A 2021, 'Progress in osmotic membrane bioreactors research: Contaminant removal, microbial community and bioenergy production in wastewater', Bioresource Technology, vol. 330, pp. 124998-124998.
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Renewable energy, water conservation, and environmental protection are the most important challenges today. Osmotic membrane bioreactor (OMBR) is an innovative process showing superior performance in bioenergy production, eliminating contaminants, and low fouling tendency. However, salinity build-up is the main drawback of this process. Identifying the microbial community can improve the process in bioenergy production and contaminant treatment. This review aims to study the recent progress and challenges of OMBRs in contaminant removal, microbial communities and bioenergy production. OMBRs are widely reported to remove over 80% of total organic carbon, PO43-, NH4+ and emerging contaminants from wastewater. The most important microbial phyla for both hydrogen and methane production in OMBR are Firmicutes, Proteobacteria and Bacteroidetes. Firmicutes' dominance in anaerobic processes is considerably increased from usually 20% at the beginning to 80% under stable condition. Overall, OMBR process has great potential to be applied for simultaneous bioenergy production and wastewater treatment.
Hou, S, Ni, W, Wang, M, Liu, X, Tong, Q & Chen, S 2021, 'Bottleneck-Aware Resource Allocation for Service Processes', International Journal of Web Services Research, vol. 18, no. 3, pp. 1-21.
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In 5G systems and beyond, traditional generic service models are no longer appropriate for highly customized and intelligent services. The process of reinventing service models involves allocating available resources, where the performance of service processes is determined by the activity node with the lowest service rate. This paper proposes a new bottleneck-aware resource allocation approach by formulating the resource allocation as a max-min problem. The approach can allocate resources proportional to the workload of each activity, which can guarantee that the service rates of activities within a process are equal or close-to-equal. Based on the business process simulator (i.e., BIMP) simulation results show that the approach is able to reduce the average cycle time and improve resource utilization, as compared to existing alternatives. The results also show that the approach can effectively mitigate the impact of bottleneck activity on the performance of service processes.
Houshyar, S, Bhattacharyya, A, Khalid, A, Rifai, A, Dekiwadia, C, Kumar, GS, Tran, PA & Fox, K 2021, 'Multifunctional Sutures with Temperature Sensing and Infection Control', Macromolecular Bioscience, vol. 21, no. 3.
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AbstractThe next‐generation sutures should provide in situ monitoring of wound condition such as temperature while reducing surgical site infection during wound closure. In this study, functionalized nanodiamond (FND) and reduced graphene oxide (rGO) into biodegradable polycaprolactone (PCL) are incorporated to develop a new multifunctional suture with such capabilities. Incorporation of FND and rGO into PCL enhances its tensile strength by about 43% and toughness by 35%. The sutures show temperature sensing capability in the range of 25–40 °C based on the shift in zero‐splitting frequency of the nitrogen‐vacancy (NV–) centers in FND via optically detected magnetic resonance, paving the way for potential detection of infection or excessive inflammation in healing wounds. The suture surface readily coats with antibiotics to reduce bacterial infection risk to the wounds. The new suture thus is promising in monitoring and supporting wound closure.
Hsieh, MH 2021, 'Preface', Leibniz International Proceedings in Informatics, LIPIcs, vol. 197.
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Hu, S, Chen, X, Ni, W, Hossain, E & Wang, X 2021, 'Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications', IEEE Communications Surveys & Tutorials, vol. 23, no. 3, pp. 1458-1493.
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Distributed machine learning (DML) techniques, such as federated learning, partitioned learning, and distributed reinforcement learning, have been increasingly applied to wireless communications. This is due to improved capabilities of terminal devices, explosively growing data volume, congestion in the radio interfaces, and increasing concern of data privacy. The unique features of wireless systems, such as large scale, geographically dispersed deployment, user mobility, and the massive amount of data, give rise to new challenges in the design of DML techniques. There is a clear gap in the existing literature that the DML techniques are yet to be systematically reviewed for their applicability to wireless systems. This survey bridges the gap by providing a contemporary and comprehensive survey of DML techniques with a focus on wireless networks. Specifically, we review the latest applications of DML in power control, spectrum management, user association, and edge cloud computing. The optimality, accuracy, convergence rate, computation cost, and communication overhead of DML are analyzed. We also discuss the potential adversarial attacks faced by DML applications, and describe state-of-the-art countermeasures to preserve privacy and security. Last but not least, we point out a number of key issues yet to be addressed, and collate potentially interesting and challenging topics for future research.
Hu, S, Ni, W, Wang, X, Jamalipour, A & Ta, D 2021, 'Joint Optimization of Trajectory, Propulsion, and Thrust Powers for Covert UAV-on-UAV Video Tracking and Surveillance', IEEE Transactions on Information Forensics and Security, vol. 16, pp. 1959-1972.
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Autonomous tracking of suspicious unmanned aerial vehicles (UAVs) by legitimate monitoring UAVs (or monitors) can be crucial to public safety and security. It is non-trivial to optimize the trajectory of a monitor while conceiving its monitoring intention, due to typically non-convex propulsion and thrust power functions. This article presents a novel framework to jointly optimize the propulsion and thrust powers, as well as the 3D trajectory of a solar-powered monitor which conducts covert, video-based, UAV-on-UAV tracking and surveillance. A multi-objective problem is formulated to minimize the energy consumption of the monitor and maximize a weighted sum of distance keeping and altitude changing, which measures the disguising of the monitor. Based on the practical power models of the UAV propulsion, thrust and hovering, and the model of the harvested solar power, the problem is non-convex and intangible for existing solvers. We convexify the propulsion power by variable substitution, and linearize the solar power. With successive convex approximation, the resultant problem is then transformed with tightened constraints and efficiently solved by the proximal difference-of-convex algorithm with extrapolation in polynomial time. The proposed scheme can be also applied online. Extensive simulations corroborate the merits of the scheme, as compared to baseline schemes with partial or no disguising.
Hu, X, Wong, S, Li, Y, Lin, J, Yang, Y, Sun, G & Zhang, L 2021, 'Broadband high‐gain slot grid array antenna for millimeter wave applications', International Journal of RF and Microwave Computer-Aided Engineering, vol. 31, no. 1.
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A broadband high-gain slot grid array antenna (SGAA) is proposed in this paper. Based on the electromagnetic complementarity principle, the metal elements in the traditional microstrip grid array antenna (GAA) are replaced by a wide slot element. Compared with the GAA, the proposed SGAA achieves broadband and high-gain performance. In order to demonstrate this concept, a prototype with 9-element SGAA is designed using wide slot radiation elements and fabricated on Rogers 5880 printed circuit board (PCB) substrates, which is fed by a 50 Ω coaxial probe. The measured and simulated results show a good agreement. The proposed SGAA achieves a measured peak gain of 14.8 dBi at 26.0 GHz, a 10-dB impedance bandwidth from 22.2 to 28.5 GHz with a fractional bandwidth of 24.9%. These results indicate that the SGAA is with high performance and it is suitable for the fifth-generation (5G) millimeter wave (mmW) wireless communication system.
Hu, X, Ye, D, Zhu, T & Huo, H 2021, 'A Differentially Private Auction Mechanism in Online Social Networks', Journal of Systems Science and Systems Engineering.
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The growing popularity of users in online social network gives a big opportunity for online auction. The famous Information Diffusion Mechanism (IDM) is an excellent method even meet the incentive compatibility and individual rationality. Although the existing auction in online social network has considered the buyers’ information which is not known by the seller, current mechanism still can not preserve the privacy information of users in online social network. In this paper, we propose a novel mechanism based on the IDM and differential privacy. Our mechanism can successfully process the auction and at the same time preserve clients’ price information from neighbours. We achieved these by adding virtual nodes to each node and Laplace noise for its price in the auction process. We also formulate this mechanism on the real network and the random network, scale-free network to show the feasibility and effectiveness of the proposed mechanism. The evaluation shows that the result of our methods only depend on the noise added to the agents. It is independent from the agents’ original price.
Hu, Z, Yuan, X, Chen, SP, Song, YH, Wang, W, Wang, SY, Wang, LQ, Feng, W, Liu, S & Sun, HS 2021, '[Comparison on short-term safety outcomes between off-pump and on-pump coronary artery bypass grafting by experienced surgeons: a single center study with 31 075 cases].', Zhonghua Xin Xue Guan Bing Za Zhi, vol. 49, no. 2, pp. 158-164.
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Objective: To compare the short-term outcomes between off-pump and on-pump coronary artery bypass graft (CABG) by experienced surgeons with similar surgical team in a single large-volume cardiac surgery center. Methods: A total of 31 075 patients with multivessel coronary disease who underwent isolated off-pump or on-pump CABG between January 1, 2009 and December 31, 2019 by experienced surgeons in Fuwai hospital were enrolled in this retrospective study. Patients was divided into on-pump CABG group and on-pump CABG group on an intention-to treat basis. Short term safety endpoints, including 30 days mortality, composite endpoint of major morbidity or mortality, prolonged postoperative length of stay (PLOS), and prolonged ICU length of stay (PICULOS), and distal anastomosis were compared between the two groups. Mortality was evaluated on 30 days post operation, other endpoints were collected before discharge. After 1∶1 propensity-score matching of baseline characteristics for on-pump and off-pump CABG, postoperative endpoints were compared with use of McNemar's test and further adjusted with the use of a logistic regression model. Results: After propensity-score matching, 10 243 matched pairs of patients were included in the final analysis, there were 4 605(22.5%) females and mean age was (60.7±8.6) years. The standardized differences were less than 5% for all baseline variables in matched cohort. Univariate analysis indicated lower risk of 30 days mortality (0.2% vs. 0.7%, P<0.001), major morbidity or mortality (5.7% vs. 8.8%, P<0.001), PLOS (3.2% vs. 4.9%, P<0.001), PICULOS (9.4% vs. 12.2, P<0.001), and lower number of distal anastomosis ((3.3±0.8) vs. (3.6±0.8), P<0.001) in off-pump CABG group than in on-pump CABG group. After adjustment of cofounders, multivariate analysis showed that off-pump CABG was still associated with a lower risk of 30 days mortality (OR=0.29, 95%CI: 0.09-0.87, P=0.027), composite endpoint of major morbidity or mortality (OR=0...
Hua, W, Sui, Y, Wan, Y, Liu, G & Xu, G 2021, 'FCCA: Hybrid Code Representation for Functional Clone Detection Using Attention Networks', IEEE Transactions on Reliability, vol. 70, no. 1, pp. 304-318.
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Code cloning, which reuses a fragment of source code via copy-and-paste with or without modifications, is a common way for code reuse and software prototyping. However, the duplicated code fragments often affect software quality, resulting in high maintenance cost. The existing clone detectors using shallow textual or syntactical features to identify code similarity are still ineffective in accurately finding sophisticated functional code clones in real-world code bases. This article proposes functional code clone detector using attention ( FCCA ), a deep-learning-based code clone detection approach on top of a hybrid code representation by preserving multiple code features, including unstructured (code in the form of sequential tokens) and structured (code in the form of abstract syntax trees and control-flow graphs) information. Multiple code features are fused into a hybrid representation, which is equipped with an attention mechanism that pays attention to important code parts and features that contribute to the final detection accuracy. We have implemented and evaluated FCCA using 275 777 real-world code clone pairs written in Java. The experimental results show that FCCA outperforms several state-of-the-art approaches for detecting functional code clones in terms of accuracy, recall, and F1 score.
Huang, H, Savkin, AV & Ni, W 2021, 'Navigation of a UAV Team for Collaborative Eavesdropping on Multiple Ground Transmitters', IEEE Transactions on Vehicular Technology, vol. 70, no. 10, pp. 10450-10460.
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Huang, H, Zhang, J, Zhang, J, Xu, J & Wu, Q 2021, 'Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification', IEEE Transactions on Multimedia, vol. 23, no. 99, pp. 1666-1680.
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Huang, H-L, Du, Y, Gong, M, Zhao, Y, Wu, Y, Wang, C, Li, S, Liang, F, Lin, J, Xu, Y, Yang, R, Liu, T, Hsieh, M-H, Deng, H, Rong, H, Peng, C-Z, Lu, C-Y, Chen, Y-A, Tao, D, Zhu, X & Pan, J-W 2021, 'Experimental Quantum Generative Adversarial Networks for Image Generation', Physical Review Applied, vol. 16, no. 2, p. 024051.
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Quantum machine learning is expected to be one of the first practical applications of near-term quantum devices. Pioneer theoretical works suggest that quantum generative adversarial networks (GANs) may exhibit a potential exponential advantage over classical GANs, thus attracting widespread attention. However, it remains elusive whether quantum GANs implemented on near-term quantum devices can actually solve real-world learning tasks. Here, we devise a flexible quantum GAN scheme to narrow this knowledge gap. In principle, this scheme has the ability to complete image generation with high-dimensional features and could harness quantum superposition to train multiple examples in parallel. We experimentally achieve the learning and generating of real-world handwritten digit images on a superconducting quantum processor. Moreover, we utilize a gray-scale bar dataset to exhibit competitive performance between quantum GANs and the classical GANs based on multilayer perceptron and convolutional neural network architectures, respectively, benchmarked by the Fréchet distance score. Our work provides guidance for developing advanced quantum generative models on near-term quantum devices and opens up an avenue for exploring quantum advantages in various GAN-related learning tasks.
Huang, J & Ji, J 2021, 'Vibration control of coupled Duffing oscillators in flexible single-link manipulators', Journal of Vibration and Control, vol. 27, no. 17-18, pp. 2058-2068.
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Motion-induced oscillations of the flexible single link and its payload at the tip have negative impact on the anticipated performance of the flexible manipulators and thus should be suppressed to achieve tip positioning accuracy and high-speed operation. Because of the structural flexibility, the dynamics of the flexible manipulator can be described by coupled Duffing oscillators when considering the inherent structural nonlinearity of the flexible link into the dynamic modeling. However, little research has been focused on addressing the dynamic coupling issue in the nonlinear modeling of flexible-link manipulators using coupled Duffing oscillators. This article presents coupled Duffing oscillators for the nonlinear modeling of flexible single-link manipulators and then proposes a control method for suppressing the nonlinear vibrations of the coupled Duffing oscillators. Simulated and experimental results obtained from a flexible single-link manipulator test bench are in good agreement with the proposed nonlinear modeling and also demonstrate the effectiveness of the proposed control techniques for vibration suppression of the flexible manipulator.
Huang, J, Li, S, Zhou, Y, Xu, T, Li, Y, Wang, H & Wang, S 2021, 'A heavy-duty magnetorheological fluid mount with flow and squeeze model', Smart Materials and Structures, vol. 30, no. 8, pp. 085012-085012.
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Huang, K-C, John, AR, Jung, T-P, Tsai, W-F, Yu, Y-H & Lin, C-T 2021, 'Comparing the Differences in Brain Activities and Neural Comodulations Associated With Motion Sickness Between Drivers and Passengers', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 1259-1267.
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It is common to believe that passengers are more adversely affected by motion sickness than drivers. However, no study has compared passengers and drivers' neural activities and drivers experiencing motion sickness (MS). Therefore, this study attempts to explore brain dynamics in motion sickness among passengers and drivers. Eighteen volunteers participated in simulating the driving winding road experiment while their subjective motion sickness levels and electroencephalogram (EEG) signals were simultaneously recorded. Independent Component Analysis (ICA) was employed to isolate MS-related independent components (ICs) from EEG. Furthermore, comodulation analysis was applied to decompose spectra of interest ICs, related to MS, to find the specific spectra-related temporally independent modulators (IMs). The results showed that passengers' alpha band (8-12 Hz) power increased in correlation with the MS level in the parietal, occipital midline and left and right motor areas, and drivers' alpha band (8-12 Hz) power showed relatively smaller increases than those in the passenger. Further, the results also indicate that the enhanced activation of alpha IMs in the passenger than the driver is due to a higher degree of motion sickness. In conclusion, compared to the driver, the passenger experience more conflicts among multimodal sensory systems and demand neuro-physiological regulation.
Huang, L, Chen, X, Zhang, Y, Zhu, Y, Li, S & Ni, X 2021, 'Dynamic network analytics for recommending scientific collaborators', Scientometrics, vol. 126, no. 11, pp. 8789-8814.
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Collaboration is one of the most important contributors to scientific advancement and a crucial aspect of an academic’s career. However, the explosion in academic publications has, for some time, been making it more challenging to find suitable research partners. Recommendation approaches to help academics find potential collaborators are not new. However, the existing methods operate on static data, which can render many suggestions less useful or out of date. The approach presented in this paper simulates a dynamic network from static data to gain further insights into the changing research interests, activities and co-authorships of scholars in a field–all insights that can improve the quality of the recommendations produced. Following a detailed explanation of the entire framework, from data collection through to recommendation modelling, we provide a case study on the field of information science to demonstrate the reliability of the proposed method, and the results provide empirical insights to support decision-making in related stakeholders—e.g., scientific funding agencies, research institutions and individual researchers in the field.
Huang, L, Liu, F & Zhang, Y 2021, 'Overlapping Community Discovery for Identifying Key Research Themes', IEEE Transactions on Engineering Management, vol. 68, no. 5, pp. 1321-1333.
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Identifying key research themes is an effective way to chart knowledge structures in a field of research and, in turn, stimulate new ideas and innovation. Most thematic analyses of a research field are based on some form of network analysis, e.g., citations and cowords, and most of these networks are made up of cohesive, highly overlapping groups of nodes. Based on the suggestion that the “universal features” of networks are to be found in these overlapping communities, we argue that these same communities in a keyword network should reveal the key research themes in a field of study. With no traditional method with which to test our theory, we combined a cluster percolation algorithm with a Word2Vec model, and in a case study on information science, we were not only able to detect the overlapping communities in a keyword similarity network, but we also found a new perspective on the importance of overlapping communities as a way to identify a field's key research themes.
Huang, Q, Wang, C, Hao, D, Wei, W, Wang, L & Ni, B-J 2021, 'Ultralight biodegradable 3D-g-C3N4 aerogel for advanced oxidation water treatment driven by oxygen delivery channels and triphase interfaces', Journal of Cleaner Production, vol. 288, pp. 125091-125091.
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The development of highly efficient and separation-free, low-cost photocatalysts have crucial prospect for sustainable wastewater treatment, because it is able to eliminate the hazards of organic pollutant with facile operation. However, the relatively high cost of previous photocatalysts highly obstructs the application of these materials. Herein, we report a cost-effective and distinct konjac/graphitic carbon nitride (KCN) aerogel, which has superior performance for advanced oxidation water treatment. The abundant porous structure of the ultralight aerogel ensures the rapid adsorption of pollutants, which is much helpful for the further photodegradation process. During the working process, the aerogel is half submerged in pollutant solution and half exposed in air, forming a distinctive gas-solid-liquid triphase system, where oxygen can be rapidly delivered into the solution via the porous channels, boosting the generation of hydroxyl and superoxide radicals. Meanwhile, the aerogel structure can separate the g-C3N4, obstruct its stacking, as well as improve the light absorption rate. The synthesis, utilization and readily biodegradable treatment of the KCN aerogels are all green and eco-friendly, which is extremely constructive for strategies to develop novel highly efficient photocatalytic materials.
Huang, Q-S, Wei, W & Ni, B-J 2021, 'Catalysts derived from Earth-abundant natural biomass enable efficient photocatalytic CO2conversion for achieving a closed-loop carbon cycle', Green Chemistry, vol. 23, no. 23, pp. 9683-9692.
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A one-pot, facile, sulfuric-acid-assisted carbonization method was developed to fabricate a series of biomass-derived metal-free carbonaceous photocatalysts for high performance CO2conversion, which satisfied a closed-loop carbon cycle.
Huang, S, Samali, B & Li, J 2021, 'Numerical and experimental investigations of a thermal break composite façade mullion under four-point bending', Journal of Building Engineering, vol. 34, pp. 101590-101590.
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© 2020 Elsevier Ltd This paper presents numerical and experimental investigations on a typical thermal break composite façade profile under four-point bending. The purpose of this study is to gain the knowledge of the interfacial behaviour between aluminum extrusion and polyamide insert beyond elastic range. Understanding the behaviour of this energy efficient façade profile within plastic range is important for the design under extreme loading, such as earthquakes, strong wind conditions and even blast loads. The experimental investigation was carried out on four types of beam specimens. The specimens were grouped by their span lengths with three specimens for each span length. As the specimens’ geometry and composite action are complicated, seven strain gauges were used per specimen including small strain gauges to fit in the limited space of the thermal break section. A three stage failure process was observed during the experiments. A numerical investigation was carried out by using Finite Element modelling to simulate behaviour of the thermal break composite façade profile under similar loading condition in order to compare with the testing results as well as to capture the corresponding failure mechanisms. Numerical simulations were setup by applying a proposed partitioned multi-phase failure model to simulate three stage failure process discovered by experiments. The results from FE models were compared and discussed with experimental counterparts. In summary, FE models showed consistent results to the experimental counterparts and it also provided the insight and more details of failure mechanism and stress distribution including interfacial condition details. Behaviour of the thermal break façade profile in the plastic range displayed excellent ductility and high strength capacity of this type of thermal break section in the plastic range after slip.
Huang, X, Dhruva, SS, Yuan, X, Bai, X, Lu, Y, Yan, X, Liu, J, Li, W, Hu, D, Ji, R, Gao, M, Miao, F, Li, J, Ge, J, Krumholz, HM & Li, J 2021, 'Characteristics, interventions and outcomes of patients with valvular heart disease hospitalised in China: a cross-sectional study', BMJ Open, vol. 11, no. 11, pp. e052946-e052946.
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ObjectivesLittle is known about contemporary characteristics and management of valvular heart disease (VHD) in China. This study aimed to examine the clinical characteristics, aetiology and type of VHD, interventions and in-hospital outcomes of patients with VHD hospitalised in China.MethodsWe used a two-stage random sampling design to create a nationally representative sample of patients with VHD hospitalised in 2015 in China and included adult patients with mild, moderate or severe VHD. We abstracted data from medical records, including echocardiogram reports, on patient characteristics, aetiology, type and severity of VHD, interventions and in-hospital outcomes. We weighted our findings to estimate nationally representative hospitalisations. We performed multivariable logistic regression analysis to identify factors associated with valve intervention.ResultsIn 2015, 38 841 patients with VHD were hospitalised in 188 randomly sampled hospitals, representing 662 384 inpatients with VHD in China. We sampled 9363 patients, mean age 68.7 years (95% CI 42.2 to 95.2) and 46.8% (95% CI 45.8% to 47.8%) male, with an echocardiogram. Degenerative origin was the predominant aetiology overall (33.3%, 95% CI 32.3% to 34.3%), while rheumatic origin was the most frequent aetiology among patients with VHD as the primary diagnosis (37.4%, 95% CI 35.9% to 38.8%). Rheumatic origin was also the most common aetiology among patients with moderate or severe VHD (27.3%, 95% CI 25.6% to 29.0% and 33.6%, 95% CI 31.9% to 35.2%, respectively). The most common VHD was mitral regurgitation (79.1%, 95% CI 78.2% to 79.9%), followed by tricuspid regurgitation (77.4%, 95% CI 76.5% to 78.2%). Among patients with a primary diagnosis of severe VHD who were admitted to facilities capable of valve intervention, 35.6% (95% CI...
Huang, X, Tuyen Le, A & Guo, YJ 2021, 'ALMS Loop Analyses With Higher-Order Statistics and Strategies for Joint Analog and Digital Self-Interference Cancellation', IEEE Transactions on Wireless Communications, vol. 20, no. 10, pp. 6467-6480.
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Joint analog and digital self-interference cancellation (SIC) is essential for enabling in-band full duplex (IBFD) communications. Analog least mean square (ALMS) loop is a promising low-complexity high-performance analog SIC technique with multi-tap adaptive filtering capability, but its properties on the tap coefficient variation have not been fully understood. In this paper, analysis based on higher-order statistics of the transmitted signal is performed to solve the problem of evaluating the variance of the ALMS loop's weighting coefficient error, which reveals two additional types of irreducible residual self-interference (SI) produced by an ALMS loop if it runs freely. The residual SI channel impulse response in digital baseband is also analysed and its unique properties are investigated. By introducing a simple track and hold control to the ALMS loop's tap coefficients, a joint analog and digital SIC scheme is proposed to stop the tap coefficient variation and achieve very low residual SI close to the IBFD receiver's noise floor. In a coordinated application scenario, the noise figure of the digital SIC algorithm is proved to be only 1.76 dB at most. Simulation results are provided to verify the theoretical analyses.
Huang, X, Tuyen Le, A & Guo, YJ 2021, 'Transmit Beamforming for Communication and Self-Interference Cancellation in Full Duplex MIMO Systems: A Trade-Off Analysis', IEEE Transactions on Wireless Communications, vol. 20, no. 6, pp. 3760-3769.
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The performance of transmit beamforming for both optimized precoding and self-interference cancellation (SIC) in full duplex multiple input multiple output (MIMO) transceivers is analysed in this paper. With sub-space dimension larger than that of the null-space of the self-interference channels, the precoding error is reduced but the interference suppression ratio (ISR) is degraded, resulting in a trade-off between multibeam communication and MIMO SIC. An analytical approach for the ISR evaluation is proposed assuming known eigenvalue distribution of the self-interference channels, and a closed-form ISR expression is derived after applying a uniform distribution approximation. The ISR and precoding error trade-off curves are also formulated. Joint SIC by transmit beamforming and beam-based analog adaptive filters over both propagation and analog domains is proposed to achieve better SIC performance and enable more flexible receive antenna selection. Simulation results verify the theoretical analyses.
Huang, Y, Lei, C, Liu, C-H, Perez, P, Forehead, H, Kong, S & Zhou, JL 2021, 'A review of strategies for mitigating roadside air pollution in urban street canyons', Environmental Pollution, vol. 280, pp. 116971-116971.
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Urban street canyons formed by high-rise buildings restrict the dispersion of vehicle emissions, which pose severe health risks to the public by aggravating roadside air quality. However, this issue is often overlooked in city planning. This paper reviews the mechanisms controlling vehicle emission dispersion in urban street canyons and the strategies for managing roadside air pollution. Studies have shown that air pollution hotspots are not all attributed to heavy traffic and proper urban design can mitigate air pollution. The key factors include traffic conditions, canyon geometry, weather conditions and chemical reactions. Two categories of mitigation strategies are identified, namely traffic interventions and city planning. Popular traffic interventions for street canyons include low emission zones and congestion charges which can moderately improve roadside air quality. In comparison, city planning in terms of building geometry can significantly promote pollutant dispersion in street canyons. General design guidelines, such as lower canyon aspect ratio, alignment between streets and prevailing winds, non-uniform building heights and ground-level building porosity, may be encompassed in new development. Concurrently, in-street barriers are widely applicable to rectify the poor roadside air quality in existing street canyons. They are broadly classified into porous (e.g. trees and hedges) and solid (e.g. kerbside parked cars, noise fences and viaducts) barriers that utilize their aerodynamic advantages to ease roadside air pollution. Post-evaluations are needed to review these strategies by real-world field experiments and more detailed modelling in the practical perspective.
Huang, Y, Ng, ECY, Zhou, JL, Surawski, NC, Lu, X, Du, B, Forehead, H, Perez, P & Chan, EFC 2021, 'Impact of drivers on real-driving fuel consumption and emissions performance', Science of The Total Environment, vol. 798, pp. 149297-149297.
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Eco-driving has attracted great attention as a cost-effective and immediate measure to reduce fuel consumption significantly. Understanding the impact of driver behaviour on real driving emissions (RDE) is of great importance for developing effective eco-driving devices and training programs. Therefore, this study was conducted to investigate the performance of different drivers using a portable emission measurement system. In total, 30 drivers, including 15 novice and 15 experienced drivers, were recruited to drive the same diesel vehicle on the same route, to minimise the effect of uncontrollable real-world factors on the performance evaluation. The results show that novice drivers are less skilled or more aggressive than experienced drivers in using the accelerator pedal, leading to higher vehicle and engine speeds. As a result, fuel consumption rates of novice drivers vary in a slightly greater range than those of experienced drivers, with a marginally higher (2%) mean fuel consumption. Regarding pollutant emissions, CO and THC emissions of all drivers are well below the standard limits, while NOx and PM emissions of some drivers significantly exceed the limits. Compared with experienced drivers, novice drivers produce 17% and 29% higher mean NOx and PM emissions, respectively. Overall, the experimental results reject the hypothesis that driver experience has significant impacts on fuel consumption performance. The real differences lie in the individual drivers, as the worst performing drivers have significantly higher fuel consumption rates than other drivers, for both novice and experienced drivers. The findings suggest that adopting eco-driving skills could deliver significant reductions in fuel consumption and emissions simultaneously for the worst performing drivers, regardless of driving experience.
Huang, Y, Song, R, Argha, A, Celler, BG, Savkin, AV & Su, SW 2021, 'Human Motion Intent Description Based on Bumpless Switching Mechanism for Rehabilitation Robot', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 673-682.
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Huang, Y, Surawski, NC, Zhuang, Y, Zhou, JL & Hong, G 2021, 'Dual injection: An effective and efficient technology to use renewable fuels in spark ignition engines', Renewable and Sustainable Energy Reviews, vol. 143, pp. 110921-110921.
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Modern spark ignition engines mostly use one injection system to deliver gasoline into the combustion chamber, using either direct injection or port fuel injection. Both technologies have their respective advantages. To integrate their advantages and to promote the use of renewable fuels, dual injection engines are in development in recent years. Dual injection represents an advanced combustion system and is a novel technology to address the urgent issues of sustainability and environmental protection. This study reviews the state-of-the-art research on dual injection spark ignition engines with a focus on renewable fuels, their advantages and engine performance. The main advantages of dual injection include greater control flexibility, enhanced cooling effect, knock mitigation, engine downsizing, extended lean-burn limits, higher thermal efficiency and reductions of several emission species. The most promising renewable fuels for dual injection are ethanol, methanol and hydrogen. Each renewable fuel is aimed at different advantages of dual injection. Alcohol-gasoline dual injection provides great anti-knock ability by taking advantage of alcohols' large enthalpies of vaporisation and high octane numbers, while hydrogen-gasoline dual injection provides extended lean-burn limits by taking advantage of hydrogen's low ignition energy, wide flammability limit and high flame speed. Direct injection of renewable fuels is the optimal injection strategy because it effectively utilises the strong cooling effect of alcohols or avoids the volumetric efficiency reduction and pre-ignition of hydrogen. Dual injection generally demonstrates higher thermal efficiency than single injection. In addition, dual injection effectively reduces particulate emissions while there are usually trade-offs among gaseous emissions.
Huang, Y, Wang, Q, Jia, W, Lu, Y, Li, Y & He, X 2021, 'See more than once: Kernel-sharing atrous convolution for semantic segmentation', Neurocomputing, vol. 443, pp. 26-34.
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The state-of-the-art semantic segmentation solutions usually leverage different receptive fields via multiple parallel branches to handle objects of different sizes. However, employing separate kernels for individual branches may degrade the generalization of the network to objects with different scales, and the computational cost increases with the increase of the number of branches. To tackle this problem, we propose a novel network structure, namely Kernel-Sharing Atrous Convolution (KSAC), where branches with different receptive fields share the same kernel, i.e., let a single kernel ‘see’ the input feature maps more than once with different receptive fields. Experiments conducted on the benchmark PASCAL VOC 2012 dataset show that our proposed sharing strategy can not only boost the network's generalization and representation abilities but also reduce the computational cost significantly. Specifically, on the validation set, when compared with DeepLabv3+, about 2.7G FLOPs and 12.7G FLOPs are saved for output stride = 16 and 8 respectively. In addition, different from the widely used ASPP structure, our proposed KSAC is able to further improve the mIOU by taking benefit of wider context with larger atrous rates. Finally, our KSAC achieves mIOUs of 88.1%, 45.47% and 80.7% on the PASCAL VOC 2012 test set (Everingham et al., 2009), ADE20K dataset (Zhou et al., 2017) and Cityscapes datasets (Marius et al., 2016), respectively. Our full code will be released on Github: https://github.com/edwardyehuang/iSeg.
Huang, Y, Wu, Q, Xu, J, Zhong, Y & Zhang, Z 2021, 'Unsupervised Domain Adaptation with Background Shift Mitigating for Person Re-Identification', International Journal of Computer Vision, vol. 129, no. 7, pp. 2244-2263.
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Unsupervised domain adaptation has been a popular approach for cross-domain person re-identification (re-ID). There are two solutions based on this approach. One solution is to build a model for data transformation across two different domains. Thus, the data in source domain can be transferred to target domain where re-ID model can be trained by rich source domain data. The other solution is to use target domain data plus corresponding virtual labels to train a re-ID model. Constrains in both solutions are very clear. The first solution heavily relies on the quality of data transformation model. Moreover, the final re-ID model is trained by source domain data but lacks knowledge of the target domain. The second solution in fact mixes target domain data with virtual labels and source domain data with true annotation information. But such a simple mixture does not well consider the raw information gap between data of two domains. This gap can be largely contributed by the background differences between domains. In this paper, a Suppression of Background Shift Generative Adversarial Network (SBSGAN) is proposed to mitigate the gaps of data between two domains. In order to tackle the constraints in the first solution mentioned above, this paper proposes a Densely Associated 2-Stream (DA-2S) network with an update strategy to best learn discriminative ID features from generated data that consider both human body information and also certain useful ID-related cues in the environment. The built re-ID model is further updated using target domain data with corresponding virtual labels. Extensive evaluations on three large benchmark datasets show the effectiveness of the proposed method.
Huang, Y, Xu, H, Gao, H, Ma, X & Hussain, W 2021, 'SSUR: An Approach to Optimizing Virtual Machine Allocation Strategy Based on User Requirements for Cloud Data Center', IEEE Transactions on Green Communications and Networking, vol. 5, no. 2, pp. 670-681.
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Huang, Y, Zhang, Y, Wu, M, Porter, A & Barrangou, R 2021, 'Determination of Factors Driving the Genome Editing Field in the CRISPR Era Using Bibliometrics', The CRISPR Journal, vol. 4, no. 5, pp. 728-738.
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Over the past two decades, the discovery of CRISPR-Cas immune systems and the repurposing of their effector nucleases as biotechnological tools have revolutionized genome editing. The corresponding work has been captured by 90,000 authors representing 7,600 affiliations in 126 countries, who have published more than 19,000 papers spanning medicine, agriculture, and biotechnology. Here, we use tech mining and an integrated bibliometric and networks framework to investigate the CRISPR literature over three time periods. The analysis identified seminal papers, leading authors, influential journals, and rising applications and topics interconnected through collaborative networks. A core set of foundational topics gave rise to diverging avenues of research and applications, reflecting a bona fide disruptive emerging technology. This analysis illustrates how bibliometrics can identify key factors, decipher rising trends, and untangle emerging applications and technologies that dynamically shape a morphing field, and provides insights into the trajectory of genome editing.
Huang, Y, Zhu, F, Porter, AL, Zhang, Y, Zhu, D & Guo, Y 2021, 'Exploring Technology Evolution Pathways to Facilitate Technology Management: From a Technology Life Cycle Perspective', IEEE Transactions on Engineering Management, vol. 68, no. 5, pp. 1347-1359.
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IEEE Technological innovation is a dynamic process that spans the life cycle of an idea, from scientific research to production. Within this process, there are often a few key innovations that significantly impact a technology's development, and the ability to identify and trace the development of these key innovations comes with a great payoff for researchers and technology managers. In this article, we present a framework for identifying the technology's main evolutionary pathway. What is unique about this framework is that we introduce new indicators that reflect the connectivity and the modularity in the interior citation network to distinguish between the stages of a technology's development. We also show how information about a family of patents can be used to build a comprehensive patent citation network. Finally, we apply integrated approaches of main path analysis (MPA)—namely global MPA and global key-route main analysis—for extracting technological trajectories at different technological stages. We illustrate this approach with dye-sensitized solar cells (DSSCs), a low-cost solar cell belonging to the group of thin-film solar cells, contributing to the remarkable growth in the renewable energy industry. The results show how this approach can trace the main development trajectory of a research field and distinguish key technologies to help decision makers manage the technological stages of their innovation processes more effectively.
Huang, Z, Lin, X, Zhang, W & Zhang, Y 2021, 'Communication-efficient distributed covariance sketch, with application to distributed PCA', Journal of Machine Learning Research, vol. 22.
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A sketch of a large data set captures vital properties of the original data while typically occupying much less space. In this paper, we consider the problem of computing a sketch of a massive data matrix A ∈ Rn×d that is distributed across s machines. Our goal is to output a matrix B ∈ Rℓ×d which is significantly smaller than but still approximates A well in terms of covariance error, i.e., kAT A - BT Bk2. Such a matrix B is called a covariance sketch of A. We are mainly focused on minimizing the communication cost, which is arguably the most valuable resource in distributed computations. We show that there is a nontrivial gap between deterministic and randomized communication complexity for computing a covariance sketch. More specifically, we first prove an almost tight deterministic communication lower bound, then provide a new randomized algorithm with communication cost smaller than the deterministic lower bound. Based on a well-known connection between covariance sketch and approximate principle component analysis, we obtain better communication bounds for the distributed PCA problem. Moreover, we also give an improved distributed PCA algorithm for sparse input matrices, which uses our distributed sketching algorithm as a key building block.
Humble, TS & Ying, M 2021, 'Editorial on Celebrating Quantum Computing with ACM', ACM Transactions on Quantum Computing, vol. 2, no. 4, pp. 1-2.
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Huo, L, Jiao Li, J, Chen, L, Yu, Z, Hutvagner, G & Li, J 2021, 'Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects', Briefings in Bioinformatics, vol. 22, no. 6, p. bbab229.
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AbstractSingle-cell sequencing is a biotechnology to sequence one layer of genomic information for individual cells in a tissue sample. For example, single-cell DNA sequencing is to sequence the DNA from every single cell. Increasing in complexity, single-cell multi-omics sequencing, or single-cell multimodal omics sequencing, is to profile in parallel multiple layers of omics information from a single cell. In practice, single-cell multi-omics sequencing actually detects multiple traits such as DNA, RNA, methylation information and/or protein profiles from the same cell for many individuals in a tissue sample. Multi-omics sequencing has been widely applied to systematically unravel interplay mechanisms of key components and pathways in cell. This survey overviews recent developments in single-cell multi-omics sequencing, and their applications to understand complex diseases in particular the COVID-19 pandemic. We also summarize machine learning and bioinformatics techniques used in the analysis of the intercorrelated multilayer heterogeneous data. We observed that variational inference and graph-based learning are popular approaches, and Seurat V3 is a commonly used tool to transfer the missing variables and labels. We also discussed two intensively studied issues relating to data consistency and diversity and commented on currently cared issues surrounding the error correction of data pairs and data imputation methods. The survey is concluded with some open questions and opportunities for this extraordinary field.
Huo, X, Luo, Q, Li, Q & Sun, G 2021, 'Measurement of fracture parameters based upon digital image correlation and virtual crack closure techniques', Composites Part B: Engineering, vol. 224, pp. 109157-109157.
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Hussain, T, Muhammad, K, Ullah, A, Ser, JD, Gandomi, AH, Sajjad, M, Baik, SW & de Albuquerque, VHC 2021, 'Multiview Summarization and Activity Recognition Meet Edge Computing in IoT Environments', IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9634-9644.
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Hussain, W, Merigo, JM, Gao, H, Alkalbani, AM & Rabhi, FA 2021, 'Integrated AHP-IOWA, POWA Framework for Ideal Cloud Provider Selection and Optimum Resource Management', IEEE Transactions on Services Computing, pp. 1-1.
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Ibrahim, I, Bhoopal, V, Seo, DH, Afsari, M, Shon, HK & Tijing, LD 2021, 'Biomass-based photothermal materials for interfacial solar steam generation: a review', Materials Today Energy, vol. 21, pp. 100716-100716.
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Ibrahim, I, Seo, DH, Angeloski, A, McDonagh, A, Shon, HK & Tijing, LD 2021, '3D microflowers CuS/Sn2S3 heterostructure for highly efficient solar steam generation and water purification', Solar Energy Materials and Solar Cells, vol. 232, pp. 111377-111377.
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Solar-driven interfacial steam generation is a promising method to produce potable water using renewable energy and help solve global clean water scarcity problems. However, the design of photothermal materials (PTMs) with excellent light absorption that can localize heat at the air/water interface, and facilitate water vapor generation remains a key challenge for its practical implementation. In this work, we demonstrate the synthesis of heterostructure microflowers composed of vertically aligned CuS/Sn2S3 nanosheets (3D CSS-NS MF) using a single-step solvothermal method for solar steam generation application. The microflower structures and the abundant nanocavities between the vertically aligned nanosheets resulted in significant sunlight harvesting over the solar spectrum, excellent heat localization through trapping and re-absorbing the heat, and fast escape of water vapor. Under 1 sun (1 kW m-2) illumination, a high water evaporation rate of 1.42 kg m-2 h-1, corresponding to an efficiency of 82.93% was obtained. The 3D CSS-NS MF based solar evaporator exhibited remarkable salt ions rejection efficiency and good reusability over 10 cycles. Furthermore, efficient removal of organic dyes was observed in application geared towards wastewater treatment with a rejection ∼99.9%. Our work demonstrates the potential of using novel semiconductor-based nanocomposites as effective photothermal materials for high-performance solar steam generation in water desalination and wastewater treatment applications.
Ibrahim, I, Seo, DH, McDonagh, AM, Shon, HK & Tijing, L 2021, 'Semiconductor photothermal materials enabling efficient solar steam generation toward desalination and wastewater treatment', Desalination, vol. 500, pp. 114853-114853.
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© 2020 Elsevier B.V. Water scarcity issues around the world have renewed interest in the use of solar water evaporation as a means of providing fresh water. Advances in photothermal materials and thermal management, together with new interfacial system designs, have considerably improved the overall efficiency of solar steam generation (SSG) for desalination and wastewater treatment. Several classes of rationally-designed photothermal materials (PTMs) and nanostructures have enabled effective absorption of broad solar spectrum resulting in improved solar evaporation efficiency. Among several classes of PTMs, semiconductor-based PTMs have demonstrated great potential for SSG. In this review, we highlight the progress and prospects in SSG with emphasis on the use and evolution of advanced semiconductor materials for PTMs and their various designs and engineered architectures. Applications and future prospects for desalination and wastewater treatment are also discussed.
Ibrahim, IA & Hossain, MJ 2021, 'Low Voltage Distribution Networks Modeling and Unbalanced (Optimal) Power Flow: A Comprehensive Review', IEEE Access, vol. 9, pp. 143026-143084.
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The rapid increase of distributed energy resources (DERs) installation at residential and commercial levels can pose significant technical issues on the voltage levels and capacity of the network assets in distribution networks. Most of these issues occur in low-voltage distribution networks (LVDNs) or near customer premises. A lack of understanding of the networks and advanced planning approaches by distribution network providers (DNSPs) has led to rough estimations for maximum DERs penetration levels that LVDNs can accommodate. These issues might under- or over-estimate the actual hosting capacity of the LVDNs. Limited available data on LVDNs' capacity to host DERs makes planning, installing, and connecting new DERs problematic and complex. In addition, the lack of transparency in LVDN data and information leads to model simplifications, such as ignoring the phase imbalance. This can lead to grossly inaccurate results. The main aim of this paper is to enable the understanding of the true extent of local voltage excursions to allow more targeted investment, improve the network's reliability, enhance solar performance distribution, and increase photovoltaic (PV) penetration levels in LVDNs. Therefore, this paper reviews the state-of-the-art best practices in modeling unbalanced LVDNs as accurately as possible to avoid under- or over-estimation of the network's hosting capacity. In addition, several PV system modeling variations are reviewed, showing their limitations and merits as a trade-off between accuracy, computational burden, and data availability. Moreover, the unbalanced power flow representations, solving algorithms, and available tools are explained extensively by providing a comparative study between these tools and the ones most commonly used in Australia. This paper also presents an overview of unbalanced optimal power flow representations with their related objectives, solving algorithms, and tools.
Ibrar, I, Yadav, S, Ganbat, N, Samal, AK, Altaee, A, Zhou, JL & Nguyen, TV 2021, 'Feasibility of H2O2 cleaning for forward osmosis membrane treating landfill leachate', Journal of Environmental Management, vol. 294, pp. 113024-113024.
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Ideris, F, Shamsuddin, AH, Nomanbhay, S, Kusumo, F, Silitonga, AS, Ong, MY, Ong, HC & Mahlia, TMI 2021, 'Optimization of ultrasound-assisted oil extraction from Canarium odontophyllum kernel as a novel biodiesel feedstock', Journal of Cleaner Production, vol. 288, pp. 125563-125563.
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In this novel study, oil was extracted from the kernel of an exotic indigenous species known as Canarium odontophyllum via an ultrasound-assisted process. The extraction process was optimized using response surface methodology (RSM) based on the Box-Behnken experimental design (BBD). The optimal conditions for the investigated parameters were determined as ultrasound amplitude level: 38.30%, ratio of n-hexane to kernel powder: 50:1 in mL/g, extraction time: 45.79 min, resulting in an oil extraction yield of 63.48%. For verification purposes, experiments were conducted using the same optimized values of the investigated parameters which resulted in the average oil yield of 63.27% and this prove the reliability of the regression model. The extracted oil's fatty acid composition was obtained using a gas chromatograph (GC) equipped with flame-ionization detection (FID). The low acid value of the extracted oil is another interesting finding. This is important because it circumvents pretreatment processes such as degumming and esterification prior to the transesterification process. Biodiesel was produced from the oil via ultrasound-assisted transesterification, with a yield of 95.2%. Physiochemical properties of the C. odontophyllum biodiesel were determined, and it was found that all the tested properties comply with fuel specifications based on ASTM D6751 and EN 14214 standards. Significant savings of 52.3% and 80.9% in energy consumption and extraction time, respectively were achieved via ultrasound-assisted extraction compared with the conventional Soxhlet extraction. This study establishes the foundation and the need to further explore the usage of C. odontophyllum as a potential feedstock for biodiesel production.
Ikram, MM, Saha, G & Saha, SC 2021, 'Conjugate forced convection transient flow and heat transfer analysis in a hexagonal, partitioned, air filled cavity with dynamic modulator', International Journal of Heat and Mass Transfer, vol. 167, pp. 120786-120786.
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Inamdar, MA, Raghavendra, U, Gudigar, A, Chakole, Y, Hegde, A, Menon, GR, Barua, P, Palmer, EE, Cheong, KH, Chan, WY, Ciaccio, EJ & Acharya, UR 2021, 'A Review on Computer Aided Diagnosis of Acute Brain Stroke', Sensors, vol. 21, no. 24, pp. 8507-8507.
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Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., ‘ischemic penumbra’) can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta–Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas.
Indraratna, B, Ngo, T, Ferreira, FB, Rujikiatkamjorn, C & Tucho, A 2021, 'Large-scale testing facility for heavy haul track', Transportation Geotechnics, vol. 28, pp. 100517-100517.
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Given the substantially increased demand for increased axle loads of heavy haul trains, there is an imperative need to develop sustainable track infrastructure. When subjected to heavy axle loading, ballast aggregates rapidly break down, compromising the particle friction and associated load bearing capacity. Therefore, understanding the deformation and degradation (breakage) of ballast subjected to various boundary and loading conditions is crucial for improved track design and performance monitoring. Ideally, field testing should be carried out in real-life tracks to avoid laboratory scale and boundary effects, but field tests are often expensive, time-consuming and may disrupt rail traffic, hence not always feasible. A prototype test facility that can simulate appropriate axle loading and boundary conditions for standard gauge heavy haul tracks is presented in this paper. In collaboration with more than a dozen Universities and Industry organisations, Australia's first and only National Facility for Heavy-haul Railroad Testing (NFHRT) has recently been constructed and is now fully operational. This new facility enables a real-size (1:1 scale) instrumented track section to be subjected to continuous cyclic loading simulated via two pairs of dynamic actuators in synchronized operation. The results of a typical test are presented in this paper including the measured track settlement and lateral deformation, transient vertical and lateral stresses, rail and sleeper accelerations, resilient modulus and breakage of ballast. The test results show that an average track settlement of about 14 mm and lateral displacements up to 9 mm are recorded after 500,000 load cycles. Subjected to a 25-tonne axle load, the maximum vertical stress measured at the sleeper-ballast interface is about 225 kPa and this attenuates with depth. The test results of this iconic facility are generally consistent with actual field measurements obtained in heavy-haul tracks located in t...
Indraratna, B, Nguyen, TT, Singh, M, Rujikiatkamjorn, C, Carter, JP, Ni, J & Truong, MH 2021, 'Cyclic loading response and associated yield criteria for soft railway subgrade – Theoretical and experimental perspectives', Computers and Geotechnics, vol. 138, pp. 104366-104366.
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Indraratna, B, Phan, NM, Nguyen, TT & Huang, J 2021, 'Simulating Subgrade Soil Fluidization Using LBM-DEM Coupling', International Journal of Geomechanics, vol. 21, no. 5, pp. 1-14.
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The loss of effective stress due to increasing excess pore pressure that results in the upward migration of soil particles, that is, subgrade fluidization and mud pumping, has been a critical issue for railways over many years. Traditional methods such as experimental and analytical approaches can capture macroscopic quantities such as the hydraulic conductivity and critical hydraulic gradient, but they have many limitations when microscopic and localized behavior must be captured. This paper, therefore, presents a novel numerical approach where the microscopic properties of fluid and particles can be better captured when the soil is subjected to an increasing hydraulic gradient. While particle behavior is simulated using the discrete element method (DEM), the fluid dynamics can be described in greater detail using the lattice Boltzmann method (LBM). The mutual LBM-DEM interaction is carried out, so the particle and fluid variables are constantly updated. To validate this numerical method, laboratory testing on a selected subgrade soil is conducted. The results show that the numerical method can reasonably predict the coupled hydraulic and soil fluidization aspects, in relation to the experimental data. Microscopic properties such as the interstitial fluid flowing through the porous spaces of the soil are also captured well by the proposed fluid-particle coupling approach.
Indraratna, B, Qi, Y, Jayasuriya, C, Rujikiatkamjorn, C & Arachchige, CMK 2021, 'Use of recycled rubber inclusions with granular waste for enhanced track performance', Transportation Engineering, vol. 6, pp. 100093-100093.
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Indraratna, B, Rujikiatkamjorn, C, Kelly, R, Kianfar, K & Sloan, LS 2021, 'COLLABORATIONS IN GEOTECHNICAL ENGINEERING: LESSONS FROM THE BALLINA BYPASS AND THE NATIONAL SOFT SOIL FIELD TESTING FACILITY', Australian Geomechanics Journal, vol. 56, no. 2, pp. 85-93.
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Collaboration assists both academics and industry partners to achieve innovations, scientific advancement, and maintain technical competencies. The Ballina Bypass is used here to demonstrate collaboration via an Australian Research Council (ARC) Linkage project on vacuum consolidation, and to discuss how the lessons learned from the Ballina Bypass led to establishing a national facility in Ballina to field test soft soils. The outcomes of the work at the field testing facility have been transferred back to the industry via an international numerical prediction symposium. The project background, roles, and responsibilities of researchers and industry members are discussed and explained, as are the innovative outcomes, stakeholder benefits, and cultural impacts.
Indraratna, B, Singh, M, Nguyen, TT, Leroueil, S, Abeywickrama, A, Kelly, R & Neville, T 2021, 'Correction: Laboratory study on subgrade fluidization under undrained cyclic triaxial loading', Canadian Geotechnical Journal, vol. 58, no. 11, pp. 1790-1790.
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In the Acknowledgements section, “(ITTC),” should be replaced with “(ITTC-Rail) at the”; and “ARTC (Australian Rail Track Corpora-tion)” should be replaced with “ACRI (Australasian Centre for Rail Innovation)”. The corrected text of the Acknowledgements section is as follows: This research was supported by the Australian Government through the Australian Research Council’s Linkage Projects funding scheme (project LP160101254) and the Industrial Transformation Training Centre for Advanced Technologies in Rail Track Infrastructure (ITTC-Rail) at the University of Wollongong. The financial and technical support from SMEC-Australia and ACRI (Australasian Centre for Rail Innovation) is acknowledged.
Indraratna, B, Soomro, MHAA & Rujikiatkamjorn, C 2021, 'Semi-empirical analytical modelling of equivalent dynamic shear strength (EDSS) of rock joint', Transportation Geotechnics, vol. 29, pp. 100569-100569.
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A systematic dynamic triaxial series of tests on replicated rough rock joints were carried out, and results clearly highlight the strength attenuation as a function of joint degradation with respect to the number of loading cycles. A novel semi-empirical mathematical model to evaluate the equivalent dynamic shear strength (EDSS) of rock joint is proposed and validated with experimental results based on two sets of rock joints using rough (JRC = 12.6) and relatively smoother (JRC = 7.2) joint specimens.
Iqbal, J 2021, 'Landslide susceptibility assessment along the Dubair-Dudishal section of the Karakoram Highway, Northwestern Himalayas, Pakistan', Acta Geodynamica et Geomaterialia, pp. 137-155.
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The primary objective of this study is to analyze and characterize landslides in North Pakistan along Karakoram Highway (KKH) to produce a landslide susceptibility map using GIS and remote sensing technology. Using satellite images followed by field investigations, spatial distribution of landslide database was generated. Next, an integrated study was undertaken in the study area to perform the landslide susceptibility mapping. Dubaur-Dudishal section of KKH (about 150 km) which is a part of Kohistan Island Arc, is investigated in this study with a buffer zone of about 8 km along both sides of the KKH. Several thematic maps, e.g., lithology, distance to faults, distance to streams, distance to roads, normalized difference vegetation index (NDVI), slope, aspect, elevation, relative relief, plan-curvature and profile-curvature were prepared. Subsequently, these thematic data layers were analyzed by frequency ratio (FR) model and weights-of-evidence (WoE) model to generate the landslide susceptibility maps. In order to check the accuracy of the models, the area under the curve (AUC) was to quantitatively compare the two models used in this study. The predictive ability of AUC values indicate that the success rates of FR model and WoE model are 0.807 and 0.866, whereas the prediction rates are 0.785 and 0.846, respectively. Both methods show that nearly 50 % landslides in the study area fall in either high or very high susceptibility zones. The landslide susceptibility maps presented in this study are of great importance to the policy makers and the engineers for highway construction as well as the mega dams construction projects (Dasu dam and Bhasha dam which lie within the vicinity of the study area); so that proper prevention as well as mitigation could be done in advance to avoid the possible economic as well as the human loss in future.
Irshad, UB, Nizami, MSH, Rafique, S, Hossain, MJ & Mukhopadhyay, SC 2021, 'A Battery Energy Storage Sizing Method for Parking Lot Equipped With EV Chargers', IEEE Systems Journal, vol. 15, no. 3, pp. 4459-4469.
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Isfeld, AC, Stewart, MG & Masia, MJ 2021, 'Stochastic finite element model assessing length effect for unreinforced masonry walls subjected to one-way vertical bending under out-of-plane loading', Engineering Structures, vol. 236, pp. 112115-112115.
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The strength of unreinforced masonry (URM) walls subjected to one-way vertical bending under out-of-plane loading (no pre-compression) is known to be affected by the tensile bond strength. Factors such as batching, workmanship, and environmental exposure alter the strength of this bond, resulting in spatial variability for any URM assembly. In narrow wall panels a single weak joint may dictate the failure load of a masonry wall, whereas for longer walls there is higher potential for weak joints to occur and load redistribution. This paper focuses on a stochastic assessment of clay brick URM walls with spatially variable tensile bond strength subjected to uniformly distributed out-of-plane loads in one-way vertical bending and assessing the effect of wall length on the ultimate failure load. Stochastic computational modelling combining 3D non-linear Finite Element Analysis (FEA) and Monte Carlo Simulation (MCS) is used to account for bond strength variability when estimating the walls ultimate failure loads. For this assessment FEA MCS has been applied to a set of existing test data for walls 1, 2, 4, and 10 units long, by ten different masons. Models were also developed to consider walls in the intermediate length range, 7 units long, and walls outside of this range, 15 units long. For each set of simulations the peak pressure and load–displacement data was extracted and analysed, showing agreement with the results of wall test data. The panel strength is shown to increase with wall length from 1 to 4 units, then stabilize with further length increase. The variability of the failure load is shown to decrease with increasing wall length.
Ishac, K & Eager, D 2021, 'Evaluating Martial Arts Punching Kinematics Using a Vision and Inertial Sensing System', Sensors, vol. 21, no. 6, pp. 1948-1948.
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Martial arts has many benefits not only in self-defence, but also in improving physical fitness and mental well-being. In our research we focused on analyzing the velocity, impulse, momentum and impact force of the Taekwondo sine-wave punch and reverse-step punch. We evaluated these techniques in comparison with the martial arts styles of Hapkido and Shaolin Wushu and investigated the kinematic properties. We developed a sensing system which is composed of an ICSensor Model 3140 accelerometer attached to a punching bag for measuring dynamic acceleration, Kinovea motion analysis software and 2 GoPro Hero 3 cameras, one focused on the practitioner’s motion and the other focused on the punching bag’s motion. Our results verified that the motion vectors associated with a Taekwondo practitioner performing a sine-wave punch, uses a unique gravitational potential energy to optimise the impact force of the punch. We demonstrated that the sine-wave punch on average produced an impact force of 6884 N which was higher than the reverse-step punch that produced an average impact force of 5055 N. Our comparison experiment showed that the Taekwondo sine-wave punch produced the highest impact force compared to a Hapkido right cross punch and a Shaolin Wushu right cross, however the Wushu right cross had the highest force to weight ratio at 82:1. The experiments were conducted with high ranking black belt practitioners in Taekwondo, Hapkido and Shaolin Wushu.
Isherwood, ZJ, Huynh-Thu, Q, Arnison, M, Monaghan, D, Toscani, M, Perry, S, Honson, V & Kim, J 2021, 'Surface properties and the perception of color', Journal of Vision, vol. 21, no. 2, pp. 7-7.
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Islam, A, Kalam, MA, Sayeed, MA, Shano, S, Rahman, MK, Islam, S, Ferdous, J, Choudhury, SD & Hassan, MM 2021, 'Escalating SARS-CoV-2 circulation in environment and tracking waste management in South Asia', Environmental Science and Pollution Research, vol. 28, no. 44, pp. 61951-61968.
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Islam, MR, Abdul Kader Jilani, MM, Miah, SJ, Akter, S & Ulhaq, A 2021, 'Discovering tourist preference for electing destinations: a pattern mining based approach', Asia Pacific Journal of Tourism Research, vol. 26, no. 10, pp. 1081-1096.
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Awareness and access to information on travel benefits may bear importance for tourist preference in selecting popular destination. Tourism businesses are continuously exploring to improve their competitive advantage and offering an effective method for assisting tourist in electing their preferred destinations. However, current studies are still at an emergent stage. This paper presents a tourists preferences dataset and introduces preference pattern mining (PPM) method as a solution framework for discovering tourist spots. Our PPM method finds and validates the most active tourist hot spots from tourist preferences dataset. The proposed framework facilitates an unbiased approach for optimal tourism destination management in tourism-friendly countries.
Islam, MR, Liu, S, Biddle, R, Razzak, I, Wang, X, Tilocca, P & Xu, G 2021, 'Discovering dynamic adverse behavior of policyholders in the life insurance industry', Technological Forecasting and Social Change, vol. 163, pp. 120486-120486.
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© 2020 Elsevier Inc. Adverse selection (AS) is one of the significant causes of market failure worldwide. Analysis and deep insights into the Australian life insurance market show the existence of adverse activities to gain financial benefits, resulting in loss to insurance companies. Understanding the behavior of policyholders is essential to improve business strategies and overcome fraudulent claims. However, policyholders’ behavior analysis is a complex process, usually involving several factors depending on their preferences and the nature of data such as data which is missing useful private information, the presence of asymmetric information of policyholders, the existence of anomalous information at the cell level rather than the data instance level and a lack of quantitative research. This study aims to analyze the life insurance policyholder's behavior to identify adverse behavior (AB). In this study, we present a novel association rule learning-based approach ‘ARLAS’ to detect the AS behavior of policyholders. In addition to the original data, we further created a synthetic AS dataset by randomly flipping the attribute values of 10% of the records in the test set. The experiment results on 31,800 Australian life insurance users show that the proposed approach achieves significant gains in performance comparatively.
Islam, MR, Lu, H, Hossain, MJ & Li, L 2021, 'Optimal Coordination of Electric Vehicles and Distributed Generators for Voltage Unbalance and Neutral Current Compensation', IEEE Transactions on Industry Applications, vol. 57, no. 1, pp. 1069-1080.
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© 1972-2012 IEEE. To maximize renewable energy usage to combat climate change, the penetration of electric vehicles (EVs) has increased significantly in developed countries. This can cause serious power quality issues, such as increased voltage imbalance and neutral currents, which severely impact the operation of power systems. Although the power quality issue is not a new problem, it requires an improved strategy for the growing penetration of photovoltaic solar energy and EVs in low-voltage distribution grids and their uncoordinated operation. This article presents a new control strategy to reduce the number of coordinated EVs to mitigate voltage unbalance and compensate for the neutral current. The proposed control strategy consists of two controllers arranged in a hierarchical structure with the central controller at the top layer and the local controller at the bottom layer. It is evident that the proposed control strategy reduces the number of EVs that need to be coordinated, and further, EV coordination is not required if the grid imbalance is less. This new hierarchical control strategy can improve power quality and reduce data processing overhead and computational complexity.
Islam, MS, Larpruenrudee, P, Hossain, SI, Rahimi-Gorji, M, Gu, Y, Saha, SC & Paul, G 2021, 'Polydisperse Aerosol Transport and Deposition in Upper Airways of Age-Specific Lung', International Journal of Environmental Research and Public Health, vol. 18, no. 12, pp. 6239-6239.
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A comprehensive understanding of airflow characteristics and particle transport in the human lung can be useful in modelling to inform clinical diagnosis, treatment, and management, including prescription medication and risk assessment for rehabilitation. One of the difficulties in clinical treatment of lung disorders lies in the patients’ variable physical lung characteristics caused by age, amongst other factors, such as different lung sizes. A precise understanding of the comparison between different age groups with various flow rates is missing in the literature, and this study aims to analyse the airflow and aerosol transport within the age-specific lung. ANSYS Fluent solver and the large-eddy simulation (LES) model were employed for the numerical simulation. The numerical model was validated with the available literature and the computational results showed airway size-reduction significantly affected airflow and particle transport in the upper airways. This study reports higher deposition at the mouth-throat region for larger diameter particles. The overall deposition efficiency (DE) increased with airway size reduction and flow rate. Lung aging effected the pressure distribution and a higher pressure drop was reported for the aged lung as compared to the younger lung. These findings could inform medical management through individualised simulation of drug-aerosol delivery processes for the patient-specific lung.
Islam, MS, Larpruenrudee, P, Paul, AR, Paul, G, Gemci, T, Gu, Y & Saha, SC 2021, 'SARS CoV-2 aerosol: How far it can travel to the lower airways?', Physics of Fluids, vol. 33, no. 6, pp. 061903-061903.
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The recent outbreak of the SARS CoV-2 virus has had a significant effect on human respiratory health around the world. The contagious disease infected a large proportion of the world population, resulting in long-term health issues and an excessive mortality rate. The SARS CoV-2 virus can spread as small aerosols and enters the respiratory systems through the oral (nose or mouth) airway. The SARS CoV-2 particle transport to the mouth–throat and upper airways is analyzed by the available literature. Due to the tiny size, the virus can travel to the terminal airways of the respiratory system and form a severe health hazard. There is a gap in the understanding of the SARS CoV-2 particle transport to the terminal airways. The present study investigated the SARS CoV-2 virus particle transport and deposition to the terminal airways in a complex 17-generation lung model. This first-ever study demonstrates how far SARS CoV-2 particles can travel in the respiratory system. ANSYS Fluent solver was used to simulate the virus particle transport during sleep and light and heavy activity conditions. Numerical results demonstrate that a higher percentage of the virus particles are trapped at the upper airways when sleeping and in a light activity condition. More virus particles have lung contact in the right lung than the left lung. A comprehensive lobe specific deposition and deposition concentration study was performed. The results of this study provide a precise knowledge of the SARs CoV-2 particle transport to the lower branches and could help the lung health risk assessment system.
Islam, MS, Larpruenrudee, P, Saha, SC, Pourmehran, O, Paul, AR, Gemci, T, Collins, R, Paul, G & Gu, Y 2021, 'How severe acute respiratory syndrome coronavirus-2 aerosol propagates through the age-specific upper airways', Physics of Fluids, vol. 33, no. 8, pp. 081911-081911.
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The recent outbreak of the COVID-19 causes significant respirational health problems, including high mortality rates worldwide. The deadly corona virus-containing aerosol enters the atmospheric air through sneezing, exhalation, or talking, assembling with the particulate matter, and subsequently transferring to the respiratory system. This recent outbreak illustrates that the severe acute respiratory syndrome (SARS) coronavirus-2 is deadlier for aged people than for other age groups. It is evident that the airway diameter reduces with age, and an accurate understanding of SARS aerosol transport through different elderly people's airways could potentially help the overall respiratory health assessment, which is currently lacking in the literature. This first-ever study investigates SARS COVID-2 aerosol transport in age-specific airway systems. A highly asymmetric age-specific airway model and fluent solver (ANSYS 19.2) are used for the investigation. The computational fluid dynamics measurement predicts higher SARS COVID-2 aerosol concentration in the airway wall for older adults than for younger people. The numerical study reports that the smaller SARS coronavirus-2 aerosol deposition rate in the right lung is higher than that in the left lung, and the opposite scenario occurs for the larger SARS coronavirus-2 aerosol rate. The numerical results show a fluctuating trend of pressure at different generations of the age-specific model. The findings of this study would improve the knowledge of SARS coronavirus-2 aerosol transportation to the upper airways which would thus ameliorate the targeted aerosol drug delivery system.
Iuliano, S, Poon, S, Robbins, J, Bui, M, Wang, X, De Groot, L, Van Loan, M, Zadeh, AG, Nguyen, T & Seeman, E 2021, 'Effect of dietary sources of calcium and protein on hip fractures and falls in older adults in residential care: cluster randomised controlled trial', BMJ, vol. 375, pp. n2364-n2364.
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AbstractObjectiveTo assess the antifracture efficacy and safety of a nutritional intervention in institutionalised older adults replete in vitamin D but with mean intakes of 600 mg/day calcium and <1 g/kg body weight protein/day.DesignTwo year cluster randomised controlled trial.Setting60 accredited residential aged care facilities in Australia housing predominantly ambulant residents.Participants7195 permanent residents (4920 (68%) female; mean age 86.0 (SD 8.2) years).InterventionFacilities were stratified by location and organisation, with 30 facilities randomised to provide residents with additional milk, yoghurt, and cheese that contained 562 (166) mg/day calcium and 12 (6) g/day protein achieving a total intake of 1142 (353) mg calcium/day and 69 (15) g/day protein (1.1 g/kg body weight). The 30 control facilities maintained their usual menus, with residents consuming 700 (247) mg/day calcium and 58 (14) g/day protein (0.9 g/kg body weight).Main outcome measuresGroup differences in incidence of fractures, falls, and all cause mortality.ResultsData from 27 intervention facilities and 29 control facilities were analysed. A total of 324 fractures (135 hip fractures), 4302 falls, and 1974 deaths were observed. The intervention was associated with risk reductions of 33% for all fractures (121v203; hazard ratio 0.67, 95% confidence interval 0.48 to 0.93; P=0.02), 46% for hip fractures (42v93; 0.54, 0.35 to 0.83; P=0.005), and 11% for falls (1879v<...
Iwanaga, T, Wang, H-H, Hamilton, SH, Grimm, V, Koralewski, TE, Salado, A, Elsawah, S, Razavi, S, Yang, J, Glynn, P, Badham, J, Voinov, A, Chen, M, Grant, WE, Peterson, TR, Frank, K, Shenk, G, Barton, CM, Jakeman, AJ & Little, JC 2021, 'Socio-technical scales in socio-environmental modeling: Managing a system-of-systems modeling approach', Environmental Modelling & Software, vol. 135, pp. 104885-104885.
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System-of-systems approaches for integrated assessments have become prevalent in recent years. Such approaches integrate a variety of models from different disciplines and modeling paradigms to represent a socio-environmental (or social-ecological) system aiming to holistically inform policy and decision-making processes. Central to the system-of-systems approaches is the representation of systems in a multi-tier framework with nested scales. Current modeling paradigms, however, have disciplinary-specific lineage, leading to inconsistencies in the conceptualization and integration of socio-environmental systems. In this paper, a multidisciplinary team of researchers, from engineering, natural and social sciences, have come together to detail socio-technical practices and challenges that arise in the consideration of scale throughout the socio-environmental modeling process. We identify key paths forward, focused on explicit consideration of scale and uncertainty, strengthening interdisciplinary communication, and improvement of the documentation process. We call for a grand vision (and commensurate funding) for holistic system-of-systems research that engages researchers, stakeholders, and policy makers in a multi-tiered process for the co-creation of knowledge and solutions to major socio-environmental problems.
Iyer, S, Velmurugan, T, Gandomi, AH, Noor Mohammed, V, Saravanan, K & Nandakumar, S 2021, 'Structural health monitoring of railway tracks using IoT-based multi-robot system', Neural Computing and Applications, vol. 33, no. 11, pp. 5897-5915.
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A multi-robot-based fault detection system for railway tracks is proposed to eliminate manual human visual inspection. A hardware prototype is designed to implement a master–slave robot mechanism capable of detecting rail surface defects, which include cracks, squats, corrugations, and rust. The system incorporates ultrasonic sensor inputs coupled with image processing using OpenCV and deep learning algorithms to classify the surface faults detected. The proposed Convolutional Neural Network (CNN) model fared better compared to the Artificial Neural Network (ANN), random forest, and Support Vector Machine (SVM) algorithms based on accuracy, R-squared value, F1 score, and Mean-Squared Error (MSE). To eliminate manual inspection, the location and status of the fault can be conveyed to a central location enabling immediate attention by utilizing GSM, GPS, and cloud storage-based technologies. The system is extended to a multi-robot framework designed to optimize energy utilization, increase the lifetime of individual robots, and improve the overall network throughput. Thus, the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol is simulated using 100 robot nodes, and the corresponding performance metrics are obtained.
Izadikhah, M, Azadi, M, Toloo, M & Hussain, FK 2021, 'Sustainably resilient supply chains evaluation in public transport: A fuzzy chance-constrained two-stage DEA approach', Applied Soft Computing, vol. 113, pp. 107879-107879.
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Owing to today's highly competitive market environments, substantial attention has been focused on sustainably resilient supply chains (SCs) over the last few years. Nevertheless, very few studies have focused on the efficiency evaluation analysis of the sustainability and resilience of SCs as an inevitable essential in any profitable business. This study aims to address this issue by proposing a novel fuzzy chance-constrained two-stage data envelopment analysis (DEA) model as an advanced and rigorous approach in the performance evaluation of sustainably resilient SCs. To the best of our knowledge, the current study is pioneering as it introduces a new fuzzy chance-constrained two-stage method that can be used to undertake the deterministic non-fuzzy programming of the proposed model. The proposed approach is validated and applied to evaluate a real case study including 21 major public transport providers in three megacities. The results demonstrate the advantages of the proposed approach in comparison to the existing approaches in the literature.
Izady, A, Khorshidi, MS, Nikoo, MR, Al-Maktoumi, A, Chen, M, Al-Mamari, H & Gandomi, AH 2021, 'Optimal Water Allocation from Subsurface Dams: A Risk-Based Optimization Approach', Water Resources Management, vol. 35, no. 12, pp. 4275-4290.
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Subsurface dams, strongly advocated in the 1992 United Nations Agenda-21, have been widely studied to increase groundwater storage capacity. However, an optimal allocation of augmented water with the construction of the subsurface dams to compensate for the water shortage during dry periods has not so far been investigated. This study, therefore, presents a risk-based simulation–optimization framework to determine optimal water allocation with subsurface dams, which minimizes the risk of water shortage in different climatic conditions. The developed framework was evaluated in Al-Aswad falaj, an ancient water supply system in which a gently sloping underground channel was dug to convey water from an aquifer via the gravity force to the surface for irrigation of downstream agricultural zones. The groundwater dynamics were modeled using MODFLOW UnStructured-Grid. The data of boreholes were used to generate a three-dimensional stratigraphic model, which was used to define materials and elevations of five-layer grid cells. The validated groundwater model was employed to assess the effects of the subsurface dam on the discharge of the falaj. A Conditional Value-at-Risk optimization model was also developed to minimize the risk of water shortage for the augmented discharge on downstream agricultural zones. Results show that discharge of the falaj is significantly augmented with a long-term average increase of 46.51%. Moreover, it was found that the developed framework decreases the water shortage percentage in 5% of the worst cases from 87%, 75%, and 32% to 53%, 32%, and 0% under the current and augmented discharge in dry, normal, and wet periods, respectively.
Jacob, A, Ashok, B, Alagumalai, A, Chyuan, OH & Le, PTK 2021, 'Critical review on third generation micro algae biodiesel production and its feasibility as future bioenergy for IC engine applications', Energy Conversion and Management, vol. 228, pp. 113655-113655.
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Jafarizadeh, S, Veitch, D, Tofigh, F, Lipman, J & Abolhasan, M 2021, 'Optimal Synchronizability in Networks of Coupled Systems: Topological View', IEEE Transactions on Network Science and Engineering, vol. 8, no. 2, pp. 1517-1530.
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Many engineered and natural systems are modeled as networks of coupled systems. Synchronization is one of their crucial and well-studied behaviors. Uniform coupling strength has been the benchmark practice in the majority of the literature. This paper considers nonuniform coupling strength, and a modified approach to the problem of synchronizability optimization, enabling a reduction to a spectral radius minimization problem, which can reach a unique optimal point on the Pareto Frontier. It is established that adding any edge to a connected graph can only improve synchronizability in this optimal measure. This result is utilized for developing a hierarchy between topologies. It is shown that several proposed structural parameters, including betweenness centrality, do not have any simple relationship to the optimal synchronizability measure.
Jahangoshai Rezaee, M, Eshkevari, M, Saberi, M & Hussain, O 2021, 'GBK-means clustering algorithm: An improvement to the K-means algorithm based on the bargaining game', Knowledge-Based Systems, vol. 213, pp. 106672-106672.
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Due to its simplicity, versatility and the diversity of applications to which it can be applied, k-means is one of the well-known algorithms for clustering data. The foundation of this algorithm is based on the distance measure. However, the traditional k-means has some weaknesses that appear in some data sets related to real applications, the most important of which is to consider only the distance criterion for clustering. Various studies have been conducted to address each of these weaknesses to achieve a balance between quality and efficiency. In this paper, a novel k-means variant of the original algorithm is proposed. This approach leverages the power of bargaining game modelling in the k-means algorithm for clustering data. In this novel setting, cluster centres compete with each other to attract the largest number of similar objectives or entities to their cluster. Thus, the centres keep changing their positions so that they have smaller distances with the maximum possible data than other cluster centres. We name this new algorithm the game-based k-means (GBK-means) algorithm. To show the superiority and efficiency of GBK-means over conventional clustering algorithms, namely, k-means and fuzzy k-means, we use the following syntactic and real-world data sets: (1) a series of two-dimensional syntactic data sets; and (2) ten benchmark data sets that are widely used in different clustering studies. The evaluation criteria show GBK-means is able to cluster data more accurately than classical algorithms based on eight evaluation metrics, namely F-measure, the Dunn index (DI), the rand index (RI), the Jaccard index (JI), normalized mutual information (NMI), normalized variation of information (NVI), the measure of concordance and error rate (ER).
Jahed Armaghani, D, Kumar, D, Samui, P, Hasanipanah, M & Roy, B 2021, 'A novel approach for forecasting of ground vibrations resulting from blasting: modified particle swarm optimization coupled extreme learning machine', Engineering with Computers, vol. 37, no. 4, pp. 3221-3235.
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Jahirul, MI, Rasul, MG, Brown, RJ, Senadeera, W, Hosen, MA, Haque, R, Saha, SC & Mahlia, TMI 2021, 'Investigation of correlation between chemical composition and properties of biodiesel using principal component analysis (PCA) and artificial neural network (ANN)', Renewable Energy, vol. 168, pp. 632-646.
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© 2020 Elsevier Ltd Biodiesel will provide a significant renewable energy source for transportation in the near future. In the present study, principal component analysis (PCA) has been used to understand the relationship between important properties of biodiesel and its chemical composition. Finally, several artificial intelligence-based models were developed to predict specific biodiesel properties based on their chemical composition. The experimental study was conducted in order to generate training data for the artificial neural network (ANN). Available (experimental) data from the literature was also employed for this modeling strategy. The analytical part of this study found a complex multi-dimensional correlation between chemical composition and biodiesel properties. Average numbers of double bonds in the chemical structure (representing the unsaturated component in biodiesel) and the poly-unsaturated component in biodiesel had a great impact on biodiesel properties. The simulation result in this study demonstrated that ANN is a useful tool for investigating the fuel properties from its chemical composition which eventually can replace the time consuming and costly experimental test.
Jain, K & Pradhan, B 2021, 'Editorial', Journal of the Indian Society of Remote Sensing, vol. 49, no. 3, pp. 461-462.
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Jaiswal, A, Kumar, S, Kaiwartya, O, Prasad, M, Kumar, N & Song, H 2021, 'Green computing in IoT: Time slotted simultaneous wireless information and power transfer', Computer Communications, vol. 168, pp. 155-169.
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Simultaneous Wireless Information and Power Transfer (SWIPT) is an emerging field to transmit information and power in IoT network through the same RF signal. Time switching (TS) protocol is more favorable in free space communication than Power Splitting (PS) protocol when the transmitted RF signal is already weak. This is because, transmitted signal loses its power due to attenuation in free space, and using PS design receiver circuit (complex), the received weak signal is further split into two fraction for energy harvesting (EH) and information decoding (ID) simultaneously, that causes inadequacy in SWIPT system. Whereas, using TS design receiver circuit (simple) insert extra delay in the network as EH and ID operations are done in two different time domain one by one. Literature on SWIPT lacks towards cooperation between more energy harvesting in case of free space communication (TS) and critical information transmission in case of delay constraint communication (PS). In this context, this paper presents a time-slotted SWIPT (T-SWIPT) focusing on maximization of energy efficiency in the relay based sensors-enabled IoT network. It enables simultaneous energy harvesting at receiver and neighboring sensors without adding extra delay in the network. The PS ratio, transmission power allotment and energy broadcast time are jointly formulated as non-convex energy efficiency maximization problem. A solution to the problem is presented using Lagrangian dual decomposition and fractional programming. The performance evaluation shows that T-SWIPT attains optimum energy efficiency by trading off transmission power allotment, power-splitting ratio and sink broadcast time slot.
Jamborsalamati, P, Garmabdari, R, Hossain, J, Lu, J & Dehghanian, P 2021, 'Planning for resilience in power distribution networks: A multi‐objective decision support', IET Smart Grid, vol. 4, no. 1, pp. 45-60.
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AbstractPower grid response against high‐impact low‐probability (HILP) events could be enhanced by (a) hardening mechanisms to boost its structural resilience and (b) corrective recovery and mitigation analytics to improve its operational resilience. Planning for structural resilience and attempts to find the optimal location of the Tie switches in radially operated power distribution networks that enable harnessing the network topology for maximised resilience against HILP disasters are focussed. This goal is achieved through a novel resilience‐oriented multi‐objective decision making platform, which employs a k‐PEM based probabilistic power flow (PPF) algorithm. The proposed framework offers a decision making analytic embedded with the fuzzy satisfying method (FSM) that characterises the system resilience features, such as robustness, restoration agility, load criticality, and recovered capacity, to assess different network reconfiguration options and select the optimal solution for implementation. The aforementioned resilience features are formulated in nodal level and then aggregated over the entire system to characterise the system‐level objective functions. The performance of the suggested framework is analysed on the IEEE 33‐Bus test system under a designated HILP event, and the applicability on larger networks has been verified on the IEEE 69‐bus test system. The results demonstrate the efficacy and applicability of the proposed framework in boosting the network resilience against future extremes.
Jamil, S, Loganathan, P, Kandasamy, J, Ratnaweera, H & Vigneswaran, S 2021, 'Comparing nanofiltration membranes effectiveness for inorganic and organic compounds removal from a wastewater-reclamation plant’s micro-filtered water', Materials Today: Proceedings, vol. 47, pp. 1389-1393.
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Jamil, S, Loganathan, P, Khan, SJ, McDonald, JA, Kandasamy, J & Vigneswaran, S 2021, 'Enhanced nanofiltration rejection of inorganic and organic compounds from a wastewater-reclamation plant’s micro-filtered water using adsorption pre-treatment', Separation and Purification Technology, vol. 260, pp. 118207-118207.
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© 2020 Elsevier B.V. Adsorption pre-treatment to enhance the nanofiltration (NF) removal of inorganic ions, dissolved organic carbon (DOC) and organic micropollutants (OMP) from microfiltered (MF) wastewater was investigated using NF 90 membrane (contact angle 79% and molecular weight cut off value of 90–200 Da). The NF showed greater rejection for divalent cations (Ca2+, Mg2+) and anions (SO42−) compared to monovalent cations (Na+, K+) and anions (Cl−, NO3−). The degree of total DOC removal was: GAC adsorption + NF (86%) > an ion exchange resin (Purolite) adsorption + NF (81%) > NF operation alone (72%). GAC + NF removed biopolymers and hydrophobic substances almost completely and the highest percentage of LMW neutral substances. In contrast, Purolite + NF almost completely removed humic substances. The degree of membrane fouling order was: LMW neutrals > building blocks > biopolymers > hydrophobics > humics. Adsorption pre-treatment reduced membrane fouling and increased solution flux, the outcome being better with GAC compared to Purolite. Of the 10 MOPs in the MF water, seven were rejected > 90% by NF without any pre-treatment. Conversely, Purolite and GAC pre-treatments rejected > 90% of all OMPs.
Jankowska, K, Grzywaczyk, A, Piasecki, A, Kijeńska-Gawrońska, E, Nguyen, LN, Zdarta, J, Nghiem, LD, Pinelo, M & Jesionowski, T 2021, 'Electrospun biosystems made of nylon 6 and laccase and its application in dyes removal', Environmental Technology & Innovation, vol. 21, pp. 101332-101332.
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Electrospun materials, due to the possibility of design of their properties, are suitable as supports for enzyme immobilization. Produced biocatalytic systems might be then apply in various biocatalytic reactions, including conversion of pollutants. In our study, electrospun fibers made from nylon 6 was produced, modified and applied as a support for laccase immobilization by adsorption and covalent binding. The systems with immobilized laccase were used in decolorization process of selected dyes, azo dye Reactive Black 5 and the anthraquinone dye Reactive Blue 4. It was found that at from dye solution at concentration 1 mg/L at pH 5, temperature 25 °C, after 24 h of process the efficiency of decolorization of Reactive Blue 4 and Reactive Black 5 reached 77% and 63%, respectively. The storage stability studies showed that after 30 days of storage, the relative activities were 60% and 95% for adsorbed and covalently bonded oxidoreductase respectively. Moreover, after 10 consecutive catalytic cycles adsorbed and covalently bonded laccase retained over 60% and 70% respectively, indicating the possibility of application of the obtained systems on a larger scale for removal of phenolic pollutants from wastewaters.
Jayathilaka, P, Indraratna, B & Heitor, A 2021, 'Influence of Salinity-Based Osmotic Suction on the Shear Strength of a Compacted Clay', International Journal of Geomechanics, vol. 21, no. 5, pp. 04021041-04021041.
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Jelich, C, Karimi, M, Kessissoglou, N & Marburg, S 2021, 'Efficient solution of block Toeplitz systems with multiple right-hand sides arising from a periodic boundary element formulation', Engineering Analysis with Boundary Elements, vol. 130, pp. 135-144.
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Jena, R, Ghansar, TAA, Pradhan, B & Rai, AK 2021, 'Estimation of fractal dimension and b-value of earthquakes in the Himalayan region', Arabian Journal of Geosciences, vol. 14, no. 10.
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Jena, R, Naik, SP, Pradhan, B, Beydoun, G, Park, H-J & Alamri, A 2021, 'Earthquake vulnerability assessment for the Indian subcontinent using the Long Short-Term Memory model (LSTM)', International Journal of Disaster Risk Reduction, vol. 66, pp. 102642-102642.
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Earthquakes are one of the most destructive and unpredictable natural hazards with a long-term physical, psychological, and economic impact to the society. In the past century, more than 1100 destructive earthquakes occurred, and caused around 1.5 million deaths worldwide. Some recent studies have suggested that a future earthquake in the Himalayan region of magnitude range MW 7.5–8 can cause more than 0.2 million human lives and around 150 billion dollar financial loss. Deep learning methods in recent studies proved very useful in natural hazards forecasting and prediction modelling. Long Short-Term Memory (LSTM) model has been particularly popular in several natural hazard forecasting. In this research, for the first time, LSTM model is implemented with suitable Geospatial Information Systems (GIS) techniques to assess the earthquake vulnerability for whole of India. In India, most of the seismic vulnerability assessment available are at city level or state level using traditional techniques. Several factors such as land use, geology, geomorphology, fault distribution, transportation facility, population density were all used to develop the social, structural, and geotechnical vulnerability maps. The results show that the areas around Delhi, NE region of India, major parts of Gujrat, West Bengal plain exhibit high to very-high seismic vulnerability. This model achieved an accuracy of 87.8%, sensitivity (90%) and specificity (84.9%). The present analysis can be helpful towards prioritization of regions which are in higher need of risk reduction interventions. Also, based on this vulnerability index map, the risk metrics can be attenuated.
Ji, A, Xue, X, Ha, QP, Luo, X & Zhang, M 2021, 'Game theory–based bilevel model for multiplayer pavement maintenance management', Automation in Construction, vol. 129, pp. 103763-103763.
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Ji, E, Weickert, CS, Purves-Tyson, T, White, C, Handelsman, DJ, Desai, R, O'Donnell, M, Liu, D, Galletly, C, Lenroot, R & Weickert, TW 2021, 'Cortisol-dehydroepiandrosterone ratios are inversely associated with hippocampal and prefrontal brain volume in schizophrenia', Psychoneuroendocrinology, vol. 123, pp. 104916-104916.
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Ji, JC, Luo, Q & Ye, K 2021, 'Vibration control based metamaterials and origami structures: A state-of-the-art review', Mechanical Systems and Signal Processing, vol. 161, pp. 107945-107945.
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Vibration and sound control is critical to many practical engineering systems in order to minimise the detrimental effects caused by unavoidable vibrations and noises. Metamaterials and origami-based structures, which have attracted increasing interests in interdisciplinary research fields, possess many peculiar physical properties, including negative Poisson's ratios, bi- or multi-stable states, nonlinear and tuneable stiffness features, and thus offer promising applications for vibration and sound control. This paper presents a review of metamaterials and origami-based structures as well as their applications to vibration and sound control. Metamaterials are artificially engineered materials having extremal properties which are not found in conventional materials. Metamaterials with abnormal features are firstly discussed on the basis of the unusual values of their elastic constants. Recent advances of auxetic, band gap and pentamode metamaterials are reviewed together with their applications to vibration and sound mitigations. Origami, as the ancient Japanese art of paper folding, has emerged as a new design paradigm for different applications. Origami-based structures can be adopted for vibration isolation by using their multi-stable states and desirable stiffness characteristics. Different origami patterns are reviewed to show their configurations and base structures. Special features, such as bi- or multi-stable states, dynamic Poisson's ratios, and nonlinear force–displacement relationships are discussed for their applications for vibration control. Finally, possible future research directions are elaborated for this emerging and promising interdisciplinary research field.
Ji, S, Pan, S, Li, X, Cambria, E, Long, G & Huang, Z 2021, 'Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications', IEEE Transactions on Computational Social Systems, vol. 8, no. 1, pp. 214-226.
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© 2014 IEEE. Suicide is a critical issue in modern society. Early detection and prevention of suicide attempts should be addressed to save people's life. Current suicidal ideation detection (SID) methods include clinical methods based on the interaction between social workers or experts and the targeted individuals and machine learning techniques with feature engineering or deep learning for automatic detection based on online social contents. This article is the first survey that comprehensively introduces and discusses the methods from these categories. Domain-specific applications of SID are reviewed according to their data sources, i.e., questionnaires, electronic health records, suicide notes, and online user content. Several specific tasks and data sets are introduced and summarized to facilitate further research. Finally, we summarize the limitations of current work and provide an outlook of further research directions.
Ji, Z, Natarajan, A, Vidick, T, Wright, J & Yuen, H 2021, 'MIP* = RE', Communications of the ACM, vol. 64, no. 11, pp. 131-138.
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Note from the Research Highlights Co-Chairs: A Research Highlights paper appearing in Communications is usually peer-reviewed prior to publication. The following paper is unusual in that it is still under review. However, the result has generated enormous excitement in the research community, and came strongly nominated by SIGACT, a nomination seconded by external reviewers. The complexity class NP characterizes the collection of computational problems that have efficiently verifiable solutions. With the goal of classifying computational problems that seem to lie beyond NP, starting in the 1980s complexity theorists have considered extensions of the notion of efficient verification that allow for the use of randomness (the class MA), interaction (the class IP), and the possibility to interact with multiple proofs, or provers (the class MIP). The study of these extensions led to the celebrated PCP theorem and its applications to hardness of approximation and the design of cryptographic protocols. In this work, we study a fourth modification to the notion of efficient verification that originates in the study of quantum entanglement. We prove the surprising result that every problem that is recursively enumerable, including the Halting problem, can be efficiently verified by a classical probabilistic polynomial-time verifier interacting with two all-powerful but noncommunicating provers sharing entanglement. The result resolves lo...
Jia, M, Gabrys, B & Musial, K 2021, 'Directed closure coefficient and its patterns', PLOS ONE, vol. 16, no. 6, pp. e0253822-e0253822.
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The triangle structure, being a fundamental and significant element, underlies many theories and techniques in studying complex networks. The formation of triangles is typically measured by the clustering coefficient, in which the focal node is the centre-node in an open triad. In contrast, the recently proposed closure coefficient measures triangle formation from an end-node perspective and has been proven to be a useful feature in network analysis. Here, we extend it by proposing the directed closure coefficient that measures the formation of directed triangles. By distinguishing the direction of the closing edge in building triangles, we further introduce the source closure coefficient and the target closure coefficient. Then, by categorising particular types of directed triangles (e.g., head-of-path), we propose four closure patterns. Through multiple experiments on 24 directed networks from six domains, we demonstrate that at network-level, the four closure patterns are distinctive features in classifying network types, while at node-level, adding the source and target closure coefficients leads to significant improvement in link prediction task in most types of directed networks.
Jia, M, Srinivasan, R, Ries, R, Bharathy, G & Weyer, N 2021, 'Investigating the Impact of Actual and Modeled Occupant Behavior Information Input to Building Performance Simulation', Buildings, vol. 11, no. 1, pp. 32-32.
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Occupant behaviors are one of the most dominant factors that influence building energy use. Understanding the influences from building occupants can promote the development of energy–efficient buildings. This paper quantifies the impact of different occupant behavior information on building energy model (BEM) from multiple perspectives. For this purpose, an occupant behavior model that uses agent–based modeling (ABM) approach is implemented via co-simulation with a BEM of an existing commercial building. Then, actual occupant behavior data in correspondence to ABM output, including operations on window, door, and blinds in selected thermal zones of the building are recorded using survey logs. A simulation experiment is conducted by creating three BEMs with constant, actual, and modeled occupant behavioral inputs. The analysis of the simulation results among these scenarios helps us gain an in–depth understanding of how occupant behaviors influence building performance. This study aims to facilitate robust building design and operation with human–in–the–loop system optimization.
Jiang, H, Xu, K, Zhang, Q, Yang, Y, Karmokar, DK, Chen, S, Zhao, P, Wang, G & Peng, L 2021, 'Backward-to-Forward Wide-Angle Fast Beam-Scanning Leaky-Wave Antenna With Consistent Gain', IEEE Transactions on Antennas and Propagation, vol. 69, no. 5, pp. 2987-2992.
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A planar periodic leaky-wave antenna (PLWA) with continuous backward-to-forward beam-scanning capability and consistent gain is presented. The PLWA is made of a new type of periodically arranging meander line structures, in which the capacitive coupling between neighboring units plays the key role in achieving a high scanning rate. In detail, the beam-scanning rate of the proposed PLWA is efficiently tuned by varying the gap between two adjacent units, without considerably changing the working frequency band. To mitigate the open stopband (OSB) and to improve the impedance matching, several inductive open stubs are introduced into a single meander line unit cell to enhance the radiation. In the experiment, a prototype of the proposed PLWA was fabricated and measured. The simulated and measured beams show good agreement in terms of scanning range and radiation performance. A continuous beam scanning from -60° to +58° through broadside in the frequency band of 5.95-7.1 GHz is observed, and hence, a 102.6°/GHz scanning rate is realized in practice. Besides, the PLWA shows an average gain level of 11.96 dBi with a variation lower than 2 dB and a sidelobe below -10 dB at all the measured frequencies. The proposed PLWA may have potential applications in radar, microwave imaging, and wireless communication due to its compact structure, easy to fabricate, and dispersionless performance.
Jiang, J, Phuntsho, S, Pathak, N, Wang, Q, Cho, J & Shon, HK 2021, 'Critical flux on a submerged membrane bioreactor for nitrification of source separated urine', Process Safety and Environmental Protection, vol. 153, pp. 518-526.
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Membrane fouling is the biggest challenge in membrane based technology operation. Studies on critical flux mainly focused on membrane bioreactor for municipal wastewater and/or greywater treatment, which can significantly differ from the ultrafiltration membrane bioreactor (UF-MBRs) to treat source separated urine. In this work, the inhibitory factors on nitrifying bacteria activity were investigated for fast acclimation of nitrifying bacteria with high ammonium concentration and optimization of a high-rate partial nitrification MBR. The maximum nitrification rate of 447 ± 50 mgN L–1 d–1 was achieved when concentration of ammonia in feed urine is approximately 4006.3 ± 225.8 mg N L–1 by maintaining desired pH around 6.2 and FA concentrations below 0.5 mgL−1. Furthermore, for the first time, the impact of different operational and filtration conditions (i.e. aeration intensity, filtration method, imposed flux, intermittent relaxation, biomass concentration) on the reversibility of membrane fouling was carried out for enhancement of membrane flux and fouling mitigation. Fouling mechanisms for minor irreversible fouling observed under sub-critical condition were pore blocking and polarization. To mitigate membrane fouling, the UF module with effective membrane surface area of 0.02 m2 is recommended to be operated at the aeration intensity of 0.4 m3 h−1, intermittent relaxation of 15 min, biomass concentration of 3.5 g L−1.
Jiang, S, Shen, L & Li, W 2021, 'An experimental study of aggregate shape effect on dynamic compressive behaviours of cementitious mortar', Construction and Building Materials, vol. 303, pp. 124443-124443.
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An experimental investigation is conducted to study the effect of aggregate shape on mortar dynamic failure behaviours. Split Hopkinson bar device is employed to compress cylindrical mortar samples containing irregular glass aggregates and rounded glass aggregates under high strain rates from 1000 s−1 to 2500 s−1. A new insight into the aggregate shape effect on the mortar cracking mechanisms is presented at the microscale using micro-CT. The cracking characteristics are found to be highly dependent on the aggregate shape, where more rounded aggregates in mortar are less likely to possess transgranular cracks after the initiation of intergranular cracks in the weak interfacial transition zone. These microscopic cracking mechanisms are validated by the cumulative distribution evolutions of particle size and morphological parameters (elongation and flatness), which are further manifested by the dynamic compressive strength. The results demonstrate that mortar with more regular aggregates exhibits higher dynamic compressive strength and strain rate sensitivity.
Jiang, T, Qiao, Y, Ruan, W, Zhang, D, Yang, Q, Wang, G, Chen, Q, Zhu, F, Yin, J, Zou, Y, Qian, R, Zheng, M & Shi, B 2021, 'Cation‐Free siRNA Micelles as Effective Drug Delivery Platform and Potent RNAi Nanomedicines for Glioblastoma Therapy', Advanced Materials, vol. 33, no. 45.
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AbstractNanoparticle‐based small interfering RNA (siRNA) therapy shows great promise for glioblastoma (GBM). However, charge associated toxicity and limited blood–brain‐barrier (BBB) penetration remain significant challenges for siRNA delivery for GBM therapy. Herein, novel cation‐free siRNA micelles, prepared by the self‐assembly of siRNA‐disulfide‐poly(N‐isopropylacrylamide) (siRNA‐SS‐PNIPAM) diblock copolymers, are prepared. The siRNA micelles not only display enhanced blood circulation time, superior cell take‐up, and effective at‐site siRNA release, but also achieve potent BBB penetration. Moreover, due to being non‐cationic, these siRNA micelles exert no charge‐associated toxicity. Notably, these desirable properties of this novel RNA interfering (RNAi) nanomedicine result in outstanding growth inhibition of orthotopic U87MG xenografts without causing adverse effects, achieving remarkably improved survival benefits. Moreover, as a novel type of polymeric micelle, the siRNA micelle displays effective drug loading ability. When utilizing temozolomide (TMZ) as a model loading drug, the siRNA micelle realizes effective synergistic therapy effect via targeting the key gene (signal transducers and activators of transcription 3, STAT3) in TMZ drug resistant pathways. The authors’ results show that this siRNA micelle nanoparticle can serve as a robust and versatile drug codelivery platform, and RNAi nanomedicine and for effective GBM treatment.
Jiang, Y, Gu, X, Wu, D, Hang, W, Xue, J, Qiu, S & Lin, C-T 2021, 'A Novel Negative-Transfer-Resistant Fuzzy Clustering Model With a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation', IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 1, pp. 40-52.
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Traditional clustering algorithms for medical image segmentation can only achieve satisfactory clustering performance under relatively ideal conditions, in which there is adequate data from the same distribution, and the data is rarely disturbed by noise or outliers. However, a sufficient amount of medical images with representative manual labels are often not available, because medical images are frequently acquired with different scanners (or different scan protocols) or polluted by various noises. Transfer learning improves learning in the target domain by leveraging knowledge from related domains. Given some target data, the performance of transfer learning is determined by the degree of relevance between the source and target domains. To achieve positive transfer and avoid negative transfer, a negative-transfer-resistant mechanism is proposed by computing the weight of transferred knowledge. Extracting a negative-transfer-resistant fuzzy clustering model with a shared cross-domain transfer latent space (called NTR-FC-SCT) is proposed by integrating negative-transfer-resistant and maximum mean discrepancy (MMD) into the framework of fuzzy c-means clustering. Experimental results show that the proposed NTR-FC-SCT model outperformed several traditional non-transfer and related transfer clustering algorithms.
Jiang, Y, Zhang, Y, Lin, C, Wu, D & Lin, C-T 2021, 'EEG-Based Driver Drowsiness Estimation Using an Online Multi-View and Transfer TSK Fuzzy System', IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 3, pp. 1752-1764.
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Jiao, Y, Wang, Y, Ding, X, Fu, B, Huang, S & Xiong, R 2021, '2-Entity Random Sample Consensus for Robust Visual Localization: Framework, Methods, and Verifications', IEEE Transactions on Industrial Electronics, vol. 68, no. 5, pp. 4519-4528.
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Jin, B, Chen, E, Zhao, H, Huang, Z, Liu, Q, Zhu, H & Yu, S 2021, 'Promotion of Answer Value Measurement With Domain Effects in Community Question Answering Systems', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 5, pp. 3068-3079.
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In the area of community question answering (CQA), answer selection and answer ranking are two tasks which are applied to help users quickly access valuable answers. Existing solutions mainly exploit the syntactic or semantic correlation between a question and its related answers (QA), where the multifacet domain effects in CQA are still underexplored. In this paper, we propose a unified model, enhanced attentive recurrent neural network (EARNN), for both answer selection and answer ranking tasks by taking full advantages of both QA semantics and multifacet domain effects (i.e., topic effects and timeliness). Specifically, we develop a serialized long short-term memory to learn the unified representations of QA, where two attention mechanisms at either sentence level or word level are designed for capturing the deep effects of topics. Meanwhile, the emphasis of QA can be automatically distinguished. Furthermore, we design a time-sensitive ranking function to model the timeliness in CQA. To effectively train EARNN, a question-dependent pairwise learning strategy is also developed. Finally, we conduct extensive experiments on a real-world dataset from Quora. Experimental results validate the effectiveness and interpretability of our proposed EARNN model.
Jin, J, Sheng, G, Bi, Y, Song, Y, Liu, X, Chen, X, Li, Q, Deng, Z, Zhang, W, Zheng, J, Coombs, T, Shen, B, Zhu, J, Zhao, Y, Wang, J, Xiang, B, Tang, Y, Ren, L, Xu, Y, Shi, J, Islam, MR, Guo, Y & Zhu, J 2021, 'Applied Superconductivity and Electromagnetic Devices - Principles and Current Exploration Highlights', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-29.
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With regard to the state-of-the-art technologies in the fields of applied superconductivity and electromagnetic devices, research and development highlights are presented. The recent progress and achievement described with principle and technical details include mainly i) applied superconducting materials; ii) superconducting magnets and their applications such as in ITER and Tokamaks; iii) high Tc superconducting (HTS) magnetic levitation and applications; iv) HTS smart grids; v) superconducting and electromagnetic material modelling and characterization; and vi) advanced electromagnetic devices. The applied superconductivity technology and availability are especially focused and verified with the trend of development prospection.
Jin, Z, Sun, X, Cai, Y, Zhu, J, Lei, G & Guo, Y 2021, 'Comprehensive Sensitivity and Cross-Factor Variance Analysis-Based Multi-Objective Design Optimization of a 3-DOF Hybrid Magnetic Bearing', IEEE Transactions on Magnetics, vol. 57, no. 2, pp. 1-4.
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Jing, H, Nikafshan Rad, H, Hasanipanah, M, Jahed Armaghani, D & Qasem, SN 2021, 'Design and implementation of a new tuned hybrid intelligent model to predict the uniaxial compressive strength of the rock using SFS-ANFIS', Engineering with Computers, vol. 37, no. 4, pp. 2717-2734.
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Jinqiang Liu, JL, Jinqiang Liu, YL, Yining Liu, LC & Lei Cui, SY 2021, 'MSAI: Masking Sensitive Area of Image on IoT Cameras', 網際網路技術學刊, vol. 22, no. 7, pp. 1553-1561.
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Johnston, E, Szabo, PSB & Bennett, NS 2021, 'Cooling silicon photovoltaic cells using finned heat sinks and the effect of inclination angle', Thermal Science and Engineering Progress, vol. 23, pp. 100902-100902.
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Jones, GT, Siwakoti, YP & Rogers, DJ 2021, 'Active Gate Drive to Increase the Power Capacity of Hard-Switched IGBTs', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 2, pp. 2247-2257.
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Jothiramalingam, R, Jude, A, Patan, R, Ramachandran, M, Duraisamy, JH & Gandomi, AH 2021, 'Machine learning-based left ventricular hypertrophy detection using multi-lead ECG signal', Neural Computing and Applications, vol. 33, no. 9, pp. 4445-4455.
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This work proposes a novel method for the detection of Left Ventricular Hypertrophy (LVH) from a multi-lead ECG signal. Left Ventricle walls become thick due to prolonged hypertension which may fail to pump heart effectively. The imaging techniques can be used as an alternative diagnose LVH; however, they are more expensive and time-consuming than proposed LVH. To overcome this issue, an algorithm to the diagnosis of LVH using ECG signal based on machine learning techniques were designed. In LVH detection, the pathological attributes such as R wave, S wave, inversion of QRS complex, changes in ST segment noticed in the ECG signal. This clinical information extracted as a feature by applying continuous wavelet transform. The signals were reconstructed with the frequency between 10 and 50 Hz from the wavelet. This followed by the detection of R wave and S wave peaks to obtain the relevant LVH diagnostic features. The Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Ensemble of Bagged Tree, AdaBoost classifiers were employed and the results are compared with four neural network classifiers including Multilayer Perceptron (MLP), Scaled Conjugate Gradient Backpropagation Neural Network (SCG NN), Levenberg–Marquardt Neural Network (LMNN) and Resilient Backpropagation Neural network (RPROP). The data source includes Left Ventricular Hypertrophy and healthy ECG signal from PTB diagnostic ECG database and St Petersburg INCART 12-Lead Arrhythmia Database. The results revealed that the proposed work can diagnose LVH successfully using neural network classifiers. The accuracy in detecting LVH is 86.6%, 84.4%, 93.3%,75.6%, 95.6%, 97.8%, 97.8%, 88.9% using SVM, KNN, Ensemble of Bagged Tree, AdaBoost, MLP, SCG NN, LMNN and RPROP classifiers, respectively.
Ju, F, Yuan, X, Li, B, Luo, X, Wu, H, Yang, T & Sun, H 2021, 'The enlargement rate of ventricular septal rupture is a risk factor for 30-day mortality in patients with delayed surgery', Annals of Translational Medicine, vol. 9, no. 24, pp. 1786-1786.
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Ju, M, Ding, C, Guo, CA, Ren, W & Tao, D 2021, 'IDRLP: Image Dehazing Using Region Line Prior', IEEE Transactions on Image Processing, vol. 30, no. 99, pp. 9043-9057.
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In this work, a novel and ultra-robust single image dehazing method called IDRLP is proposed. It is observed that when an image is divided into n regions, with each region having a similar scene depth, the brightness of both the hazy image and its haze-free correspondence are positively related with the scene depth. Based on this observation, this work determines that the hazy input and its haze-free correspondence exhibit a quasi-linear relationship after performing this region segmentation, which is named as region line prior (RLP). By combining RLP and the atmospheric scattering model (ASM), a recovery formula (RF) can be easily obtained with only two unknown parameters, i.e., the slope of the linear function and the atmospheric light. A 2D joint optimization function considering two constraints is then designed to seek the solution of RF. Unlike other comparable works, this 'joint optimization' strategy makes efficient use of the information across the entire image, leading to more accurate results with ultra-high robustness. Finally, a guided filter is introduced in RF to eliminate the adverse interference caused by the region segmentation. The proposed RLP and IDRLP are evaluated from various perspectives and compared with related state-of-the-art techniques. Extensive analysis verifies the superiority of IDRLP over state-of-the-art image dehazing techniques in terms of both the recovery quality and efficiency. A software release is available at https://sites.google.com/site/renwenqi888/.
Ju, M, Ding, C, Ren, W, Yang, Y, Zhang, D & Guo, YJ 2021, 'IDE: Image Dehazing and Exposure Using an Enhanced Atmospheric Scattering Model', IEEE Transactions on Image Processing, vol. 30, pp. 2180-2192.
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Atmospheric scattering model (ASM) is one of the most widely used model to describe the imaging processing of hazy images. However, we found that ASM has an intrinsic limitation which leads to a dim effect in the recovered results. In this paper, by introducing a new parameter, i.e., light absorption coefficient, into ASM, an enhanced ASM (EASM) is attained, which can address the dim effect and better model outdoor hazy scenes. Relying on this EASM, a simple yet effective gray-world-assumption-based technique called IDE is then developed to enhance the visibility of hazy images. Experimental results show that IDE eliminates the dim effect and exhibits excellent dehazing performance. It is worth mentioning that IDE does not require any training process or extra information related to scene depth, which makes it very fast and robust. Moreover, the global stretch strategy used in IDE can effectively avoid some undesirable effects in recovery results, e.g., over-enhancement, over-saturation, and mist residue, etc. Comparison between the proposed IDE and other state-of-the-art techniques reveals the superiority of IDE in terms of both dehazing quality and efficiency over all the comparable techniques.
Ju, R, Zhou, P, Wen, S, Wei, W, Xue, Y, Huang, X & Yang, X 2021, '3D-CNN-SPP: A Patient Risk Prediction System From Electronic Health Records via 3D CNN and Spatial Pyramid Pooling', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 5, no. 2, pp. 247-261.
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IEEE The problem of extracting useful clinical representations from longitudinal electronic health record (EHR) data, also known as the computational phenotyping problem, is an important yet challenging task in the health-care academia and industry. Recent progress in the design and applications of deep learning methods has shown promising results towards solving this problem. In this paper, we propose 3D-CNN-SPP (3D Convolutional Neural Networks and Spatial Pyramid Pooling), a novel patient risk prediction system, to investigate the application of deep neural networks in modeling longitudinal EHR data. Particularly, we propose a 3D CNN structure, which is featured by SPP. Compared with 2D CNN methods, our proposed method can capture the complex relationships in EHRs more effectively and efficiently. Furthermore, previous works handle the issue of variable length in patient records by padding zeros to all vectors so that they have a fixed length. In our work, the proposed spatial pyramid pooling divides the records into several length sections for respective pooling processing, hence handling the variable length problem easily and naturally. We take heart failure and diabetes as examples to test the performance of the system, and the experiment results demonstrate great effectiveness in patient risk prediction, compared with several strong baselines.
Jung, MC, Chai, R, Zheng, J & Nguyen, H 2021, 'Sparse Gaussian process regression in real-time myoelectric control', International Journal of Modelling, Identification and Control, vol. 39, no. 1, pp. 51-51.
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Kalam, MA, Davis, TP, Islam, MA, Islam, S, Kittle, BL & Casas, MP 2021, 'Exploring behavioral determinants of handwashing with soap after defecation in an urban setting in Bangladesh: findings from a barrier analysis', Journal of Water, Sanitation and Hygiene for Development, vol. 11, no. 6, pp. 1006-1015.
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Abstract Social and behavior change (SBC) has long been recognized as a necessary step in the promotion of handwashing with soap (HHWS), and identifying the barriers and enablers of this behavior are key to increasing its adoption. Based on the health belief model (HBM), the theory of reasoned action (TRA) and other behavioral models, this barrier analysis study was conducted to identify the barriers and enablers of HWWS after defecation in an urban setting in Bangladesh. We conducted interviews with 45 adults who washed their hands with soap after defecation (doers) and compared them to 45 adults who did not (non-doers). The analysis showed that the main barriers of HWWS after defecation were related to perceived self-efficacy, difficulty to remember to buy soap, access to low-cost soap, low perceived severity of diarrhea, and not believing that HWWS would reduce diarrhea. Believing that it is Allah's will when one gets diarrhea was mentioned more frequently by the non-doers, while feeling clean and keeping free from illness were reported as benefits of HWWS significantly by the doers. The results suggest that an SBC strategy that addresses these key barriers and enablers would be more effective in promoting the adoption of HWWS.
Kaliaraj, GS, Vishwakarma, V, Dawn, SS, Karthik, A, Vigneshwaran, S & Naidu, GD 2021, 'Reduction of sulphate reducing bacterial survival by Cu-Ni, Zn-Ni and Cu-Zn-Ni coatings using electroless plating technique for oil/diesel pipeline applications', Materials Today: Proceedings, vol. 45, pp. 6804-6806.
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Kallam, S, Patan, R, Ramana, TV & Gandomi, AH 2021, 'Linear Weighted Regression and Energy-Aware Greedy Scheduling for Heterogeneous Big Data', Electronics, vol. 10, no. 5, pp. 554-554.
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Data are presently being produced at an increased speed in different formats, which complicates the design, processing, and evaluation of the data. The MapReduce algorithm is a distributed file system that is used for big data parallel processing. Current implementations of MapReduce assist in data locality along with robustness. In this study, a linear weighted regression and energy-aware greedy scheduling (LWR-EGS) method were combined to handle big data. The LWR-EGS method initially selects tasks for an assignment and then selects the best available machine to identify an optimal solution. With this objective, first, the problem was modeled as an integer linear weighted regression program to choose tasks for the assignment. Then, the best available machines were selected to find the optimal solution. In this manner, the optimization of resources is said to have taken place. Then, an energy efficiency-aware greedy scheduling algorithm was presented to select a position for each task to minimize the total energy consumption of the MapReduce job for big data applications in heterogeneous environments without a significant performance loss. To evaluate the performance, the LWR-EGS method was compared with two related approaches via MapReduce. The experimental results showed that the LWR-EGS method effectively reduced the total energy consumption without producing large scheduling overheads. Moreover, the method also reduced the execution time when compared to state-of-the-art methods. The LWR-EGS method reduced the energy consumption, average processing time, and scheduling overhead by 16%, 20%, and 22%, respectively, compared to existing methods.
Kamal, MS, Northcote, A, Chowdhury, L, Dey, N, Crespo, RG & Herrera-Viedma, E 2021, 'Alzheimer’s Patient Analysis Using Image and Gene Expression Data and Explainable-AI to Present Associated Genes', IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-7.
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Kamali, N, Saberi, M, Sadeghipour, A & Tarnian, F 2021, 'Effect of Different Concentrations of Titanium Dioxide Nanoparticles on Germination and Early Growth of Five Desert Plant Species', Ecopersia, vol. 9, no. 1, pp. 53-59.
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Aims Studying the effects of nanoparticles on living organisms seems to be necessary, especially in plants as the first trophic level. Thus the phytotoxicity of different concentrations of nano-TiO2 on five desert plant species was investigated in the present study. Materials & Methods The phytotoxicity of different concentrations (0, 10, 100, 500, 1500mgl-1) of nano-TiO2 on five desert plant species of Halothamnus glaucus Botsch, Haloxylon aphyllum L., Nitraria schoberi L., Zygophyllum eurypterum Boiss. & Buhse, Halocnemum strobilaceum were investigated using seed germination percentage, radicle, and plumule elongation measurement. Experiments were conducted based on a completely randomized design with four replications. Findings Outcomes of the study demonstrated that the application of nano-TiO2 had no adverse effect on germination at low concentrations (up to 500mgl-1), it also increased the germination of H. aphyllum (72 to 88%). The concentration of 1500mgl-1 had a negative effect on germination and radicle growth of three species of N. schoberi (decrease in germination from 32 to 20% and radicle length from 13.85 to 10.68cm), H. aphyllum (decrease in germination from 72 to 44% and radicle length from 6.105 to 4.03cm). Conclusion Generally, in most plants, low concentrations of nano-Tio2 did not significantly affect germination and seedling growth, but in high concentrations (1500mgl-1) due to toxicity effect, germination and seedling growth were reduced. Therefore, in using nanoparticles, attention to dosage, which is useful and not causes toxicity, is significant.
Kamranifar, M, Al-Musawi, TJ, Amarzadeh, M, Hosseinzadeh, A, Nasseh, N, Qutob, M & Arghavan, FS 2021, 'Quick adsorption followed by lengthy photodegradation using FeNi3@SiO2@ZnO: A promising method for complete removal of penicillin G from wastewater', Journal of Water Process Engineering, vol. 40, pp. 101940-101940.
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In this study, FeNi @SiO @ZnO nanoparticles were prepared via coprecipitation and sol–gel methods and were used as both PNG adsorbent and photodegradation catalyst. Thereafter, an aqueous solution containing penicillin G (PNG) was subjected to adsorption for 20 min followed by photodegradation for 200 min. To optimize the treatment method, the PNG removal efficiencies of the adsorption and photodegradation processes were measured under different experimental conditions, and it was determined that the increase in FeNi @SiO @ZnO nanoparticle concentration from 0.005 to 1 g/L favored adsorption but hindered photodegradation, as PNG removal efficiency noticeably decreased after increasing the catalyst concentration beyond 0.01 g/L. The results revealed that a PNG removal efficiency of 100 % could be achieved after 220 min of successive adsorption and photodegradation at a pH of 5, FeNi @SiO @ZnO concentration of 0.01 g/L, PNG concentration of 10 mg/L, and H O concentration of 150 mg/L. Analysis of the PNG photodegradation mechanism demonstrated that the superoxide anion radicals generated during photodegradation played a major role in PNG degradation. FeNi @SiO @ZnO is a sustainable adsorbent/catalyst because it can be reused for six consecutive treatment cycles with minor losses in efficiency (<3 %) and quantity (<1 %). Our results indicated that the prepared FeNi @SiO @ZnO nanoparticles were highly effective treatment agents and presented great practical application potential for the treatment of PNG-laden wastewater using adsorption–photodegradation. 3 2 3 2 3 2 2 2 3 2 3 2
Kan, ME, Indraratna, B & Rujikiatkamjorn, C 2021, 'On numerical simulation of vertical drains using linear 1-dimensional drain elements', Computers and Geotechnics, vol. 132, pp. 103960-103960.
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Kan, WH, Huang, S, Man, Z, Yang, L, Huang, A, Chang, L, Nadot, Y, Cairney, JM & Proust, G 2021, 'Effect of T6 treatment on additively-manufactured AlSi10Mg sliding against ceramic and steel', Wear, vol. 482-483, pp. 203961-203961.
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Karaosmanoglu, S, Zhou, M, Shi, B, Zhang, X, Williams, GR & Chen, X 2021, 'Carrier-free nanodrugs for safe and effective cancer treatment', Journal of Controlled Release, vol. 329, pp. 805-832.
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Karimi, F, Green, D, Matous, P, Varvarigos, M & Khalilpour, KR 2021, 'Network of networks: A bibliometric analysis', Physica D: Nonlinear Phenomena, vol. 421, pp. 132889-132889.
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This study explores the evolving structure of the rising field of “network of networks” (NoN). Reviewing publications dating back to 1931, we describe the evolution of major NoN research themes in different scientific disciplines and the gradual emergence of an integrated field. We analyse the co-occurrence networks of keywords used in all 7818 scientific publications in Scopus database that mention NoN and other related terms (i.e., “interconnected networks”, “multilayer networks”, “multiplex networks”, “interdependent networks”, “multinetworks”, “multilevel networks”, and “multidimensional networks”). The results show that the NoN began to form as a field mainly in the 1990s around research on neural networks. Diverse aspects of NoN research, indicated by dominant keywords such as “interconnection”, “multilayer”, and “interdependence”, gradually spread to computer and physical sciences. As of 2018, network interdependence – with its application in network resilience and prevention of cascading failure – seems to be one of the key topics attracting broad academic attention. Another noteworthy observation is the emergence of a distinct cluster of terms relevant to nanoscience and nanotechnology. It is envisaged from the analysis that NoN concepts will develop stronger ties with nanoscience with increasing understanding and data acquisition from the molecular, atomic, and subatomic levels.
Karimi, M, Croaker, P, Skvortsov, A, Maxit, L & Kirby, R 2021, 'Simulation of airfoil surface pressure due to incident turbulence using realizations of uncorrelated wall plane waves', The Journal of the Acoustical Society of America, vol. 149, no. 2, pp. 1085-1096.
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A numerical technique is proposed for synthesizing realizations of airfoil surface pressure induced by incoming turbulence. In this approach, realization of the surface pressure field is expressed as a set of uncorrelated wall plane waves. The amplitude of these plane waves is determined from the power spectrum density function of the incoming upwash velocity fluctuation and the airfoil aeroacoustic transfer function. The auto-spectrum of the surface pressure is obtained from an ensemble average of different realizations. The numerical technique is computationally efficient as it rapidly converges using a relatively small number of realizations. The surface pressures for different airfoils excited by incoming turbulence are numerically predicted, and the results are compared with experimental data in the literature. Further, the unsteady force exerted on an airfoil due to the airfoil-turbulence interaction is also computed, and it is shown to be in very good agreement with analytical results.
Karki, D, Far, H & Saleh, A 2021, 'Numerical studies into factors affecting structural behaviour of composite cold-formed steel and timber flooring systems', Journal of Building Engineering, vol. 44, pp. 102692-102692.
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Lightweight flooring system made up of cold-formed steel joist, and timber floorboard is widespread but the benefits of composite action that arise due to the interaction of top flange of cold-formed steel joist and the bottom surface of timber floorboard as a result of mobilising the shear connection are not considered in their design. A three-dimensional (3D) finite element model was developed and validated against the experimental results for cold-formed steel and particle board flooring system. The validated numerical model was used for parametric studies to investigate the influence of various factors that affect the structural behaviour of the composite flooring system. The results from the parametric studies showed that higher strength and stiffness values of engineered timber product, as well as their increased thickness, enhances the moment capacity and stiffness of the flooring system. The reduction in the spacing of the cold-formed steel joist was found to increase the stiffness and hence the load-carrying capacity of the flooring system. The high strength to weight ratio of cold-formed steel flooring system is also demonstrated in this study. A simplified design method is proposed herein to predict flexural capacity of composite beams taking into account for the composite action. The finding in this study indicates that the design and construction of composite cold-formed steel and timber flooring system should be subjected to availability of the engineered timber product in the region,choice of timber floorboard thickness and joist spacing can be based on the ultimate strength and serviceability requirements of the flooring systems to make it cost-effective.
Kashani, AR, Chiong, R, Dhakal, S & Gandomi, AH 2021, 'Investigating bound handling schemes and parameter settings for the interior search algorithm to solve truss problems', Engineering Reports, vol. 3, no. 10, pp. 1-31.
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AbstractThe interior search algorithm (ISA) is an optimization algorithm inspired by esthetic techniques used for interior design and decoration. The algorithm has only one parameter, controlled by θ, and uses an evolutionary boundary constraint handling (BCH) strategy to keep itself within an admissible solution space while approaching the optimum. We apply the ISA to find optimal weight design of truss structures with frequency constraints. Sensitivity of the ISA's performance to the θ parameter and the BCH strategy is investigated by considering different values of θ and BCH techniques. This is the first study in the literature on the sensitivity of truss optimization problems to various BCH approaches. Moreover, we also study the impact of different BCH methods on diversification and intensification. Through extensive numerical simulations, we identified the best BCH methods that provide consistently better results for all truss problems studied, and obtained a range of θ that maximizes the ISA's performance for all problem classes studied. However, results also recommend further fine‐tuning of parameter settings for specific case studies to obtain the best results.
Kashani, AR, Chiong, R, Mirjalili, S & Gandomi, AH 2021, 'Particle Swarm Optimization Variants for Solving Geotechnical Problems: Review and Comparative Analysis', Archives of Computational Methods in Engineering, vol. 28, no. 3, pp. 1871-1927.
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© 2020, CIMNE, Barcelona, Spain. Optimization techniques have drawn much attention for solving geotechnical engineering problems in recent years. Particle swarm optimization (PSO) is one of the most widely used population-based optimizers with a wide range of applications. In this paper, we first provide a detailed review of applications of PSO on different geotechnical problems. Then, we present a comprehensive computational study using several variants of PSO to solve three specific geotechnical engineering benchmark problems: the retaining wall, shallow footing, and slope stability. Through the computational study, we aim to better understand the algorithm behavior, in particular on how to balance exploratory and exploitative mechanisms in these PSO variants. Experimental results show that, although there is no universal strategy to enhance the performance of PSO for all the problems tackled, accuracies for most of the PSO variants are significantly higher compared to the original PSO in a majority of cases.
Kashif, M, Hossain, MJ, Fernandez, E, Nizami, MSH, Ali, SMN & Sharma, V 2021, 'An Optimal Allocation of Reactive Power Capable End-User Devices for Grid Support', IEEE Systems Journal, vol. 15, no. 3, pp. 3249-3260.
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Kashyap, PK, Kumar, S, Jaiswal, A, Prasad, M & Gandomi, AH 2021, 'Towards Precision Agriculture: IoT-Enabled Intelligent Irrigation Systems Using Deep Learning Neural Network', IEEE Sensors Journal, vol. 21, no. 16, pp. 17479-17491.
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Kaw, HY, Jin, X, Liu, Y, Cai, L, Zhao, X, Wang, J, Zhou, JL, He, M & Li, D 2021, 'Gas-liquid microextraction coupled with magnetic-assisted dispersive solid-phase extraction clean-up for multi-residue pesticide analysis in fatty foods of animal origin', LWT, vol. 137, pp. 110448-110448.
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An effective sequential clean-up method by coupling gas-liquid microextraction (GLME) and magnetic-assisted dispersive solid phase extraction (d-SPE) termed as GLME-MA-d-SPE has been developed for multi-residue pesticide analysis in different fatty foods of animal origin. GLME is applied as a primary clean-up step to remove low-volatile interferences, followed by a secondary clean-up technique through adsorptive removal using d-SPE to eliminate other co-extracts like organic acids in fatty biological samples. As much as 99.3% of lipid substances were effectively eliminated by this powerful clean-up method, and the chromatographic analysis by GC-MS showed at least two orders of magnitude reduction for peaks of interference. Analytical results verified the accuracy and precision of this method with recoveries of 50 pesticides ranged from 60.5% to 119.7%, and RSDs of less than 20%. Permethrin was present in salmon, pork and egg samples, but the concentrations were within the maximum residue levels (MRLs) permitted by both national and international regulations. The GLME-MA-d-SPE technique minimizes matrix effects, and it exhibits significant potential as an analytical technique of food safety control systems for broad-spectrum screening trace-level environmental pollutants in complex biological matrices.
Ke, B, Khandelwal, M, Asteris, PG, Skentou, AD, Mamou, A & Armaghani, DJ 2021, 'Rock-Burst Occurrence Prediction Based on Optimized Naïve Bayes Models', IEEE Access, vol. 9, pp. 91347-91360.
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Ke, Z, Li, Z, Cao, Z & Liu, P 2021, 'Enhancing Transferability of Deep Reinforcement Learning-Based Variable Speed Limit Control Using Transfer Learning', IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4684-4695.
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The study aims to evaluate the performance of the transfer learning algorithm to enhance the transferability of a deep reinforcement learning-based variable speed limits (VSL) control. The Double Deep Q Network (DDQN)-based VSL control strategy is proposed for reducing total time spent (TTS) on freeways. A real merging bottleneck is developed in the simulation and considered for the VSL control as the source scenario. Three types of target scenarios are considered, including the overspeed scenarios, adverse weather scenarios, and diverse capacity drop scenarios. A stable testing demand and a fluctuating testing demand are adopted to evaluate the effects of VSL control. The results show that by updating the neural networks, the transfer learning in the DDQN-based VSL control agent successfully transfers knowledge learned in the source scenario to other target scenarios. With the transfer learning, the entire training process is shortened by 32.3% to 69.8%, while keeping a similar maximum reward level, as compared to the VSL control with full learning from scratch. With the transferred DDQN-based VSL strategy, the TTS is reduced by 26.02% to 67.37% with the stable testing demand and 21.31% to 69.98% with the fluctuating testing demand in various scenarios, respectively. The results also show that when the task similarity between the source scenario and target scenario is relatively low, the transfer learning could lead to local optimum and may not achieve the global optimal control effects.
Kelly, R, Huang, J, Poulos, H & Stewart, MG 2021, 'Geotechnical and Structural stochastic analysis of piled solar farm foundations', Computers and Geotechnics, vol. 132, pp. 103988-103988.
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Development of large scale solar farms supported by large numbers of short piles has created new challenges for engineers to address. Solar arrays are highly flexible structures and the piles can be designed to move to enable more cost effective design. The structural reliability of the above-ground pile can be assessed and probabilities of failure for different section sizes calculated. Economic analysis incorporating capital cost and whole-of-life maintenance cost can be performed to work out whether adopting smaller section sizes provide the best cost outcome. Assessment of pile movements using Monte-Carlo calculations, unsaturated soil mechanics and updating material parameters with suction have been performed. The results show that soil movements are typically larger than pile movements and that soil can slip past the pile with no pile movement when the limiting conditions occur. The results also highlight that the largest soil and pile movements occur infrequently as a result of extreme wetting or drying conditions. Structural reliability analyses showed that correlating wind speed and direction results in a lower probability of failure than if wind load is considered to be uncorrelated with wind direction. The outcomes of the assessment were sensitive to the adopted probabilistic model for pile durability. The main limitation of the analyses is that there is limited information in the literature relating to the types of probability distributions and their input parameters. This adds uncertainty to the stochastic analysis.
Kennedy, PJ, Catchpoole, DR, Tafavogh, S, Harvey, BL & Aloqaily, AA 2021, 'Feature prioritisation on big genomic data for analysing gene-gene interactions', International Journal of Bioinformatics Research and Applications, vol. 17, no. 2, pp. 158-158.
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Keshavarz, R, Lipman, J, Schreurs, DMM-P & Shariati, N 2021, 'Highly Sensitive Differential Microwave Sensor for Soil Moisture Measurement', IEEE Sensors Journal, vol. 21, no. 24, pp. 27458-27464.
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This paper presents a highly sensitive differential soil moisture sensor (DSMS) using a microstrip line loaded with triangular two-turn resonator (T2-SR) and complementary of the rectangular two-turn spiral resonator (CR2-SR), simultaneously. Volumetric Water Content (VWC) or permittivity sensing is conducted by loading the T2-SR side with dielectric samples. Two transmission notches are observed for identical loads relating to T2-SR and CR2-SR. The CR2-SR notch at 4.39 GHz is used as a reference for differential permittivity measurement method. Further, the resonance frequency of T2-SR is measured relative to the reference value. Based on this frequency difference, the permittivity of soil is calculated which is related to the soil VWC. Triangular two-turn resonator (T2-SR) resonance frequency changes from 4 to 2.38 GHz when VWC varies 0% to 30%. The sensor's operation principle is described through circuit model analysis and simulations. To validate the differential sensing concept, prototype of the designed 3-cell DSMS is fabricated and measured. The proposed sensor exhibits frequency shift of 110 MHz for 1% change at the highest soil moisture content (30%) for sandy-type soil. This work proves the differential microwave sensing concept for precision agriculture.
Keshavarz, R, Mohammadi, A & Abdipour, A 2021, 'Linearity improvement of a dual-band Doherty power amplifier using E-CRLH transmission line', AEU - International Journal of Electronics and Communications, vol. 131, pp. 153584-153584.
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Keshavarz, S, Keshavarz, R & Abdipour, A 2021, 'COMPACT ACTIVE DUPLEXER BASED ON CSRR AND INTERDIGITAL LOADED MICROSTRIP COUPLED LINES FOR LTE APPLICATION', Progress In Electromagnetics Research C, vol. 109, pp. 27-37.
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Khade, S, Gite, S, Thepade, SD, Pradhan, B & Alamri, A 2021, 'Detection of Iris Presentation Attacks Using Hybridization of Discrete Cosine Transform and Haar Transform With Machine Learning Classifiers and Ensembles', IEEE Access, vol. 9, no. 99, pp. 169231-169249.
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Iris biometric identification allows for contactless authentication, which helps to avoid the transmission of diseases like COVID-19. Biometric systems become unstable and hazardous due to spoofing attacks involving contact lenses, replayed video, cadaver iris, synthetic Iris, and printed iris. This work demonstrates the iris presentation attacks detection (Iris-PAD) approach that uses fragmental coefficients of transform iris images as features obtained using Discrete Cosine Transform (DCT), Haar Transform, and hybrid Transform. In experimental validations of the proposed method, three main types of feature creation are investigated. The extracted features are utilized for training seven different machine learning classifiers alias Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), and decision tree(J48) with ensembles of SVM+RF+NB, SVM+RF+RT, and RF+SVM+MLP (multi-layer perceptron) for proposed iris liveness detection. The proposed iris liveness detection variants are evaluated using various statistical measures: accuracy, Attack Presentation Classification Error Rate (APCER), Normal Presentation Classification Error Rate (NPCER), Average Classification Error Rate (ACER). Six standard datasets are used in the investigations. Total nine iris spoofing attacks are getting identified in the proposed method. Among all investigated variations of proposed iris-PAD methods, the 4 ×4 of fragmental coefficients of a Hybrid transformed iris image with RF algorithm have shown superior iris liveness detection with 99.95% accuracy. The proposed hybridization of transform for features extraction has demonstrated the ability to identify all nine types of iris spoofing attacks and proved it robust. The proposed method offers exceptional performances against the Synthetic iris spoofing images by using a random forest classifier. Machine learning has massive potential in a similar domain and could be explored further based on the research requirements.
Khan, AA, Abolhasan, M, Ni, W, Lipman, J & Jamalipour, A 2021, 'An End-to-End (E2E) Network Slicing Framework for 5G Vehicular Ad-Hoc Networks', IEEE Transactions on Vehicular Technology, vol. 70, no. 7, pp. 7103-7112.
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Network slicing is emerging as a promising solution for end-to-end resource management and orchestration together with Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies. In this paper, a comprehensive network slicing framework is presented to achieve end-to-end (E2E) QoS provisioning among customized services in 5G-driven VANETs. The proposed scheme manages the cooperation of both RAN and Core Network (CN), using SDN, NFV and Edge Computing technologies. Furthermore, a dynamic radio resource slice optimization scheme is formulated mathematically, that handles a mixture of mission-critical and best effort traffic, by delivering the QoS provisioning of Ultra-reliability and low latency. The proposed scheme adjusts the optimal bandwidth slicing and dynamically adapts to instantaneous network load conditions in a way that a targeted performance is guaranteed. The problem is solved using a Genetic Algorithm (GA) and results are compared with the previously proposed 5 G VANET architecture. Simulation reveal that the proposed slicing framework is able to optimize resources and deliver on the key performance metrics for mission critical communication.
Khan, AUH, Liu, Y, Naidu, R, Fang, C, Dharmarajan, R & Shon, H 2021, 'Interactions between zinc oxide nanoparticles and hexabromocyclododecane in simulated waters', Environmental Technology & Innovation, vol. 24, pp. 102078-102078.
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The zinc oxide nanoparticles (ZnO-NPs) have been increasingly applied in industries and consumer products, causing release of these nanoparticles in environments. The behaviour of ZnO-NPs in the water systems is complicated due to the presence of different cations, anions, organic substances (e.g. humic acid HA) and other organic pollutants (e.g. commonly used brominated flame retardant, BFR). In particular, the aggregation and alteration of these nanoparticles can be influenced by co-existence contaminants. In this study, the interactions between hexabromocyclododecane (HBCD) and ZnO-NPs were investigated for the physicochemical properties and colloidal stability changes in various simulated waters. This is significant to understand the fate and behaviour of ZnO-NPs at environmental relevant conditions. The surface chemistry and particle size distribution (PSD) of ZnO-NPs with and without the existence of HBCD, HA and electrolytes (NaCl, CaCl2 and MgCl2) after different periods (1 and 3 weeks) were investigated at pH 7.00 ± 0.02. The size of the ZnO-NPs increased from nanometres to micrometres with the addition of numerous concentrations of HBCD, HA, and cations and their mixtures. The zeta potential of ZnO-NPs increased upon addition of HBCD, HA and electrolytes indicating a more stable agglomeration form while less agglomeration was observed in the ZnO-NPs and HA suspension after 3 weeks. Hydrophobic and electrostatic interactions, van der Waals forces, including hydrogen bonding and cation bridging could be potential interactive driving forces. The results indicated agglomeration of ZnO-NPs in the existence of organic substances, salts and contaminants, thus sedimentation and precipitation are promising under salty surface water/sea water.
Khan, HM, Iqbal, T, Mujtaba, MA, Soudagar, MEM, Veza, I & Fattah, IMR 2021, 'Microwave Assisted Biodiesel Production Using Heterogeneous Catalysts', Energies, vol. 14, no. 23, pp. 8135-8135.
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As a promising renewable fuel, biodiesel has gained worldwide attention to replace fossil-derived mineral diesel due to the threats concerning the depletion of fossil reserves and ecological constraints. Biodiesel production via transesterification involves using homogeneous, heterogeneous and enzymatic catalysts to speed up the reaction. The usage of heterogeneous catalysts over homogeneous catalysts are considered more advantageous and cost-effective. Therefore, several heterogeneous catalysts have been developed from variable sources to make the overall production process economical. After achieving optimum performance of these catalysts and chemical processes, the research has been directed in other perspectives, such as the application of non-conventional methods such as microwave, ultrasonic, plasma heating etc, aiming to enhance the efficiency of the overall process. This mini review is targeted to focus on the research carried out up to this date on microwave-supported heterogeneously catalysed biodiesel production. It discusses the phenomenon of microwave heating, synthesis techniques for heterogeneous catalysts, microwave mediated transesterification reaction using solid catalysts, special thermal effects of microwaves and parametric optimisation under microwave heating. The review shows that using microwave technology on the heterogeneously catalysed transesterification process greatly decreases reaction times (5–60 min) while maintaining or improving catalytic activity (>90%) when compared to traditional heating.
Khan, HM, Iqbal, T, Yasin, S, Irfan, M, Kazmi, M, Fayaz, H, Mujtaba, MA, Ali, CH, Kalam, MA, Soudagar, MEM & Ullah, N 2021, 'Production and utilization aspects of waste cooking oil based biodiesel in Pakistan', Alexandria Engineering Journal, vol. 60, no. 6, pp. 5831-5849.
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Excessive fuel demand thrusts the Pakistani government to import large volumes of fuel from foreign sources, creating adverse effects on the country's economy. Therefore, exploring an alternative to fossil fuels is unavoidable. The option of environmentally friendly fuel like biodiesel produced from indigenous waste is an additional bonus for the populous developing country like Pakistan where likelihood of waste generation is huge. There exists a potential option for sustainable biodiesel production utilizing excessive waste cooking oil available in the country which otherwise is an ecological burden. The present work is focused to sturdily vindicate the appropriateness of waste cooking oil-based biodiesel generation and utilization in Pakistan through SWOT-AHP, TOWS and PESTLE analysis. The prioritization of SWOT through AHP in view of experts’ perception displayed the strengths and opportunities in highest group priority values (Strengths: 0.51, Opportunities: 0.29). Furthermore, TOWS analysis suggests promising strategies for the sustainable implementation of commercial aspect of waste oil-based biodiesel in Pakistan. Political, Economic, Social, Technological, Legal and Environmental (PESTLE) analysis favors the strengths and opportunities factors of SWOT and TOWS strategies for the application of waste cooking oil based biodiesel in country. At the end, regional recommendations have been provided for the implementation of biodiesel production scenario in country.
Khan, HU, Niazi, M, El-Attar, M, Ikram, N, Khan, SU & Gill, AQ 2021, 'Empirical Investigation of Critical Requirements Engineering Practices for Global Software Development.', IEEE Access, vol. 9, pp. 93593-93613.
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There is a need to identify requirements engineering (RE) practices that are important to global software development (GSD) project success. The objective of this paper is to report our recent empirical study results which aimed to identify the RE practices that are important to GSD projects. This study used an online survey questionnaire to elicit data from 56 RE experts of GSD projects. The survey included 66 RE practices identified by Sommerville et al. for non-GSD projects. The participants were asked to rank each RE practice on a four-point scale to determine the degree of importance of each practice in the context of GSD projects. This research identified a set of six key RE practices that mainly focuses on GSD project stakeholders, scope, standards and requirements traceability management. One common theme that is evident from the RE experts' feedback analysis is the standardization of requirements documents to reduce requirements inconsistencies and improve communication in diverse and distributed GSD project environments Our results show that not all 66 RE best practices are important for GSD projects. We believe that a good understanding of the identified RE practices is vital in developing and implementing the situation-specific RE processes for GSD projects.
Khan, JA, Vu, MT & Nghiem, LD 2021, 'A preliminary assessment of forward osmosis to extract water from rumen fluid for artificial saliva', Case Studies in Chemical and Environmental Engineering, vol. 3, pp. 100095-100095.
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Khan, MNH, Siwakoti, YP, Scott, MJ, Li, L, Khan, SA, Lu, DD-C, Barzegarkhoo, R, Sidorski, F, Blaabjerg, F & Hasan, SU 2021, 'A Common Grounded Type Dual-Mode Five-Level Transformerless Inverter for Photovoltaic Applications.', IEEE Trans. Ind. Electron., vol. 68, no. 10, pp. 9742-9754.
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This article presents a novel dual-mode five-level common grounded type (5L-DM-CGT) transformerless inverter topology for a medium-power application with a wide input voltage range (200–400 V). It consists of nine semiconductor switches, two inner flying-capacitors, and a small LC filter at the output side. Due to the direct connection of the negative terminal of the photovoltaic to the neutral point of the grid, there is no leakage current in the 5L-DM-CGT. Depending on the magnitude of the input voltage, the converter can operate in both buck and boost mode to produce the same ac output voltage. The theoretical analysis shows the advantages of the dual-mode inverter for various industrial applications. Finally, the laboratory test results are presented to verify the theoretical analysis. Measurement results show that the proposed inverter rated at 1 kW has around 97±1% efficiency over a wide range of load with a peak efficiency of 98.96% at 130 VA in buck mode and peak efficiency of 99% at 122 VA in boost mode
Khan, P, Khan, Y, Kumar, S, Khan, MS & Gandomi, AH 2021, 'HVD-LSTM based recognition of epileptic seizures and normal human activity', Computers in Biology and Medicine, vol. 136, pp. 104684-104684.
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In this paper, we detect the occurrence of epileptic seizures in patients as well as activities namely stand, walk, and exercise in healthy persons, leveraging EEG (electroencephalogram) signals. Using Hilbert vibration decomposition (HVD) on non-linear and non-stationary EEG signal, we obtain multiple monocomponents varying in terms of amplitude and frequency. After decomposition, we extract features from the monocomponent matrix of the EEG signals. The instantaneous amplitude of the HVD monocomponents varies because of the motion artifacts present in EEG signals. Hence, the acquired statistical features from the instantaneous amplitude help in identifying the epileptic seizures and the normal human activities. The features selected by correlation-based Q-score are classified using an LSTM (Long Short Term Memory) based deep learning model in which the feature-based weight update maximizes the classification accuracy. For epilepsy diagnosis using the Bonn dataset and activity recognition leveraging our Sensor Networks Research Lab (SNRL) data, we achieve testing classification accuracies of 96.00% and 83.30% respectively through our proposed method.
Khan, S, Hussain, FK & Hussain, OK 2021, 'Guaranteeing end-to-end QoS provisioning in SOA based SDN architecture: A survey and Open Issues', Future Generation Computer Systems, vol. 119, pp. 176-187.
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Ensuring end-to-end Quality of Services (QoS) is a challenging aspect in traditional network architectures. Software-Defined Network (SDN), as the new norm of the network, has ascended in response to a traditional network's limitations. SDN's benefits are its ability to provide a global networking view, programmability, decouple the data plane with the control plane. Integrating SDN architecture with Service-Oriented Architecture (SOA) paradigm brings a novel network-based notion for service delivery. However, it also introduces new challenges for maintaining the QoS in these networks. Researchers from both academia and industry have proposed and developed several resolutions for QoS management in SDNs. However, gaps still exist in developing and applying such resolutions for QoS management in SOA-based SDNs. This review paper aims to identify these gaps by representing a sketch of the effectiveness of the existing QoS management techniques in SOA-based SDNs. We first identify the four different requirements that QoS management techniques need to meet to be applied in SOA-based SDNs. We then categorize the relevant QoS management approaches into five main categories of QoS based controller design, Resource allocation-based approach, Queue scheduling and management-based approach, QoS-driven optimal routing, and Service Level Agreement (SLA) based quality management in SDN. We then compare the working of techniques in each category against the identified requirements for guaranteeing end-to-end QoS provisioning in SOA based SDN architecture and present directions for future research.
Khan, S, Solano-Paez, P, Suwal, T, Lu, M, Al-Karmi, S, Ho, B, Mumal, I, Shago, M, Hoffman, LM, Dodgshun, A, Nobusawa, S, Tabori, U, Bartels, U, Ziegler, DS, Hansford, JR, Ramaswamy, V, Hawkins, C, Dufour, C, André, N, Bouffet, E, Huang, A, Gonzalez CV, A, Stephens, D, Leary, S, Marrano, P, Fonseca, A, Thacker, N, Li, BK, Lindsay, HB, Lassaletta, A, Bendel, AE, Moertel, C, Morales La Madrid, A, Santa-Maria, V, Lavarino, C, Rivas, E, Perreault, S, Ellezam, B, Weil, AG, Jabado, N, Oviedo, A, Yalon-Oren, M, Amariglio, L, Toledano, H, Dvir, R, Loukides, J, Van Meter, TE, Nakamura, H, Wong, T-T, Wu, K-S, Cheng, C-J, Ra, Y-S, La Spina, M, Massimi, L, Buccoliero, AM, Reddy, A, Li, R, Gillespie, GY, Adamek, D, Fangusaro, J, Scharnhorst, D, Torkildson, J, Johnston, D, Michaud, J, LafayCousin, L, Chan, J, Van Landeghem, F, Wilson, B, Camelo-Piragua, S, Kabbara, N, Boutarbouch, M, Hanson, D, Jacobsen, C, Wright, K, Vibhakar, R, Levy, JM, Wang, Y, Catchpoole, D, Gerber, N, Grotzer, MA, Shen, V, Plant, A, Dunham, C, Joao Gil da Costa, M, Ramanujachar, R, Raabe, E, Rubens, J, Phillips, J, Gupta, N, Demir, HA, Dahl, C, Jorgensen, M, Hwang, EI, Packer, RJ, Smith, A, Tan, E, Low, S, Lu, J-Q, Ng, H-K, Kresak, JL, Gururangan, S, Pomeroy, SL, Sirachainan, N, Hongeng, S, Magimairajan, V, Sinha, R, Mushtaq, N, Antony, R, Sato, M, Samuel, D, Zapotocky, M, Afzal, S, Walter, A, Tihan, T, Tsang, DS, Gajjar, A, Wood, P, Cain, JE, Downie, PA, Gottardo, N, Branson, H, Laughlin, S, Ertl-Wagner, B, Kulkarni, AV, Taylor, MD, Drake, J, Ibrahim, GM, Dirks, PB, Rutka, JT, Somers, GR, Hazrati, L-N, Bourdeaut, F, Padovani, L, Grundy, RG, Mazewski, CM & Fouladi, M 2021, 'Clinical phenotypes and prognostic features of embryonal tumours with multi-layered rosettes: a Rare Brain Tumor Registry study', The Lancet Child & Adolescent Health, vol. 5, no. 11, pp. 800-813.
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BACKGROUND: Embryonal tumours with multi-layered rosettes (ETMRs) are a newly recognised, rare paediatric brain tumour with alterations of the C19MC microRNA locus. Due to varied diagnostic practices and scarce clinical data, disease features and determinants of outcomes for these tumours are poorly defined. We did an integrated clinicopathological and molecular analysis of primary ETMRs to define clinical phenotypes, and to identify prognostic factors of survival and key treatment modalities for this orphan disease. METHODS: Paediatric patients with primary ETMRs and tissue available for analyses were identified from the Rare Brain Tumor Consortium global registry. The institutional histopathological diagnoses were centrally re-reviewed as per the current WHO CNS tumour guidelines, using histopathological and molecular assays. Only patients with complete clinical, treatment, and survival data on Nov 30, 2019, were included in clinicopathological analyses. Among patients who received primary multi-modal curative regimens, event-free survival and overall survival were determined using Cox proportional hazard and log-rank analyses. Univariate and multivariable Cox proportional hazard regression was used to estimate hazard ratios (HRs) with 95% CIs for clinical, molecular, or treatment-related prognostic factors. FINDINGS: 159 patients had a confirmed molecular diagnosis of primary ETMRs (median age at diagnosis 26 months, IQR 18-36) and were included in our clinicopathological analysis. ETMRs were predominantly non-metastatic (94 [73%] of 128 patients), arising from multiple sites; 84 (55%) of 154 were cerebral tumours and 70 (45%) of 154 arose at sites characteristic of other brain tumours. Hallmark C19MC alterations were seen in 144 (91%) of 159 patients; 15 (9%) were ETMR not otherwise specified. In patients treated with curative intent, event-free survival was 57% (95% CI 47-68) at 6 months and 31% (21-42) at 2 years; overall survival was 29% (20-38) ...
Khan, SA, Barzegarkhoo, R, Guo, Y, Siwakoti, Y, Khan, MNH, Lu, DD-C & Zhu, J 2021, 'Topology, Modeling and Control Scheme for a new Seven-Level Inverter With Reduced DC-Link Voltage', IEEE Transactions on Energy Conversion, vol. 36, no. 4, pp. 2734-2746.
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Khan, T, Bari, G, Kang, H-J, Lee, T-G, Park, J-W, Hwang, H, Hossain, S, Mun, J, Suzuki, N, Fujishima, A, Kim, J-H, Shon, H & Jun, Y-S 2021, 'Synthesis of N-Doped TiO2 for Efficient Photocatalytic Degradation of Atmospheric NOx', Catalysts, vol. 11, no. 1, pp. 109-109.
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Titanium oxide (TiO2) is a potential photocatalyst for removing toxic NOx from the atmosphere. Its practical application is, however, significantly limited by its low absorption into visible light and a high degree of charge recombination. The overall photocatalytic activity of TiO2 remains too low since it can utilize only about 4–5% of solar energy. Nitrogen doping into the TiO2 lattice takes advantage of utilizing a wide range of solar radiation by increasing the absorption capability towards the visible light region. In this work, N-doped TiO2, referred to as TC, was synthesized by a simple co-precipitation of tri-thiocyanuric acid (TCA) with P25 followed by heat treatment at 550 degrees C. The resulting nitrogen doping increased the visible-light absorption and enhanced the separation/transfer of photo-excited charge carriers by capturing holes by reduced titanium ions. As a result, TC samples exhibited excellent photocatalytic activities of 59% and 51% in NO oxidation under UV and visible light irradiation, in which the optimum mass ratio of TCA to P25 was found to be 10.
Khanafer, D, Ibrahim, I, Yadav, S, Altaee, A, Hawari, A & Zhou, J 2021, 'Brine reject dilution with treated wastewater for indirect desalination', Journal of Cleaner Production, vol. 322, pp. 129129-129129.
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Khanafer, D, Yadav, S, Ganbat, N, Altaee, A, Zhou, J & Hawari, AH 2021, 'Performance of the Pressure Assisted Forward Osmosis-MSF Hybrid Desalination Plant', Water, vol. 13, no. 9, pp. 1245-1245.
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An osmotically driven membrane process was proposed for seawater pretreatment in a multi-stage flashing (MSF) thermal plant. Brine reject from the MSF plant was the draw solution (DS) in the forward osmosis (FO) process in order to reduce chemical use. The purpose of FO is the removal of divalent ions from seawater prior the thermal desalination. In this study, seawater at 80 g/L and 45 g/L concentrations were used as the brine reject and seawater, respectively. The temperature of the brine reject was 40 °C and of seawater was 25 °C. Commercial thin-film composite (TFC) and cellulose triacetate (CTA) membranes were evaluated for the pretreatment of seawater in the FO and the pressure-assisted FO (PAFO) processes. Experimental results showed 50% more permeation flux by increasing the feed pressure from 1 to 4 bar, and permeation flux reached 16.7 L/m2h in the PAFO process with a TFC membrane compared to 8.3 L/m2h in the PAFO process with CTA membrane. TFC membrane experienced up to 15% reduction in permeation flux after cleaning with DI water while permeation flux reduction in the CTA membrane was >6%. The maximum recovery rate was 11.5% and 8.8% in the PAFO process with TFC and CTA membrane, respectively. The maximum power consumption for the pretreatment of seawater was 0.06 kWh/m3 and 0.1 kWh/m3 for the PAFO process with a TFC and CTA membrane, respectively.
Khanh Nguyen, V, Kumar Chaudhary, D, Hari Dahal, R, Hoang Trinh, N, Kim, J, Chang, SW, Hong, Y, Duc La, D, Nguyen, XC, Hao Ngo, H, Chung, WJ & Nguyen, DD 2021, 'Review on pretreatment techniques to improve anaerobic digestion of sewage sludge', Fuel, vol. 285, pp. 119105-119105.
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Anaerobic digestion (AD) of sewage sludge is one of the most efficient, effective, and environmentally sustainable remediation techniques; however, the presence of complex floc structures, hard cell walls, and large amounts of molecular organic matter in the sludge hinder AD hydrolysis. Consequently, sewage sludge pretreatment is a prerequisite to accelerate hydrolysis and improve AD efficiency. This review focuses on pretreatment techniques for improving sewage sludge AD, which include mechanical, chemical, thermal, and biological processes. The various pretreatment process effects are discussed in terms of advantages and disadvantages, including their effectiveness, and recent achievements are reviewed for improved biogas production.
Khawaldeh, HA, Al-Soeidat, M, Farhangi, M, Lu, DD-C & Li, L 2021, 'Efficiency Improvement Scheme for PV Emulator Based on a Physical Equivalent PV-Cell Model', IEEE Access, vol. 9, pp. 83929-83939.
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Recently, a photovoltaic (PV) emulator is proposed which is based on a combination of a constant current source and a one-diode photovoltaic model. Its superior dynamic performance is compatible with that of a real PV system. Although it is power efficient at the maximum power point (MPP), it suffers from high power loss around and at the open-circuit voltage (OCV) operation condition. The PV emulator can be used for PV system analysis and testing, such as maximum power point tracking (MPPT). This paper presents a new switching circuit which is placed in parallel with the diode string to minimize the power loss. The switching circuit consists of a two-switch non-inverting buck-boost DC/DC converter. When the operating point of the PV emulator moves from the current source region to the voltage source region, the converter, which is more efficient, switches in to replace the diode string seamlessly to maintain the circuit operation of the emulator. Experimental results show that in the worst case scenario, i.e. OCV condition, the efficiency and temperature of the proposed solution reach 81.47% and 30.1 °C respectively, as compared with 2.8% and 94.2 °C respectively for the diode string only case. In terms of dynamic response, the proposed PV emulator lags the real PV panel by only 3.5 ms as compared with 120 ms by a commercial emulator under the 30% to 60% insolation change test.
Khawaldeh, HA, Al‐soeidat, M, Lu, DD & Li, L 2021, 'Simple and Fast Dynamic Photovoltaic Emulator based on a Physical Equivalent PV‐cell Model', The Journal of Engineering, vol. 2021, no. 5, pp. 276-285.
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AbstractPhotovoltaic emulators are a specific type of power electronics system to mimic the behaviour of a photovoltaic (PV) panel or array and facilitate the testing of energy systems. Existing solutions usually require sophisticated hardware design and fast computing. This paper presents a simple, reliable, and effective circuit‐based photovoltaic (PV) emulator based on the equivalent PV stacked cells. The PV emulator can be used for solar system testing and analysis, such as maximum power point tracking (MPPT) and partial shading effect. The – and – characteristic curves of the emulator have been generated by using an LTspice simulator. It is experimentally investigated and compared with a real PV panel and existing emulator products. The experiment results show good agreement with the mimicked actual PV panel. The proposed PV emulator shows a better dynamic response and shorter settling time than several benchmarked commercial products. The enhancement in the time response is due to the simplicity of the emulator, where a few power diodes and some resisters are used. In addition to simplicity, the PV emulator is very cost‐effective.
Khorshidi Paji, M, Gordan, B, Biklaryan, M, Armaghani, DJ, Zhou, J & Jamshidi, M 2021, 'Neuro-swarm and neuro-imperialism techniques to investigate the compressive strength of concrete constructed by freshwater and magnetic salty water', Measurement, vol. 182, pp. 109720-109720.
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Khosravi, K, Bordbar, M, Paryani, S, Saco, PM & Kazakis, N 2021, 'New hybrid-based approach for improving the accuracy of coastal aquifer vulnerability assessment maps', Science of The Total Environment, vol. 767, pp. 145416-145416.
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Khosravi, K, Miraki, S, Saco, PM & Farmani, R 2021, 'Short-term River streamflow modeling using Ensemble-based additive learner approach', Journal of Hydro-environment Research, vol. 39, pp. 81-91.
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Khuat, TT & Gabrys, B 2021, 'An in-depth comparison of methods handling mixed-attribute data for general fuzzy min–max neural network', Neurocomputing, vol. 464, pp. 175-202.
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A general fuzzy min–max (GFMM) neural network is one of the efficient neuro-fuzzy systems for classification problems. However, a disadvantage of most of the current learning algorithms for GFMM is that they can handle effectively numerical valued features only. Therefore, this paper provides some potential approaches to adapting GFMM learning algorithms for classification problems with mixed-type or only categorical features as they are very common in practical applications and often carry very useful information. We will compare and assess three main methods of handling datasets with mixed features, including the use of encoding methods, the combination of the GFMM model with other classifiers, and employing the specific learning algorithms for both types of features. The experimental results showed that the target and James–Stein are appropriate categorical encoding methods for learning algorithms of GFMM models, while the combination of GFMM neural networks and decision trees is a flexible way to enhance the classification performance of GFMM models on datasets with the mixed features. The learning algorithms with the mixed-type feature abilities are potential approaches to deal with mixed-attribute data in a natural way, but they need further improvement to achieve a better classification accuracy. Based on the analysis, we also identify the strong and weak points of different methods and propose potential research directions.
Khuat, TT & Le, MH 2021, 'Balanced Random Hyperboxes for Class Imbalanced Problems', IAENG International Journal of Computer Science, vol. 48, no. 2, pp. 1-11.
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A Random Hyperboxes (RH) classifier is a simple but powerful randomization-based ensemble model, including hyperbox-based classifiers used as base learners. Individual learners in this ensemble model are trained on random subspaces of both instance and feature spaces. This facet results in a flexible mechanism to form a high-performing classifier competitive with other ensemble models in the literature. Like other machine learning models, however, the RH classifier also faces inefficiency when dealing with class-imbalanced datasets. Meanwhile, data containing highly imbalanced class distributions are prevalent in practical applications. Hence, this paper proposes a new variant of the original RH model, namely Balance Random Hyperboxes (BRH), to bypass this drawback effectively. The proposed method uses an undersampling strategy to build individual learners instead of the random sampling method employed in the original RH model. The experiment conducted on software fault datasets, which show a highly class-imbalanced property, indicated the proposed method's efficiency compared to the original RH model and other ensemble models.
Kieu, BT, Unanue, IJ, Pham, SB, Phan, HX & Piccardi, M 2021, 'NeuSub: A Neural Submodular Approach for Citation Recommendation', IEEE Access, vol. 9, pp. 148459-148468.
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Kim, J, Guivant, J, Sollie, ML, Bryne, TH & Johansen, TA 2021, 'Compressed pseudo-SLAM: pseudorange-integrated compressed simultaneous localisation and mapping for unmanned aerial vehicle navigation', Journal of Navigation, vol. 74, no. 5, pp. 1091-1103.
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AbstractThis paper addresses the fusion of the pseudorange/pseudorange rate observations from the global navigation satellite system and the inertial–visual simultaneous localisation and mapping (SLAM) to achieve reliable navigation of unmanned aerial vehicles. This work extends the previous work on a simulation-based study [Kim et al. (2017). Compressed fusion of GNSS and inertial navigation with simultaneous localisation and mapping. IEEE Aerospace and Electronic Systems Magazine, 32(8), 22–36] to a real-flight dataset collected from a fixed-wing unmanned aerial vehicle platform. The dataset consists of measurements from visual landmarks, an inertial measurement unit, and pseudorange and pseudorange rates. We propose a novel all-source navigation filter, termed a compressed pseudo-SLAM, which can seamlessly integrate all available information in a computationally efficient way. In this framework, a local map is dynamically defined around the vehicle, updating the vehicle and local landmark states within the region. A global map includes the rest of the landmarks and is updated at a much lower rate by accumulating (or compressing) the local-to-global correlation information within the filter. It will show that the horizontal navigation error is effectively constrained with one satellite vehicle and one landmark observation. The computational cost will be analysed, demonstrating the efficiency of the method.
Kim, J, Light, N, Subasri, V, Young, EL, Wegman-Ostrosky, T, Barkauskas, DA, Hall, D, Lupo, PJ, Patidar, R, Maese, LD, Jones, K, Wang, M, Genome Research Laboratory, C, Tavtigian, SV, Wu, D, Shlien, A, Telfer, F, Goldenberg, A, Skapek, SX, Wei, JS, Wen, X, Catchpoole, D, Hawkins, DS, Schiffman, JD, Khan, J, Malkin, D & Stewart, DR 2021, 'Pathogenic Germline Variants in Cancer Susceptibility Genes in Children and Young Adults With Rhabdomyosarcoma', JCO Precision Oncology, vol. 5, no. 5, pp. 75-87.
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PURPOSE Rhabdomyosarcoma (RMS) is the most common pediatric soft-tissue sarcoma and accounts for 3% of all pediatric cancer. In this study, we investigated germline sequence and structural variation in a broad set of genes in two large, independent RMS cohorts. MATERIALS AND METHODS Genome sequencing of the discovery cohort (n = 273) and exome sequencing of the secondary cohort (n = 121) were conducted on germline DNA. Analyses were performed on 130 cancer susceptibility genes (CSG). Pathogenic or likely pathogenic (P/LP) variants were predicted using the American College of Medical Genetics and Genomics (ACMG) criteria. Structural variation and survival analyses were performed on the discovery cohort. RESULTS We found that 6.6%-7.7% of patients with RMS harbored P/LP variants in dominant-acting CSG. An additional approximately 1% have structural variants ( ATM, CDKN1C) in CSGs. CSG variants did not influence survival, although there was a significant correlation with an earlier age of tumor onset. There was a nonsignificant excess of P/LP variants in dominant inheritance genes in the patients with FOXO1 fusion–negative RMS patients versus the patients with FOXO1 fusion–positive RMS. We identified pathogenic germline variants in CSGs previously ( TP53, NF1, DICER1, mismatch repair genes), rarely ( BRCA2, CBL, CHEK2, SMARCA4), or never ( FGFR4) reported in RMS. Numerous genes ( TP53, BRCA2, mismatch repair) were on the ACMG Secondary Findings 2.0 list. CONCLUSION In two cohorts of patients with RMS, we identified pathogenic germline variants for which gene-specific therapies and surveillance guidelines may be beneficial. In families with a proband with an RMS-risk P/LP variant, genetic counseling and cascade testing should be ...
Kolanu, N, Brown, AS, Beech, A, Center, JR & White, CP 2021, 'Natural language processing of radiology reports for the identification of patients with fracture', Archives of Osteoporosis, vol. 16, no. 1.
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Kolekar, S, Gite, S, Pradhan, B & Kotecha, K 2021, 'Behavior Prediction of Traffic Actors for Intelligent Vehicle Using Artificial Intelligence Techniques: A Review', IEEE Access, vol. 9, pp. 135034-135058.
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Intelligent vehicle technology has made tremendous progress due to Artificial Intelligence (AI) techniques. Accurate behavior prediction of surrounding traffic actors is essential for the safe and secure navigation of the intelligent vehicle. Minor misbehavior of these vehicles on the busy roads may lead to an accident. Due to this, there is a need for vehicle behavior research work in today's era. This research article reviews traffic actors' behavior prediction techniques for intelligent vehicles to perceive, infer, and anticipate other vehicles' intentions and future actions. It identifies the key strategies and methods for AI, emerging trends, datasets, and ongoing research issues in these fields. As per the authors' knowledge, this is the first systematic literature review dedicated to the vehicle behavior study examining existing academic literature published by peer review venues between 2011 and 2021. A systematic review was undertaken to examine these papers, and five primary research questions have been addressed. The findings show that using sophisticated input representation that includes traffic rules and road geometry, artificial intelligence-based solutions applied to behavior prediction of traffic actors for intelligent vehicles have shown promising success, particularly in complex driving scenarios. Finally, the paper summarizes the most widely used approaches in behavior prediction of traffic actors for intelligent vehicles, which the authors believe serves as a foundation for future research in behavior prediction of surrounding traffic actors for secure and accurate intelligent vehicle navigation.
Kolekar, SS, Gite, SS & Pradhan, B 2021, 'Demystifying Artificial Intelligence based Behavior Prediction of Traffic Actors for Autonomous Vehicle- A Bibliometric Analysis of Trends and Techniques', Library Philosophy and Practice, vol. 2021, pp. 1-25.
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Background: The purpose of this study is to examine, using bibliometric methods, the work done on behavior prediction of traffic actors for autonomous vehicles using various artificial intelligence algorithms from 2011 to 2020. Methods: Using one of the most common databases, Scopus, numerous papers on behavior prediction of traffic actors for autonomous vehicles were retrieved. The research papers are being considered for the period from 2011 to 2020. The Scopus analyzer is used to obtain some results of the study, such as documents by year, source, and country and so on. VOSviewer Version 1.6.16 is used for the analysis of different units such as co-authorship, co-occurrences, citation analysis etc. Results: In our study, a database search outputs a total of 275 articles on behavior prediction for autonomous vehicle from 2011 to 2020. Statistical analysis and network analysis shows the maximum articles are published in the years 2019 and 2020 with United State contributed the largest number of documents. Network analysis of different parameters shows a good potential of the topic in terms of research. Conclusions: Scopus keyword search outcome has 272 articles with English language having the largest number. Authors, documents, country, affiliation etc are statically analyzed and indicates the potential of the topic. Network analysis of different parameters indicates that, there is a lot of scope to contribute in the further research in terms of advanced algorithms of computer vision, deep learning, machine learning and explainable artificial intelligence.
Koli, MNY, Afzal, MU & Esselle, KP 2021, 'Significant Bandwidth Enhancement of Radial-Line Slot Array Antennas Using a Radially Nonuniform TEM Waveguide', IEEE Transactions on Antennas and Propagation, vol. 69, no. 6, pp. 3193-3203.
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IEEE Radial line slot array (RLSA) antennas have attractive features such as high gain, high efficiency, and planar low profile, but their gain bandwidths have been limited to less than 10%. This paper presents a method to significantly increase the gain bandwidth of RLSAs to over 30%. The key to the method is the application of a non-uniform radial TEM waveguide as opposed to the radially uniform TEM waveguide used in conventional RLSAs. Hence, the condition for maximum radiation is satisfied at a wide range of frequencies by different sections of the RLSA. To demonstrate the concept, several circularly polarised RLSA designs and one prototype are presented. The measured results of the prototype demonstrate an unprecedented 3dB gain bandwidth of 27.6%, a peak gain of 27.3 dBic, 3dB axial ratio bandwidth greater than 31.1% and a 10dB return loss bandwidth greater than 34.8%. The overall measured bandwidth of the RLSA in which gain variation and axial ratio are within 3dB and return loss is greater than 10dB is from 9.7 GHz to 12.8 GHz or 27.6%. Its extremely high measured gain bandwidth product per unit area (GBP/A) of 88 indicates excellent overall performance in terms of bandwidth, gain and area.
Koli, MNY, Afzal, MU, Esselle, KP & Mehta, A 2021, 'Use of Narrower Reflection-Canceling Slots to Design Linearly Polarized Radial Line Slot Arrays With Improved Radiation Performance', IEEE Antennas and Wireless Propagation Letters, vol. 20, no. 12, pp. 2275-2279.
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Kołodziejczak-Radzimska, A, Nghiem, LD & Jesionowski, T 2021, 'Functionalized Materials as a Versatile Platform for Enzyme Immobilization in Wastewater Treatment', Current Pollution Reports, vol. 7, no. 3, pp. 263-276.
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Abstract Purpose of Review Untreated wastewater discharge can significantly and negatively impact the state of the environment. Rapid industrialization and economic development have directly contributed to land and water pollution resulting from the application of many chemicals such as organic dyes, pharmaceuticals, and industrial reagents. The removal of these chemicals before effluent discharge is crucial for environmental protection. This review aims to explore the importance of functionalized materials in the preparation of biocatalytic systems and consider their application in eliminating water pollutants. Recent Findings Wastewater treatment methods can be classified into three groups: (i) chemical (e.g., chemical oxidation and ozonation), (ii) physical (e.g., membrane separation and ion exchange), and (iii) biological processes. Biological treatment is the most widely used method due to its cost-effectiveness and eco-friendliness. In particular, the use of immobilized enzymes has recently become more attractive as a result of scientific progress in advanced material synthesis. The selection of an appropriate support plays an important role in the preparation of such biologically active systems. Recent studies have demonstrated the use of various materials for enzyme immobilization in the purification of water. Summary This review identifies and discusses different biocatalytic systems used in the enzymatic degradation of various water pollutants. Materials functionalized by specific groups can serve as good support matrices for enzyme immobilization, providing chemical and thermal stability to support catalytic reactions. Enzymatic...
Komalla, V, Mehta, M, Achi, F, Dua, K & Haghi, M 2021, 'The Potential for Phospholipids in the Treatment of Airway Inflammation: An Unexplored Solution', Current Molecular Pharmacology, vol. 14, no. 3, pp. 333-349.
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:Asthma, chronic obstructive pulmonary disease (COPD), and cystic fibrosis (CF) are major inflammatory respiratory diseases. Current mainstay therapy for asthma, and chronic obstructive pulmonary disease are corticosteroids, which have well-established side effect profiles. Phospholipids (PLs) are ubiquitous, diverse compounds with varying functions such as their structural role in the cell membrane, energy storage, and cell signaling. Recent advances in understanding PLs role as inflammatory mediators in the body as well as their widespread long-standing use as carrier molecules in drug delivery demonstrate the potential application of PLs in modulating inflammatory conditions.:This review briefly explains the main mechanisms of inflammation in chronic respiratory diseases, current anti-inflammatory treatments and areas of unmet need. The structural features, roles of endogenous and exogenous phospholipids, including their use as pharmaceutical excipients, are reviewed. Current research on the immunomodulatory properties of PLs and their potential application in inflammatory diseases is the major section of this review.:Considering the roles of PLs as inflammatory mediators and their safety profile established in pharmaceutical formulations, these small molecules demonstrate great potential as candidates in respiratory inflammation. Future studies need to focus on the immunomodulatory properties and the underlying mechanisms of PLs in respiratory inflammatory diseases.
Kordi Ghasrodashti, E & Sharma, N 2021, 'Hyperspectral image classification using an extended Auto-Encoder method', Signal Processing: Image Communication, vol. 92, pp. 116111-116111.
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Kousik, N, Natarajan, Y, Arshath Raja, R, Kallam, S, Patan, R & Gandomi, AH 2021, 'Improved salient object detection using hybrid Convolution Recurrent Neural Network', Expert Systems with Applications, vol. 166, pp. 114064-114064.
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Salient object detection is a critical and active field that aims at the detection of objects in a video, however, it draws increased attention among researchers. With increasing dynamic video data, the performance of saliency object detection method has been degrading with conventional object detection methods. The challenges lie with blurry moving targets, rapid movement of objects and background occlusion or dynamic background change on foreground regions in video frames. Such challenges result in poor saliency detection. In this paper, we design a deep learning model to address the issues, which uses a novel framework by combining the idea of Convolutional Neural Network (CNN) with Recurrent Neural Network (RNN) for video saliency detection. The proposed method aims at developing a spatiotemporal model that exploits temporal, spatial and local constraint cues to achieve global optimization. The task of finding the salient objects in benchmark dynamic video datasets is then carried out by capturing the temporal, spatial and local constraint features with the Convolution Recurrent Neural Network (CRNN). The CRNN is evaluated on benchmark datasets against conventional video salient object detection methods in terms of precision, F-measure, mean absolute error (MAE) and computational load. The experiments reveal that the CRNN model achieves improved performance than other state-of-the-art saliency models in terms of increased speed and reduced computational load.
Krätzig, O & Sick, N 2021, 'Exploring the role of entrepreneurial passion for facilitating university technology commercialization: Insights from battery research as an interdisciplinary field', Journal of Engineering and Technology Management, vol. 60, pp. 101627-101627.
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University-industry technology commercialization (UTC) from interdisciplinary environments is promising to contribute to solutions for major socio-economic challenges. However, UTC requires considerable coordination and mediation effort and thus intrinsic motivation from the involved researchers. Thus, the objective of the present study is to explore entrepreneurial passion as a means to facilitate researchers’ intrinsic motivation for UTC activities. The interdisciplinary field of battery research is used as a representative environment for the expert interview study. Drawing on qualitative content analysis, a framework is developed, which links researchers’ intrinsic motivation to respective UTC activities, resulting in three distinct UTC-promoting roles. Implications for policy makers seeking to promote UTC, for research managers responsible for the implementation of transfer projects as well as actors from industry who have an interest in collaborative R&D with public research institutions are provided.
Krishankumar, R, Nimmagadda, SS, Rani, P, Mishra, AR, Ravichandran, KS & Gandomi, AH 2021, 'Solving renewable energy source selection problems using a q-rung orthopair fuzzy-based integrated decision-making approach', Journal of Cleaner Production, vol. 279, pp. 123329-123329.
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This paper proposes an integrated decision-making framework for the systematic selection of a renewable energy source (RES) from a set of RESs based on sustainability attributes. A real case study of RES selection in Karnataka, India, using the framework is demonstrated, and the results are compared with state-of-the-art methods. The main reason for developing this framework is to handle uncertainty and vagueness effectively by reducing human intervention. Systematic selection of RESs also reduces inaccuracies and promotes rational decision-making. In this paper, q-rung orthopair fuzzy information is adopted to minimize subjective randomness by providing a flexible and generalized preference style. Further, the study found systematic approaches for imputing missing values, calculating attributes’ and decision-makers’ weights, aggregation or preferences, and prioritizing RESs, which are integrated into the framework. Comparing the proposed framework with state-of-the-art-methods shows that (i) biomass and solar are suitable RESs for the process under consideration in Karnataka, (ii) the proposed framework is consistent with state-of-the-art methods, (iii) the proposed framework is sufficiently stable even after weights of attributes and decision makers are altered, and (iv) the proposed framework produces broad and sensible rank values for efficient backup management. These results validate the significance of the proposed framework.
Krishankumar, R, Ravichandran, KS, Gandomi, AH & Kar, S 2021, 'Interval-valued probabilistic hesitant fuzzy set-based framework for group decision-making with unknown weight information', Neural Computing and Applications, vol. 33, no. 7, pp. 2445-2457.
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This paper aims at presenting a new decision framework under an interval-valued probabilistic hesitant fuzzy set (IVPHFS) context with fully unknown weight information. At first, the weights of the attributes are determined by using the interval-valued probabilistic hesitant deviation method. Later, the DMs’ weights are determined by using a recently proposed evidence theory-based Bayesian approximation method under the IVPHFS context. The preferences are aggregated by using a newly extended generalized Maclaurin symmetric mean operator under the IVPHFS context. Further, the alternatives are prioritized by using an interval-valued probabilistic hesitant complex proportional assessment method. From the proposed framework, the following significances are inferred; for example, it uses a generalized preference structure that provides ease and flexibility to the decision-makers (DMs) during preference elicitation; weights are calculated systematically to mitigate inaccuracies and subjective randomness; interrelationship among attributes are effectively captured; and alternatives are prioritized from different angles by properly considering the nature of the attributes. Finally, the applicability of the framework is validated by using green supplier selection for a leading bakery company, and from the comparison, it is observed that the framework is useful, practical and systematic for rational decision-making and robust and consistent from sensitivity analysis of weights and Spearman correlation of rank values, respectively.
Krishankumar, R, Ravichandran, KS, Liu, P, Kar, S & Gandomi, AH 2021, 'A decision framework under probabilistic hesitant fuzzy environment with probability estimation for multi-criteria decision making', Neural Computing and Applications, vol. 33, no. 14, pp. 8417-8433.
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With growing hesitation in human perception, hesitant fuzzy set, an important extension of fuzzy set, has gained much attention from the research community. The concept of HFS gives decision makers the ability to provide multiple preferences for the same instance. However, the chance of these preferences occurring is assumed to be equal, which is unreasonable in practice. To circumvent this issue, probabilistic hesitant fuzzy set (PHFS) is adopted in this work, which is an extension of hesitant fuzzy set with associated probability values. Based on the literature review on PHFS, it is evident that (i) occurrence probability of each element was not methodically calculated; (ii) hesitation was not properly captured during criteria weight calculation; (iii) interrelationship among criteria was not captured during aggregation; and (iv) broad/rational ranking of alternatives with compromise solution was lacking. Motivated by these challenges and to alleviate the same, a systematic procedure is proposed in this paper to estimate these probabilities. Additionally, in this procedure, decision makers’ preferences are aggregated using the newly proposed probabilistic hesitant fuzzy generalized Maclaurin symmetric mean operator and criteria weights are calculated using the proposed statistical variance method in the context of PHFS. A new ranking method is also proposed that extends a well-known VIKOR method to the PHFS context. Further, the practical use of the proposed decision framework is demonstrated by two examples viz., selecting a suitable coordinator for a research and development project and selection of a doctoral candidate for the supervisor position. Finally, the strength and weakness of the proposed decision framework are realized by comparing it with state-of-the-art methods.
Kularatna, N & Gunawardane, K 2021, 'Preface', pp. xiii-xiii.
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Kulkarni, AJ, Mezura-Montes, E, Wang, Y, Gandomi, AH & Krishnasamy, G 2021, 'Preface', Constraint Handling in Metaheuristics and Applications, pp. v-x.
Kumar, A, Esmaili, N & Piccardi, M 2021, 'Topic-Document Inference With the Gumbel-Softmax Distribution', IEEE Access, vol. 9, pp. 1313-1320.
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© 2013 IEEE. Topic modeling is an important application of natural language processing (NLP) that can automatically identify the set of main topics of a given, typically large, collection of documents. In addition to identifying the main topics in the given collection, topic modeling infers which combination of topics is addressed by each individual document (the so-called topic-document inference), which can be useful for their classification and organization. However, the distributional assumptions for this inference are typically restricted to the Dirichlet family which can limit the performance of the model. For this reason, in this paper we propose modeling the topic-document inference with the Gumbel-Softmax distribution, a distribution recently introduced to expand differentiability in deep networks. To set up a performing system, the proposed approach integrates Gumbel-Softmax topic-document inference in a state-of-the-art topic model based on a deep variational autoencoder. Experimental results over two probing datasets show that the proposed approach has been able to outperform the original deep variational autoencoder and other popular topic models in terms of test-set perplexity and two topic coherence measures.
Kumar, A, Kim, Y, Su, X, Fukuda, H, Naidu, G, Du, F, Vigneswaran, S, Drioli, E, Hatton, TA & Lienhard, JH 2021, 'Advances and challenges in metal ion separation from water', Trends in Chemistry, vol. 3, no. 10, pp. 819-831.
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Technologies for selective metal ion separation from water and wastewater are currently attracting strong research interest as a pathway to greater sustainability. The chemistry of metal ion separation processes is critical for understanding the mechanisms of selectivity and making the technologies viable. This paper discusses current advances and challenges in metal ion separation technologies from chemical points of view and proposes how they should be approached in the future.
Kumar, A, Naidu, G, Fukuda, H, Du, F, Vigneswaran, S, Drioli, E & Lienhard, JH 2021, 'Metals Recovery from Seawater Desalination Brines: Technologies, Opportunities, and Challenges', ACS Sustainable Chemistry & Engineering, vol. 9, no. 23, pp. 7704-7712.
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The urgent need for environmental sustainability has increasingly prompted policy makers to emphasize resource recovery from desalination brine streams. Recent research on resource recovery from waste streams has shown rising momentum with near term viability for several new technologies. In this perspective, we focus on new opportunities for metal resource recovery from seawater desalination brine, while outlining associated sustainability challenges and opportunities. The potential of metals recovery is discussed.
Kumar, A, Varadarajan, V, Kumar, A, Dadheech, P, Choudhary, SS, Kumar, VDA, Panigrahi, BK & Veluvolu, KC 2021, 'Black hole attack detection in vehicular ad-hoc network using secure AODV routing algorithm', Microprocessors and Microsystems, vol. 80, pp. 103352-103352.
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Kumar, BA, Ling, SH, Ching, PHC & Torii, S 2021, 'Guest Editorial: Artificial-intelligence-based network security and computing technologies in wireless networks.', IET Networks, vol. 10, no. 3, pp. 101-102.
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Kumar, S, Jangir, P, Tejani, GG, Premkumar, M & Alhelou, HH 2021, 'MOPGO: A New Physics-Based Multi-Objective Plasma Generation Optimizer for Solving Structural Optimization Problems', IEEE Access, vol. 9, pp. 84982-85016.
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Kumari, P, Bahadur, N, Cretin, M, Kong, L, O'Dell, LA, Merenda, A & Dumée, LF 2021, 'Electro-catalytic membrane reactors for the degradation of organic pollutants – a review', Reaction Chemistry & Engineering, vol. 6, no. 9, pp. 1508-1526.
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Electro-catalytic membrane reactor exhibiting electro-oxidation degradation of organic pollutants on anodic membrane.
Kumari, P, Bahadur, N, O'Dell, LA, Kong, L, Sadek, A, Merenda, A & Dumée, LF 2021, 'Nanoscale 2D semi-conductors – Impact of structural properties on light propagation depth and photocatalytic performance', Separation and Purification Technology, vol. 258, pp. 118011-118011.
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The performance of photocatalytic materials are largely dictated by the crystalline and optical properties of the semi-conductors. The density of photo-generated electron and hole pairs is greatly influenced by the light penetration in photocatalytic material, leading to specific degradation kinetics. In this work, the relationship between the metal oxide film thickness and the overall materials optical and photocatalytic performances are systematically established for the first time. Thin films of semiconductor metal oxides such as TiO2 and ZnO were prepared by atomic layer deposition (ALD) on stainless steel sputtered silicon wafers. The thickness of the metal oxide thin films was controlled by varying the number of deposition cycles (50–1000 cycles). The fabricated films were fully characterized to examine the change in morphology, roughness, crystallinity, optical and structural properties with varying thickness by several techniques such as Scanning Electron Microscopy (SEM), Atomic Force Microscopy (AFM), X-Ray Diffraction (XRD), Diffuse Reflectance Spectroscopy (DRS) and X-Ray Photo-electro Spectroscopy (XPS). The films were generated to yield very consistent crystallinity, roughness and light absorption properties. Critical thicknesses were observed when a plateau in photocatalytic efficiency was reached at the thickness of 31 nm in the case of the TiO2 and 89 nm thickness for ZnO films. The dependency of the thickness of nanometric ALD films on their photocatalytic efficiency results from the light diffusion and penetration within the material which was investigated through fundamentals and modelling of light-matter interaction in photocatalytic processes. This work establishes a new fundamental understanding of the operation and performance of photocatalysts for further development of advanced reactors and their scale-up.
Kundariya, N, Mohanty, SS, Varjani, S, Hao Ngo, H, W. C. Wong, J, Taherzadeh, MJ, Chang, J-S, Yong Ng, H, Kim, S-H & Bui, X-T 2021, 'A review on integrated approaches for municipal solid waste for environmental and economical relevance: Monitoring tools, technologies, and strategic innovations', Bioresource Technology, vol. 342, pp. 125982-125982.
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Rapid population growth, combined with increased industrialization, has exacerbated the issue of solid waste management. Poor management of municipal solid waste (MSW) not only has detrimental environmental consequences but also puts public health at risk and introduces several other socioeconomic problems. Many developing countries are grappling with the problem of safe disposing of large amounts of produced municipal solid waste. Unmanaged municipal solid waste pollutes the environment, so its use as a potential renewable energy source would aid in meeting both increased energy needs and waste management. This review investigates emerging strategies and monitoring tools for municipal solid waste management. Waste monitoring using high-end technologies and energy recovery from MSW has been discussed. It comprehensively covers environmental and economic relevance of waste management technologies based on innovations achieved through the integration of approaches.
Kurian, JC, Goh, DH-L & John, BM 2021, 'Organizational culture on the Facebook page of an emergency management agency: a thematic analysis', Online Information Review, vol. 45, no. 2, pp. 336-355.
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PurposeThe purpose of this study is to identify organizational cultural factors and overarching themes on emergency management evident across the Facebook page of an emergency management organization. This study also aims to understand the dimensions of social capital that influence the reputation of emergency management organization using the lens of organizational culture.Design/methodology/approachThe organizational cultural factors defined in the literature were used to classify content posted by the organization during a six-month period. The posts were read and analyzed thematically to determine the overarching themes evident across the collected posts. The dimensions of social capital defined in the literature were used to determine its influence on the reputation of an emergency management organization.FindingsThe organizational cultural factors that emerged from the analysis are openness and future orientation without any evidence on risk-taking and flexibility. An analysis of cultural factors indicates that organizational culture facilitates knowledge exchange and knowledge combination. The key themes embedded in the organization's posts are emergency preparedness, communication devices for emergency management, coordination and admiration. The dimensions of social capital that influenced the reputation of emergency management organization were group characteristics, volunteerism, generalized norms and togetherness. Though previous studies have found the influence of culture on social capital, this study extends those findings by identifying the dimensions of culture (i.e. openness and future orientation) that reflects the social capital dimensions (i.e. generalized norms an...
Kusakunniran, W, Charoenpanich, P, Samunyanoraset, P, Suksai, S, Karnjanapreechakorn, S, Wu, Q & Zhang, J 2021, 'Hybrid Learning of Vessel Segmentation in Retinal Images', ECTI Transactions on Computer and Information Technology (ECTI-CIT), vol. 15, no. 1, pp. 1-11.
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This paper aims to develop a technique of vessel segmentation in retinal images. Interpreting the segmented vessels is necessary for the automatic detection of the severe stage of the diabetic retinopathy. Thus, it is important to have the technique for segmenting vessels in an automatic way with high performance, for the sake of further analysis. In this paper, the proposed method is developed based on the double layer combining supervised and non-supervised learning aspects. The first layer is to detect the initial seeds of vessels using the supervised learning. It learns based on three types of features including green intensity, line operators, and Gabor filters. Then, the support vector machine (SVM) is applied as the classification tool. In the second layer, the segmentation results from the first layer is further revised and completed using the non-supervised learning. The morphological operations with the watershed technique are applied on the results obtained from the first layer, to remain with the segmented pixels with high confidential to be vessels. Then, these pixels are used as the initial seeds of foreground in the iterative graph cut. As the result, the more completed and comprehensive foreground (i.e. vessels) can be obtained. The proposed method is evaluated using two well-known datasets including DRIVE and STARE. The experimental results show the promising performance of the proposed method when compared with other existing methods in the literature.
Kute, DV, Pradhan, B, Shukla, N & Alamri, A 2021, 'Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering–A Critical Review', IEEE Access, vol. 9, pp. 82300-82317.
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Money laundering has been a global issue for decades, which is one of the major threat for economy and society. Government, regulatory and financial institutions are combating it together in their respective capacity, however still billions of dollars in fines by authorities make the headlines in the news. High-speed internet services have enabled financial institutions to deliver better customer experience through multi-channel engagements, which has led to exponential growth in transactions and new avenues for laundering the money for fraudsters. Literature shows the usage of statistical methods, data mining and Machine Learning (ML) techniques for money laundering detection, but limited research on Deep Learning (DL) techniques, primarily due to lack of model interpretability and explainability of the decisions made. Several studies are conducted on application of ML for Anti-Money Laundering (AML), and Explainable Artificial Intelligence (XAI) techniques in general, but lacks the study on usage of DL techniques together with XAI. This paper aims to review the current state-of-the-art literature on DL together with XAI for identifying suspicious money laundering transactions and identify future research areas. Key findings of the review are, researchers have preferred variants of Convolutional Neural Networks, and AutoEncoder; graph deep learning together with natural language processing is emerging as an important technology for AML; XAI use is not seen in AML domain; 51% ML methods used in AML are non-interpretable, 58% studies used sample of old real data; key challenges for researchers are access to recent real transaction data and scarcity of labelled training data; and data being highly imbalanced. Future research directions are, application of XAI techniques to bring-out explainability, graph deep learning using natural language processing (NLP), unsupervised and reinforcement learning to handle lack of labelled data; and joint research progra...
Kwon, S, Tomonaga, A, Lakshmi Bhai, G, Devitt, SJ & Tsai, J-S 2021, 'Gate-based superconducting quantum computing', Journal of Applied Physics, vol. 129, no. 4, pp. 041102-041102.
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In this Tutorial, we introduce basic conceptual elements to understand and build a gate-based superconducting quantum computing system.
Labeeuw, L, Commault, AS, Kuzhiumparambil, U, Emmerton, B, Nguyen, LN, Nghiem, LD & Ralph, PJ 2021, 'A comprehensive analysis of an effective flocculation method for high quality microalgal biomass harvesting', Science of The Total Environment, vol. 752, pp. 141708-141708.
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Laccone, F, Malomo, L, Pietroni, N, Cignoni, P & Schork, T 2021, 'Integrated computational framework for the design and fabrication of bending-active structures made from flat sheet material', Structures, vol. 34, pp. 979-994.
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Laengle, S, Lobos, V, Merigó, JM, Herrera-Viedma, E, Cobo, MJ & De Baets, B 2021, 'Forty years of Fuzzy Sets and Systems: A bibliometric analysis', Fuzzy Sets and Systems, vol. 402, pp. 155-183.
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© 2020 Elsevier B.V. Fuzzy Sets and Systems is a leading international journal in computer science and applied mathematics that was created in 1978. In 2018, the journal celebrated its 40th anniversary. The aim of this study is to present a bibliometric overview of the leading trends occurring in the journal between 1978 and 2016 by analysing the most productive and influential authors, institutions and countries as well as the publication and citation structure. Additionally, this work presents a graphical visualization of the bibliographic data by using the visualization of similarities (VOS) viewer and the science mapping analysis tool (SciMAT) software. The results show the strong growth of fuzzy set theory over time and a huge diversity of publications from all over the world, especially from Europe, North America and East Asia.
Lalbakhsh, A, Afzal, MU, Hayat, T, Esselle, KP & Mandal, K 2021, 'All-metal wideband metasurface for near-field transformation of medium-to-high gain electromagnetic sources', Scientific Reports, vol. 11, no. 1.
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AbstractElectromagnetic (EM) metasurfaces are essential in a wide range of EM engineering applications, from incorporated into antenna designs to separate devices like radome. Near-field manipulators are a class of metasurfaces engineered to tailor an EM source’s radiation patterns by manipulating its near-field components. They can be made of all-dielectric, hybrid, or all-metal materials; however, simultaneously delivering a set of desired specifications by an all-metal structure is more challenging due to limitations of a substrate-less configuration. The existing near-field phase manipulators have at least one of the following limitations; expensive dielectric-based prototyping, subject to ray tracing approximation and conditions, narrowband performance, costly manufacturing, and polarization dependence. In contrast, we propose an all-metal wideband phase correcting structure (AWPCS) with none of these limitations and is designed based on the relative phase error extracted by post-processing the actual near-field distributions of any EM sources. Hence, it is applicable to any antennas, including those that cannot be accurately analyzed with ray-tracing, particularly for near-field analysis. To experimentally verify the wideband performance of the AWPCS, a shortened horn antenna with a large apex angle and a non-uniform near-field phase distribution is used as an EM source for the AWPCS. The measured results verify a significant improvement in the antenna’s aperture phase distribution in a large frequency band of 25%.
Lalbakhsh, A, Mohamadpour, G, Roshani, S, Ami, M, Roshani, S, Sayem, ASM, Alibakhshikenari, M & Koziel, S 2021, 'Design of a Compact Planar Transmission Line for Miniaturized Rat-Race Coupler With Harmonics Suppression', IEEE Access, vol. 9, pp. 129207-129217.
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This paper presents an elegant yet straightforward design procedure for a compact rat-race coupler (RRC) with an extended harmonic suppression. The coupler's conventional \lambda /4 transmission lines (TLs) are replaced by a specialized TL that offers significant size reduction and harmonic elimination capabilities in the proposed approach. The design procedure is verified through the theoretical, circuit, and electromagnetic (EM) analyses, showing excellent agreement among different analyses and the measured results. The circuit and EM results show that the proposed TL replicates the same frequency behaviour of the conventional one at the design frequency of 1.8 GHz while enables harmonic suppression up to the 7 {\mathrm {th}} harmonic and a size reduction of 74%. According to the measured results, the RRC has a fractional bandwidth of 20%, with input insertion losses of around 0.2 dB and isolation level better than 35 dB. Furthermore, the total footprint of the proposed RRC is only 31.7 mm \times15.9 mm, corresponding to 0.28\,\,\lambda \times 0.14\,\,\lambda , where \lambda is the guided wavelength at 1.8 GHz.
Lan, T, Hutvagner, G, Lan, Q, Liu, T & Li, J 2021, 'Sequencing dropout-and-batch effect normalization for single-cell mRNA profiles: a survey and comparative analysis', Briefings in Bioinformatics, vol. 22, no. 4.
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AbstractSingle-cell mRNA sequencing has been adopted as a powerful technique for understanding gene expression profiles at the single-cell level. However, challenges remain due to factors such as the inefficiency of mRNA molecular capture, technical noises and separate sequencing of cells in different batches. Normalization methods have been developed to ensure a relatively accurate analysis. This work presents a survey on 10 tools specifically designed for single-cell mRNA sequencing data preprocessing steps, among which 6 tools are used for dropout normalization and 4 tools are for batch effect correction. In this survey, we outline the main methodology for each of these tools, and we also compare these tools to evaluate their normalization performance on datasets which are simulated under the constraints of dropout inefficiency, batch effect or their combined effects. We found that Saver and Baynorm performed better than other methods in dropout normalization, in most cases. Beer and Batchelor performed better in the batch effect normalization, and the Saver–Beer tool combination and the Baynorm–Beer combination performed better in the mixed dropout-and-batch effect normalization. Over-normalization is a common issue occurred to these dropout normalization tools that is worth of future investigation. For the batch normalization tools, the capability of retaining heterogeneity between different groups of cells after normalization can be another direction for future improvement.
Laranjo, L, Ding, D, Heleno, B, Kocaballi, B, Quiroz, JC, Tong, HL, Chahwan, B, Neves, AL, Gabarron, E, Dao, KP, Rodrigues, D, Neves, GC, Antunes, ML, Coiera, E & Bates, DW 2021, 'Do smartphone applications and activity trackers increase physical activity in adults? Systematic review, meta-analysis and metaregression', British Journal of Sports Medicine, vol. 55, no. 8, pp. 422-432.
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ObjectiveTo determine the effectiveness of physical activity interventions involving mobile applications (apps) or trackers with automated and continuous self-monitoring and feedback.DesignSystematic review and meta-analysis.Data sourcesPubMed and seven additional databases, from 2007 to 2020.Study selectionRandomised controlled trials in adults (18–65 years old) without chronic illness, testing a mobile app or an activity tracker, with any comparison, where the main outcome was a physical activity measure. Independent screening was conducted.Data extraction and synthesisWe conducted random effects meta-analysis and all effect sizes were transformed into standardised difference in means (SDM). We conducted exploratory metaregression with continuous and discrete moderators identified as statistically significant in subgroup analyses.Main outcome measuresPhysical activity: daily step counts, min/week of moderate-to-vigorous physical activity, weekly days exercised, min/week of total physical activity, metabolic equivalents.ResultsThirty-five studies met inclusion criteria and 28 were included in the meta-analysis (n=7454 participants, 28% women). The meta-analysis showed a small-to-moderate positive effect on physical activity measures (SDM 0.350, 95% CI 0.236 to 0.465, I2=69%,T2=0.051) corresponding to 1850 steps per day (95% CI 1247 to 2457). Interventions including text-messaging and personalisation features were significantly more effective in subgroup analyses and metar...
Law, AMK, Rodriguez de la Fuente, L, Grundy, TJ, Fang, G, Valdes-Mora, F & Gallego-Ortega, D 2021, 'Advancements in 3D Cell Culture Systems for Personalizing Anti-Cancer Therapies', Frontiers in Oncology, vol. 11, p. 782766.
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Over 90% of potential anti-cancer drug candidates results in translational failures in clinical trials. The main reason for this failure can be attributed to the non-accurate pre-clinical models that are being currently used for drug development and in personalised therapies. To ensure that the assessment of drug efficacy and their mechanism of action have clinical translatability, the complexity of the tumor microenvironment needs to be properly modelled. 3D culture models are emerging as a powerful research tool that recapitulatesin vivocharacteristics. Technological advancements in this field show promising application in improving drug discovery, pre-clinical validation, and precision medicine. In this review, we discuss the significance of the tumor microenvironment and its impact on therapy success, the current developments of 3D culture, and the opportunities that advancements thatin vitrotechnologies can provide to improve cancer therapeutics.
Le, AT, Huang, X & Guo, YJ 2021, 'Analog Self-Interference Cancellation in Dual-Polarization Full-Duplex MIMO Systems', IEEE Communications Letters, vol. 25, no. 9, pp. 3075-3079.
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Full-duplex (FD) technology combined with dual-polarization (DP) multiple-input multiple-output (MIMO) systems is attractive to improve spectral efficiency and to enhance link capacity. Cancelling self-interference (SI) in such DPFD MIMO systems using beamforming techniques is very challenging due to a significant difference of the co-polarization and cross-polarization SI channels. In this letter, an analog adaptive filter structure is proposed to mitigate both co-polarization and cross-polarization SIs in DPFD MIMO systems. Stationary analysis is applied to evaluate the performance of the proposed structure. Simulation results show that about 45 dB to 55 dB of SI cancellation can be achieved regardless of the isolation differences between cross-polarization and co-polarization channels.
Le, AT, Tran, LC, Huang, X, Guo, YJ & Hanzo, L 2021, 'Analog Least Mean Square Adaptive Filtering for Self-Interference Cancellation in Full Duplex Radios', IEEE Wireless Communications, vol. 28, no. 1, pp. 12-18.
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Le, NP, Tran, LC, Huang, X, Choi, J, Dutkiewicz, E, Phung, SL & Bouzerdoum, A 2021, 'Performance Analysis of Uplink NOMA Systems With Hardware Impairments and Delay Constraints Over Composite Fading Channels', IEEE Transactions on Vehicular Technology, vol. 70, no. 7, pp. 6881-6897.
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In this paper, we propose a mixture gamma distribution based analytical framework for NOMA wireless systems over composite fading channels. We analyze the outage probability (OP), delay-limited throughput (TP) and effective capacity (EC) in uplink NOMA with imperfect successive interference cancellation (SIC) due to the presence of residual hardware impairments and delay constraints. A mixture gamma distribution is used to approximate the probability density functions of fading channels. Based on this, we obtain closed-form expressions in terms of Meijer-G functions for the OP, the TP and the EC. We also perform asymptotic analysis of these metrics to characterize system behaviors at the high signal-to-noise ratio regime. Moreover, upper-bounds for the EC is derived. Efficacy of NOMA over orthogonal multiple access is analytically examined. Unlike the existing works, our analytical expressions hold for NOMA systems with an arbitrary number of users per cluster over a wide range of channel models, including lognormal-Nakagami-m, KG, η-μ, Nakagami-q (Hoyt), κ-μ, Nakagami-n (Rician), Nakagami-m, and Rayleigh fading channels. This unified analysis facilitates evaluations of impacts of the residual interference, the power allocation among users, the delay quality-of-service exponent as well as the shadowing and small-scale fading parameters on the performance metrics. Simulation results are provided to validate theoretical analysis.
Le, NP, Tran, LC, Huang, X, Dutkiewicz, E, Ritz, C, Phung, SL, Bouzerdoum, A, Franklin, DR & Hanzo, L 2021, 'Energy-Harvesting Aided Unmanned Aerial Vehicles for Reliable Ground User Localization and Communications Under Lognormal-Nakagami-$m$ Fading Channels.', IEEE Trans. Veh. Technol., vol. 70, no. 2, pp. 1632-1647.
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Lee, A, Mondon, J, Merenda, A, Dumée, LF & Callahan, DL 2021, 'Surface adsorption of metallic species onto microplastics with long-term exposure to the natural marine environment', Science of The Total Environment, vol. 780, pp. 146613-146613.
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Microplastics are ubiquitous in most biomes and environments, representing one of the most pressing global environmental challenges. This study investigated the ability of pre-production microplastic pellets to accumulate metals from the marine environment. An accidental ocean discharge of poly(propylene) pellets occurred via a wastewater treatment centre at the coastal city of Warrnambool, Victoria - Australia. These pellets were collected routinely from Shelly Beach, adjacent to the ocean discharge site over a period of 16-months following the spill. This collection formed a unique time-series that accurately represented the degree to which metal ions in the coastal marine environment accumulate on plastic debris. Elemental analysis indicated an increase in concentration over time of rare earth elements and a selection of other metals supporting the hypothesis that microplastics selectively adsorb metals from the environment. A subset of the poly(propylene) pellets contained a surfactant coating which significantly increased the adsorption capacity. The surface properties in relation to adsorption were further explored with surface imaging and these results are also discussed. This study shows how microplastics act as nucleation points and carriers of trace metal ions in marine environments.
Lee, SS, Siwakoti, YP, Barzegarkhoo, R & Lee, K-B 2021, 'Switched-Capacitor-Based Five-Level T-Type Inverter (SC-5TI) With Soft-Charging and Enhanced DC-Link Voltage Utilization', IEEE Transactions on Power Electronics, vol. 36, no. 12, pp. 13958-13967.
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The emerging switched-capacitor-based multilevel inverters offer interesting merits such as self-balancing of capacitor voltages and boosting of voltage gain. While the switched capacitors (SCs) in these topologies are charged in parallel with the dc source, severe current spikes issue is inevitable, rendering them impractical at high power. This article proposes a novel switched-capacitor-based T-type inverter that mitigates the current spikes by enabling soft charging for its integrated SCs, where both SC in the topological structure charges through a dedicated circuit comprises of an inductor and two switches. The proposed topology is capable of five-level ac voltage generation and when compared to a classical T-type/ANPC (active neutral-point-clamped) inverter, it achieves higher dc-link voltage utilization since its maximum attainable voltage gain is doubled. Theoretical findings of the proposed topology are validated by both the simulation and experimental results.
Lee, SS, Yang, Y & Siwakoti, YP 2021, 'A Novel Single-Stage Five-Level Common-Ground-Boost-Type Active Neutral-Point-Clamped (5L-CGBT-ANPC) Inverter', IEEE Transactions on Power Electronics, vol. 36, no. 6, pp. 6192-6196.
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Lee, SS, Yang, Y, Siwakoti, YP & Lee, K-B 2021, 'A Novel Boost Cascaded Multilevel Inverter.', IEEE Trans. Ind. Electron., vol. 68, no. 9, pp. 8072-8080.
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Lee, XJ, Ong, HC, Gao, W, Ok, YS, Chen, W-H, Goh, BHH & Chong, CT 2021, 'Solid biofuel production from spent coffee ground wastes: Process optimisation, characterisation and kinetic studies', Fuel, vol. 292, pp. 120309-120309.
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Legg, R 2021, 'A legal geography of the regulation of contaminated land in Williamtown, New South Wales', Geographical Research, vol. 59, no. 2, pp. 242-254.
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AbstractLegal geographers have recently been interested in interlegality: the interactions between different legal orders governing one particular space. Contaminated land is one such space governed by multiple legal orders; however, it is yet to receive great attention in legal geography. In Australia, different authorities and bodies cooperate together in a responsive regulatory framework to prevent contamination and then manage cases where contamination has occurred, although this process is mostly coordinated at the level of the state. This article evaluates the framework by reference to a case where the contamination, by perfluoroalkyl and polyfluoroalkyl substances (PFAS), has crossed jurisdictional boundaries in Williamtown, in New South Wales, Australia. This cross‐boundary incursion has complicated management responses, and may have resulted in contradicting forms of regulatory implementation by different legal orders and a break down in accountability. Ultimately, such findings point to the need for the current regulatory framework to be adapted to better deal with situations of spatial and legal complexity.
Lei, B, Li, W, Luo, Z, Li, X, Tam, VWY & Tang, Z 2021, 'Performance deterioration of sustainable recycled aggregate concrete under combined cyclic loading and environmental actions', Journal of Sustainable Cement-Based Materials, vol. 10, no. 1, pp. 23-45.
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The effects of strength grades, loading modes, and stress levels on the performance deterioration of sustainable recycled aggregate concrete (RAC) subjected to mechanical loading or coupled mechanical loading and environmental actions are investigated in this study. Comparison analysis of residual properties of RAC suffered from single mechanical loading, coupled actions of mechanical loading, and salt-solution freeze-thaw cycles, as well as the combined actions of mechanical loading and salt-solution corrosion were experimentally studied. The results indicate that the stress level is the most influential factor affecting the durability of RAC followed by the RAC strength grade and the number of times the alternating load applied. Moreover, applying novel intermittent loading mode to simulate the durability of RAC under the coupled actions of sustained loading and environmental factors is feasible and reasonable. Additionally, the fitting equations were proposed to discuss the effects of number of times the alternating load applied, stress level, and mechanical strength on the mechanical strength loss and durability of RAC.
Lei, F, Lv, X, Fang, J, Li, Q & Sun, G 2021, 'Nondeterministic multi-objective and multi-case discrete optimization of functionally-graded front-bumper structures for pedestrian protection', Thin-Walled Structures, vol. 167, pp. 106921-106921.
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Pedestrian lower-leg protection and lower-speed crashworthiness often present two important yet competing criteria on the design of front-bumper structures. Conventional design optimization is largely focused on a single loading condition without considering multiple impact cases. Furthermore, design of front-bumper structures is usually discrete in engineering practice and impacting conditions are commonly random. To cope with such a sophisticated nondeterministic design problem, this study aimed to develop a successive multiple attribute decision making (MADM) algorithm for optimizing a functionally graded thickness (FGT) front-bumper structure subject to multiple impact loading cases. The finite element (FE) model of front-end vehicle was constructed and validated with the in-house experimental tests under the loads of both Flexible Pedestrian Legform Impactor (Flex-PLI) impact and lower-speed impact. In the proposed successive MADM algorithm, the order preference by similarity to ideal solution (TOPSIS) based upon relative entropy was coupled with the analytic hierarchy process (AHP) to develop a MADM model for converting multiple conflicting objectives into a unified single cost function. The presented optimization procedure is algorithmically iterated using the successive Taguchi method to deal with a large number of design variables and design levels. The results showed that not only the algorithm enabled to generate an optimal design efficiently, but also the robustness of Flex-PLI impact is significantly enhanced. The proposed algorithm can be potentially used for other engineering design problems with similar complexity.
Lei, G, Bramerdorfer, G, Liu, C, Guo, Y & Zhu, J 2021, 'Robust Design Optimization of Electrical Machines: A Comparative Study and Space Reduction Strategy', IEEE Transactions on Energy Conversion, vol. 36, no. 1, pp. 300-313.
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This article presents a comparative study on different types of robust design optimization methods for electrical machines. Three robust design approaches, Taguchi parameter design, worst-case design and design for six-sigma, are compared for low-dimensional and high-dimensional design optimization scenarios, respectively. For the high-dimensional scenario, the computational burden is normally massive due to the robustness evaluation of a huge number of design candidates. To attempt this challenge, as the second aim of this paper, a space reduction optimization (SRO) strategy is proposed for these robust design approaches, yielding three new robust optimization methods. To illustrate and compare the performance of different robust design optimization methods, a permanent magnet motor with soft magnetic composite cores is investigated with the consideration of material diversities and manufacturing tolerances. 3-D finite element model and thermal network model are employed in the optimization process and the accuracy of both models has been verified by experimental results. Based on the theoretical analysis and optimization results, a detailed comparison is provided for all investigated and proposed robust design optimization methods in terms of different aspects. It shows that the proposed SRO strategy can greatly improve the design optimization effectiveness and efficiency of those three conventional robust design methods.
Lei, G, Bramerdorfer, G, Ma, B, Guo, Y & Zhu, J 2021, 'Robust Design Optimization of Electrical Machines: Multi-Objective Approach', IEEE Transactions on Energy Conversion, vol. 36, no. 1, pp. 390-401.
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This article presents a new method for multi-objective robust design optimization of electrical machines and provides a detailed comparison with so far introduced techniques. First, two robust design approaches, worst-case design and design for six-sigma, are compared with the conventional deterministic approach for multi-objective optimization. Through a case study on a permanent magnet motor, it is found that the reliabilities of motors produced based on robust designs are 100% under the investigated constraints, while the reliabilities of deterministic designs can be lower than 30%. A major disadvantage of robust optimization is the huge computation cost, especially for high-dimensional problems. To attempt this problem, a new multi-objective sequential optimization method (MSOM) with an orthogonal design technique and hypervolume indicator (as a measure of convergence) is proposed for both deterministic and robust design optimization of electrical machines. Through another case study, it is found that the new MSOM can improve motor performance and greatly reduce the computational cost. For the robust optimization, the number of required finite element simulations can be reduced by more than 40%, compared with that required by the conventional approach. The proposed method can be applied to many-objective (robust) design optimization of electrical machines.
Lei, H, Chen, S, Wang, M, He, X, Jia, W & Li, S 2021, 'A New Algorithm for Sketch‐Based Fashion Image Retrieval Based on Cross‐Domain Transformation', Wireless Communications and Mobile Computing, vol. 2021, no. 1, pp. 1-14.
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Due to the rise of e‐commerce platforms, online shopping has become a trend. However, the current mainstream retrieval methods are still limited to using text or exemplar images as input. For huge commodity databases, it remains a long‐standing unsolved problem for users to find the interested products quickly. Different from the traditional text‐based and exemplar‐based image retrieval techniques, sketch‐based image retrieval (SBIR) provides a more intuitive and natural way for users to specify their search need. Due to the large cross‐domain discrepancy between the free‐hand sketch and fashion images, retrieving fashion images by sketches is a significantly challenging task. In this work, we propose a new algorithm for sketch‐based fashion image retrieval based on cross‐domain transformation. In our approach, the sketch and photo are first transformed into the same domain. Then, the sketch domain similarity and the photo domain similarity are calculated, respectively, and fused to improve the retrieval accuracy of fashion images. Moreover, the existing fashion image datasets mostly contain photos only and rarely contain the sketch‐photo pairs. Thus, we contribute a fine‐grained sketch‐based fashion image retrieval dataset, which includes 36,074 sketch‐photo pairs. Specifically, when retrieving on our Fashion Image dataset, the accuracy of our model ranks the correct match at the top‐1 which is 96.6%, 92.1%, 91.0%, and 90.5% for clothes, pants, skirts, and shoes, respectively. Extensive experiments conducted on our dataset and two fine‐grained instance‐level datasets, i.e., QMUL‐shoes and QMUL‐chairs, show that our model has achieved a better performance than other existing methods.
Lei, J, Li, M, Xie, W, Li, Y & Jia, X 2021, 'Spectral mapping with adversarial learning for unsupervised hyperspectral change detection', Neurocomputing, vol. 465, pp. 71-83.
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Lei, J, Yang, G, Xie, W, Li, Y & Jia, X 2021, 'A Low-Complexity Hyperspectral Anomaly Detection Algorithm and Its FPGA Implementation', IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 907-921.
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Leng, D, Zhu, Z, Xu, K, Li, Y & Liu, G 2021, 'Vibration control of jacket offshore platform through magnetorheological elastomer (MRE) based isolation system', Applied Ocean Research, vol. 114, pp. 102779-102779.
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With high flexibility and low damping, offshore wind turbines (OWTs) are prone to external vibrations such as wind, wave and earthquake, either attacked individually or as combined loading cases. This study proposes a semi-active variable-stiffness tuned mass damper (VSTMD) with magnetorheological elastomer (MRE) materials to mitigate undesired dynamic responses of OWT. A jacket supported OWT with MRE-TMD installed at the top of the tower is adopted as an example to demonstrate the effectiveness of the proposed design under multiple hazards. A semi-active frequency tracing algorithm is proposed through which the current-dependent stiffness of MRE-TMD is controlled by tracking the acceleration of OWT tower. The numerical results demonstrate that the semi-active MRE-TMD can effectively attenuate the dynamic responses of OWT under multi-hazard loadings, and it outperforms the passive TMD in reducing the peak and RMS displacements of tower structure. Robustness analysis of semi-active MRE-TMD is also validated by considering OWT sudden loss of partial stiffness under multiple-loadings.
Leng, D, Zhu, Z, Xu, K, Li, Y & Liu, G 2021, 'Vibration control of jacket offshore platform through magnetorheological elastomer (MRE) based isolation system', Applied Ocean Research, vol. 114.
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Undesirable vibrations in offshore platforms due to ocean loadings may reduce platform productivity and increase the fatigue failure. This study proposes a magnetorheological elastomer (MRE) based isolation system to control the jacket platform oscillations and its effectiveness is numerically evaluated. The working principle and design method of MRE-based isolation system are proposed, and MRE materials with high magnetorheological effects are conceptually designed. Practical jacket offshore platforms are selected for case studies. Semi-active fuzzy controller (SFC) is utilized to achieve real-time non-resonance vibration control. The proposed fuzzy core is constructed conceptually by the dynamic analysis of object structure. Numerical results demonstrate that MRE isolation system with SFC significantly reduces the maximum, minimum and RMS of the deck displacement and acceleration under realistic irregular waves at different sea states. MRE system could also reduce the response spectrum peaks and present robustness under various deck's mass. The present study proves the feasibility of MRE isolation systems in the application of vibration control for marine structures.
Leon-Castro, E, Blanco-Mesa, F, Alfaro-Garcia, V, Gil-Lafuente, AM & Merigo, JM 2021, 'Fuzzy systems and applications in innovation and sustainability', Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 1723-1726.
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Leon-Castro, E, Blanco-Mesa, F, Alfaro-Garcia, V, Gil-Lafuente, AM & Merigo, JM 2021, 'Fuzzy systems in innovation and sustainability', Computational and Mathematical Organization Theory, vol. 27, no. 4, pp. 377-383.
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Fuzzy systems in innovation and sustainability are important topics in literature nowadays. A lot of new formulations in fuzzy systems are being made including interesting applications in different topics. The aim of this special issue is to present different works made in this line of research that were presented in the IV International Congress of Innovation and Sustainability (ICONIS).
Leon-Castro, E, Blanco-Mesa, FR, Gil-Lafuente, AM & Merigo Lindahl, JM 2021, 'Editorial', International Journal of Entrepreneurship and Innovation Management, vol. 25, no. 2-3, pp. 105-109.
León-Castro, E, Espinoza-Audelo, LF, Merigó, JM, Herrera-Viedma, E & Herrera, F 2021, 'Measuring volatility based on ordered weighted average operators: The case of agricultural product prices', Fuzzy Sets and Systems, vol. 422, pp. 161-176.
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León-Castro, E, Perez-Arellano, LA, Olazabal-Lugo, M & Merigó, JM 2021, 'Prioritized Induced Heavy Operators Applied to Political Modelling', International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 29, no. 04, pp. 603-620.
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This paper presents the prioritized induced heavy ordered weighted average (PIHOWA) operator. This operator combines an unbounded weighting vector, an induced vector and a prioritized vector and can be applied to the group decision-making process where the information provided by each decision maker does not have the same importance. An application of this operator is done in governmental transparency in Mexico based on the Open Government Metric (OGM). Among the main results it is possible to visualize how the relative importance of each component can generate important change in the top 10 ranking.
Leung, D, Nayak, A, Shayeghi, A, Touchette, D, Yao, P & Yu, N 2021, 'Capacity Approaching Coding for Low Noise Interactive Quantum Communication Part I: Large Alphabets', IEEE Transactions on Information Theory, vol. 67, no. 8, pp. 5443-5490.
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We consider the problem of implementing two-party interactive quantum communication over noisy channels, a necessary endeavor if we wish to fully reap quantum advantages for communication. For an arbitrary protocol with n messages, designed for a noiseless qudit channel over a size alphabet, our main result is a simulation method that fails with probability less than and uses a qudit channel over the same alphabet n(1 + times, of which an fraction can be corrupted adversarially. The simulation is thus capacity achieving to leading order, and we conjecture that it is optimal up to a constant factor in the term. Furthermore, the simulation is in a model that does not require pre-shared resources such as randomness or entanglement between the communicating parties. Our work improves over the best previously known quantum result where the overhead is a non-explicit large constant [Brassard et al., SICOMP'19] for low.
Leung, D, Winter, A & Yu, N 2021, 'LOCC protocols with bounded width per round optimize convex functions', Reviews in Mathematical Physics, vol. 33, no. 05, pp. 2150013-2150013.
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We start with the task of discriminating finitely many multipartite quantum states using LOCC protocols, with the goal to optimize the probability of correctly identifying the state. We provide two different methods to show that finitely many measurement outcomes in every step are sufficient for approaching the optimal probability of discrimination. In the first method, each measurement of an optimal LOCC protocol, applied to a [Formula: see text]-dimensional local system, is replaced by one with at most [Formula: see text] outcomes, without changing the probability of success. In the second method, we decompose any LOCC protocol into a convex combination of a number of “slim protocols” in which each measurement applied to a [Formula: see text]-dimensional local system has at most [Formula: see text] outcomes. To maximize any convex functions in LOCC (including the probability of state discrimination or fidelity of state transformation), an optimal protocol can be replaced by the best slim protocol in the convex decomposition without using shared randomness. For either method, the bound on the number of outcomes per measurement is independent of the global dimension, the number of parties, the depth of the protocol, how deep the measurement is located, and applies to LOCC protocols with infinite rounds, and the “measurement compression” can be done “top-down” — independent of later operations in the LOCC protocol. The second method can be generalized to implement LOCC instruments with finitely many classical outcomes: if the instrument has [Formula: see text] coarse-grained final measurement outcomes, global input dimension [Formula: see text] and global output dimension [Formula: see text] for [Formula: see text] conditioned on the [Formula: see text]th outcome, then one can obtain the instrument as a convex combination of no more than [Formula: see text] slim protocols; that is, [Formula: see text] bits of shared randomness ...
Li, A, Yang, B, Huo, H & Hussain, FK 2021, 'Leveraging implicit relations for recommender systems', Information Sciences, vol. 579, pp. 55-71.
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Li, B, Guo, T, Li, R, Wang, Y, Ou, Y & Chen, F 2021, 'Delay Propagation in Large Railway Networks with Data-Driven Bayesian Modeling', Transportation Research Record: Journal of the Transportation Research Board, vol. 2675, no. 11, pp. 472-485.
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Reliability and punctuality are the key evaluation criteria in railway service for both passengers and operators. Delays spanning over spatial and temporal dimensions significantly affect the reliability and punctuality level of train operation. The optimization of capacity utilization and timetable design requires the prediction of the reliability and punctuality level of train operations, which is determined by train delays and delay propagation. To predict the punctuality level of train operations, the distributions of arrival and departure delays must be estimated as realistically as possible by taking into account the complex railway network structure and different types of delays caused by route conflict and connected trips. This paper aims to predict the propagation of delays on the railway network in the Greater Sydney area by developing a conditional Bayesian model. In the model, the propagation satisfies the Markov property if one can predict future delay propagation in the network based solely on its present state just as well as one could knowing the process’s full history, so that it is independent of such historical procedures. Meanwhile, we consider the throughput estimation for the cases of delay caused by interchange line conflicts and train connection in this model. To the best of the authors’ knowledge, this is the first work of data-driven delay propagation modeling that examines both spatial and temporal dimensions under four different scenarios for railway networks. Implementation on real-world railway network operation data shows the feasibility and accuracy of the proposed model compared with traditional probability models.
Li, B, Wen, G, Peng, Z, Wen, S & Huang, T 2021, 'Time-Varying Formation Control of General Linear Multi-Agent Systems Under Markovian Switching Topologies and Communication Noises', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 4, pp. 1303-1307.
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This brief studies the time-varying formation (TVF) control of linear multi-agent systems (MASs), where the communication topology switches from several different topologies and the switching signal is depicted by a right-continuous Markov process. The communication noises are taken into account simultaneously, which are described as independent white noises with noisy intensities. A class of stochastic-approximation type control protocols is given and certain sufficient conditions are derived for realizing the TVF stabilization in mean square sense. Moreover, the indicative function and infinitesimal generator are imported in Lyapunov functions to help proving that the MASs can be formed into stable TVF shapes with the proposed control protocols. In the end, an simulation example is performed to state the availability of the approach.
Li, C, Xie, H-B, Fan, X, Xu, RYD, Van Huffel, S & Mengersen, K 2021, 'Kernelized Sparse Bayesian Matrix Factorization', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, pp. 391-404.
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Extracting low-rank and/or sparse structures using matrix factorization techniques has been extensively studied in the machine learning community. Kernelized matrix factorization (KMF) is a powerful tool to incorporate side information into the low-rank approximation model, which has been applied to solve the problems of data mining, recommender systems, image restoration, and machine vision. However, most existing KMF models rely on specifying the rows and columns of the data matrix through a Gaussian process prior and have to tune manually the rank. There are also computational issues of existing models based on regularization or the Markov chain Monte Carlo. In this article, we develop a hierarchical kernelized sparse Bayesian matrix factorization (KSBMF) model to integrate side information. The KSBMF automatically infers the parameters and latent variables including the reduced rank using the variational Bayesian inference. In addition, the model simultaneously achieves low-rankness through sparse Bayesian learning and columnwise sparsity through an enforced constraint on latent factor matrices. We further connect the KSBMF with the nonlocal image processing framework to develop two algorithms for image denoising and inpainting. Experimental results demonstrate that KSBMF outperforms the state-of-the-art approaches for these image-restoration tasks under various levels of corruption.
Li, C, Zhang, F, Zhang, Y, Qin, L, Zhang, W & Lin, X 2021, 'Discovering fortress-like cohesive subgraphs.', Knowl. Inf. Syst., vol. 63, no. 12, pp. 3217-3250.
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Morris (Rev Econ Stud 67:57–78, 2000) defines the p-cohesion by a connected subgraph in which every vertex has at least a fraction p of its neighbors in the subgraph, i.e., at most a fraction (1 - p) of its neighbors outside. We can find that a p-cohesion ensures not only inner-cohesiveness but also outer-sparseness. The textbook on networks by Easley and Kleinberg (Networks, Crowds, and Markets - Reasoning About a Highly Connected World, Cambridge University Press, 2010) shows that p-cohesions are fortress-like cohesive subgraphs which can hamper the entry of the cascade, following the contagion model. Despite the elegant definition and promising properties, to our best knowledge, there is no existing study on p-cohesion regarding problem complexity and efficient computing algorithms. In this paper, we fill this gap by conducting a comprehensive theoretical analysis on the complexity of the problem and developing efficient computing algorithms. We focus on the minimal p-cohesion because they are elementary units of p-cohesions and the combination of multiple minimal p-cohesions is a larger p-cohesion. We demonstrate that the discovered minimal p-cohesions can be utilized to solve the MinSeed problem: finding a smallest set of initial adopters (seeds) such that all the network users are eventually influenced. Extensive experiments on 8 real-life social networks verify the effectiveness of this model and the efficiency of our algorithms.
Li, C, Zhou, J, Armaghani, DJ & Li, X 2021, 'Stability analysis of underground mine hard rock pillars via combination of finite difference methods, neural networks, and Monte Carlo simulation techniques', Underground Space, vol. 6, no. 4, pp. 379-395.
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Li, C, Zhou, J, Armaghani, DJ, Cao, W & Yagiz, S 2021, 'Stochastic assessment of hard rock pillar stability based on the geological strength index system', Geomechanics and Geophysics for Geo-Energy and Geo-Resources, vol. 7, no. 2.
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Li, D, Koopialipoor, M & Armaghani, DJ 2021, 'A Combination of Fuzzy Delphi Method and ANN-based Models to Investigate Factors of Flyrock Induced by Mine Blasting', Natural Resources Research, vol. 30, no. 2, pp. 1905-1924.
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Li, DL, Prasad, M, Liu, C-L & Lin, C-T 2021, 'Multi-View Vehicle Detection Based on Fusion Part Model With Active Learning', IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 5, pp. 3146-3157.
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IEEE Computer vision-based vehicle detection techniques are widely used in real-world applications. However, most of these techniques aim to detect only single-view vehicles, and their performances are easily affected by partial occlusion. Therefore, this paper proposes a novel multi-view vehicle detection system that uses a part model to address the partial occlusion problem and the high variance between all types of vehicles. There are three features in this paper; firstly, different from Deformable Part Model, the construction of part models in this paper is visual and can be replaced at any time. Secondly, this paper proposes some new part models for detection of vehicles according to the appearance analysis of a large number of modern vehicles by the active learning algorithm. Finally, this paper proposes the method that contains color transformation along with the Bayesian rule to filter out the background to accelerate the detection time and increase accuracy. The proposed method outperforms other methods on given dataset.
Li, E, Zhou, J, Shi, X, Jahed Armaghani, D, Yu, Z, Chen, X & Huang, P 2021, 'Developing a hybrid model of salp swarm algorithm-based support vector machine to predict the strength of fiber-reinforced cemented paste backfill', Engineering with Computers, vol. 37, no. 4, pp. 3519-3540.
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Li, F, Jiang, L, Liao, Y, Si, Y, Yi, C, Zhang, Y, Zhu, X, Yang, Z, Yao, D, Cao, Z & Xu, P 2021, 'Brain variability in dynamic resting-state networks identified by fuzzy entropy: a scalp EEG study', Journal of Neural Engineering, vol. 18, no. 4, pp. 046097-046097.
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Abstract Objective. Exploring the temporal variability in spatial topology during the resting state attracts growing interest and becomes increasingly useful to tackle the cognitive process of brain networks. In particular, the temporal brain dynamics during the resting state may be delineated and quantified aligning with cognitive performance, but few studies investigated the temporal variability in the electroencephalogram (EEG) network as well as its relationship with cognitive performance. Approach. In this study, we proposed an EEG-based protocol to measure the nonlinear complexity of the dynamic resting-state network by applying the fuzzy entropy. To further validate its applicability, the fuzzy entropy was applied into simulated and two independent datasets (i.e. decision-making and P300). Main results. The simulation study first proved that compared to the existing methods, this approach could not only exactly capture the pattern dynamics in time series but also overcame the magnitude effect of time series. Concerning the two EEG datasets, the flexible and robust network architectures of the brain cortex at rest were identified and distributed at the bilateral temporal lobe and frontal/occipital lobe, respectively, whose variability metrics were found to accurately classify different groups. Moreover, the temporal variability of resting-state network property was also either positively or negatively related to individual cognitive performance. Significance. This outcome suggested the potential of fuzzy entropy for evaluating the temporal variability of the dynamic resting-state brain networks, and the fuzzy entropy is also helpful for uncovering the fluctuating network variability that accounts for the individual decision differences.
Li, F, Li, Y, Zheng, H, Jiang, L, Gao, D, Li, C, Peng, Y, Cao, Z, Zhang, Y, Yao, D, Xu, T, Yuan, T-F & Xu, P 2021, 'Identification of the General Anesthesia Induced Loss of Consciousness by Cross Fuzzy Entropy-Based Brain Network', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 2281-2291.
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Li, F, Yi, C, Liao, Y, Jiang, Y, Si, Y, Song, L, Zhang, T, Yao, D, Zhang, Y, Cao, Z & Xu, P 2021, 'Reconfiguration of Brain Network Between Resting State and P300 Task', IEEE Transactions on Cognitive and Developmental Systems, vol. 13, no. 2, pp. 383-390.
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IEEE Previous studies explore the power spectra from the resting-state condition to the oddball task, but whether brain network existing significant difference is still unclear. Our study aims to address how the brain reconfigures its architecture from a resting-state condition (i.e., baseline) to the P300 task in the visual oddball task. In this study, electroencephalograms (EEGs) were collected from 24 subjects, who were required to only mentally count the number of target stimulus; afterwards, EEG networks constructed in different bands were compared between baseline and task to evaluate the reconfiguration of functional connectivity. Compared to the baseline, our results showed the significantly enhanced delta/theta functional connectivity and decreased alpha default mode network in the progress of brain reconfiguration to the task. Furthermore, the reconfigured coupling strengths were found to relate to P300 amplitudes, which were then regarded as features to train a classifier to differentiate the brain states and the high and low P300 groups with an accuracy of 100% and 77.78%, respectively. The findings of our study help us to under-stand the updates in functional connectivity from resting-state to the oddball task, and the reconfigured network structure has the potential for the selection of good subjects for P300-based brain-computer interface.
Li, F, Zheng, J, Zhang, Y-F, Liu, N & Jia, W 2021, 'AMDFNet: Adaptive multi-level deformable fusion network for RGB-D saliency detection', Neurocomputing, vol. 465, pp. 141-156.
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Effective exploration of useful contextual information in multi-modal images is an essential task in salient object detection. Nevertheless, the existing methods based on the early-fusion or the late-fusion schemes cannot address this problem as they are unable to effectively resolve the distribution gap and information loss. In this paper, we propose an adaptive multi-level deformable fusion network (AMDFNet) to exploit the cross-modality information. We use a cross-modality deformable convolution module to dynamically adjust the boundaries of salient objects by exploring the extra input from another modality. This enables incorporating the existing features and propagating more contexts so as to strengthen the model's ability to perceiving scenes. To accurately refine the predicted maps, a multi-scaled feature refinement module is proposed to enhance the intermediate features with multi-level prediction in the decoder part. Furthermore, we introduce a selective cross-modality attention module in the fusion process to exploit the attention mechanism. This module captures dense long-range cross-modality dependencies from a multi-modal hierarchical feature's perspective. This strategy enables the network to select more informative details and suppress the contamination caused by the negative depth maps. Experimental results on eight benchmark datasets demonstrate the effectiveness of the components in our proposed model, as well as the overall saliency model.
Li, H, Askari, M, Li, J, Li, Y & Yu, Y 2021, 'A novel structural seismic protection system with negative stiffness and controllable damping', Structural Control and Health Monitoring, vol. 28, no. 10.
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In this paper, an innovative controllable negative stiffness system (CNSS) integrating adaptive negative stiffness and controllable damping characteristics is proposed to realise desirable vibration protection and improve adaptability, hence being effective to various earthquakes. The force-displacement relationship of the CNSS is derived as the forward model to describe its nonlinear properties. Three representative control algorithms, i.e., Linear Quadratic Regular (LQR) control, H∞ control and Sliding Mode (SM) control, are utilised for the CNSS to attain optimal control force. Based on the Takagi-Sugeno-Kang (TSK) Fuzzy inference system optimised by Non-Dominated Sorted Genetic Algorithm II (NSGAII), a novel inverse model is proposed accordingly to obtain input current according to the required control force and real-time system responses. To demonstrate the feasibility and efficiency of the CNSS for structural seismic protection, a numerical case study is conducted on a three-storey building model with CNSS installed on its first floor. Four scaled benchmark earthquakes are employed as excitations for the case study. Ten evaluation criteria are adopted to assess and verify the performance of the CNSS, and comparisons are made with that of uncontrolled and passive controlled systems. The numerical results indicate that the proposed CNSS can significantly improve the vibration control performance on all evaluation criteria simultaneously in comparison with the other two conventional systems. In addition to having good suppression effects on peak floor displacement and peak inter-storey drift, the CNSS with the SM controller demonstrates superior performance on mitigating peak structure shear and peak acceleration response of the first floor.
Li, H, Li, J, Yu, Y & Li, Y 2021, 'Modified Adaptive Negative Stiffness Device with Variable Negative Stiffness and Geometrically Nonlinear Damping for Seismic Protection of Structures', International Journal of Structural Stability and Dynamics, vol. 21, no. 08, pp. 2150107-2150107.
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Adaptive negative stiffness device is one of the promising seismic protection devices since it can generate seismic isolation effect through negative stiffness when it is mostly needed and achieve similar vibration mitigation as a semi-active control device. However, the adaptive negative stiffness device generally combined with linear viscous damping underpins the drawback of degrading the vibration isolation effect during the high-frequency region. In this paper, a modified adaptive negative stiffness device (MANSD) with the ability to provide both lateral negative stiffness and nonlinear damping by configuring linear springs and linear viscous dampers is proposed to address the above issue. The negative stiffness and nonlinear damping are realised through a linkage mechanism. The fundamentals and dynamic characteristics of a SDOF system with such a device are analyzed and formulated using the Harmonic Balance Method, with a special focus on the amplitude–frequency response and transmissibility of the system. The system with damping nonlinearity as a function of displacement and velocity has been proven to have attractive advantages over linear damping in reducing the transmissibility in the resonance region without increasing that in the high-frequency region. The effect of nonlinear damping on suppressing displacement and acceleration responses is numerically verified under different sinusoidal excitations and earthquakes with different intensities. Compared with linear damping, the MANSD with nonlinear damping could achieve additional reductions on displacement and acceleration under scaled earthquakes, especially intensive earthquakes.
Li, H, Li, Y, Wang, K, Lai, L, Xu, X, Sun, B, Yang, Z & Ding, G 2021, 'Ultra-high sensitive micro-chemo-mechanical hydrogen sensor integrated by palladium-based driver and high-performance piezoresistor', International Journal of Hydrogen Energy, vol. 46, no. 1, pp. 1434-1445.
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AbstractA novel resistive chemical-mechanical sensor for hydrogen gas detection was designed and manufactured by using MEMS processing technology. The sensor combines a composite piezoresistor of silver nanowires-polyimide and a palladium sputtered microcantilever, and the optimized structure of which has been obtained through theoretical and simulation analysis. With a series of experimental testing, the fabricated sensor achieved the ultra-high sensitivity of 2825, 8071, 28250 and 47083 for hydrogen detection at the concentration of 0.4%, 0.8%, 1.2%, 1.6% and 2.0%, respectively. The ultra-high sensitive detection for hydrogen was enabled from the synergistic function of both the surface resistance effect between the palladium coated cantilever and silver nanowires-polyimide piezoresistor, and the bulk resistance effect of the silver nanowires-polyimide piezoresistor. In addition, the sensor also demonstrates excellent stability, which has high potential for practical hydrogen gas detection.
Li, H, Yu, Y, Li, J & Li, Y 2021, 'Analysis and optimization of a typical quasi-zero stiffness vibration isolator', Smart Structures and Systems, vol. 27, no. 3, pp. 525-536.
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To isolate vibration at a low-frequency range and at the same time to provide sufficient loading support to the isolated structure impose a challenge in vibration isolation. Quasi-zero stiffness (QZS) vibration isolator, as a potential solution to the challenge, has been widely investigated due to its unique property of high-static & low-dynamic stiffness. This paper provides an in-depth analysis and potential optimization of a typical QZS vibration isolator to illustrate the complexity and importance of design optimization. By carefully examining the governing fundamentals of the QZS vibration isolator, a simplified approximation of force and stiffness relationship is derived to enable the characteristic analysis of the QZS vibration isolator. The explicit formulae of the amplitude-frequency response (AFR) and transmissibility of the QZS vibration isolator are obtained by employing the Harmonic Balance Method. The transmissibility curves under force excitation with different values of nonlinear coefficient, damping ratio, and amplitude of excitation are further investigated. As the result, an optimization of the structural parameter has been demonstrated using a comprehensive objective function with considering multiple dynamic characteristic parameters simultaneously. Finally, the genetic algorithm (GA) is adopted to minimise the objective function to obtain the optimal stiffness ratios under different conditions. General recommendations are provided and discussed in the end.
Li, H, Yu, Y, Li, J, Li, Y & Askari, M 2021, 'Multi-objective optimisation for improving the seismic protection performance of a multi-storey adaptive negative stiffness system based on modified NSGA-II with DCD', Journal of Building Engineering, vol. 43, pp. 103145-103145.
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Installing adaptive negative stiffness devices (ANSD) on multiple storeys of a building structure to develop a smart seismic protection system, namely multi-storey adaptive negative stiffness system (MANSS), is an effective approach to mitigate the structural responses under earthquake events. However, like other base isolators, the MANSS cannot reach its full potential to address the contradiction between effective vibration isolation and suppression, due to improper setting of structural parameters. In this paper, a comprehensive multi-objective nonlinear optimisation for obtaining the optimal structural parameters of the ANSD is conducted to effectively improve the performance of MANSS on seismic protection. After the characteristic analysis of the ANSD, six optimisation variables and one constraint are determined. Four objective functions are defined by considering the two adverse requirements simultaneously, i.e. enhancing vibration isolation and improving vibration suppression. The highly nonlinear optimisation problem can be adequately resolved by the modified non-dominated sorting genetic algorithm type II (NSGA-II) with dynamic crowding distance (DCD) algorithm, which generates a series of Pareto front, hence obtaining the optimal parameter combination. Furthermore, to verify and evaluate the feasibility and capacity of the proposed optimisation method, a numerical case study is conducted based on a five-storey benchmark building model subjected to six different earthquakes. Four systems, including bare building, bare building with ANSD on the first floor, bare building with dampers on each floor and preliminarily designed MANSS are also investigated to conduct comparative analysis. The results demonstrate that the optimised MANSS can largely reduce both peak and root mean square (RMS) values of inter-storey drift, acceleration, and displacement responses of the benchmark building under all six earthquakes, which proves the effectiveness and su...
Li, H, Zhao, S, Pei, L, Qiao, Z, Han, D, Liu, Z, Lian, Q, Zhao, G & Wang, Z 2021, 'Thermal properties of polybenzoxazine exhibiting improved toughness: Blending with cyclodextrin and its derivatives', High Performance Polymers, vol. 33, no. 9, pp. 1012-1024.
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Polybenzoxazines are emerging as a class of high-performance thermoset polymers that can find their applications in various fields. However, its practical application is limited by its low toughness. The cyclic β-cyclodextrin and a newly synthesized derivative (β-cyclodextrin-MAH) were separately blended with benzoxazine to improve the toughness of polybenzoxazine. The results revealed that the maximum impact strength of the blend was 12.24 kJ·m−2 and 14.29 kJ·m−2 when 1 wt.% of β-Cyclodextrin and β-Cyclodextrin-MAH, respectively, were used. The strengths were 53% and 86% higher than that of pure polybenzoxazine. The curing reaction, possible chemical structures, and fractured surface were examined using differential scanning calorimetry, Fourier transform infrared spectroscopy, and scanning electron microscopy techniques to understand the mechanism of generation of toughness. The results revealed that the sea-island structure and the presence of hydrogen bonds between polybenzoxazine and β-cyclodextrin and β-cyclodextrin-MAH resulted in the generation of toughness. Furthermore, the curves generated during thermogravimetric analysis did not significantly change, revealing the good thermal properties of the system. The phase-separated structure and the hydrogen bonds present in the system can be exploited to prepare synergistically tough polybenzoxazine exhibiting excellent thermal properties. This can be a potential way of modifying the thermoset resins.
Li, J, Guo, J & Zhu, X 2021, 'Time-Varying Parameter Identification of Bridges Subject to Moving Vehicles Using Ridge Extraction Based on Empirical Wavelet Transform', International Journal of Structural Stability and Dynamics, vol. 21, no. 04, pp. 2150046-2150046.
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For a vehicle moving over a bridge, the vehicle-bridge interaction (VBI) embraces the time-varying modal parameters of the system. The identification of non-stationary characteristics of bridge responses due to moving vehicle load is important and remains a challenging task. A new method based on the improved empirical wavelet transform (EWT) along with ridge detection of signals in time-frequency representation (TFR) is proposed to estimate the instantaneous frequencies (IFs) of the bridge. Numerical studies are conducted using a VBI model to investigate the time-varying characteristics of the system. The effects of the measurement noise, road surface roughness and structural damage on the bridge IFs are investigated. Finally, the dynamic responses of an in-situ cable-stayed bridge subjected to a passing vehicle are analyzed to further explore the time varying characteristics of the VBI system. Numerical and experimental studies demonstrate the feasibility and effectiveness of the proposed method on the IF estimation. The identified IFs reveal important time-varying characteristics of the bridge dynamics that is significant to evaluating the actual performance of operational bridges in operation and may be used for structural health assessment.
Li, J, Jin, J, Lyu, L, Yuan, D, Yang, Y, Gao, L & Shen, C 2021, 'A fast and scalable authentication scheme in IOT for smart living', Future Generation Computer Systems, vol. 117, pp. 125-137.
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Li, J, Zheng, M, Shimoni, O, Banks, WA, Bush, AI, Gamble, JR & Shi, B 2021, 'Development of Novel Therapeutics Targeting the Blood–Brain Barrier: From Barrier to Carrier', Advanced Science, vol. 8, no. 16, pp. 1-27.
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AbstractThe blood–brain barrier (BBB) is a highly specialized neurovascular unit, initially described as an intact barrier to prevent toxins, pathogens, and potentially harmful substances from entering the brain. An intact BBB is also critical for the maintenance of normal neuronal function. In cerebral vascular diseases and neurological disorders, the BBB can be disrupted, contributing to disease progression. While restoration of BBB integrity serves as a robust biomarker of better clinical outcomes, the restrictive nature of the intact BBB presents a major hurdle for delivery of therapeutics into the brain. Recent studies show that the BBB is actively engaged in crosstalk between neuronal and the circulatory systems, which defines another important role of the BBB: as an interfacing conduit that mediates communication between two sides of the BBB. This role has been subject to extensive investigation for brain‐targeted drug delivery and shows promising results. The dual roles of the BBB make it a unique target for drug development. Here, recent developments and novel strategies to target the BBB for therapeutic purposes are reviewed, from both barrier and carrier perspectives.
Li, K, Lu, N, Zheng, J, Zhang, P, Ni, W & Tovar, E 2021, 'BloothAir', ACM Transactions on Cyber-Physical Systems, vol. 5, no. 3, pp. 1-22.
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Thanks to flexible deployment and excellent maneuverability, autonomous drones have been recently considered as an effective means to act as aerial data relays for wireless ground devices with limited or no cellular infrastructure, e.g., smart farming in a remote area. Due to the broadcast nature of wireless channels, data communications between the drones and the ground devices are vulnerable to eavesdropping attacks. This article develops BloothAir, which is a secure multi-hop aerial relay system based on Bluetooth Low Energy ( BLE ) connected autonomous drones. For encrypting the BLE communications in BloothAir, a channel-based secret key generation is proposed, where received signal strength at the drones and the ground devices is quantized to generate the secret keys. Moreover, a dynamic programming-based channel quantization scheme is studied to minimize the secret key bit mismatch rate of the drones and the ground devices by recursively adjusting the quantization intervals. To validate the design of BloothAir, we build a multi-hop aerial relay testbed by using the MX400 drone platform and the Gust radio transceiver, which is a new lightweight onboard BLE communicator specially developed for the drone. Extensive real-world experiments demonstrate that the BloothAir system achieves a significantly lower secret key bit mismatch rate than the key generation benchmarks, which use the static quantization intervals. In addition, the high randomness of the generated secret keys is verified by the standard NIST test, thereby effectively protecting the BLE communications in BloothAir from the eavesdropping attacks.
Li, K, Ni, W, Tovar, E & Guizani, M 2021, 'Joint Flight Cruise Control and Data Collection in UAV-Aided Internet of Things: An Onboard Deep Reinforcement Learning Approach', IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9787-9799.
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Employing unmanned aerial vehicles (UAVs) as aerial data collectors in Internet-of-Things (IoT) networks is a promising technology for large-scale environment sensing. A key challenge in UAV-aided data collection is that UAV maneuvering gives rise to buffer overflow at the IoT node and unsuccessful transmission due to lossy airborne channels. This article formulates a joint optimization of flight cruise control and data collection schedule to minimize network data loss as a partially observable Markov decision process (POMDP), where the states of individual IoT nodes can be obscure to the UAV. The problem can be optimally solvable by reinforcement learning, but suffers from the curse of dimensionality and becomes rapidly intractable with the growth in the number of IoT nodes. In practice, a UAV-aided IoT network contains a large number of network states and actions in POMDP while the up-to-date knowledge is not available at the UAV. We propose an onboard deep Q -network-based flight resource allocation scheme (DQN-FRAS) to optimize the online flight cruise control of the UAV and data scheduling given outdated knowledge on the network states. Numerical results demonstrate that DQN-FRAS reduces the packet loss by over 51%, as compared to existing nonlearning heuristics.
Li, K, Ni, W, Tovard, E & Jamalipour, A 2021, 'Online Velocity Control and Data Capture of Drones for the Internet of Things: An Onboard Deep Reinforcement Learning Approach', IEEE Vehicular Technology Magazine, vol. 16, no. 1, pp. 49-56.
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Applications of unmanned aerial vehicles (UAVs) for data collection are a promising means to extend Internet of Things (IoT) networks to remote and hostile areas and to locations where there is no access to power supplies. The adequate design of UAV velocity control and communication decision making is critical to minimize the data packet losses at ground IoT nodes that result from overflowing buffers and transmission failures. However, online velocity control and communication decision making are challenging in UAV-enabled IoT networks, due to a UAV?s lack of up-to-date knowledge about the state of the nodes, e.g., the battery energy, buffer length, and channel conditions.
Li, K, Ni, W, Zheng, J, Tovar, E & Guizani, M 2021, 'Confidentiality and Timeliness of Data Dissemination in Platoon-based Vehicular Cyber-Physical Systems', IEEE Network, vol. 35, no. 4, pp. 248-254.
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Li, M, Cao, Z & Li, Z 2021, 'A Reinforcement Learning-Based Vehicle Platoon Control Strategy for Reducing Energy Consumption in Traffic Oscillations', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 12, pp. 5309-5322.
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The vehicle platoon will be the most dominant driving mode on future roads. To the best of our knowledge, few reinforcement learning (RL) algorithms have been applied in vehicle platoon control, which has large-scale action and state spaces. Some RL-based methods were applied to solve single-agent problems. If we need to tackle multiagent problems, we will use multiagent RL algorithms since the parameters space grows exponentially with the increasing number of agents involved. Previous multiagent RL algorithms generally may provide redundant information to agents, indicating a large amount of useless or unrelated information, which may cause to be difficult for convergence training and pattern extractions from shared information. Also, random actions usually contribute to crashes, especially at the beginning of training. In this study, a communication proximal policy optimization (CommPPO) algorithm was proposed to tackle the above issues. In specific, the CommPPO model adopts a parameter-sharing structure to allow the dynamic variation of agent numbers, which can well handle various platoon dynamics, including splitting and merging. The communication protocol of the CommPPO consists of two parts. In the state part, the widely used predecessor-leader follower typology in the platoon is adopted to transmit global and local state information to agents. In the reward part, a new reward communication channel is proposed to solve the spurious reward and ``lazy agent'' problems in some existing multiagent RLs. Moreover, a curriculum learning approach is adopted to reduce crashes and speed up training. To validate the proposed strategy for platoon control, two existing multiagent RLs and a traditional platoon control strategy were applied in the same scenarios for comparison. Results showed that the CommPPO algorithm gained more rewards and achieved the largest fuel consumption reduction (11.6%).
Li, M, Chen, Q, Wen, K, Nimbalkar, S & Dai, R 2021, 'Improved Vacuum Preloading Method Combined with Sand Sandwich Structure for Consolidation of Dredged Clay-Slurry Fill and Original Marine Soft Clay', International Journal of Geomechanics, vol. 21, no. 10, pp. 04021182-04021182.
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Li, M, Liu, Y & Guo, YJ 2021, 'Design of Sum and Difference Patterns by Optimizing Element Rotations and Positions for Linear Dipole Array', IEEE Transactions on Antennas and Propagation, vol. 69, no. 5, pp. 3027-3032.
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IEEE This communication presents a novel method of synthesizing both sum and difference patterns by optimizing the element rotations and positions for linear dipole array. The common element rotations and positions are optimized by using the particle swarm optimization (PSO) method to produce sum and difference patterns with reduced sidelobe levels (SLLs) and cross-polarization levels (XPLs), and as steep slope as possible for the difference pattern at the target direction. Such method leads to a sum-and-difference array with sparsely distributed uniform amplitude elements, thus saving many antenna elements and unequal power dividers. Three examples for synthesizing sparse rotated dipole arrays with sum and difference patterns are provided. Synthesis results show that the obtained arrays with uniform amplitudes can produce satisfactory sum and difference patterns while saving about 34.69% ~ 42.27% of the antenna elements when compared with λ/2-spaced arrays occupying the same aperture.
Li, M, Wang, B & Jiang, J 2021, 'Siamese Pre-Trained Transformer Encoder for Knowledge Base Completion', Neural Processing Letters, vol. 53, no. 6, pp. 4143-4158.
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In this paper, we aim at leveraging a Siamese textual encoder to efficiently and effectively tackle knowledge base completion problem. Traditional graph embedding-based methods straightforwardly learn the embeddings by considering a knowledge base’s structure but are inherently vulnerable to the graph’s sparsity or incompleteness issue. In contrast, previous textual encoding-based methods capture such structured knowledge from a semantic perspective and employ deep neural textual encoder to model graph triples in semantic space, but they fail to trade off the contextual features with model’s efficiency. Therefore, in this paper we propose a Siamese textual encoder operating on each graph triple from the knowledge base, where the contextual features between a head/tail entity and a relation are well-captured to highlight relation-aware entity embedding while a Siamese structure is also adapted to avoid combinatorial explosion during inference. In the experiments, the proposed method reaches state-of-the-art or comparable performance on several link prediction datasets. Further analyses demonstrate that the proposed method is much more efficient than its baseline with similar evaluating results.
Li, M, Yang, Y, Iacopi, F, Yamada, M & Nulman, J 2021, 'Compact Multilayer Bandpass Filter Using Low-Temperature Additively Manufacturing Solution', IEEE Transactions on Electron Devices, vol. 68, no. 7, pp. 3163-3169.
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This article presented an additively manufactured bandpass filter (BPF) based on a second-order stub-loaded resonator consisting of multimetal layer components. The proposed BPF is fabricated by a low-temperature (140°) additively manufactured electronics (AME) solution that can fabricate conductive and dielectric materials simultaneously with multimetal-layer and flexible interlayer distance. By reducing the interlayer distance, constant inductance and capacitance can be realized in smaller sizes, which helps to achieve device minimization. Taking advantage of this inkjet printing technology, a second-order multimetal layer resonator is proposed. To understand the principle of the BPF, an equivalent circuit with odd- and even-mode analysis is demonstrated. For verification, the frequency response of the circuit's mathematical model is calculated to compare with the electromagnetic simulation results. Good agreement can be achieved among the calculated, simulated, and measured results. The proposed BPF is designed at 12.25 GHz with a bandwidth of 40.8% and a compact size of 2.7 mm \times1.425 mm \times0.585 mm or 0.186\lambda {g} \times 0.098\lambda {g}\times 0.040\lambda {g} , which is suitable for circuit-in-package applications in television programs, radar detection, and satellite communications.
Li, P, Li, W, Sun, Z, Shen, L & Sheng, D 2021, 'Development of sustainable concrete incorporating seawater: A critical review on cement hydration, microstructure and mechanical strength', Cement and Concrete Composites, vol. 121, pp. 104100-104100.
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Many countries are experiencing freshwater crises due to the increasing growth of the population together with the infrastructure construction that is aligned with the needs of freshwater for concrete production. There are also deficiencies in freshwater in many coastal areas where seawater is more accessible. To reduce unnecessary resource-wasting and meanwhile drive sustainable development in the construction industry, great efforts have been made to utilize seawater as the alternative mixing water for concrete casting, which presents potential economical and environmental benefits in the coastal and island regions. This paper comprehensively reviews the current studies on the predominant performance differences between seawater-mixed and conventional concretes with freshwater. Particular attention is paid to the chloride-induced hydration mechanism due to the chloride ions in seawater. The main findings of this review reveal that although harmful ingredients in seawater may weaken some of the concrete performances, applying proper curing conditions and adding moderate additives and admixtures could significantly and effectively mitigate these defects in properties. However, the unstable chloride binding ability in cement hydrates cannot eliminate the risk of rebar corrosion caused by chlorides in seawater, resulting in a limited scope of practical application. Finally, some trade-offs are recommended in using seawater in concrete, suggesting prospects of applications in the future construction industry. This study guides for the safer use of seawater in sustainable concrete through reviewing the advanced research progress.
Li, Q, Meng, S, Sang, X, Zhang, H, Wang, S, Bashir, AK, Yu, K & Tariq, U 2021, 'Dynamic Scheduling Algorithm in Cyber Mimic Defense Architecture of Volunteer Computing', ACM Transactions on Internet Technology, vol. 21, no. 3, pp. 1-33.
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Volunteer computing uses computers volunteered by the general public to do distributed scientific computing. Volunteer computing is being used in high-energy physics, molecular biology, medicine, astrophysics, climate study, and other areas. These projects have attained unprecedented computing power. However, with the development of information technology, the traditional defense system cannot deal with the unknown security problems of volunteer computing . At the same time, Cyber Mimic Defense (CMD) can defend the unknown attack behavior through its three characteristics: dynamic, heterogeneous, and redundant. As an important part of the CMD, the dynamic scheduling algorithm realizes the dynamic change of the service centralized executor, which can enusre the security and reliability of CMD of volunteer computing . Aiming at the problems of passive scheduling and large scheduling granularity existing in the existing scheduling algorithms, this article first proposes a scheduling algorithm based on time threshold and task threshold and realizes the dynamic randomness of mimic defense from two different dimensions; finally, combining time threshold and random threshold, a dynamic scheduling algorithm based on multi-level queue is proposed. The experiment shows that the dynamic scheduling algorithm based on multi-level queue can take both security and reliability into account, has better dynamic heterogeneous redundancy characteristics, and can effectively prevent the transformation rule of heterogeneous executors from being mastered by attackers.
Li, Q, Tian, Y, Wu, D, Gao, W, Yu, Y, Chen, X & Yang, C 2021, 'The nonlinear dynamic buckling behaviour of imperfect solar cells subjected to impact load', Thin-Walled Structures, vol. 169, pp. 108317-108317.
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Solar cells are becoming a strong competitor in the new energy market due to their superior ability to generate electricity in an environmentally friendly and sustainable manner. The present study is devoted to presenting a theoretical framework for nonlinear dynamic buckling behaviours of imperfect multilayer solar cells subjected to impact loading resting on the elastic foundation. Two types of solar cell models, namely, organic solar cell (OSC) and perovskite solar cell (PSC), with simply supported and clamped boundary conditions are investigated. Sinusoidal, exponential, rectangular, and damping pulse functions are considered Based on the first-order shear deformation plate theory, the nonlinearity are introduced with the aid of von Kármán theory. The equations of the dynamic system of the plate with the consideration of large-deflection are derived by the Galerkin method and then solved by the fourth-order Runge–Kutta methods. After validation, some parametric experiments are performed to explore the influences of the pulse duration, pulse function pulse amplitude, initial imperfection, boundary conditions, Winkler–Pasternak elastic foundation coefficients, and damping ratios on the dynamic stability of the structures.
Li, R-H, Dai, Q, Qin, L, Wang, G, Xiao, X, Yu, JX & Qiao, S 2021, 'Signed Clique Search in Signed Networks: Concepts and Algorithms.', IEEE Trans. Knowl. Data Eng., vol. 33, no. 2, pp. 710-727.
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© 1989-2012 IEEE. Mining cohesive subgraphs from a network is a fundamental problem in network analysis. Most existing cohesive subgraph models are mainly tailored to unsigned networks. In this paper, we study the problem of seeking cohesive subgraphs in a signed network, in which each edge can be positive or negative, denoting friendship or conflict, respectively. We propose a novel model, called maximal (α, k)(α,k)-clique, that represents a cohesive subgraph in signed networks. Specifically, a maximal (α, k)(α,k)-clique is a clique in which every node has at most kk negative neighbors and at least ⌈ α k ⌈αk⌉ positive neighbors (α ≥q 1α≥1). We show that the problem of enumerating all maximal (α, k)(α,k)-cliques in a signed network is NP-hard. To enumerate all maximal (α, k)(α,k)-cliques efficiently, we first develop an elegant signed network reduction technique to significantly prune the signed network. Then, we present an efficient branch and bound enumeration algorithm with several carefully-designed pruning rules to enumerate all maximal (α, k) (α,k)-cliques in the reduced signed network. In addition, we also propose an efficient algorithm with three novel upper-bounding techniques to find the maximum (α, k) (α,k)-clique in a signed network. The results of extensive experiments on five large real-life datasets demonstrate the efficiency, scalability, and effectiveness of our algorithms.
Li, S, Li, W, Wen, S, Shi, K, Yang, Y, Zhou, P & Huang, T 2021, 'Auto-FERNet: A Facial Expression Recognition Network With Architecture Search', IEEE Transactions on Network Science and Engineering, vol. 8, no. 3, pp. 2213-2222.
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Deep convolutional neural networks have achieved great success in facial expression datasets both under laboratory conditions and in the wild. However, most of these related researches use general image classification networks (e.g., VGG, GoogLeNet) as backbones, which leads to inadaptability while applying to Facial Expression Recognition (FER) task, especially those in the wild. In the meantime, these manually designed networks usually have large parameter size. To tackle with these problems, we propose an appropriative and lightweight Facial Expression Recognition Network Auto-FERNet, which is automatically searched by a differentiable Neural Architecture Search (NAS) model directly on FER dataset. Furthermore, for FER datasets in the wild, we design a simple yet effective relabeling method based on Facial Expression Similarity (FES) to alleviate the uncertainty problem caused by natural factors and the subjectivity of annotators. Experiments have shown the effectiveness of the searched Auto-FERNet on FER task. Concretely, our architecture achieves a test accuracy of 73.78% on FER2013 without ensemble or extra training data. And noteworthily, experimental results on CK+ and JAFFE outperform the state-of-The-Art with an accuracy of 98.89% (10 folds) and 97.14%, respectively, which also validate the robustness of our system.
Li, S, Li, Y & Li, J 2021, 'Thixotropy of magnetorheological gel composites: Experimental testing and modelling', Composites Science and Technology, vol. 214, pp. 108996-108996.
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Li, S, Liao, S, Yang, Y, Che, W & Xue, Q 2021, 'Low-Profile Circularly Polarized Isoflux Beam Antenna Array Based on Annular Aperture Elements for CubeSat Earth Coverage Applications', IEEE Transactions on Antennas and Propagation, vol. 69, no. 9, pp. 5489-5502.
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This article presents an antenna array to produce a circularly polarized (CP) isoflux beam for CubeSat earth coverage applications. Distinguished from conventional arrays formed by identical elements, the proposed one is based on sequential rotation-fed concentric annular aperture elements. By adjusting the excitation phase, magnitude, and polarization of each annular aperture element, the radiation patterns of the array can be shaped to form isoflux beams. Radiation pattern modeling method is developed to obtain array radiation parameters. Besides, to effectively characterize the impedance matching of the radiators of the array with multiple excitations, an overall reflection coefficient is deduced from signal decomposition. A prototype operating at C -band (5 GHz) is designed, fabricated, and measured. It consists of a radiator and a feeding network. The radiator is formed by two annular aperture elements, of which the inner one is realized by a circular patch antenna while the outer one is formed by eight planar inverted-F antennas (PIFAs). The feeding network drives annular aperture elements in the manner of sequential rotation. In this way, a CP isoflux beam array with a low profile ( 0.078~{\lambda }_{0}) , lightweight, and ease-of-integration for CubeSat is realized. The proposed array can also be extended to realize other shaped beams.
Li, S, Zhou, X & Feng, Y 2021, 'Qubit Mapping Based on Subgraph Isomorphism and Filtered Depth-Limited Search', IEEE Transactions on Computers, vol. 70, no. 11, pp. 1777-1788.
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Mapping logical quantum circuits to Noisy Intermediate-Scale Quantum (NISQ) devices is a challenging problem which has attracted rapidly increasing interests from both quantum and classical computing communities. This article proposes an efficient method by (i) selecting an initial mapping that takes into consideration the similarity between the architecture graph of the given NISQ device and a graph induced by the input logical circuit and (ii) searching, in a filtered and depth-limited way, a most useful swap combination that makes executable as many as possible two-qubit gates in the logical circuit. The proposed circuit transformation algorithm can significantly decrease the number of auxiliary two-qubit gates required to be added to the logical circuit, especially when it has a large number of two-qubit gates. For an extensive benchmark set of 131 circuits and IBM's current premium Q system, viz., IBM Q Tokyo, our algorithm needs, in average, 0.3801 extra two-qubit gates per input two-qubit gate, while the corresponding figures for three state-of-the-art algorithms are 0.4705, 0.8154, and 1.0066, respectively.
Li, W, Dong, W, Castel, A & Sheng, D 2021, 'Self-sensing cement-based sensors for structural health monitoring toward smart infrastructure', Journal and Proceedings of the Royal Society of New South Wales, vol. 154, pp. 24-32.
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Since its first appearance more than 100 years ago, concrete has had a significant impact on urban development — buildings, roads, bridges, ports, tunnels, railways and other structures. While traditional concrete is a structural material without any function, a new branch of concrete technology has produced smart (or intelligent) concrete, with superior self-sensing capabilities that can detect stress, strain, cracks and damage, and monitor temperature and humidity. With the incorporation of functional conductive fillers, traditional concrete can exhibit electrical conductivity with intrinsic piezoresistivity. This piezoresistivity means that the electrical resistivity of concrete is synchronously altered under applied load or environmental factors. The self-sensing electrical resistivity thus obtained can be an index or parameter to detect stress or strain changes in concrete, or cracks and damage to concrete. On the other hand, because of the relationship between electrical resistivity, temperature and humidity, self-sensing concrete can also monitor environmental factors. This smart self-sensing concrete can therefore be a promising alternative to conventional sensors for monitoring structural health and detecting traffic information from concrete roads, all of which are critical to achieving smart automation in concrete infrastructures.
Li, W, Ji, J, Huang, L & Guo, Z 2021, 'Global dynamics of a controlled discontinuous diffusive SIR epidemic system', Applied Mathematics Letters, vol. 121, pp. 107420-107420.
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In this work, we investigate the global dynamics of a controlled discontinuous diffusive SIR epidemic system under Neumann boundary conditions. We first establish the conditions for the existence of the solution and obtain the boundedness of the solution for the controlled discontinuous diffusive system. Then, under the framework of differential inclusion, we study the existence of constant equilibria of the controlled diffusive epidemic system. Furthermore, we discuss the global dynamical behaviour of the controlled discontinuous diffusive epidemic system.
Li, W, Luo, Z, Gan, Y, Wang, K & Shah, SP 2021, 'Nanoscratch on mechanical properties of interfacial transition zones (ITZs) in fly ash-based geopolymer composites', Composites Science and Technology, vol. 214, pp. 109001-109001.
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Interfacial transition zones (ITZs) of cementitious concrete are highly heterogeneous, which cause many challenges in accurately obtaining their properties. In this paper, regular aggregates were applied to prepare modelled geopolymer composites, in which ITZs exhibited neat boundaries. Nanoscratch technique with the ability to quickly scan a long distance was adopted to investigate mechanical properties of ITZ and geopolymer paste. To compare the properties of the ITZs and paste, abundant scratch data were analyzed in the form of histograms and Gaussian mixture models. The results showed that the ITZs in geopolymer with silica modulus of 1.5 presented similar properties with the paste, while the ITZs in geopolymer with silica modulus of 1.0 showed significantly higher scratch hardness but lower scratch friction coefficient than paste. Deconvolution analysis revealed that the abnormal hardness and friction coefficient of the paste in geopolymer with silica modulus of 1.0 could be caused by the defects related points. Compared with the traditional scratch scheme, the parallel scratch scheme based on modelled ITZ gave more stable results with a given number of test data, which can provide in-depth information for comparative studies.
Li, W, Wang, G-G & Gandomi, AH 2021, 'A Survey of Learning-Based Intelligent Optimization Algorithms', Archives of Computational Methods in Engineering, vol. 28, no. 5, pp. 3781-3799.
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A large number of intelligent algorithms based on social intelligent behavior have been extensively researched in the past few decades, through the study of natural creatures, and applied to various optimization fields. The learning-based intelligent optimization algorithm (LIOA) refers to an intelligent optimization algorithm with a certain learning ability. This is how the traditional intelligent optimization algorithm combines learning operators or specific learning mechanisms to give itself some learning ability, thereby achieving better optimization behavior. We conduct a comprehensive survey of LIOAs in this paper. The research includes the following sections: Statistical analysis about LIOAs, classification of LIOA learning method, application of LIOAs in complex optimization scenarios, and LIOAs in engineering applications. The future insights and development direction of LIOAs are also discussed.
Li, X, Guan, R, Ou, K, Fu, Q, Yang, G & Sun, Y 2021, 'Ultra-high stability and magnetic response of magnetorheological fluids based on magnetic ionic liquids and carbonyl iron fibers', Journal of Rheology, vol. 65, no. 6, pp. 1347-1359.
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Li, X, He, Y, Zhang, JA & Jing, X 2021, 'Supervised Domain Adaptation for Few-Shot Radar-Based Human Activity Recognition', IEEE Sensors Journal, vol. 21, no. 22, pp. 25880-25890.
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With the application of deep learning (DL) techniques, radar-based human activity recognition (HAR) attracts increasing attention thanks to its high accuracy and good privacy. However, training a DL model requires a large volume of data, and generally the trained model cannot be adapted to a new scenario. In this paper, we propose a supervised few-shot adversarial domain adaptation (FS-ADA) method for HAR, where only limited radar training data is collected from a new application scenario. We adopt the domain adaptation method to learn a common feature space between a pre-existing radar dataset and the newly acquired training data. We also design a multi-class discriminator network, which integrates the category classifier and the binary domain discriminator, to employ the supervised label information in the limited radar data for model training. Then, a multitask generative adversarial training mechanism is proposed to optimize FS-ADA. In this way, both domain-invariant and category-discriminative features can be extracted for HAR in a new scenario. Experimental results for two few-shot radar-based HAR tasks show that the proposed FS-ADA method is effective and outperforms state-of-the-art methods.
Li, X, Kulandaivelu, J, O'Moore, L, Wilkie, S, Hanzic, L, Bond, PL, Yuan, Z & Jiang, G 2021, 'Synergistic effect on concrete corrosion control in sewer environment achieved by applying surface washing on calcium nitrite admixed concrete', Construction and Building Materials, vol. 302, pp. 124184-124184.
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Microbially influenced concrete corrosion (MICC) is often the major cause for the structure-failure in sewers. To mitigate the MICC, surface washing was considered as a cost efficient approach, however its effect is temporary and limited due to the fast re-establishment of sulfide oxidizing microorganisms (SOMs). Recently calcium nitrite admixed concrete was found as a promising approach to mitigate MICC. In this study, we hypothesize that applying a single surface washing on calcium nitrite admixed concrete would yield synergistic and effective control of MICC in sewers. The corrosion development and re-establishment after high-pressure washing on concrete coupons without calcium nitrite and with 1% (N1) and 4% (N4) (by the weight of cement) of calcium nitrite was investigated by exposing these coupons in a pilot-scale gravity sewer system for sixteen months. The corrosion process was monitored by measuring surface pH, corrosion product composition, corrosion loss and the microbial community. With one wash, the corrosion loss reduced by 45% and 58% in N1 and N4 coupons, respectively. The corrosion mitigation effect of surface washing for coupons without calcium nitrite was negligible. The combined application of surface washing and calcium nitrite admixed concrete showed synergetic effects and promising efficiency in MICC mitigation.
Li, X, Kulandaivelu, J, Zhang, S, Shi, J, Sivakumar, M, Mueller, J, Luby, S, Ahmed, W, Coin, L & Jiang, G 2021, 'Data-driven estimation of COVID-19 community prevalence through wastewater-based epidemiology', Science of The Total Environment, vol. 789, pp. 147947-147947.
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Wastewater-based epidemiology (WBE) has been regarded as a potential tool for the prevalence estimation of coronavirus disease 2019 (COVID-19) in the community. However, the application of the conventional back-estimation approach is currently limited due to the methodological challenges and various uncertainties. This study systematically performed meta-analysis for WBE datasets and investigated the use of data-driven models for the COVID-19 community prevalence in lieu of the conventional WBE back-estimation approach. Three different data-driven models, i.e. multiple linear regression (MLR), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS) were applied to the multi-national WBE dataset. To evaluate the robustness of these models, predictions for sixteen scenarios with partial inputs were compared against the actual prevalence reports from clinical testing. The performance of models was further validated using unseen data (data sets not included for establishing the model) from different stages of the COVID-19 outbreak. Generally, ANN and ANFIS models showed better accuracy and robustness over MLR models. Air and wastewater temperature played a critical role in the prevalence estimation by data-driven models, especially MLR models. With unseen datasets, ANN model reasonably estimated the prevalence of COVID-19 (cumulative cases) at the initial phase and forecasted the upcoming new cases in 2-4 days at the post-peak phase of the COVID-19 outbreak. This study provided essential information about the feasibility and accuracy of data-driven estimation of COVID-19 prevalence through the WBE approach.
Li, X, Li, M, Mei, Q, Niu, S, Wang, X, Xu, H, Dong, B, Dai, X & Zhou, JL 2021, 'Aging microplastics in wastewater pipeline networks and treatment processes: Physicochemical characteristics and Cd adsorption', Science of The Total Environment, vol. 797, pp. 148940-148940.
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Despite a wealth of information on removal of the microplastics (MPs) in wastewater treatment plants (WWTPs), little attention has been paid to how wastewater treatment process affect the MP physicochemical and adsorption characteristics. In this study, changes in physicochemical property of three MPs, i.e. polyamide (PA), polyethylene (PE) and polystyrene (PS) through the wastewater pipeline, grit and biological aeration tanks were investigated. The results show that compared with virgin MPs, the treated MPs have higher specific surface area and O content, and lower C and H contents, and glass transition temperature, implying that the three treatments cause the chain scission and oxidation of the MPs. Cd adsorption capacities of the MPs are higher than the corresponding virgin MPs after sulfidation in the pipeline (SWPN) and biological treatment in aeration tank (BTAT). Pearson correlation analysis shows that the increase is mainly resulted from the enhancement of the O-containing groups on the MPs. However, Cd adsorption capacities of the MPs decrease after mechanical abrasion in grit tank (MAGT), corresponding to the decrease in carbonyl index. Two dimensional FTIR correlation spectroscopy demonstrates that the NH bond in the PA plays a more important role than CH bond in the adsorption of Cd, but only change of the CH bond is found in the PE and PS. The findings provide new insights into the effect of WWTPs on the MP aging and physicochemical characteristics.
Li, X, Xia, J, Cao, L, Zhang, G & Feng, X 2021, 'Driver fatigue detection based on convolutional neural network and face alignment for edge computing device', Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 235, no. 10-11, pp. 2699-2711.
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Most current vision-based fatigue detection methods don’t have high-performance and robust face detector. They detect driver fatigue using single detection feature and cannot achieve real-time efficiency on edge computing devices. Aimed at solving these problems, this paper proposes a driver fatigue detection system based on convolutional neural network that can run in real-time on edge computing devices. The system firstly uses the proposed face detection network LittleFace to locate the face and classify the face into two states: small yaw angle state “normal” and large yaw angle state “distract.” Secondly, the speed-optimized SDM algorithm is conducted only in the face region of the “normal” state to deal with the problem that the face alignment accuracy decreases at large angle profile, and the “distract” state is used to detect driver distraction. Finally, feature parameters EAR, MAR and head pitch angle are calculated from the obtained landmarks and used to detect driver fatigue respectively. Comprehensive experiments are conducted to evaluate the proposed system and the results show its practicality and superiority. Our face detection network LittleFace can achieve 88.53% mAP on AFLW test set at 58 FPS on the edge computing device Nvidia Jetson Nano. Evaluation results on YawDD, 300 W, and DriverEyes show the average detection accuracy of the proposed system can reach 89.55%.
Li, X, Xiang, J, Wang, J, Li, J, Wu, F-X & Li, M 2021, 'FUNMarker: Fusion Network-Based Method to Identify Prognostic and Heterogeneous Breast Cancer Biomarkers', IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 6, pp. 2483-2491.
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Breast cancer is a heterogeneous disease with many clinically distinguishable molecular subtypes each corresponding to a cluster of patients. Identification of prognostic and heterogeneous biomarkers for breast cancer is to detect cluster-specific gene biomarkers which can be used for accurate survival prediction of breast cancer outcomes. In this study, we proposed a FUsion Network-based method (FUNMarker) to identify prognostic and heterogeneous breast cancer biomarkers by considering the heterogeneity of patient samples and biological information from multiple sources. To reduce the affect of heterogeneity of patients, samples were first clustered using the K-means algorithm based on the principal components of gene expression. For each cluster, to comprehensively evaluate the influence of genes on breast cancer, genes were weighted from three aspects: biological function, prognostic ability and correlation with known disease genes. Then they were ranked via a label propagation model on a fusion network that combined physical protein interactions from seven types of networks and thus could reduce the impact of incompleteness of interactome. We compared FUNMarker with three state-of-the-art methods and the results showed that biomarkers identified by FUNMarker were biological interpretable and had stronger discriminative power than the existing methods in differentiating patients with different prognostic outcomes.
Li, X, Zhang, J, Shen, L, Qin, L, Fu, Q, Sun, Y & Liu, Y 2021, 'Magnetoresistive micro-displacement sensor based on magnetorheological fluid', Smart Materials and Structures, vol. 30, no. 4, pp. 045025-045025.
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Abstract A novel magnetoresistance material based on magnetorheological fluid (MRF) was developed for applications in micro-displacement sensor. The MRF samples were fabricated by dispersing carbonyl iron particles (CIP) into a magnetic ion liquid (MIL) composed of 1-methylethyl ether-3-butylimidazole cation and [Fe2Cl7]− anions. The magnetoresistance characteristics were also systematically tested. It was found that the resistance value of MRF with a CIP content of 20 vol% decreased from 125 to 24.4 KΩ when increasing the magnetic field from 0 to 0.2 T. A sensor device was developed to study the displacement sensing characteristics of MRF, and found that the sensor had a high sensitivity of 0.1 Ω μm−1 and a high resolution of 10.0 μm. The excellent performance can be attributed to the low modulus and good stability of the MIL matrix, allowing for easy change of the resistance by controlling the magnetic field or displacement. In summary, these unique characters make the present MRF a promising magnetoresistance material with potential applications in displacement sensor.
Li, X, Zhang, S, Shi, J, Luby, SP & Jiang, G 2021, 'Uncertainties in estimating SARS-CoV-2 prevalence by wastewater-based epidemiology', Chemical Engineering Journal, vol. 415, pp. 129039-129039.
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Wastewater-based epidemiology (WBE) is a promising approach for estimating population-wide COVID-19 prevalence through detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater. However, various methodological challenges associated with WBE would affect the accuracy of prevalence estimation. To date, the overall uncertainty of WBE and the impact of each step on the prevalence estimation are largely unknown. This study divided the WBE approach into five steps (i.e., virus shedding; in-sewer transportation; sampling and storage; analysis of SARS-CoV-2 RNA concentration in wastewater; back-estimation) and further summarized and quantified the uncertainties associated with each step through a systematic review. Although the shedding of SARS-CoV-2 RNA varied greatly between COVID-19 positive patients, with more than 10 infected persons in the catchment area, the uncertainty caused by the excretion rate became limited for the prevalence estimation. Using a high-frequency flow-proportional sampling and estimating the prevalence through actual water usage data significantly reduced the overall uncertainties to around 20-40% (relative standard deviation, RSD). And under such a scenario, the analytical uncertainty of SARS-CoV-2 RNA in wastewater was the dominant factor. This highlights the importance of using surrogate viruses as internal or external standards during the wastewater analysis, and the need for further improvement on analytical approaches to minimize the analytical uncertainty. This study supports the application of WBE as a complementary surveillance strategy for monitoring COVID-19 prevalence and provides methodological improvements and suggestions to enhance the reliability for future studies.
Li, Y, Huang, C, Ngo, HH, Yin, S, Dong, Z, Zhang, Y, Chen, Y, Lu, Y & Guo, W 2021, 'Analysis of event stratigraphy and hydrological reconstruction of low-frequency flooding: A case study on the Fenhe River, China', Journal of Hydrology, vol. 603, pp. 127083-127083.
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Li, Y, Lei, G, Bramerdorfer, G, Peng, S, Sun, X & Zhu, J 2021, 'Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions', Applied Sciences, vol. 11, no. 4, pp. 1627-1627.
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This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.
Li, Y, Shi, W, Liu, Z, Li, J, Wang, Q, Yan, X, Cao, Z & Wang, G 2021, 'Effective Brain State Estimation During Propofol-Induced Sedation Using Advanced EEG Microstate Spectral Analysis', IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 4, pp. 978-987.
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Brain states are patterns of neuronal synchrony, and the electroencephalogram (EEG) microstate provides a promising tool to characterize and analyze the synchronous neural firing. However, the topographical spectral information for each predominate microstate is still unclear during the switch of consciousness, such as sedation, and the practical usage of the EEG microstate is worth probing. Also, the mechanism behind the anesthetic-induced alternations of brain states remains poorly understood. In this study, an advanced EEG microstate spectral analysis was utilized using multivariate empirical mode decomposition in Hilbert-Huang transform. The practicability was further investigated in scalp EEG recordings during the propofol-induced transition of consciousness. The process of transition from the awake baseline to moderate sedation was accompanied by apparent increases in microstate (A, B, and F) energy, especially in the whole-brain delta band, frontal alpha band and beta band. In comparison to other effective EEG-based parameters that commonly used to measure anesthetic depth, using the selected spectral features reached better performance (80% sensitivity, 90% accuracy) to estimate the brain states during sedation. The changes in microstate energy also exhibited high correlations with individual behavioral data during sedation. In a nutshell, the EEG microstate spectral analysis is an effective method to estimate brain states during propofol-induced sedation, giving great insights into the underlying mechanism. The generated spectral features can be promising markers to dynamically assess the consciousness level.
Li, Y, Wang, D, Yang, G, Yuan, X, Li, H, Wang, Q, Ni, B, He, D, Fu, Q, Jiang, L, Tang, W, Yang, F & Chen, H 2021, 'Comprehensive investigation into in-situ chemical oxidation of ferrous iron/sodium percarbonate (Fe(II)/SPC) processing dredged sediments for positive feedback of solid–liquid separation', Chemical Engineering Journal, vol. 425, pp. 130467-130467.
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Before disposal of dredged sediments (DS), filtrating DS is commonly used for their volume reduction. The work, for the first time, investigated Fe(II)/SPC processing DS to advance their solid–liquid separation from filtering feasibility, operational mechanism, technic reinforcement to potential implication. 16 mg Fe(II)/TSS & 60 mg SPC/TSS treatment elevated solid content of DS from 25.7% to 55.7% (vacuum filtration for 10 min), along with filtrate volume increased from 45.0 mL to 77.5 mL. •OH and Fe(III) with their hydrolyzed polymers, from Fe(II)/SPC system, are mainly lying behind the improved solid–liquid separation. Detailedly, the dilapidation of extracellular polymeric substances (EPS) with the destruction of biomolecules in EPS was completed by •OH invasion, which might rearrange the extracellular/intracellular protein configuration, with the increments of β-sheet & random coil but the decrement of α-helices. Simultaneously, Fe(III) and their hydrolyzed polymers promoted the relief of electrostatic repulsive-forces and the squeezing of double-electric layers, and the gathered DS could be held by integration of Fe(III) with –COOH and –OH. Additionally, CaO strengthened the filtering velocity/extent of Fe(II)/SPC-treated DS. After 70 mg/g CaO treatment, its solid content further elevated to 61.7% after vacuum filtration for 5.5 min, mainly resulting from skeleton construction by CaO, charge neutrality by released Ca2+, bridging cell debris and biopolymers by released Ca2+, compression of colloids double layers by released Ca2+, and binding PO43- in outer centrate liquid by released Ca2+.
Li, Y, Wang, D, Yang, G, Yuan, X, Yuan, L, Li, Z, Xu, Q, Liu, X, Yang, Q, Tang, W, Jiang, L, Li, H, Wang, Q & Ni, B 2021, 'In-depth research on percarbonate expediting zero-valent iron corrosion for conditioning anaerobically digested sludge', Journal of Hazardous Materials, vol. 419, pp. 126389-126389.
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Anaerobically digested sludge (ADS) is commonly hard to dewater for the presence of extracellular polymeric substances (EPS) and the liberation of glutinous soluble microbic products during anaerobic digestion. Sodium percarbonate (SPC) expediting zero-valent iron (ZVI) corrosion (SPC/ZVI) process firstly conditioned ADS to amend its dewaterability. Results showed that SPC/ZVI conditioning decreased moisture content of dewatered cake from 90.5% (control) to 69.9% with addition of 0.10 g/g TS SPC and 0.20 g/g TS ZVI. Mechanistic research indicated that the enhanced ADS dewaterability mainly resulted from •OH and Fe(III)/iron polymers yielded in SPC/ZVI. •OH disrupted EPS, damaged cytoderm & cytomembrane, and lysed intracellular substances, unbinding the bound water. Meanwhile, the breakage and inactivation of microbe by •OH prompted the production of macro-pores in ADS. •OH adjusted the conformation of extracellular/intracellular proteins by intervening in the H-bonds and S-S bonds, availing the hydrophobicity and slight flocculation of ADS. •OH further facilitated the despiralization of α-helical to β-sheet structure in ADS pellets, benefiting cell-to-cell aggregation. Additionally, Fe(III)/iron polymers from ZVI corrosion accelerated to gather ADS and maintained its floc structure. Consequently, SPC/ZVI conditioning not only adjusted the natures of ADS and its EPS but also the features of residual pellets, which further induced the advancement of ADS dewaterability. In addition, SPC/ZVI conditioning possibly surmounts some limitations existing in ZVI/Peroxide or ZVI/Persulfate technique.
Li, Y, Xue, B, Zhang, M, Zhang, L, Hou, Y, Qin, Y, Long, H, Su, QP, Wang, Y, Guan, X, Jin, Y, Cao, Y, Li, G & Sun, Y 2021, 'Transcription-coupled structural dynamics of topologically associating domains regulate replication origin efficiency', Genome Biology, vol. 22, no. 1, pp. 1-29.
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Abstract Background Metazoan cells only utilize a small subset of the potential DNA replication origins to duplicate the whole genome in each cell cycle. Origin choice is linked to cell growth, differentiation, and replication stress. Although various genetic and epigenetic signatures have been linked to the replication efficiency of origins, there is no consensus on how the selection of origins is determined. Results We apply dual-color stochastic optical reconstruction microscopy (STORM) super-resolution imaging to map the spatial distribution of origins within individual topologically associating domains (TADs). We find that multiple replication origins initiate separately at the spatial boundary of a TAD at the beginning of the S phase. Intriguingly, while both high-efficiency and low-efficiency origins are distributed homogeneously in the TAD during the G1 phase, high-efficiency origins relocate to the TAD periphery before the S phase. Origin relocalization is dependent on both transcription and CTCF-mediated chromatin structure. Further, we observe that the replication machinery protein PCNA forms immobile clusters around TADs at the G1/S transition, explaining why origins at the TAD periphery are preferentially fired. Conclusion Our work reveals a new origin selection mechanism that the replication efficiency of origins is determined by their physical distribution in the chromatin domain, which undergoes a transcription-dependent structural re-organization process. Our model explains the complex links between replication origin efficiency and many genetic and epigenetic signatures that mark active transcription. The coordination bet...
Li, Y, Yin, J & Chen, L 2021, 'SEAL: Semisupervised Adversarial Active Learning on Attributed Graphs', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 7, pp. 3136-3147.
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Li, Y, Zeng, X, Zhou, J, Shi, Y, Umar, HA, Long, G & Xie, Y 2021, 'Development of an eco-friendly ultra-high performance concrete based on waste basalt powder for Sichuan-Tibet Railway', Journal of Cleaner Production, vol. 312, pp. 127775-127775.
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Generally, tunnel waste is stacked in the slag field nearby for landfilling, which is harmful to sustainable development. The broken rocks and rock powder among the tunnel waste can be recycled to produce machine-made sand, producing many by-products calling rock powder. Based on the practical project, three types of waste basalt powder (BP), from tunnel excavation waste and by-products (rock powder) of machine-made sand producing from tunnel excavation waste in Sichuan-Tibet railway construction sites, was used to prepare an eco-friendly UHPC. The BP is used to replace the cement and is included in the design UHPC based on Modified Andreasen &Andersen particle packing model (MAA). Moreover, the chemical and physical behaviors and ecological evaluation of the designed UHPC and UHPC pasted were discussed. The results showed that when BP (Specific surface area 4.6582 m2/g) replaces up to 15%, the highest compressive strength of designed UHPC (220 MPa) was obtained. Compared with quartz powder, the pozzolanic activity of BP was generally low and increased with the increase of reaction temperature. However, the presence of BP and its fineness in UHPC pastes increased the values of the total autogenous shrinkage and decreased the total heat release at an early age of designed UHPC pastes, this effect is more pronounced with temperature increasing. Based on a quartering method with embodied carbon dioxide emissions and the compressive strength, UHPC with waste BP reduced embodied carbon dioxide and possessed higher compressive strength and lower environmental impact than the control samples of UHPC.
Li, Z, Du, H, Lu, J, Wu, L, He, L & Liu, H 2021, 'Self-assembly of antimony sulfide nanowires on three-dimensional reduced GO with superior electrochemical lithium storage performances', Chemical Physics Letters, vol. 771, pp. 138529-138529.
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Li, Z, Yang, C, Shen, Q & Wen, S 2021, 'A Document Image Dataset for Quality Assessment', Journal of Physics: Conference Series, vol. 1828, no. 1, pp. 012033-012033.
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Abstract Mobile device plays an very important role in capturing document image. However, the quality of the captured image is influenced by many factors, such as device quality and shooting conditions. In this context, it is necessary to automatically assess the quality of captured document image. Although there has a lot of work in the filed of image quality assessment (IQA), insufficient attention has been paid to the establishment of document images dataset. Thus, we propose a large dataset of document images containing 19,943 images which are collected by mobile devices. During the process of image acquisition, many factors such as light intensity, distortion type, document material are considered. After capturing images, multiple volunteers participated in the evaluation and collection of Mean Opinion Score (MOS) of the document images. We use two no-reference image quality assessment algorithms to test the proposed dataset. The experimental results show the validity of our dataset and the reliability of MOS. The proposed dataset can be used in the field of image quality assessment and Optical Character Recognition.
Li, Z-X, Zhang, X, Shi, Y, Wu, C & Li, J 2021, 'Finite element modeling of FRP retrofitted RC column against blast loading', Composite Structures, vol. 263, pp. 113727-113727.
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Fiber-reinforced polymer (FRP) wrap could considerably improve the shear capacity and ductility of RC columns. FRP is therefore considered a potential material to strengthen the RC column against blast loading. Due to the high expense and safety concern of field blast tests, a very limited number of explosion tests on FRP retrofitted RC columns have been conducted, which hinders the understanding of the response of FRP retrofitted RC columns against blast loading. With advanced computational technology, it is convenient to develop a Finite Element (FE) model that can accurately capture the structural response of FRP retrofitted columns under blast loading. In this paper, a refined FE model was established to simulate the FRP retrofitted RC columns under blast loading. Strain rate effects on the concrete and steel reinforcing bar as well as the FRP composite of which the strain rate effect was commonly ignored, were all considered in the model. Comprehensive modifications were made to the Karagozian and Case concrete (KCC) model to accurately capture the mechanical properties of FRP-confined concrete. Finally, the FE model was validated with several available experimental tests. The developed FE model could capture the blast response of FRP retrofitted columns with good accuracy.
Li, Z-X, Zhang, X, Shi, Y, Wu, C & Li, J 2021, 'Predication of the residual axial load capacity of CFRP-strengthened RC column subjected to blast loading using artificial neural network', Engineering Structures, vol. 242, pp. 112519-112519.
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In this study, two genetic algorithm optimized backpropagation neural networks (GA-BPNN) were established to predict the ratio of residual axial load capacity to the maximum axial load capacity (referred to as RCI hereafter) of the non- and CFRP-strengthened RC columns based on a huge amount of simulation data. The first one can be used to predict the residual axial load capacity of the damaged non- and CFRP-strengthened RC columns induced by blast load with the input of several parameters including column dimensions, concrete strength, transverse reinforcement ratio, longitudinal reinforcement ratio, axial load ratio, CFRP stiffness, carbon fiber strength, peak pressure and impulse of the blast load. Therefore it can be used for the blast-resistant design of non- and CFRP-strengthened RC columns. The input variables of the second GA-BPNN were changed to be the ratio of residual mid-height deflection to the column height after the explosion, column dimensions, concrete strength, transverse reinforcement ratio, longitudinal reinforcement ratio, CFRP stiffness and carbon fiber strength. Since the input variables of the second GA-BPNN could be easily derived after the explosion, thus it could be used for the rapid damage assessment of RC columns. Damage assessments for three non- and CFRP-strengthened columns were also conducted using the first GA-BPNN.
Lian, J-W, Ban, Y-L & Guo, YJ 2021, 'Wideband Dual-Layer Huygens’ Metasurface for High-Gain Multibeam Array Antennas', IEEE Transactions on Antennas and Propagation, vol. 69, no. 11, pp. 7521-7531.
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A wideband dual-layer Huygens’ unit cell based on offset electric dipole pair (OEDP) is proposed. Different from traditional designs with a combination of electric and magnetic polarizabilities, the proposed Huygens’ unit cell employs electric polarizabilities exclusively. By doing so, it practically avoids the unbalanced resonant frequencies between two polarizabilities, thereby achieving wideband transmission. Based on the proposed unit cell, a wideband and high-gain multibeam array antenna is developed. Firstly, a Rotman lens is designed by using a substrate integrated waveguide (SIW) technology. Then a parallel-fed slot antenna array is connected to the Rotman lens to generate multiple beams. Without using a series-fed slot antenna array, the multibeam array antenna based on Rotman lens can operate within a relatively wide bandwidth (28 GHz to 32 GHz). Secondly, a wideband dual-layer Huygens’ metasurface is developed that serves as a superstrate of the multibeam array antenna for increasing the antenna gain further. A wideband and high-gain multibeam array antenna is finally realized, which is comprised of a Rotman lens, a parallel-fed slot antenna array, and a Huygens’ metasurface. To verify the performance of this design, a prototype is fabricated and its measured results are compared to the simulated counterparts.
Liao, J, Zhou, J, Song, Y, Liu, B, Chen, Y, Wang, F, Chen, C, Lin, J, Chen, X, Lu, J & Jin, D 2021, 'Preselectable Optical Fingerprints of Heterogeneous Upconversion Nanoparticles', Nano Letters, vol. 21, no. 18, pp. 7659-7668.
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The control in optical uniformity of single nanoparticles and tuning their diversity in multiple dimensions, dot to dot, holds the key to unlocking nanoscale applications. Here we report that the entire lifetime profile of the single upconversion nanoparticle (τ2 profile) can be resolved by confocal, wide-field, and super-resolution microscopy techniques. The advances in both spatial and temporal resolutions push the limit of optical multiplexing from microscale to nanoscale. We further demonstrate that the time-domain optical fingerprints can be created by utilizing nanophotonic upconversion schemes, including interfacial energy migration, concentration dependency, energy transfer, and isolation of surface quenchers. We exemplify that three multiple dimensions, including the excitation wavelength, emission color, and τ2 profile, can be built into the nanoscale derivative τ2-dots. Creating a vast library of individually preselectable nanotags opens up a new horizon for diverse applications, spanning from sub-diffraction-limit data storage to high-throughput single-molecule digital assays and super-resolution imaging.
Liao, J, Zhou, J, Song, Y, Liu, B, Lu, J & Jin, D 2021, 'Optical Fingerprint Classification of Single Upconversion Nanoparticles by Deep Learning', The Journal of Physical Chemistry Letters, vol. 12, no. 41, pp. 10242-10248.
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Highly controlled synthesis of upconversion nanoparticles (UCNPs) can be achieved in the heterogeneous design, so that a library of optical properties can be arbitrarily produced by depositing multiple lanthanide ions. Such a control offers the potential in creating nanoscale barcodes carrying high-capacity information. With the increasing creation of optical information, it poses more challenges in decoding them in an accurate, high-throughput, and speedy fashion. Here, we reported that the deep-learning approach can recognize the complexity of the optical fingerprints from different UCNPs. Under a wide-field microscope, the lifetime profiles of hundreds of single nanoparticles can be collected at once, which offers a sufficient amount of data to develop deep-learning algorithms. We demonstrated that high accuracies of over 90% can be achieved in classifying 14 kinds of UCNPs. This work suggests new opportunities in handling the diverse properties of nanoscale optical barcodes toward the establishment of vast luminescent information carriers.
Liaqat, R, Sajjad, IA, Waseem, M & Alhelou, HH 2021, 'Appliance Level Energy Characterization of Residential Electricity Demand: Prospects, Challenges and Recommendations', IEEE Access, vol. 9, pp. 148676-148697.
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Lim, JHK, Gan, YY, Ong, HC, Lau, BF, Chen, W-H, Chong, CT, Ling, TC & Klemeš, JJ 2021, 'Utilization of microalgae for bio-jet fuel production in the aviation sector: Challenges and perspective', Renewable and Sustainable Energy Reviews, vol. 149, pp. 111396-111396.
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Aviation sector discharges approximately 2% of the global anthropogenic CO2, and the proportion is growing. The search for cost-effective and environmental-friendly bio-jet fuels derived from natural resources is gaining momentum. The microalgae cultivation conditions including temperature, pH, light intensity and nutrients have shown significant influence on the microalgae growth rate and chemical composition, which create the opportunities to enhance the yield and quality of microalgae bio-jet fuel. This review is focused on the hydroprocessing method for converting microalgae oil into bio-jet fuel, as well as the novel conceptual approaches for bio-jet fuel production such as gasification with Fischer-Tropsch and sugar-to-jet. Fischer-Tropsch synthesis of biomass is one of the best alternative ways to replace natural aviation fuel due to the high maximum energy efficiency and low emission of greenhouse gas. In addition, hydroprocessing with the aid of Ni and zeolites catalysts has successfully converted the microalgae biodiesel to bio-jet fuel with high yield and alkane selectivity. Among these techniques, hydroprocessing used the lowest production cost with the longest duration, whereas the bio-jet fuel with high selectivity (C8–C16) could be produced by using gasification with the Fischer-Tropsch process. Consequently, gasification and Fischer-Tropsch and sugar-to-jet can become the future alternative process to convert microalgae to bio-jet fuel. The development of microalgae bio-jet fuel will increase the security of energy supply and reduce the fuel expenses in aviation industry.
Lin, C-T & Do, T-TN 2021, 'Direct-Sense Brain–Computer Interfaces and Wearable Computers', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 1, pp. 298-312.
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Brain-computer interfaces (BCIs) allow users to communicate directly with external devices via their brain signals. Recently, BCIs, and wearable computers in particular, have been receiving more attention by government and industry as an alternative means of interacting with technology. Wearable computers can combine highly immersive virtual/augmented/mixed reality experiences for entertainment, health monitoring, utilitarian purposes, and, most importantly at present, research. With wearable computers, researchers can design, simulate, and finely control experiments to examine human-brain dynamics outside the laboratory. Yet despite the power of BCIs, take-up is slow. This form of interaction is unnatural to humans and often requires external stimuli. Further, the response feedback produced by the computer part of the system is nowhere near as quick as our brains. Hence, we undertook a review of the current state-of-the-art in BCI research and distilled the current findings into a stimulus-free BCI, called direct-sense BCIs, that operates directly and seamlessly from our thinking. This is a novel paradigm that, in the short term, could substantially improve the quality of a user's experience with BCI, and, over the long term, lead to much more widespread take-up of BCI technology.
Lin, C-T, Chuang, C-H, Hung, Y-C, Fang, C-N, Wu, D & Wang, Y-K 2021, 'A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks', IEEE Transactions on Cybernetics, vol. 51, no. 10, pp. 4959-4967.
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Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral lapses. Thus, there is a need to predict the cognitive state of drivers, particularly their fatigue level. Electroencephalography (EEG) has been demonstrated to be effective for monitoring changes in the human brain state and behavior. Thirty-seven subjects participated in this driving experiment and performed a perform lane-keeping task in a visual-reality environment. Three domains, namely, frequency, temporal, and 2-D spatial information, of the EEG channel location were comprehensively considered. A 4-D convolutional neural-network (4-D CNN) algorithm was then proposed to associate all information from the EEG signals and the changes in the human state and behavioral performance. A 4-D CNN achieves superior forecasting performance over 2-D CNN, 3-D CNN, and shallow networks. The results showed a 3.82% improvement in the root mean-square error, a 3.45% improvement in the error rate, and a 11.98% improvement in the correlation coefficient with 4-D CNN compared with 3-D CNN. The 4-D CNN algorithm extracts the significant θ and alpha activations in the frontal and posterior cingulate cortices under distinct fatigue levels. This work contributes to enhancing our understanding of deep learning methods in the analysis of EEG signals. We even envision that deep learning might serve as a bridge between translation neuroscience and further real-world applications.
Lin, C-T, Jiang, W-L, Chen, S-F, Huang, K-C & Liao, L-D 2021, 'Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation', Biosensors, vol. 11, no. 9, pp. 343-343.
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In the assistive research area, human–computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life applications.
Lin, C-T, King, J-T, John, AR, Huang, K-C, Cao, Z & Wang, Y-K 2021, 'The Impact of Vigorous Cycling Exercise on Visual Attention: A Study With the BR8 Wireless Dry EEG System', Frontiers in Neuroscience, vol. 15, p. 621365.
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Many studies have reported that exercise can influence cognitive performance. But advancing our understanding of the interrelations between psychology and physiology in sports neuroscience requires the study of real-time brain dynamics during exercise in the field. Electroencephalography (EEG) is one of the most powerful brain imaging technologies. However, the limited portability and long preparation time of traditional wet-sensor systems largely limits their use to laboratory settings. Wireless dry-sensor systems are emerging with much greater potential for practical application in sports. Hence, in this paper, we use the BR8 wireless dry-sensor EEG system to measure P300 brain dynamics while cycling at various intensities. The preparation time was mostly less than 2 min as BR8 system’s dry sensors were able to attain the required skin-sensor interface impedance, enabling its operation without any skin preparation or application of conductive gel. Ten participants performed four sessions of a 3 min rapid serial visual presentation (RSVP) task while resting and while cycling. These four sessions were pre-CE (RSVP only), low-CE (RSVP in 40–50% of max heart rate), vigorous-CE (RSVP in 71–85% of max heart rate) and post-CE (RSVP only). The recorded brain signals demonstrate that the P300 amplitudes, observed at the Pz channel, for the target and non-target responses were significantly different in all four sessions. The results also show decreased reaction times to the visual attention task during vigorous exercise, enriching our understanding of the ways in which exercise can enhance cognitive performance. Even though only a single channel was evaluated in this study, the quality and reliability of the measurement using these dry sensor-based EEG systems is clearly demonstrated by our results. Further, the smooth implementation of the experiment with a dry system and the success of the data analysis demonstrate that wireless dry EEG devices can o...
Lin, C-T, Wang, C-Y, Huang, K-C, Horng, S-J & Liao, L-D 2021, 'Wearable, Multimodal, Biosignal Acquisition System for Potential Critical and Emergency Applications', Emergency Medicine International, vol. 2021, pp. 1-10.
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For emergency or intensive-care units (ICUs), patients with unclear consciousness or unstable hemodynamics often require aggressive monitoring by multiple monitors. Complicated pipelines or lines increase the burden on patients and inconvenience for medical personnel. Currently, many commercial devices provide related functionalities. However, most devices measure only one biological signal, which can increase the budget for users and cause difficulty in remote integration. In this study, we develop a wearable device that integrates electrocardiography (ECG), electroencephalography (EEG), and blood oxygen machines for medical applications with the hope that it can be applied in the future. We develop an integrated multiple-biosignal recording system based on a modular design. The developed system monitors and records EEG, ECG, and peripheral oxygen saturation (SpO2) signals for health purposes simultaneously in a single setting. We use a logic level converter to connect the developed EEG module (BR8), ECG module, and SpO2 module to a microcontroller (Arduino). The modular data are then smoothly encoded and decoded through consistent overhead byte stuffing (COBS). This developed system has passed simulation tests and exhibited proper functioning of all modules and subsystems. In the future, the functionalities of the proposed system can be expanded with additional modules to support various emergency or ICU applications.
Lin, J-Y, Yang, Y, Wong, S-W & Li, Y 2021, 'High-Order Modes Analysis and Its Applications to Dual-Band Dual-Polarized Filtering Cavity Slot Arrays', IEEE Transactions on Microwave Theory and Techniques, vol. 69, no. 6, pp. 3084-3092.
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In this article, a series of filtering cavity slot arrays using high-order modes are investigated. It is found that each unit of the cavity slot arrays in the proposed high-order mode resonator is in phase with the same amplitude, which helps enhance the antenna gain and reduce the sidelobe level. Meanwhile, the filtering function is integrated into the design for frequency selectivity and harmonic mode suppression. The higher order response can be achieved by cascading more high-order mode resonators with required external quality factor (Qₑ) and coupling coefficient (K). The fractional bandwidth (FBW) and out-of-band suppression of proposed designs are also discussed. For proof-of-concept, a single-band third-order 4 x 5 filtering cavity slot array using a TM₄₅₀ mode resonator and a dual-band dual-polarized third-order 4 x 3 filtering cavity slot array, using TM₄₃₀ and TM₃₄₀ mode resonators, are fabricated and tested. The good agreement between the simulated and measured results verifies that the proposed design methodology is feasible for designing high-order mode filtering cavity slot array antennas.
Lin, S, Liao, S, Yang, Y, Che, W & Xue, Q 2021, 'Gain Enhancement of Low-Profile Omnidirectional Antenna Using Annular Magnetic Dipole Directors', IEEE Antennas and Wireless Propagation Letters, vol. 20, no. 1, pp. 8-12.
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An annular array antenna is proposed in this letter. The proposed low-profile antenna produces an enhanced gain with omnidirectional vertically polarized radiation patterns. Center-fed via-shorted circular patch antenna with omnidirectional radiation pattern can be low profile and wide bandwidth but suffers from tilted beam leading to a low gain in the azimuth plane. To improve the azimuth gain, instead of the traditional way of increasing the profile, a low-profile omnidirectional director is exploited, which is basically a passive magnetic dipole consisting of an annular side-coupling open-cavity. Several directors cooperating with the driven element form the proposed antenna, whose working principle is similar to the Yagi-Uda antenna. A prototype with a low profile of 0.11 λ0 is designed and fabricated. Measured results show that the prototype can realize -10-dB impedance bandwidth of 13% (4.72-5.42 GHz) and gain of 3.72 dBi at 5.34 GHz with good omnidirectivity. Compared with that of the driven element only, the omnidirectional azimuth gain is significantly improved within the bandwidth, with the maximum gain being enhanced from -4.2 to 3.72 dBi at 5.34 GHz. The proposed antenna is designed for a portable 5G sub-6 GHz wireless channel measurement application.
Lin, Z, Lv, T, Ni, W, Zhang, JA & Liu, RP 2021, 'Nested Hybrid Cylindrical Array Design and DoA Estimation for Massive IoT Networks', IEEE Journal on Selected Areas in Communications, vol. 39, no. 4, pp. 919-933.
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IEEE Reducing cost and power consumption while maintaining high network access capability is a key physical-layer requirement of massive Internet of Things (mIoT) networks. Deploying a hybrid array is a cost-and energy-efficient way to meet the requirement, but would penalize system degree of freedom (DoF) and channel estimation accuracy. This is because signals from multiple antennas are combined by a radio frequency (RF) network of the hybrid array. This paper presents a novel hybrid uniform circular cylindrical array (UCyA) for mIoT networks. We design a nested hybrid beamforming structure based on sparse array techniques and propose the corresponding channel estimation method based on the second-order channel statistics. As a result, only a small number of RF chains are required to preserve the DoF of the UCyA. We also propose a new tensor-based two-dimensional (2-D) direction-of-arrival (DoA) estimation algorithm tailored for the proposed hybrid array. The algorithm suppresses the noise components in all tensor modes and operates on the signal data model directly, hence improving estimation accuracy with an affordable computational complexity. Corroborated by a Cramér-Rao lower bound (CRLB) analysis, simulation results show that the proposed hybrid UCyA array and the DoA estimation algorithm can accurately estimate the 2-D DoAs of a large number of IoT devices.
Lin, Z, Lv, T, Ni, W, Zhang, JA, Zeng, J & Liu, RP 2021, 'Joint Estimation of Multipath Angles and Delays for Millimeter-Wave Cylindrical Arrays With Hybrid Front-Ends', IEEE Transactions on Wireless Communications, vol. 20, no. 7, pp. 4631-4645.
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Accurate channel parameter estimation is challenging for wideband millimeter-wave (mmWave) large-scale hybrid arrays, due to beam squint and much fewer radio frequency (RF) chains than antennas. This article presents a novel joint angle and delay estimation (JADE) approach for wideband mmWave fully-connected hybrid uniform cylindrical arrays. We first design a new hybrid beamformer to reduce the dimension of received signals on the horizontal plane by exploiting the convergence of the Bessel function, and to reduce the active beams in the vertical direction through preselection. The important recurrence relationship of the received signals needed for subspace-based angle and delay estimation is preserved, even with substantially fewer RF chains than antennas. Then, linear interpolation is generalized to reconstruct the received signals of the hybrid beamformer, so that the signals can be coherently combined across the whole band to suppress the beam squint. As a result, efficient subspace-based algorithm algorithms can be developed to estimate the angles and delays of multipath components. The estimated delays and angles are further matched and correctly associated with different paths in the presence of non-negligible noises, by putting forth perturbation operations. Simulations show that the proposed approach can approach the Cramér-Rao lower bound (CRLB) of the estimation with a significantly lower computational complexity than existing techniques.
Linares-Mustarós, S, Ferrer-Comalat, JC, Corominas-Coll, D & Merigó, JM 2021, 'The weighted average multiexperton', Information Sciences, vol. 557, pp. 355-372.
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© 2020 Experton theory, a generalization of probabilistic set theory, that is of great usefulness to group decision analysis, was first proposed as a means of improving the processing and analysis of opinions issued by experts. This theory produces an information-fusion mathematical object, the experton, which can be used in predictive problems to justify decisions based on well-constructed reasoning. The aim of this paper is to present an aggregative method of several expertons, with the idea that some of the groups of experts involved in producing these expertons may have more influence than others in the decision-making process. In this article, we carry out an aggregation analysis of expertons, not experts, which culminates in the creation of a new mathematical object. This object, which is called the weighted average multiexperton, is coherent with an experton-type object created from a weighting of the initial data provided by all experts. Since the aggregation method presented has been devised to represent the decision-maker's attitude regarding the importance of different groups of experts, this approach represents a new tool for dealing with group decision-making problems. Additionally, the study presents some properties of the new object. Finally, the paper ends with an application for business decision-making.
Ling, L, Yelland, N, Hatzigianni, M & Dickson-Deane, C 2021, 'Toward a conceptualization of the internet of toys', Australasian Journal of Early Childhood, vol. 46, no. 3, pp. 249-262.
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The Internet of Things is reshaping many households’ digital landscape and influencing children’s play and learning, especially in the form of toys that are named the Internet of Toys (IoToys). IoToys may generate a significant influence on children’s growth. While increasing attention is drawn to the IoToys, confusion around their conceptualization and use is evident. Without a thorough understanding of what the IoToys are, the progress of meaningful research on this topic will be greatly hindered. We, thus, conducted a systematic review to determine existing definitions of the IoToys using seven major databases over the past 20 years. After analyzing the definitions identified, we found that the previous definitions neglected the significance of defining “toys” in their work. The review led to a discussion around how to understand “toys” and then a more precise conceptualization of the IoToys, based on which implications for future research are offered.
Ling, Y, Wang, K, Wang, X & Li, W 2021, 'Prediction of engineering properties of fly ash-based geopolymer using artificial neural networks', Neural Computing and Applications, vol. 33, no. 1, pp. 85-105.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Fly ash-based geopolymer has been studied extensively in recent years due to its comparable properties to Portland cement and its environmental benefits. However, the uncertainty and complexity of design parameters, such as the SiO2/Na2O mole ratio in alkaline solution, the alkaline solution concentration in liquid phase, and the liquid-to-fly ash mass ratio (L/F), have made it very difficult to create a systematic approach for geopolymer mix design. These mix design parameters, along with fly ash properties and curing conditions (temperature and time), significantly influence key properties of the material, such as setting time and compressive strength. In this study, an artificial neural network (ANN) was used to develop models for predicting the key properties of high-calcium fly ash-based geopolymer according to its mix design parameters. The correlations between experimental measurements and ANN model predictions of setting time, compressive strength, and heat of geopolymerization were established based on the results of tests on 36, 273, and 72 geopolymer mixes, respectively. The results show that the correlations between the experimental measurements and ANN model predictions of the properties studied are all strong. ANN modeling was found to be a suitable computing method to analyze the effects of design parameters on geopolymer properties and showed that L/F exhibited the greatest effect on setting time, alkaline solution concentration had the greatest influence on compressive strength, and a mole ratio larger than 1.5 significantly impacted heat at the geopolymerization peak. The developed ANN models can be used as guidance for mix design of high-calcium fly ash geopolymer in engineering applications.
Liu Chung Ming, C, Sesperez, K, Ben-Sefer, E, Arpon, D, McGrath, K, McClements, L & Gentile, C 2021, 'Considerations to Model Heart Disease in Women with Preeclampsia and Cardiovascular Disease', Cells, vol. 10, no. 4, pp. 899-899.
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Preeclampsia is a multifactorial cardiovascular disorder diagnosed after 20 weeks of gestation, and is the leading cause of death for both mothers and babies in pregnancy. The pathophysiology remains poorly understood due to the variability and unpredictability of disease manifestation when studied in animal models. After preeclampsia, both mothers and offspring have a higher risk of cardiovascular disease (CVD), including myocardial infarction or heart attack and heart failure (HF). Myocardial infarction is an acute myocardial damage that can be treated through reperfusion; however, this therapeutic approach leads to ischemic/reperfusion injury (IRI), often leading to HF. In this review, we compared the current in vivo, in vitro and ex vivo model systems used to study preeclampsia, IRI and HF. Future studies aiming at evaluating CVD in preeclampsia patients could benefit from novel models that better mimic the complex scenario described in this article.
Liu, A, Lu, J & Zhang, G 2021, 'Concept Drift Detection via Equal Intensity k-Means Space Partitioning', IEEE Transactions on Cybernetics, vol. 51, no. 6, pp. 3198-3211.
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The data stream poses additional challenges to statistical classification tasks because distributions of the training and target samples may differ as time passes. Such a distribution change in streaming data is called concept drift. Numerous histogram-based distribution change detection methods have been proposed to detect drift. Most histograms are developed on the grid-based or tree-based space partitioning algorithms which makes the space partitions arbitrary, unexplainable, and may cause drift blind spots. There is a need to improve the drift detection accuracy for the histogram-based methods with the unsupervised setting. To address this problem, we propose a cluster-based histogram, called equal intensity k-means space partitioning (EI-kMeans). In addition, a heuristic method to improve the sensitivity of drift detection is introduced. The fundamental idea of improving the sensitivity is to minimize the risk of creating partitions in distribution offset regions. Pearson's chi-square test is used as the statistical hypothesis test so that the test statistics remain independent of the sample distribution. The number of bins and their shapes, which strongly influence the ability to detect drift, are determined dynamically from the sample based on an asymptotic constraint in the chi-square test. Accordingly, three algorithms are developed to implement concept drift detection, including a greedy centroids initialization algorithm, a cluster amplify-shrink algorithm, and a drift detection algorithm. For drift adaptation, we recommend retraining the learner if a drift is detected. The results of experiments on the synthetic and real-world datasets demonstrate the advantages of EI-kMeans and show its efficacy in detecting concept drift.
Liu, A, Lu, J & Zhang, G 2021, 'Concept Drift Detection: Dealing With Missing Values via Fuzzy Distance Estimations', IEEE Transactions on Fuzzy Systems, vol. 29, no. 11, pp. 3219-3233.
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In data streams, the data distribution of arriving observations at different time points may change—a phenomenon called concept drift. While detecting concept drift is a relatively mature area of study, solutions to the uncertainty introduced by observations with missing values have only been studied in isolation. No one has yet explored whether or how these solutions might impact drift detection performance. We, however, believe that data imputation methods may actually increase uncertainty in the data rather than reducing it. We also conjecture that imputation can introduce bias into the process of estimating distribution changes during drift detection, which can make it more difficult to train a learning model. Our idea is to focus on estimating the distance between observations rather than estimating the missing values, and to define membership functions that allocate observations to histogram bins according to the estimation errors. Our solution comprises a novel masked distance learning (MDL) algorithm to reduce the cumulative errors caused by iteratively estimating each missing value in an observation and a fuzzy-weighted frequency (FWF) method for identifying discrepancies in the data distribution. The concept drift detection algorithm proposed in this article is a singular and unified algorithm that can handle missing values, but not an imputation algorithm combined with a concept drift detection algorithm. Experiments on both synthetic and real-world datasets demonstrate the advantages of this method and show its robustness in detecting drift in data with missing values. The results show that compared to the best-performing algorithm that handles imputation and drift detection separately, MDL-FWF reduced the average drift detection difference from 10.75% to 5.83%. This is a nearly 46% improvement. These findings reveal that missing values exert a profound impact on concept drift detection, but using fuzzy set theory to model observations can p...
Liu, A, Lu, J & Zhang, G 2021, 'Diverse Instance-Weighting Ensemble Based on Region Drift Disagreement for Concept Drift Adaptation', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, pp. 293-307.
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Concept drift refers to changes in the distribution of underlying data and is an inherent property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved to be an efficient method of handling concept drift. However, the best way to create and maintain ensemble diversity with evolving streams is still a challenging problem. In contrast to estimating diversity via inputs, outputs, or classifier parameters, we propose a diversity measurement based on whether the ensemble members agree on the probability of a regional distribution change. In our method, estimations over regional distribution changes are used as instance weights. Constructing different region sets through different schemes will lead to different drift estimation results, thereby creating diversity. The classifiers that disagree the most are selected to maximize diversity. Accordingly, an instance-based ensemble learning algorithm, called the diverse instance-weighting ensemble (DiwE), is developed to address concept drift for data stream classification problems. Evaluations of various synthetic and real-world data stream benchmarks show the effectiveness and advantages of the proposed algorithm.
Liu, B, Wang, F, Chen, C & McGloin, D 2021, 'Single-Pixel Diffuser Camera', IEEE Photonics Journal, vol. 13, no. 6, pp. 1-5.
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We present a compact, diffuser assisted, single-pixel computational camera. A rotating ground glass diffuser is adopted, in preference to a commonly used digital micro-mirror device (DMD), to encode a two-dimensional (2D) image into single-pixel signals. We retrieve images with an 8.8% sampling ratio after the calibration of the pseudo-random pattern of the diffuser under light-emitting diode (LED) illumination. Furthermore, we demonstrate hyperspectral imaging with line array detection by adding a diffraction grating. As the random and fixed patterns of a rotating diffuser placed in the image plane can serve as 2D modulation patterns in single-pixel imaging, we do not need further calibration for spectral imaging case since we use a parallel recovery strategy for images at all wavelengths. The implementation results in a cost-effective single-pixel camera for high-dimensional imaging, with potential for imaging in non-visible wavebands.
Liu, B, Wang, F, Chen, C, Dong, F & McGloin, D 2021, 'Self-evolving ghost imaging', Optica, vol. 8, no. 10, pp. 1340-1340.
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Ghost imaging captures 2D images with a point detector instead of an array sensor. It could therefore solve the challenge of building cameras in wave bands where sensors are difficult and expensive to produce and could open up more routine THz, near-infrared, lifetime, and hyperspectral imaging simply by using single-pixel detectors. Traditionally, ghost imaging retrieves the image of an object offline by correlating measured light intensities with pre-designed illuminating patterns. Here we present a “self-evolving” ghost imaging (SEGI) strategy for imaging objects bypassing offline post-processing. It also offers the capability to image objects in turbid media. By inspecting the optical feedback, we evaluate the illumination patterns by a cost function and generate offspring illumination patterns that mimic the object’s image, bypassing the reconstruction process. At the initial evolving state, the object’s “genetic information” is stored in the patterns. At the following imaging stage, the object’s image ( 48 × 48 p i x e l s ) can be updated at a 40 Hz imaging rate. We numerically and experimentally demonstrate this concept for static and moving objects. The frame-memory effect between the self-evolving illumination patterns provided by the genetic algorithm enables SEGI imaging through turbid media. We further demonst...
Liu, C, Bano, M, Zowghi, D & Kearney, M 2021, 'Analysing user reviews of inquiry-based learning apps in science education.', Comput. Educ., vol. 164, pp. 104119-104119.
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© 2020 Elsevier Ltd The science education community is increasingly valuing the use of mobile apps in inquiry-based learning (IBL) to improve learner’ attitudes and their understanding of science concepts. Although there exists a body of research on mobile apps used for IBL in science education, limited attention has been paid to linking apps' features with their pedagogical affordances. Our study addresses this research gap by evaluating science mobile learning apps with respect to IBL pedagogy. Nine functional features of apps that support educational aspects of inquiry-based pedagogy are identified from user reviews, including: fingertip interaction, graphics visualisation, informative materials, location-based services, offline access, search by question, timeline scrolling, user tutorials, and zoom control. The information contained in the version history of the apps is analysed and four educational aspects of IBL supported by the nine functional features are identified as: motivation, conceptualisation, exploration, and conclusion. We have further compared the evolution of the functional features of apps to the educational aspects of inquiry-based pedagogy identified from different versions of apps. The findings of this study show the trend of updated functional features that support IBL and inform practitioners seeking to improve their use of mobile apps to support students' learning in science. We conclude by proposing areas of future research in this field.
Liu, C, Indraratna, B & Rujikiatkamjorn, C 2021, 'An analytical model for particle-geogrid aperture interaction', Geotextiles and Geomembranes, vol. 49, no. 1, pp. 41-44.
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© 2020 The shear strength of geogrid-reinforced ballast is often dependent on the aperture size of geogrids and the nominal size of ballast. This paper presents a theoretical analysis based on probabilistic mechanics of how aperture size affects the interaction between particles and geogrid. Unlike past literature, in this study, the properties of the particle size distribution is analysed using a Weibull distribution. The probability of grain interlock is proposed to describe the interactive mechanisms between particles and geogrids based on the relative particle size, which is defined as the ratio of particle size to aperture size. The mathematical model is calibrated by a set of large-scale direct shear tests with almost single-size (highly uniform) ballast aggregates, and then validated by independent set of data taken from both literature and current study. The study concludes that more uniform particle size distribution increases the probability of grain interlock at the optimum aperture size but decreases it at non-optimum aperture sizes.
Liu, C, Liu, Q, Wang, S, Wang, Y, Lei, G, Guo, Y & Zhu, J 2021, 'A novel flux switching claw pole machine with soft magnetic composite cores', International Journal of Applied Electromagnetics and Mechanics, vol. 67, no. 2, pp. 183-203.
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This paper proposes a novel flux switching claw pole machine (FSCPM) with soft magnetic composite (SMC) cores. The proposed FSCPM holds advantages of the conventional flux switching permanent magnet machine (FSPMM) and claw pole machine (CPM) with SMC cores. As permanent magnets are installed between the stator claw pole teeth, FSCPM has good flux concentrating ability, and the air gap flux density can be significantly improved. The torque coefficient of FSCPM is relatively high due to the applied claw pole teeth and global winding. FSCPM is mechanically robust because there are no windings or PMs on its rotor. Moreover, the core loss of FSCPM is relatively low for the SMC material has lower core loss at high frequency compared with silicon steels. The topology and operational principle of FSCPM are explained first. Several main dimensions of the machine are optimized to achieve better performance, based on 3D finite element method (FEM). Furthermore, the rotor skewing technology is adopted to reduce the cogging torque and torque ripple.
Liu, C, Wang, D, Wang, S, Niu, F, Wang, Y, Lei, G & Zhu, J 2021, 'Design and Analysis of a New Permanent Magnet Claw Pole Machine With S-Shape Winding', IEEE Transactions on Magnetics, vol. 57, no. 2, pp. 1-5.
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With the continuous improvement of magnetic and mechanical properties of soft magnetic composite (SMC) material, there is a trend to develop novel electrical machines with SMC cores for some special applications. Among these electrical machines, permanent magnet claw pole machine (CPM) has been extensively studied over the past few decades. As linear global winding has been used in this machine, it can be regarded as a linear winding CPM (LWCPM). To improve the performance of LWCPM, a new S-shape winding CPM (SWCPM) is proposed in this article. The main stator structures of the LWCPM and SWCPM are optimized to achieve maximum torque ability. Compared with LWCPM, SWCPM provides higher average torque, power factor, and higher efficiency. The main disadvantage of the proposed SWCPM is its lower flux weakening ability. 3-D finite element model is used to evaluate the performance of the proposed LWCPM and SWCPM. The accuracy of the 3-D finite element model is verified by using a previous prototype.
Liu, C, Zowghi, D, Kearney, M & Bano, M 2021, 'Inquiry-based mobile learning in secondary school science education: A systematic review.', J. Comput. Assist. Learn., vol. 37, no. 1, pp. 1-23.
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Recent years have seen a growing call for inquiry‐based learning in science education, and mobile technologies are perceived as increasingly valuable tools to support this approach. However, there is a lack of understanding of mobile technology‐supported inquiry‐based learning (mIBL) in secondary science education. More evidence‐based, nuanced insights are needed into how using mobile technologies might facilitate students' engagement with various levels of inquiry and enhance their science learning. We, therefore, conducted a robust systematic literature review (SLR) of the research articles on mIBL in secondary school science education that have been published from 2000 to 2019. We reviewed and analysed 31 empirical studies (34 articles) to explore the types of mIBL, and the benefits and constraints of mIBL in secondary school science education. The findings of this SLR suggest new research areas for further exploration and provide implications for science teachers' selection, use and design of mIBL approaches in their teaching.
Liu, D, Chen, Y, Tran, TT & Zhang, G 2021, 'Facile and rapid assembly of high-performance tannic acid thin-film nanofiltration membranes via Fe3+ intermediated regulation and coordination', Separation and Purification Technology, vol. 260, pp. 118228-118228.
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Non-polyamide (non-PA) thin-film composite nanofiltration (TFC-NF) membranes have received tremendous attention in recent years, but their development is strongly hindered by the complicated fabrication process and the trade-off between permeability and selectivity. Here, we report a highly perm-selective non-PA TFC-NF membrane through 2-minute rapid assembly of tannic acid (TA) on hydrolyzed polyacrylonitrile (PAN) substrate via Fe3+ intermediated regulation and coordination. The optimized membrane with a molecular weight cut off of ~390 Da showed high rejections for salts in a sequence of Na2SO4 (90.2%) > MgSO4 (83.4%) > NaCl (50.0%) > MgCl2 (35.2%) and desirable rejections for organic pollutants (e.g. >99.0% dyes, 92.2% streptomycin and 81.8% chloramphenicol) while maintaining a pure water permeability of as high as 13.6 L·m−2·h−1·bar−1, which clearly outperforms the reported non-PA membranes. In addition, the assembled TFC membrane showed excellent antifouling performance and reasonable structural stability against operation pressure and solution alkalinity. These results are highly promising and indicate a great potential for the membrane to be used in practical nanofiltration application, e.g. water purification and wastewater reclamation. Our work outlines the production of novel high-performance non-PA membrane with a fast fabrication process in a green chemistry context.
Liu, D, Wu, Q, Huang, Y, Huang, X & An, P 2021, 'Learning from EPI-Volume-Stack for Light Field image angular super-resolution', Signal Processing: Image Communication, vol. 97, pp. 116353-116353.
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Light Field (LF) image angular super-resolution aims to synthesize a high angular resolution LF image from a low angular resolution one, and is drawing increased attention because of its wide applications. In order to reconstruct a high angular resolution LF image, many learning based LF image angular super-resolution methods have been proposed. However, most existing methods are based on LF Epipolar Plane Image or Epipolar Plane Image volume representation, which underuse the LF image structure. The LF view spatial correlation and neighboring LF views angular correlations which can reflect LF image structure are not fully explored, which reduces LF angular super-resolution quality. In order to alleviate this problem, this paper introduces an Epipolar Plane Image Volume Stack (EPI-VS) representation for LF angular super-resolution. The EPI-VS is constituted by arranging all LF views in a raster order, which benefits in exploring LF view spatial correlation and neighboring LF views angular correlations. Based on such representation, we further propose an LF angular super-resolution network. 3D convolutions are applied in the whole super-resolution network to better accommodate the input EPI-VS data and allow information propagation between two spatial and one directional dimensions of EPI-VS data. Extensive experiments on synthetic and real-world LF scenes demonstrate the effectiveness of the proposed network. Moreover, we also illustrate the superiority of our network by applying it in scene depth estimation task.
Liu, D, Xu, X, Du, Y, Liao, J, Wen, S, Dong, X, Jin, Y, Liu, L, Jin, D, Capobianco, JA & Shen, D 2021, 'Reconstructing the Surface Structure of NaREF4 Upconversion Nanocrystals with a Novel K+ Treatment', Chemistry of Materials, vol. 33, no. 7, pp. 2548-2556.
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Property of the nanocrystals' surface structure plays a key role in developing novel nanomaterials with high performance and new functionalities. Conventional methods of nanocrystal surface engineering are commonly based on tuning the synthesis reaction parameters or growing core-shell structures, which usually results in increasing the size of the nanoparticles. Here, we report an approach to tailoring the surface crystalline structure of β-NaYF4 nanocrystals by reheating the nanocrystals in a K+-rich environment of the oleic acid-1-octadecene (OA-ODE) system. We found that the crystal surface stability of nanocrystals was decreased in the K+-rich solution, which reconstructs the nanocrystals' surface into a porous surface structure. With a systematic design of experiments, the roles of the cations, such as K+, K+-Gd3+, and Na+-Y3+, are individually identified, which leads to a reformation of the surface structure of the hexagonal NaYF4 nanocrystal into different forms, e.g., a mesostructured, spherical, and diamond surface. The technique of tailoring the surface crystalline structures will provide new insight for the shape and surface-dependent property studies and luminescence enhancement without a size increase.
Liu, F, Han, R, Naficy, S, Casillas, G, Sun, X & Huang, Z 2021, 'Few-Layered Boron Nitride Nanosheets for Strengthening Polyurethane Hydrogels', ACS Applied Nano Materials, vol. 4, no. 8, pp. 7988-7994.
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Two-dimensional hexagonal boron nitride nanosheets (BNNS) are an outstanding filler and additive, since they are transparent, thermally stable, and chemically inert. However, it is difficult to obtain few-layered BNNS with large lateral sizes in an efficient way due to the strong interlayer interactions in h-BN. Herein, a facile and efficient molten salt-assisted synthesis has been developed to prepare few-layered BNNS with a few microns in lateral size. Ammonia borane was mixed with KCl and NaCl and then heated to 1000 °C and held for 2 min, and the resultant powders were sonicated in water to produce hydroxylated BNNS. Used as an additive with 0.066 wt % loading, the functionalized BNNS can effectively improve the mechanical modulus of polyurethane (PU) hydrogels from 1635 to 2776 kPa, and the optical property of the hydrogel is not compromised. The BNNS-reinforced PU hydrogel with significantly improved mechanical properties can be highly useful in the application of printed electronics.
Liu, F, Han, R, Nattestad, A, Sun, X & Huang, Z 2021, 'Carbon- and oxygen-doped hexagonal boron nitride for degradation of organic pollutants', Surface Innovations, vol. 9, no. 4, pp. 222-230.
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Carbon- and oxygen-doped hexagonal boron nitrides (BCNOs) with good chemical stability and photoresponsiveness to visible light are found to be promising metal-free catalysts for degradation of Rhodamine B (RhB). By doping with heteroatoms of carbon and oxygen, insulating hexagonal boron nitride was transformed into semiconducting BCNO. The BCNO photocatalyst presents photodegradation performance towards RhB, with degradation rates up to 1.39 h−1 (0.05 wt% catalyst loading). The active species involved in the photoreaction were demonstrated to be superoxide anion radical (˙O2 −) and holes (h+), as opposed to ˙OH in the most studied titanium dioxide. The stability of BCNO in highly acidic environments was exploited for catalyst regeneration, as is necessary after long-term use and poisoning. This work demonstrates that BCNO is a promising low-cost and metal-free photocatalyst for environmental pollution remediation.
Liu, F, Zhang, G & Lu, J 2021, 'Multisource Heterogeneous Unsupervised Domain Adaptation via Fuzzy Relation Neural Networks', IEEE Transactions on Fuzzy Systems, vol. 29, no. 11, pp. 3308-3322.
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In unsupervised domain adaptation (UDA), a classifier for a target domain is trained with labeled source data and unlabeled target data. Existing UDA methods assume that the source data come from the same source domain (i.e., single-source scenario) or from multiple source domains, whose feature spaces have the same dimension ( homogeneous ) but different distributions (i.e., multihomogeneous-source scenario). However, in the real world, for a specific target domain, we probably have multiple different-dimension ( heterogeneous ) source domains, which do not satisfy the assumption of existing UDA methods. To remove this assumption and move forward to a realistic UDA problem, this article presents a shared-fuzzy-equivalence-relation neural network (SFERNN) for addressing the multisource heterogeneous UDA problem. The SFERNN is a five-layer neural network containing c source branches and one target branch. The network structure of the SFERNN is first confirmed by a novel fuzzy relation called multisource shared fuzzy equivalence relation. Then, we optimize parameters of the SFERNN via minimizing cross-entropy loss on c source branches and the distributional discrepancy between each source branch and the target branch. Experiments distributed across eight real-world datasets are conducted to validate the SFERNN. This testing regime demonstrates that the SFERNN outperforms the existing single-source heterogeneous UDA methods, especially when the target domain contains few data.
Liu, H 2021, 'Research on Performance Prediction of Technological Innovation Enterprises Based on Deep Learning', Wireless Communications and Mobile Computing, vol. 2021, no. 1.
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High‐tech enterprises are the leaders in promoting economic development. The study of the relationship between their scientific and technological innovation capabilities and corporate performance is of far‐reaching practical significance for guiding companies to formulate independent innovation strategies scientifically, improving their independent innovation capabilities, and promoting further transformation into an innovative country. In view of the large‐scale technological innovation enterprise network, the traditional technological innovation enterprise performance prediction method cannot fully reflect the real‐time technological innovation enterprise status. Aiming at the deficiencies of the existing short‐term technology innovation enterprise forecasting methods, this paper proposes a technology innovation enterprise performance forecasting method based on deep learning. I analyze the temporal and spatial characteristics of the data of technological innovation enterprises and divide the data according to the temporal characteristics of technological innovation enterprises. According to the spatial relevance of technological innovation enterprises, grouping is carried out by setting different correlation coefficient thresholds. The method of spectral decomposition is used to divide the data of scientific and technological innovation enterprises into trend items and random fluctuation items, to decompose the matrix of scientific and technological innovation enterprises, and to construct a compressed matrix using correlation. Using the deep belief network model in deep learning combined with support vector regression to establish a prediction model for technological innovation enterprises, this paper proposes a convolutional neural network model for performance prediction of scientific and technological innovation enterprises. Through the convolution operation and subsampling operation based on the concept of local window, the feature learn...
Liu, H, Du, H, Zhao, W, Qiang, X, Zheng, B, Li, Y & Cao, B 2021, 'Fast potassium migration in mesoporous carbon with ultrathin framework boosting superior rate performance for high-power potassium storage', Energy Storage Materials, vol. 40, pp. 490-498.
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Liu, H, Li, X, Zhang, Z, Nghiem, LD, Gao, L & Wang, Q 2021, 'Semi-continuous anaerobic digestion of secondary sludge with free ammonia pretreatment: Focusing on volatile solids destruction, dewaterability, pathogen removal and its implications', Water Research, vol. 202, pp. 117481-117481.
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Our previous work has reported the pretreatment of secondary sludge with free ammonia (NH3, FA) enhanced the methane production in batch biochemical methane potential tests. However, the batch biochemical methane potential test could only provide conservative results compared to continuous/semi-continuous anaerobic digestion. Also, the impacts of FA pretreatment on the key anaerobic digestion parameters, including volatile solids (VS) destruction, sludge dewaterability and pathogen removal, are still unknown. This study for the first time investigated these impacts using semi-continuous anaerobic digestion systems for 130 days. Pretreatment of secondary sludge for 24 h at an FA concentration of 560 mg NH3-N/L improved VS destruction by 26.4% (from 22.0 to 27.8%), supported by a similar increase of 28.6% in methane production (from 126.7 to 162.9 ml CH4/g VSfed). Model based analysis revealed that FA pretreatment improved the sludge degradability extent, which may be the reason for the enhanced VS destruction. Equally importantly, the dewaterability of the digested sludge with FA pretreatment was also enhanced by 9.2% (from 12.0 to 13.1% in solids content of the dewatered digested sludge), which could be partly attributed to the increased zeta potential from -16.7 to -14.5 mV. Anaerobic digestion with FA pretreatment enhanced the removals of Fecal Coliform and E. Coli by 1.3 and 1.4 log MPN/g TS (MPN: Most Probable Number; TS: Total Solids), indicating FA pretreatment was effective in enhancing pathogen removal. With inorganic solids representing 21% of the sludge used, the volume of dewatered sludge to be disposed of was reduced by 14.5% via FA pretreatment. This will substantially decrease the cost as evaluated by economic analysis. In brief, this study provides a promising strategy to enhance sludge reduction in anaerobic digestion and is of great significance in promoting the application of FA pretreatment strategy in the real world.
Liu, H, Wang, Z, Nghiem, LD, Gao, L, Zamyadi, A, Zhang, Z, Sun, J & Wang, Q 2021, 'Solid-Embedded Microplastics from Sewage Sludge to Agricultural Soils: Detection, Occurrence, and Impacts', ACS ES&T Water, vol. 1, no. 6, pp. 1322-1333.
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Liu, H, Zhu, X, Wang, Y, Men, K & Yeo, KS 2021, 'A 60 GHz 8-Way Combined Power Amplifier in 0.18 μm SiGe BiCMOS', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 6, pp. 1847-1851.
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IEEE A 60 GHz fully-integrated 8-way combined power amplifier (PA) is developed in a standard 0.18 μm SiGe BiCMOS technology. The 8-way power splitter and combiner are co-optimized with transformer based baluns inside the eight differential PA cells, and hence resulting in minimum loss and high gain, linearity and efficiency. The measurement shows that the PA can achieve a gain of 22.2 dB around 60 GHz and 3-dB bandwidth from 53.5 GHz to 66.5 GHz, which covers all the channels specified in IEEE 802.11ad standard. It also attains a 1-dB power compression point (P1dB) of 21.8 dBm and saturated output power (PSAT) of 22.6 dBm, with power-added-efficiency of 10.7% and 12%, respectively.
Liu, J, Itchins, M, Nagrial, A, Cooper, WA, De Silva, M, Barnet, M, Varikatt, W, Sivasubramaniam, V, Davis, A, Gill, AJ, Blinman, P, Lee, K, Hui, R, Gao, B, Pavlakis, N, Clarke, S, Lee, J, Boyer, M & Kao, S 2021, 'Relationship between PD-L1 expression and outcome in EGFR-mutant lung cancer patients treated with EGFR tyrosine kinase inhibitors', Lung Cancer, vol. 155, pp. 28-33.
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Liu, J, Li, H, Ji, J & Luo, J 2021, 'Bipartite Consensus Control for a Swarm of Robots', Journal of Dynamic Systems, Measurement, and Control, vol. 143, no. 1.
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Abstract This paper studies the bipartite consensus problem of a swarm of robots whose dynamics are formulated by Lagrangian equations. Two distributed bipartite consensus control protocols are proposed for a swarm of robots without a leader or with a virtual leader. For the nonleader case, the networked Lagrangian system can reach static bipartite consensus under the control protocol developed, and the final convergent states can be explicitly determined by the specific structure of the Laplacian matrix associated with the cooperative–competitive network topology. For the virtual leader case, all the followers can track the leader's state in a bipartite formation to realize bipartite tracking consensus. Finally, the simulation results are given to verify the theoretical results.
Liu, J, Wang, X, Shen, S, Yue, G, Yu, S & Li, M 2021, 'A Bayesian Q-Learning Game for Dependable Task Offloading Against DDoS Attacks in Sensor Edge Cloud', IEEE Internet of Things Journal, vol. 8, no. 9, pp. 7546-7561.
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To enhance dependable resource allocation against increasing DDoS attacks, in this paper, we investigate interactions between a sensor device-edgeVM pair and a DDoS attacker using a game-theoretic framework, under the constraints of the task time, resource budget, and incomplete knowledge of the processing time of machine learning tasks. In this game, the sensor device expects an edgeVM to cooperate and choose its resource allocation strategy with the objective of satisfying the minimum resource required of machine learning tasks at the corresponding sensor device. Similarly, the attacker’s objective is to strategically allocate resources so that the resource constraint of the machine learning tasks is not satisfied. Owing to a lack of complete information of the processing time of the machine learning tasks, this strategic resource allocation problem between the two players is modeled as a Bayesian Q-learning game, in which the optimal strategies of the sensor device-edgeVM pair and the attacker are analyzed. Furthermore, probability distributions are employed by the corresponding players to model the incomplete nature of the game and a greedy Q-learning algorithm is proposed to dependable resource allocation against DDoS attacks. Numerical simulation results demonstrate that the proposed mechanism is superior to other dependable resource allocation mechanisms under incomplete information for DDoS attacks in the sensor edge cloud.
Liu, J, Wu, C, Li, J, Liu, Z, Xu, S, Liu, K, Su, Y, Fang, J & Chen, G 2021, 'Projectile impact resistance of fibre-reinforced geopolymer-based ultra-high performance concrete (G-UHPC)', Construction and Building Materials, vol. 290, pp. 123189-123189.
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This paper describes the mix design of geopolymer-based ultra-high performance concrete (G-UHPC) with the compressive strength from 100 to 150 MPa. Projectile impact tests at two striking velocities of ~550 m/s and ~800 m/s were then performed to explore the impact resistance of G-UHPC targets. G-UHPC without the addition of fibres yielded better impact resistance than Ordinary Portland Cement (OPC) concrete regarding crater damage and crack propagation, but inferior performance on reducing depth of penetration (DOP). The addition of fibres in G-UHPC effectively helped reduced DOP, crater damage and crack propagation. Steel fibres with a length of 10 mm and a volumetric fraction of 2% were most effective in resisting projectile impact compared with other G-UHPC specimens. To further comprehend the projectile impact performance of G-UHPC, a calibrated Karagozian and Case Concrete (KCC) model accounting for the strain rate effect was successfully used for G-UHPC in projectile analysis. Numerical results including single element and full-scale quasi-static tests, deceleration-time histories of projectiles during penetration and DOP of G-UHPC targets were obtained to validate the numerical models. After that, trendlines were regressed to predict DOP of G-UHPC at two striking velocities of ~550 m/s and ~800 m/s. Perforation limits of G-UHPC were also proposed for the design of both safe and cost-effective protective structures against projectile impact, in which the perforation limits of G-UHPC were taken as 1.1 times of DOP.
Liu, J, Wu, C, Liu, Z, Li, J, Xu, S, Liu, K, Su, Y & Chen, G 2021, 'Investigations on the response of ceramic ball aggregated and steel fibre reinforced geopolymer-based ultra-high performance concrete (G-UHPC) to projectile penetration', Composite Structures, vol. 255, pp. 112983-112983.
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This paper presents experimental and numerical studies on projectile impact resistance of ceramic ball aggregated and steel fibre reinforced geopolymer-based ultra-high performance concrete (G-UHPC) targets. Compared with plain G-UHPC, ceramic ball aggregated G-UHPC enhanced projectile impact resistance regarding crack propagation, crater damage and depth of penetration (DOP). A further improvement of projectile impact resistance was observed if a combined addition of steel fibres and ceramic balls was used. Numerical simulations were then performed to further comprehend the projectile impact on G-UHPC targets using the HJC constitutive model in the finite element software LS-DYNA. Numerically simulated DOP, projectile velocity and displacement histories were obtained and then validated through comparing with the existing models. The numerical perforation limits for 20 vol-% ceramic ball aggregated and 1.5 vol-% steel fibre reinforced G-UHPC were 240 mm at 568 m/s and 380 mm at 798 m/s, respectively.
Liu, K, Wu, C, Li, X, Liu, J, Tao, M, Fang, J & Xu, S 2021, 'The influences of cooling regimes on fire resistance of ultra-high performance concrete under static-dynamic coupled loads', Journal of Building Engineering, vol. 44, pp. 103336-103336.
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Liu, L, Fryc, S, Wu, L, Vu, TL, Paul, G & Vidal-Calleja, T 2021, 'Active and Interactive Mapping With Dynamic Gaussian Process Implicit Surfaces for Mobile Manipulators', IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 3679-3686.
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In this letter, we present an interactive probabilistic mapping framework for a mobile manipulator picking objects from a pile. The aim is to map the scene, actively decide where to go next and which object to pick, make changes to the scene by picking the chosen object, and then map these changes alongside. The proposed framework uses a novel dynamic Gaussian Process (GP) Implicit Surface method to incrementally build and update the scene map that reflects environment changes. Actively the framework computes the next-best-view, balancing the terms of object reachability for picking and map information gain (IG) for fidelity and coverage. To enforce a priority of visiting boundary segments over unknown regions, the IG formulation includes an uncertainty gradient-based frontier score by exploiting the GP kernel derivative. This leads to an efficient strategy that addresses the often conflicting requirement of unknown environment exploration and object picking exploitation given a limited execution horizon. We demonstrate the effectiveness of our framework with software simulation and real-life experiments.
Liu, L, Guo, Y, Lei, G & Zhu, JG 2021, 'Iron Loss Calculation for High-Speed Permanent Magnet Machines Considering Rotating Magnetic Field and Thermal Effects', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-5.
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Thanks to their many merits such as high power density and fast dynamic response, the high-speed permanent magnet machines (HSPMMs) have attracted increasing industrial and domestic applications. However, the iron loss may become significantly higher at higher operating speed and frequency and it should be carefully considered in the machine design and analysis. In this paper, an advanced iron loss analytical calculation method is applied for HSPMMs in which the influences of rotating magnetic field and thermal field are both considered. A 30 kW, 45000 r/min HSPMM is studied to demonstrate that the proposed model is feasible and advantageous. Analysis results reveal that the predicted iron loss by using the proposed method has satisfactory accuracy with small errors (maximum error of 3.73% and absolute average error of 3.03%) under different operating conditions. The proposed method can also be applied in other electromagnetic devices such as superconducting electrical machines.
Liu, L, Jiang, J, Jia, W, Amirgholipour, S, Wang, Y, Zeibots, M & He, X 2021, 'DENet: A Universal Network for Counting Crowd With Varying Densities and Scales', IEEE Transactions on Multimedia, vol. 23, no. 99, pp. 1060-1068.
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Liu, M, Xie, K, Nothling, MD, Zu, L, Zhao, S, Harvie, DJE, Fu, Q, Webley, PA & Qiao, GG 2021, 'Ultrapermeable Composite Membranes Enhanced Via Doping with Amorphous MOF Nanosheets', ACS Central Science, vol. 7, no. 4, pp. 671-680.
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Thin-film composite (TFC) polymeric membranes have attracted increasing interest to meet the demands of industrial gas separation. However, the development of high-performance TFC membranes within their current configuration faces two key challenges: (i) the thickness-dependent gas permeability of polymeric materials (mainly poly(dimethylsiloxane) (PDMS)) and (ii) the geometric restriction effect due to the limited pore accessibility of the underlying porous substrate. Here we demonstrate that the incorporation of trace amounts (∼1.8 wt %) of amorphous metal-organic framework (MOF) nanosheets into the gutter layer of TFC assemblies can simultaneously address these two limitations by the creation of rapid, transmembrane gas diffusion pathways. The resultant PDMS&MOF membrane displayed excellent CO2 permeance of 10450 GPU and CO2/N2 selectivity of 9.1. Leveraging this strategy, we successfully fabricate a novel TFC membrane, consisting of a PDMS&MOF gutter and an ultrathin (∼54 nm) poly(ethylene glycol) top selective layer via surface-initiated atom transfer radical polymerization. The complete TFC membrane exhibits excellent processability and remarkable CO2/N2 separation performance (1990 GPU with a CO2/N2 ideal selectivity of 39). This study reveals a strategy for the design and fabrication of a new TFC membrane system with unprecedented gas-separation performance.
Liu, P, Li, Y, Cheng, W, Gao, X & Huang, X 2021, 'Intelligent Reflecting Surface Aided NOMA for Millimeter-Wave Massive MIMO With Lens Antenna Array', IEEE Transactions on Vehicular Technology, vol. 70, no. 5, pp. 4419-4434.
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Liu, Q, Do, TDT & Cao, L 2021, 'Answer Keyword Generation for Community Question Answering by Multiaspect Gamma–Poisson Matrix Completion', IEEE Intelligent Systems, vol. 36, no. 4, pp. 35-47.
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IEEE Community question answering (CQA) recommends appropriate answers to existing and new questions. Such answer recommendation is challenging since CQA data is often sparse and decentralized and lacks sufficient information to generate suitable answers to existing questions. Matching answers to new questions is more challenging in modeling Q/A sparsity, generating answers to cold-start/novel questions, and integrating metadata about Q/A into models, etc. This paper addresses these issues by a novel statistical model to automatically generate answer keywords in CQA with multi-aspect Gamma-Poisson matrix completion (MAGIC). MAGIC is the first trial in CQA to model multiple aspects of Q/A sentence information in CQA by involving Q/A metadata, Q/A sparsity, and both lexical and semantic Q/A information in a hierarchical Gamma-Poisson model. MAGIC can efficiently generate answer keywords for both existing and new questions against nonnegative matrix factorization (MF), probability MF, and relevant Poisson factorization models w.r.t. recommending appropriate and informative answer keywords.
Liu, Q, Huang, H, Xuan, J, Zhang, G, Gao, Y & Lu, J 2021, 'A Fuzzy Word Similarity Measure for Selecting Top-$k$ Similar Words in Query Expansion', IEEE Transactions on Fuzzy Systems, vol. 29, no. 8, pp. 2132-2144.
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Liu, Q, Lu, J, Zhang, G, Shen, T, Zhang, Z & Huang, H 2021, 'Domain-specific meta-embedding with latent semantic structures', Information Sciences, vol. 555, pp. 410-423.
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Liu, S, Wang, S, Liu, X, Gandomi, AH, Daneshmand, M, Muhammad, K & De Albuquerque, VHC 2021, 'Human Memory Update Strategy: A Multi-Layer Template Update Mechanism for Remote Visual Monitoring', IEEE Transactions on Multimedia, vol. 23, no. 99, pp. 2188-2198.
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Liu, S, Wang, S, Liu, X, Lin, C-T & Lv, Z 2021, 'Fuzzy Detection Aided Real-Time and Robust Visual Tracking Under Complex Environments', IEEE Transactions on Fuzzy Systems, vol. 29, no. 1, pp. 90-102.
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Liu, T, Lu, J, Yan, Z & Zhang, G 2021, 'Statistical generalization performance guarantee for meta-learning with data dependent prior', Neurocomputing, vol. 465, pp. 391-405.
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Meta-learning aims to leverage experience from previous tasks to achieve an effective and fast adaptation ability when encountering new tasks. However, it is unclear how the generalization property applies to new tasks. Probably approximately correct (PAC) Bayes bound theory provides a theoretical framework to analyze the generalization performance for meta-learning with an explicit numerical generalization error upper bound. A tighter upper bound may achieve better generalization performance. However, for the PAC-Bayes meta-learning bound, the prior distribution is selected randomly which results in poor generalization performance. In this paper, we derive three novel generalization error upper bounds for meta-learning based on the PAC-Bayes relative entropy bound. Furthermore, in order to avoid randomly prior distribution, based on the empirical risk minimization (ERM) method, a data-dependent prior for the PAC-Bayes meta-learning bound algorithm is developed and the sample complexity and computational complexity are analyzed. The experiments illustrate that the proposed three PAC-Bayes bounds for meta-learning achieve a competitive generalization guarantee, and the extended PAC-Bayes bound with a data-dependent prior can achieve rapid convergence ability.
Liu, W, Wang, H, Zhang, Y, Wang, W, Qin, L & Lin, X 2021, 'EI-LSH: An early-termination driven I/O efficient incremental c-approximate nearest neighbor search.', VLDB J., vol. 30, pp. 215-235.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Nearest neighbor in high-dimensional space has been widely used in various fields such as databases, data mining and machine learning. The problem has been well solved in low-dimensional space. However, when it comes to high-dimensional space, due to the curse of dimensionality, the problem is challenging. As a trade-off between accuracy and efficiency, c-approximate nearest neighbor (c-ANN) is considered instead of an exact NN search in high-dimensional space. A variety of c-ANN algorithms have been proposed, one of the important schemes for the c-ANN problem is called Locality-sensitive hashing (LSH), which projects a high-dimensional dataset into a low-dimensional dataset and can return a c-ANN with a constant probability. In this paper, we propose a new aggressive early-termination (ET) condition which stops the algorithm with LSH scheme earlier under the same theoretical guarantee, leading to a smaller I/O cost and less running time. Unlike the “conservative” early termination conditions used in previous studies, we propose an “aggressive” early termination condition which can stop much earlier. Though it is not absolutely safe and may result in the probability of failure, we can still devise more efficient algorithms under the same theoretical guarantee by carefully considering the failure probabilities brought by LSH scheme and early termination. Furthermore, we also introduce an incremental searching strategy. Unlike the previous LSH methods, which expand the bucket width in an exponential way, we employ a more natural search strategy to incrementally access the hash values of the objects. We also provide a rigorous theoretical analysis to underpin our incremental search strategy and the new early termination technique. Our comprehensive experiment results show that, compared with the state-of-the-art I/O efficient c-ANN techniques, our proposed algorithm, namely EI-LSH, can achieve much bette...
Liu, X, Chen, Z, Tian, K, Zhu, F, Hao, D, Cheng, D, Wei, W, Zhang, L & Ni, B-J 2021, 'Fe3+ Promoted the Photocatalytic Defluorination of Perfluorooctanoic Acid (PFOA) over In2O3', ACS ES&T Water, vol. 1, no. 11, pp. 2431-2439.
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Liu, X, Fu, Q, Liu, Z, Zeng, T, Du, M, He, D, Lu, Q, Ni, B-J & Wang, D 2021, 'Alkaline pre-fermentation for anaerobic digestion of polyacrylamide flocculated sludge: Simultaneously enhancing methane production and polyacrylamide degradation', Chemical Engineering Journal, vol. 425, pp. 131407-131407.
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The residual Polyacrylamide (PAM) in sewage sludge might cause severe disturbance in anaerobic digestion, and appropriate solutions to alleviate such situation are urgently required. In present study, alkaline pre-fermentation was proposed for PAM-flocculated sewage sludge (PFS) pretreatment, by which both PFS methane production and PAM degradation were remarkably enhanced. Under the optimal alkaline pre-fermentation condition (pH 10 for 12 d), the biochemical methane potential of PFS (12 g PAM/kg TS) increased from 107.2 to 246.6 mL/g VS, the hydrolysis rate increased from 0.109 to 0.197 d−1, and the degradation efficiency of PAM increased from 30.6% to 80.1%. Mechanism analysis indicated that the alkaline pre-fermentation broke the large “PAM-sludge” floccules, decreased the molecular weight of PAM, which alleviated the disturbance situation of PAM-present digester and made PAM more available for microbes to be biodegraded. Moreover, PFS hydrolysis and acidification were simultaneously accelerated by alkaline pre-fermentation, thereby providing more bioavailable carbon substrates for subsequent methane producing and PAM co-metabolism. Microbial community analysis demonstrated syntrophic bacteria such as Petrimonas and Sedimentibacter, which had ability to degrade an extensive range of various types of organics including carbohydrates and PAM, were enriched in alkaline pre-fermenter, and the acetotrophic methanogen Methanosaeta, were elevated in anaerobic digester. This work provides an effective microbial based strategy to improve the efficiency of anaerobic digestion of PFS.
Liu, X, Ren, Z, Ngo, HH, He, X, Desmond, P & Ding, A 2021, 'Membrane technology for rainwater treatment and reuse: A mini review', Water Cycle, vol. 2, pp. 51-63.
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Because of the current situation of global water shortage, finding strategies that can effectively guarantee water safety and sustainable use has become an urgent problem that needs to be solved at present. Rainwater is a type of clean energy and the method of the treatment and reuse of rainwater has become a pivotal problem that is worthy of consideration. Membrane technology has become the preferred method in the field of wastewater treatment due to its small footprint, good treatment effect, and low cost, and has also received increasing attention in rainwater treatment field. This review aims to retrospect the existing research technology of rainwater treatment with membrane technology and seek out the most critical research gaps to meet future research needs and technological exploration. The characteristics of different types of membrane technologies in rainwater treatment were summarized, the water quality after treatment and the feasibility in practical applications was analyzed. Membrane fouling has been identified as the main challenge. Nowadays, the research on membrane surface modification and membrane process optimization is gradually deepening, and the exploration and synthesis of new membrane materials and the process of treating rainwater with various technology combinations are still under research. The future application prospects are worth looking forward to.
Liu, X, Wu, Y, Xu, Q, Du, M, Wang, D, Yang, Q, Yang, G, Chen, H, Zeng, T, Liu, Y, Wang, Q & Ni, B-J 2021, 'Mechanistic insights into the effect of poly ferric sulfate on anaerobic digestion of waste activated sludge', Water Research, vol. 189, pp. 116645-116645.
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Poly ferric sulfate (PFS), one of the typical inorganic flocculants widely used in wastewater management and waste activated sludge (WAS) dewatering, could be accumulated in WAS and inevitably entered in anaerobic digestion system at high levels. However, knowledge about its impact on methane production is virtually absent. This study therefore aims to fill this gap and provide insights into the mechanisms involved through both batch and long-term tests using either real WAS or synthetic wastewaters as the digestion substrates. Experimental results showed that the maximum methane potential and production rate of WAS was respectively retarded by 39.0% and 66.4%, whereas the lag phase was extended by 237.0% at PFS of 40 g per kg of total solids. Mechanism explorations exhibited that PFS induced the physical enmeshment and disrupted the enzyme activity involved in anaerobic digestion, resulting in an inhibitory state of the bioprocess of hydrolysis, acidogenesis, and methanogenesis. Furthermore, PFS's inhibition to hydrogenotrophic methanogenesis was much severer than that to acetotrophic methanogenesis, which could be supported by the elevated abundances of Methanosaeta sp and the dropped abundances of Methanobacterium sp in PFS-present digester, and probably due to the severe mass transfer resistance of hydrogen between the syntrophic bacteria and methanogens, as well as the higher hydrogen appetency of PFS-induced sulfate reducing bacteria. Among the derivatives of PFS, 'multinucleate and multichain-hydroxyl polymers' and sulfate were unveiled to be the major contributors to the decreased methane potential, while the 'multinucleate and multichain-hydroxyl polymers' were identified to be the chief buster to the slowed methane-producing rate and the extended lag time.
Liu, X, Xu, B, Duan, X, Hao, Q, Wei, W, Wang, S & Ni, B-J 2021, 'Facile preparation of hydrophilic In2O3 nanospheres and rods with improved performances for photocatalytic degradation of PFOA', Environmental Science: Nano, vol. 8, no. 4, pp. 1010-1018.
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This study used metal–organic-framework (MOF) derived In2O3 for the photocatalytic degradation of PFOA for the first time. MOF derived In2O3 demonstrated significantly enhanced performance for PFOA decomposition compared to commercial In2O3.
Liu, X, Yang, B, Chen, H, Musial, K, Chen, H, Li, Y & Zuo, W 2021, 'A Scalable Redefined Stochastic Blockmodel', ACM Transactions on Knowledge Discovery from Data, vol. 15, no. 3, pp. 1-28.
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Stochastic blockmodel (SBM) is a widely used statistical network representation model, with good interpretability, expressiveness, generalization, and flexibility, which has become prevalent and important in the field of network science over the last years. However, learning an optimal SBM for a given network is an NP-hard problem. This results in significant limitations when it comes to applications of SBMs in large-scale networks, because of the significant computational overhead of existing SBM models, as well as their learning methods. Reducing the cost of SBM learning and making it scalable for handling large-scale networks, while maintaining the good theoretical properties of SBM, remains an unresolved problem. In this work, we address this challenging task from a novel perspective of model redefinition. We propose a novel redefined SBM with Poisson distribution and its block-wise learning algorithm that can efficiently analyse large-scale networks. Extensive validation conducted on both artificial and real-world data shows that our proposed method significantly outperforms the state-of-the-art methods in terms of a reasonable trade-off between accuracy and scalability. 1
Liu, X, Yang, B, Song, W, Musial, K, Zuo, W, Chen, H & Yin, H 2021, 'A block-based generative model for attributed network embedding', World Wide Web, vol. 24, no. 5, pp. 1439-1464.
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Attributed network embedding has attracted plenty of interest in recent years. It aims to learn task-independent, low-dimensional, and continuous vectors for nodes preserving both topology and attribute information. Most of the existing methods, such as random-walk based methods and GCNs, mainly focus on the local information, i.e., the attributes of the neighbours. Thus, they have been well studied for assortative networks (i.e., networks with communities) but ignored disassortative networks (i.e., networks with multipartite, hubs, and hybrid structures), which are common in the real world. To model both assortative and disassortative networks, we propose a block-based generative model for attributed network embedding from a probability perspective. Specifically, the nodes are assigned to several blocks wherein the nodes in the same block share the similar linkage patterns. These patterns can define assortative networks containing communities or disassortative networks with the multipartite, hub, or any hybrid structures. To preserve the attribute information, we assume that each node has a hidden embedding related to its assigned block. We use a neural network to characterize the nonlinearity between node embeddings and node attributes. We perform extensive experiments on real-world and synthetic attributed networks. The results show that our proposed method consistently outperforms state-of-the-art embedding methods for both clustering and classification tasks, especially on disassortative networks.
Liu, X, Zheng, G, Luo, Q, Li, Q & Sun, G 2021, 'Fatigue behavior of carbon fibre reinforced plastic and aluminum single-lap adhesive joints after the transverse pre-impact', International Journal of Fatigue, vol. 144, pp. 105973-105973.
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Liu, Y & Li, J 2021, 'Hamming-shifting graph of genomic short reads: Efficient construction and its application for compression', PLOS Computational Biology, vol. 17, no. 7, pp. e1009229-e1009229.
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Graphs such as de Bruijn graphs and OLC (overlap-layout-consensus) graphs have been widely adopted for the de novo assembly of genomic short reads. This work studies another important problem in the field: how graphs can be used for high-performance compression of the large-scale sequencing data. We present a novel graph definition named Hamming-Shifting graph to address this problem. The definition originates from the technological characteristics of next-generation sequencing machines, aiming to link all pairs of distinct reads that have a small Hamming distance or a small shifting offset or both. We compute multiple lexicographically minimal k-mers to index the reads for an efficient search of the weight-lightest edges, and we prove a very high probability of successfully detecting these edges. The resulted graph creates a full mutual reference of the reads to cascade a code-minimized transfer of every child-read for an optimal compression. We conducted compression experiments on the minimum spanning forest of this extremely sparse graph, and achieved a 10 − 30% more file size reduction compared to the best compression results using existing algorithms. As future work, the separation and connectivity degrees of these giant graphs can be used as economical measurements or protocols for quick quality assessment of wet-lab machines, for sufficiency control of genomic library preparation, and for accurate de novo genome assembly.
Liu, Y, Bai, J, Zheng, J, Liao, H, Ren, Y & Guo, YJ 2021, 'Efficient Shaped Pattern Synthesis for Time Modulated Antenna Arrays Including Mutual Coupling by Differential Evolution Integrated With FFT via Least-Square Active Element Pattern Expansion', IEEE Transactions on Antennas and Propagation, vol. 69, no. 7, pp. 4223-4228.
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IEEE The fast Fourier transform (FFT) via the least-square active element pattern expansion (LSAEPE) is generalized to speed up the computation of array patterns including mutual coupling and platform effect for time-modulated antenna arrays (TMAAs) at the central and sideband frequencies. By integrating the LSAEPE-FFT with differential evolution algorithm (DEA), the resulting DEA-LSAEPE-FFT method can realize efficient shaped pattern synthesis with accurate control of mainlobe shape, sidelobe level (SLL) and sideband level (SBL). Two examples of synthesizing different shaped patterns for different TMAAs mounted on a nonuniform platform or with metal scatters are conducted to validate the effectiveness and robustness of the proposed method. Synthesis results show that the proposed method has much better accuracy performance than the conventional DEA-FFT while costing much less CPU time than that of using DEA combined with direct summation.
Liu, Y, Li, H, Li, Y, Xu, X, Yang, Z & Ding, G 2021, 'Optimization of the Discrete Structure in a Pressure Sensor Based on a Multiple-Contact Mechanism to Improve Sensitivity and Nonlinearity', IEEE Sensors Journal, vol. 21, no. 19, pp. 21259-21267.
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Liu, Y, Ma, C, Zhang, X, Ngo, HH, Guo, W, Zhang, M & Zhang, D 2021, 'Role of structural characteristics of MoS2 nanosheets on Pb2＋ removal in aqueous solution', Environmental Technology & Innovation, vol. 22, pp. 101385-101385.
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In this study, ultrasonic-assisted liquid-phase stripping and hydrothermal synthesis were used to prepare the two structural types of MoS2 nanosheets, namely u-MoS2 and h-MoS2, respectively. The u-MoS2 and h-MoS2 were characterized by various techniques, and the profound relationship between the structure and preparation method was also identified. Results indicated that adsorptions of Pb2＋ onto both u-MoS2 and h-MoS2 nanosheets reached equilibrium after 30 min at higher rates. The removal efficiencies of Pb2＋ by h-MoS2 and u-MoS2 nanosheets were 98.4% and 20.6% under the condition of low dosage (60 mg/L). The Pb2＋ by h-MoS2 adsorption fitted well to the Langmuir adsorption isotherm with the adsorption capacity of 174.0 mg/g while the Pb2＋ adsorption by u-MoS2 fitted well to the Freundlich isotherm (n=1). The obvious discrepancy suggested that the adsorption performance was directly associated with their structural properties, which were induced by two different synthesis methods. Based on these results, the effects of operational parameters (pH, dosage and existing ions) on Pb2＋ adsorption using h-MoS2 were further investigated. The dosage greatly affected the adsorption capacity and removal efficiency, while pH and coexisting ions had small effects on adsorption performance. In short, this study could help to better understand the role of MoS2 nanosheets’ structures obtained by different preparation methods for adsorption of heavy metal ions in aqueous solution.
Liu, Y, Shi, B & Liang, X 2021, 'Exploration: Explore a better future with advanced science and technology', Exploration, vol. 1, no. 1, pp. 6-8.
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Liu, Y, Yang, Y, Wu, P, Ma, X, Li, M, Xu, K-D & Guo, YJ 2021, 'Synthesis of Multibeam Sparse Circular-Arc Antenna Arrays Employing Refined Extended Alternating Convex Optimization', IEEE Transactions on Antennas and Propagation, vol. 69, no. 1, pp. 566-571.
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IEEE A refined extended alternating convex optimization (REACO) method is presented to synthesize multibeam sparse circular-arc antenna arrays with minimum element spacing control by considering real antenna array structure characteristics. This method consists of initial step and a few refining steps. At the initial step, an initial array with dense elements distributed on a circular-arc is considered, and its array manifold vector is described by rotating a simulated isolated element pattern (IEP) without considering element mutual coupling. The collective excitation coefficient vector (CECV) and its energy bound are introduced for each element, and consequently the common element positions for generating desired multibeam patterns can be found by minimizing the number of active CECVs under multiple constraints. This minimization problem is further formulated as performing a sequence of alternating convex optimization (ACO) in which the CECV and an auxiliary weighting vector are alternately chosen as the optimization variables, so that the mimimum element spacing constraint can be easily dealt with. Once the initial optimization step is finished, a few refining steps are performed in which the element positions and excitations are successively updated in each step by renewing the array manifold vector through rotating the simulated nearby active element patterns (AEPs) of the antenna array obtained at the previous step. In such a way, the mutual coupling can be incorporated into the multibeam sparse array synthesis. An example of synthesizing a sparse circular-arc conformal array with 23 beams covering the space from–63.25° to 63.25° is conducted to validate the effectiveness and advantage of the proposed method.
Liu, Y, Zhang, L, Li, HL, Liang, BM, Wang, J, Zhang, X, Chen, ZH, Zhang, HP, Xie, M, Wang, L, Wang, G & Oliver, BG 2021, 'Small Airway Dysfunction in Asthma Is Associated with Perceived Respiratory Symptoms, Non-Type 2 Airway Inflammation, and Poor Responses to Therapy', Respiration, vol. 100, no. 8, pp. 767-779.
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<b><i>Background:</i></b> Emerging evidence has indicated that small airway dysfunction (SAD) contributes to the clinical expression of asthma. <b><i>Objectives:</i></b> The aim of the study was to explore the relationships of SAD assessed by forced expiratory flow between 25 and 75% (FEF<sub>25–75</sub>%), with clinical and inflammatory profile and treatment responsiveness in asthma. <b><i>Method:</i></b> In study I, dyspnea intensity (Borg scale), chest tightness, wheezing and cough (visual analog scales, VASs), and pre- and post-methacholine challenge testing (MCT) were analyzed in asthma patients with SAD and non-SAD. In study II, asthma subjects with SAD and non-SAD underwent sputum induction, and inflammatory mediators in sputum were detected. Asthma patients with SAD and non-SAD receiving fixed treatments were prospectively followed up for 4 weeks in study III. Spirometry, Asthma Control Questionnaire (ACQ), and Asthma Control Test (ACT) were carried out to define treatment responsiveness. <b><i>Results:</i></b> SAD subjects had more elevated ΔVAS for dyspnea (<i>p</i> = 0.027) and chest tightness (<i>p</i> = 0.032) after MCT. Asthma patients with SAD had significantly elevated interferon (IFN)-γ in sputum (<i>p</i> < 0.05), and Spearman partial correlation found FEF<sub>25–75</sub>% significantly related to IFN-γ and interleukin-8 (both having <i>p</i> < 0.05). Furthermore, multivariable regression analysis indicated SAD was significantly associated with worse treatment responses (decrease in ACQ ≥0.5 and increase in ACT ≥3) (<i>p</i> = 0.022 and <i>p</i> = 0.032). <b><i>Conclusions:</i></b> This study indicates that SAD in asthma predisposes patients to greater dyspnea intensity and chest tightness during b...
Liu, Y, Zheng, M, Jiao, M, Yan, C, Xu, S, Du, Q, Morsch, M, Yin, J & Shi, B 2021, 'Polymeric nanoparticle mediated inhibition of miR-21 with enhanced miR-124 expression for combinatorial glioblastoma therapy', Biomaterials, vol. 276, pp. 121036-121036.
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Liu, Y, Zhou, Z, Wang, F, Kewes, G, Wen, S, Burger, S, Ebrahimi Wakiani, M, Xi, P, Yang, J, Yang, X, Benson, O & Jin, D 2021, 'Axial localization and tracking of self-interference nanoparticles by lateral point spread functions', Nature Communications, vol. 12, no. 1, pp. 1-9.
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AbstractSub-diffraction limited localization of fluorescent emitters is a key goal of microscopy imaging. Here, we report that single upconversion nanoparticles, containing multiple emission centres with random orientations, can generate a series of unique, bright and position-sensitive patterns in the spatial domain when placed on top of a mirror. Supported by our numerical simulation, we attribute this effect to the sum of each single emitter’s interference with its own mirror image. As a result, this configuration generates a series of sophisticated far-field point spread functions (PSFs), e.g. in Gaussian, doughnut and archery target shapes, strongly dependent on the phase difference between the emitter and its image. In this way, the axial locations of nanoparticles are transferred into far-field patterns. We demonstrate a real-time distance sensing technology with a localization accuracy of 2.8 nm, according to the atomic force microscope (AFM) characterization values, smaller than 1/350 of the excitation wavelength.
Liu, Z, Gao, Y, Yang, J, Xu, X, Fang, J & Xu, Y 2021, 'Effect of discretized transfer paths on abnormal vibration analysis and door structure improvement to reduce its vibration in the door slamming event', Applied Acoustics, vol. 183, pp. 108306-108306.
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Liu, Z, Gao, Y, Yang, J, Xu, X, Fang, J, Duan, Y & Ma, C 2021, 'Transfer path analysis and its application to diagnosis for low-frequency transient vibration in the automotive door slamming event', Measurement, vol. 183, pp. 109896-109896.
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Liu, Z, Liu, J, Pei, Q, Yu, H, Li, C & Wu, C 2021, 'Seismic response of tunnel near fault fracture zone under incident SV waves', Underground Space, vol. 6, no. 6, pp. 695-708.
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This study investigated the impact of a non-causative fault on the dynamic response of a nearby lined tunnel under the incidence of plane SV waves using the indirect boundary element method. The effects of several critical parameters, such as the incident frequency, the inclination degree of the fault, the distance between the fault and the tunnel on the hoop stress of the lined inner and outer walls, were explored intensively. The numerical results indicated that the non-causative fault could significantly change the hoop stress distribution of inner and outer surfaces of the tunnels. In general, for the vertically incident seismic waves, when the tunnel was located in the foot wall (under the fault), the hoop stress within the tunnel was significantly greater than that of the tunnels in the non-fault half space, with an amplification factor of up to 117%. The amplification effect became more pronounced as the fault dip angle increased. However, when the tunnel was located in the hanging wall (above the fault), the non-causative fault could produce a significant shielding effect on the dynamic response of the tunnel under high frequency wave incidence, with the reduction of hoop stress being up to 81%. For low-frequency waves, though, the fault could lead to an increase of the hoop stress of the tunnel of up to 152%. The research results will provide a reference for the seismic design and safety protection of underground structures in non-causative fault sites.
Liu, Z, Xiao, F, Lin, C-T, Kang, BH & Cao, Z 2021, 'A Generalized Golden Rule Representative Value for Multiple-Criteria Decision Analysis', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 5, pp. 3193-3204.
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Liu, Z, Yang, M, Cheng, J, Wu, D & Tan, J 2021, 'Meta-model based stochastic isogeometric analysis of composite plates', International Journal of Mechanical Sciences, vol. 194, pp. 106194-106194.
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A stochastic isogeometric analysis approach (SIGA) is presented for functionally graded porous plates with graphene platelets reinforcement (FGP-GPLs). Different kinds of random fields and variables are applied to describe the uncertain system inputs which are including material properties of the FGP matrix and graphene platelets, magnitudes and directions of applied loads. A Nyström based Karhunen-Loève expansion is presented for random field discretization within the IGA scheme. The arbitrary polynomial chaos-Kriging (aPCK) method is presented for uncertainty quantification. To sustain the robustness of the aPCK approach for engineering problems involving high-dimension of uncertainty, a new Dagum kernel function is introduced in Kriging. The mean, standard deviation, probability density function (PDF) and cumulative distribution function (CDF) of structural outputs can be effectively estimated. Three illustrative examples are investigated to assess the performance of the proposed method for mathematical and engineering applications.
Liu, Z, Yao, L, Wang, X, Monaghan, JJM, Schaette, R, He, Z & McAlpine, D 2021, 'Generalizable Sample-Efficient Siamese Autoencoder for Tinnitus Diagnosis in Listeners With Subjective Tinnitus', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, no. 99, pp. 1452-1461.
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Electroencephalogram (EEG)-based neurofeedback has been widely studied for tinnitus therapy in recent years. Most existing research relies on experts' cognitive prediction, and studies based on machine learning and deep learning are either data-hungry or not well generalizable to new subjects. In this paper, we propose a robust, data-efficient model for distinguishing tinnitus from the healthy state based on EEG-based tinnitus neurofeedback. We propose trend descriptor, a feature extractor with lower fineness, to reduce the effect of electrode noises on EEG signals, and a siamese encoder-decoder network boosted in a supervised manner to learn accurate alignment and to acquire high-quality transferable mappings across subjects and EEG signal channels. Our experiments show the proposed method significantly outperforms state-of-the-art algorithms when analyzing subjects' EEG neurofeedback to 90dB and 100dB sound, achieving an accuracy of 91.67%-94.44% in predicting tinnitus and control subjects in a subject-independent setting. Our ablation studies on mixed subjects and parameters show the method's stability in performance.
Loban, R 2021, 'Modding Europa Universalis IV: An informal gaming practice transposed into a formal learning setting', E-Learning and Digital Media, vol. 18, no. 6, pp. 530-556.
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This article explores the use of modding as a formal tool for learning history. The article examines data from a formal analysis of Europa Universalis IV (EUIV), a survey of 331 EUIV forum participants and a case study of 18 university participants. Significant quantitative survey data indicated that 45% (149/331) of participants had modified EUIV, and of the 125 participants who responded with comments about modding, a significant number (86/125 responses or 68.8%) explained how they had learnt about history, geography or other subjects through the modding process. Closer analysis of survey and case study responses and mods reveals the variety of ways participants learnt and critiqued history through the modding process. The article discusses the data and the pedagogical affordance of modding in a few steps. First, the article briefly explores the evidence that indicates modding is popular within the EUIV gaming community. In this instance, it examines whether given the popularity of gaming practice, modding might also be seen as a new casual form of engagement with games. Second, the article reviews the modding process in EUIV and examines how both playing and creating mods may be beneficial for learning history. Modding is examined in terms of its pedagogical importance and the unique educational opportunities it may offer that are not otherwise accessible through other forms of game-based learning. Finally, the article explores how and what the case study participants learnt when they were tasked with creating and implementing playable mods to demonstrate their understanding of history. Overall, the article considers the growing importance of mods, how learners can create and represent history using mods and how mods can provide a platform for learners to develop their own critique and analysis of official history.
Loban, R 2021, 'The Transformation from Manual Wargames to Grand Strategy Video Games, and the Opportunities for Sophisticated and Efficient Historical Gaming Experiences', Digital Culture & Education.
Logan, J, Kennedy, PJ & Catchpoole, D 2021, 'The Untapped Social Impact of Artificial Intelligence for Breast Cancer Screening in Developing Countries: A Critical Commentary of DeepMind', Innovations in Digital Health, Diagnostics, and Biomarkers, vol. 1, no. 2, pp. 29-32.
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Lotfi, I, Niyato, D, Sun, S, Dinh, HT, Li, Y & Kim, DI 2021, 'Protecting Multi-Function Wireless Systems From Jammers With Backscatter Assistance: An Intelligent Strategy', IEEE Transactions on Vehicular Technology, vol. 70, no. 11, pp. 11812-11826.
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In this paper, we present a novel unified framework to protect multi-function wireless systems from jamming attacks. Examples of such multi-function system include joint radar and communication (JRC) systems and simultaneous wireless information and power transfer (SWIPT) systems. By abstracting the system functionalities as a joint optimization problem of multiple queues, we achieve effective resistance against jammers for the multi-functions simultaneously.We incorporate different antijamming techniques into one framework. Deception mechanism is adopted to lure the jammer to attack and make its actions more predictable, and ambient backscatter technology is used to leverage the jamming signals. Since conventional Markov decision process (MDP) has only one decision epoch at every time slot, it cannot be used to model the deception strategy which needs two decision epochs to leverage the jamming signals. We therefore formulate the problem using an advanced two-step MDP. After that, a deep reinforcement learning algorithm with a prioritized double deep Q-Learning architecture is proposed to learn optimal strategies in different system states. We show that by jointly considering the multi-functions of the system with potential jamming attacks during design phase, significant improvement can be achieved for both of the system functionalities.
Lowe, D, Wilkinson, T, Willey, K, Kadi, A, Goldfinch, T & Lim, TJ 2021, 'Educating the Evolving Engineer: Lessons From the University of Sydney', IEEE Potentials, vol. 40, no. 2, pp. 7-12.
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Lu, H, Zhu, Y, Yuan, Y, Gong, W, Li, J, Shi, K, Lv, Y, Niu, Z & Wang, F-Y 2021, 'Social Signal-Driven Knowledge Automation: A Focus on Social Transportation', IEEE Transactions on Computational Social Systems, vol. 8, no. 3, pp. 737-753.
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Lu, J, Zheng, X, Tang, L, Zhang, T, Sheng, QZ, Wang, C, Jin, J, Yu, S & Zhou, W 2021, 'Can Steering Wheel Detect Your Driving Fatigue?', IEEE Transactions on Vehicular Technology, vol. 70, no. 6, pp. 5537-5550.
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Automated Driving System (ADS) has attracted increasing attention but the state-of-the-art ADS largely depend on vehicle driving parameters and facial features, which lacks reliability. Approaches using physiological based sensors (e.g., electroencephalogram or electrocardiogram) are either too clumsy to wear or impractical to install. In this paper, we propose a novel driver fatigue detection method by embedding surface electromyography (sEMG) sensors on a steering wheel. Compared with the existing methods, our approach is able to collect bio-signals in a non-intrusive way and detect driver fatigue at an earlier stage. The experimental results show that our approach outperforms existing methods with the weighted average F1 scores of about 90%. We also propose promising future directions to deploy this approach in real-life settings, such as applying multimodal learning using several supplementary sensors.
Lu, W, Yu, R, Wang, S, Wang, C, Jian, P & Huang, H 2021, 'Sentence Semantic Matching Based on 3D CNN for Human–Robot Language Interaction', ACM Transactions on Internet Technology, vol. 21, no. 4, pp. 1-24.
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The development of cognitive robotics brings an attractive scenario where humans and robots cooperate to accomplish specific tasks. To facilitate this scenario, cognitive robots are expected to have the ability to interact with humans with natural language, which depends on natural language understanding ( NLU ) technologies. As one core task in NLU, sentence semantic matching ( SSM ) has widely existed in various interaction scenarios. Recently, deep learning–based methods for SSM have become predominant due to their outstanding performance. However, each sentence consists of a sequence of words, and it is usually viewed as one-dimensional ( 1D ) text, leading to the existing available neural models being restricted into 1D sequential networks. A few researches attempt to explore the potential of 2D or 3D neural models in text representation. However, it is hard for their works to capture the complex features in texts, and thus the achieved performance improvement is quite limited. To tackle this challenge, we devise a novel 3D CNN-based SSM ( 3DSSM ) method for human–robot language interaction. Specifically, first, a specific architecture called feature cube network is designed to transform a 1D sentence into a multi-dimensional representation named as semantic feature cube. Then, a 3D CNN module is employed to learn a semantic representation for the semantic feature cube by capturing both the local features embedded in word representations and the sequential information among successive words in a sentence. Given a pair of sentences, their representations are c...
Lu, W, Zhang, Y, Wang, S, Huang, H, Liu, Q & Luo, S 2021, 'Concept Representation by Learning Explicit and Implicit Concept Couplings', IEEE Intelligent Systems, vol. 36, no. 1, pp. 6-15.
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IEEE Generating the precise semantic representation of a word/concept is a fundamental task in natural language processing. Recent studies which incorporate semantic knowledge into word embedding have shown their potential in improving the semantic representation of a concept. However, existing approaches only achieved limited performance improvement as they usually (1) model a word's semantics from some explicit aspects while ignoring the intrinsic aspects of the word, (2) treat semantic knowledge as a supplement of word embeddings, and (3) consider partial relations between concepts while ignoring rich coupling relations between them, such as explicit concept co-occurrences in descriptive texts in a corpus as well as concept hyperlink relations in a knowledge network, and implicit couplings between the explicit relations. In human consciousness, concepts are associated with various coupling relations, which inspires us to capture as many concept couplings as possible for building a better concept representation. We thus propose a neural coupled concept representation (CoupledCR) framework and its instantiation: a coupled concept embedding (CCE) model. CCE first learns two types of explicit couplings from concept cooccurrences and hyperlink relations respectively, and then learns a type of high-level implicit couplings between these two types of explicit couplings. Extensive experimental results on real-world datasets show that CCE significantly outperforms state-of-the-art semantic representation methods.
Lu, X, Liu, L, Nie, L, Chang, X & Zhang, H 2021, 'Semantic-Driven Interpretable Deep Multi-Modal Hashing for Large-Scale Multimedia Retrieval', IEEE Transactions on Multimedia, vol. 23, pp. 4541-4554.
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Multi-modal hashing focuses on fusing different modalities and exploring the complementarity of heterogeneous multi-modal data for compact hash learning. However, existing multi-modal hashing methods still suffer from several problems, including: 1) Almost all existing methods generate unexplainable hash codes. They roughly assume that the contribution of each hash code bit to the retrieval results is the same, ignoring the discriminative information embedded in hash learning and semantic similarity in hash retrieval. Moreover, the length of hash code is empirically set, which will cause bit redundancy and affect retrieval accuracy. 2) Most existing methods exploit shallow models which fail to fully capture higher-level correlation of multi-modal data. 3) Most existing methods adopt online hashing strategy based on immutable direct projection, which generates query codes for new samples without considering the differences of semantic categories. In this paper, we propose a Semantic-driven Interpretable Deep Multi-modal Hashing (SIDMH) method to generate interpretable hash codes driven by semantic categories within a deep hashing architecture, which can solve all these three problems in an integrated model. The main contributions are: 1) A novel deep multi-modal hashing network is developed to progressively extract hidden representations of heterogeneous modality features and deeply exploit the complementarity of multi-modal data. 2) Learning interpretable hash codes, with discriminant information of different categories distinctively embedded into hash codes and their different impacts on hash retrieval intuitively explained. Besides, the code length depends on the number of categories in the dataset, which can reduce the bit redundancy and improve the retrieval accuracy. 3) The semantic-driven online hashing strategy encodes the significant branches and discards the negligible branches of each query sample according to the semantics contained in it, th...
Lu, Z-H, Wu, S-Y, Tang, Z, Zhao, Y-G & Li, W 2021, 'Effect of chloride-induced corrosion on the bond behaviors between steel strands and concrete', Materials and Structures, vol. 54, no. 3.
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The corrosion of steel strands due to the chloride contamination is one of the most common causes for the degradation of prestressed concrete infrastructure. In this paper, an experimental study was performed to investigate the bond behaviors between steel strands and concrete after suffered the chloride corrosion. Total twenty central and off-center pull-out specimens with different corrosion levels were prepared and tested, in which the electrochemical acceleration method was employed to induce various corrosion levels. The effects of corrosion rate, stirrup configuration and holding condition of concrete to the steel strands on the bond behaviors of steel strands were studied and compared, in terms of the failure mode, bond-slip relationship, bond strength, and bond toughness. The results show that both the ultimate bond strength and characteristic bond strength decreased with the increase of corrosion degree. The presence of stirrups can significantly enhance the bond performance, indicating the more ductile failure characteristic and increased bond toughness. Moreover, the prediction results using empirical and analytical models are also compared with the experimental results to verify their applicability and accuracies in predicting the bond strength of steel strands after corrosion.
Luo, H, Wang, P, Chen, H & Xu, M 2021, 'Object Detection Method Based on Shallow Feature Fusion and Semantic Information Enhancement', IEEE Sensors Journal, vol. 21, no. 19, pp. 21839-21851.
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Luo, J, Zhou, C, Li, W, Chen, S, Habibnejad Korayem, A & Duan, W 2021, 'Using graphene oxide to improve physical property and control ASR expansion of cement mortar', Construction and Building Materials, vol. 307, pp. 125006-125006.
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Alkali-silica reaction (ASR) is a slowly occurring reaction in concrete between alkaline pore solution and reactive non-crystalline silica in aggregates, which is a challenge to physical property and durability of concrete. The oxygen-containing functional groups coupled with large surface area of graphene oxide (GO) nanomaterial renders highly reactive interaction with cement-based composite. Here, the physical properties and ASR expansion test of cement mortars modified with varied loadings of GO (wGO) or/and Pyrex glass (GOPM) were implemented after optimizing GO dispersion efficiency in water. The water absorption and microstructures of GOPM were observed to figure out the mechanism of GO's effect on mechanical strength, permeability, and ASR expansion of GOPM. Results show, 15 kJ is the optimal sonication energy for the dispersion of 300 mL pristine GO-water suspension with 0.04% wGO; the effect of GO on improving the flexural and compressive strength of GOPM is remarkable, the maximal amplitude is up to 24.16%, 43.03% compared with the baseline, respectively; GO has great influence on long-term anti-permeability and controlling expansion, the nano-nucleation and interlocking effect of GO render the expansion rate of GOPM be well below 0.1% threshold.
Luo, L, Jiang, Z, Wei, D & Jia, F 2021, 'A study of influence of hydraulic pressure on micro-hydromechanical deep drawing considering size effects and surface roughness', Wear, vol. 477, pp. 203803-203803.
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Luo, L, Wei, D, Zu, G & Jiang, Z 2021, 'Influence of blank holder-die gap on micro-deep drawing of SUS304 cups', International Journal of Mechanical Sciences, vol. 191, pp. 106065-106065.
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Luo, S, Chu, VW, Li, Z, Wang, Y, Zhou, J, Chen, F & Wong, RK 2021, 'Multi-task learning by hierarchical Dirichlet mixture model for sparse failure prediction', International Journal of Data Science and Analytics, vol. 12, no. 1, pp. 15-29.
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© 2020, Springer Nature Switzerland AG. Sparsity and noisy labels occur inherently in real-world data. Previously, strong assumptions were made by domain experts to use their experience and expertise to select parameters for their models. Similar approach has been adopted in machine learning for hyper-parameter setting. However, these assumptions are often subjective and are not necessarily the optimal choice. To address this problem, we propose a data-driven approach to automate model parameter learning via a Bayesian nonparametric formulation. We propose hierarchical Dirichlet process mixture model (HDPMM) as a multi-task learning framework. It is used to learn the common parameters across different datasets in the same industry. In our experiments, we verified the capability of HDPMM for multi-task learning in infrastructure failure predictions. It was done by combining HDPMM with hierarchical beta process, which is our failure prediction model. In particular, multi-task learning was used to gain additional knowledge from failure records of water supply networks managed by other utility companies to improve prediction accuracy of our model. Notably, we have achieved superior accuracy for sparse predictions than previous state-of-the-art models. Moreover, we have demonstrated the capability of our proposed model in supporting preventive maintenance of critical infrastructure.
Luo, Z, Li, W, Gan, Y, He, X, Castel, A & Sheng, D 2021, 'Nanoindentation on micromechanical properties and microstructure of geopolymer with nano-SiO2 and nano-TiO2', Cement and Concrete Composites, vol. 117, pp. 103883-103883.
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Fly ash-based geopolymers incorporated with 2% nano-SiO2 (NS)/nano-TiO2 (NT) particles were subjected to microstructural and statistical nanoindentation analysis. With the addition of both types of nanoparticles, the compressive strength of geopolymer and the micromechanical properties of N-A-S-H gel were increased. NS exhibited higher reinforcement effect than NT on macro-strength. However, NT more significantly enhanced gel micromechanical properties. NT and especially the NS were found to have a positive effect on the early reaction rate of geopolymer. After 28 days, the gel proportion obtained by Backscattered electron (BSE) images analysis was close values of 49.16%, 55.69% and 54.02% for reference sample and NS, NT reinforced geopolymer, which were more than two times of that from the statistical nanoindentation. The effects of NS and NT on microstructure, gel proportion and gel micromechanical properties were discussed to reveal the macro-strength reinforcement mechanism. The results obtained from different techniques were also compared and discussed.
Luo, Z, Li, W, Li, P, Wang, K & Shah, SP 2021, 'Investigation on effect of nanosilica dispersion on the properties and microstructures of fly ash-based geopolymer composite', Construction and Building Materials, vol. 282, pp. 122690-122690.
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Nanosilica-geopolymer composites with different dispersion levels of nanoparticles were investigated on the physical properties and gel properties. The results indicated that compared with the nanosilica-geopolymer composites without ultrasonic dispersion, the 20 and 120 min of sonication brought 3.88% and 13.59% of additional strength enhancement, respectively. For the micromechanical properties of N-A-S-H gel, the better dispersed samples exhibited higher elastic modulus. In comparison to the reference sample, the elastic modulus was increased by 15.18% and 29.93% respectively for samples with 20 and 120 min of sonication. The better dispersed nanoparticles exhibited smaller sizes and increased the physical filling effect which could result in densified microstructures, better bonding behaviors and the both improved micro and macro mechanical properties.
Luo, Z, Li, W, Wang, K, Castel, A & Shah, SP 2021, 'Comparison on the properties of ITZs in fly ash-based geopolymer and Portland cement concretes with equivalent flowability', Cement and Concrete Research, vol. 143, pp. 106392-106392.
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This paper aims to compare the properties of interfacial transition zones (ITZs) in Portland cement (PC) concrete and geopolymer concrete. Portland cement and geopolymer pastes were designed with the equivalent flowability to provide similar mix and casting condition of ITZs. Two types of modelled ITZs were prepared to facilitate the nanoindentations across ITZs, microstructural characterization, and comparison on the properties of ITZs with less influential factors. The results showed that the interfacial bonding of ITZs between geopolymer matrix and aggregate is relatively stronger than the counterpart in the PC concrete. There is a high amount of crystalline hydration products in the ITZs of PC concrete, but a layer gel-rich paste with denser microstructures in the ITZs of geopolymer concrete. Additionally, the interface morphology and nanoindentation analysis indicate that the property of ITZs in the modelled geopolymer concrete is not poorer than that of the corresponding geopolymer paste.
Luong, NC, Lu, X, Hoang, DT, Niyato, D & Kim, DI 2021, 'Radio Resource Management in Joint Radar and Communication: A Comprehensive Survey', IEEE Communications Surveys & Tutorials, vol. 23, no. 2, pp. 780-814.
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Joint radar and communication (JRC) has recently attracted substantial attention. The first reason is that JRC allows individual radar and communication systems to share spectrum bands and thus improves the spectrum utilization. The second reason is that JRC enables a single hardware platform, e.g., an autonomous vehicle or a UAV, to simultaneously perform the communication function and the radar function. As a result, JRC is able to improve the efficiency of resources, i.e., spectrum and energy, reduce the system size, and minimize the system cost. However, there are several challenges to be solved for the JRC design. In particular, sharing the spectrum imposes the interference caused by the systems, and sharing the hardware platform and energy resource complicates the design of the JRC transmitter and compromises the performance of each function. To address the challenges, several resource management approaches have been recently proposed, and this paper presents a comprehensive literature review on resource management for JRC. First, we give fundamental concepts of JRC, important performance metrics used in JRC systems, and applications of the JRC systems. Then, we review and analyze resource management approaches, i.e., spectrum sharing, power allocation, and interference management, for JRC. In addition, we present security issues to JRC and provide a discussion of countermeasures to the security issues. Finally, we highlight important challenges in the JRC design and discuss future research directions related to JRC.
Luu, HM, van Walsum, T, Franklin, D, Pham, PC, Vu, LD, Moelker, A, Staring, M, VanHoang, X, Niessen, W & Trung, NL 2021, 'Efficiently compressing 3D medical images for teleinterventions via CNNs and anisotropic diffusion', Medical Physics, vol. 48, no. 6, pp. 2877-2890.
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PurposeEfficient compression of images while preserving image quality has the potential to be a major enabler of effective remote clinical diagnosis and treatment, since poor Internet connection conditions are often the primary constraint in such services. This paper presents a framework for organ‐specific image compression for teleinterventions based on a deep learning approach and anisotropic diffusion filter.MethodsThe proposed method, deep learning and anisotropic diffusion (DLAD), uses a convolutional neural network architecture to extract a probability map for the organ of interest; this probability map guides an anisotropic diffusion filter that smooths the image except at the location of the organ of interest. Subsequently, a compression method, such as BZ2 and HEVC‐visually lossless, is applied to compress the image. We demonstrate the proposed method on three‐dimensional (3D) CT images acquired for radio frequency ablation (RFA) of liver lesions. We quantitatively evaluate the proposed method on 151 CT images using peak‐signal‐to‐noise ratio (), structural similarity (), and compression ratio () metrics. Finally, we compare the assessments of two radiologists on the liver lesion detection and the liver lesion center annotation using 33 sets of the original images and the compressed images.
Lv, Y, Miao, J, Liang, J, Chen, L & Qian, Y 2021, 'BIC-based node order learning for improving Bayesian network structure learning', Frontiers of Computer Science, vol. 15, no. 6, p. 156337.
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Node order is one of the most important factors in learning the structure of a Bayesian network (BN) for probabilistic reasoning. To improve the BN structure learning, we propose a node order learning algorithm based on the frequently used Bayesian information criterion (BIC) score function. The algorithm dramatically reduces the space of node order and makes the results of BN learning more stable and effective. Specifically, we first find the most dependent node for each individual node, prove analytically that the dependencies are undirected, and then construct undirected subgraphs UG. Secondly, the UG is examined and connected into a single undirected graph UGC. The relation between the subgraph number and the node number is analyzed. Thirdly, we provide the rules of orienting directions for all edges in UGC, which converts it into a directed acyclic graph (DAG). Further, we rank the DAG’s topology order and describe the BIC-based node order learning algorithm. Its complexity analysis shows that the algorithm can be conducted in linear time with respect to the number of samples, and in polynomial time with respect to the number of variables. Finally, experimental results demonstrate significant performance improvement by comparing with other methods.
Lyu, B, Ramezani, P, Hoang, DT, Gong, S, Yang, Z & Jamalipour, A 2021, 'Optimized Energy and Information Relaying in Self-Sustainable IRS-Empowered WPCN', IEEE Transactions on Communications, vol. 69, no. 1, pp. 619-633.
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This paper proposes a hybrid-relaying scheme empowered by a self-sustainable intelligent reflecting surface (IRS) in a wireless powered communication network (WPCN), to simultaneously improve the performance of downlink energy transfer (ET) from a hybrid access point (HAP) to multiple users and uplink information transmission (IT) from users to the HAP. We propose time-switching (TS) and power-splitting (PS) schemes for the IRS, where the IRS can harvest energy from the HAP's signals by switching between energy harvesting and signal reflection in the TS scheme or adjusting its reflection amplitude in the PS scheme. For both the TS and PS schemes, we formulate the sum-rate maximization problems by jointly optimizing the IRS's phase shifts for both ET and IT and network resource allocation. To address each problem's non-convexity, we propose a two-step algorithm to obtain the near-optimal solution with high accuracy. To show the structure of resource allocation, we also investigate the optimal solutions for the schemes with random phase shifts. Through numerical results, we show that our proposed schemes can achieve significant system sum-rate gain compared to the baseline scheme without IRS.
Lyu, J, Bi, X, Banerjee, S, Huang, Z, Leung, FHF, Lee, TT-Y, Yang, D-D, Zheng, Y-P & Ling, SH 2021, 'Dual-task ultrasound spine transverse vertebrae segmentation network with contour regularization', Computerized Medical Imaging and Graphics, vol. 89, pp. 101896-101896.
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3D ultrasound imaging has become one of the common diagnosis ways to assess scoliosis since it is radiation-free, real-time, and low-cost. Spine curvature angle measurement is an important step to assess scoliosis precisely. One way to calculate the angle is using the vertebrae features of the 2-D coronal images to identify the most tilted vertebrae. To do the measurement, the segmentation of the transverse vertebrae is an important step. In this paper, we propose a dual-task ultrasound transverse vertebrae segmentation network (D-TVNet) based on U-Net. First, we arrange an auxiliary shape regularization network to learn the contour segmentation of the bones. It improves the boundary segmentation and anti-interference ability of the U-Net by fusing some of the features of the auxiliary task and the main task. Then, we introduce the atrous spatial pyramid pooling (ASPP) module to the end of the down-sampling stage of the main task stream to improve the relative feature extraction ability. To further improve the boundary segmentation, we extendedly fuse the down-sampling output features of the auxiliary network in the ASPP. The experiment results show that the proposed D-TVNet achieves the best dice score of 86.68% and the mean dice score of 86.17% based on cross-validation, which is an improvement of 5.17% over the baseline U-Net. An automatic ultrasound spine bone segmentation network with promising results has been achieved.
Lyu, J, Ling, SH, Banerjee, S, Zheng, JY, Lai, KL, Yang, D, Zheng, YP, Bi, X, Su, S & Chamoli, U 2021, 'Ultrasound volume projection image quality selection by ranking from convolutional RankNet', Computerized Medical Imaging and Graphics, vol. 89, pp. 101847-101847.
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Periodic inspection and assessment are important for scoliosis patients. 3D ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique. With the generation of a 3D ultrasound volume projection spine image using our Scolioscan system, a series of 2D coronal ultrasound images are produced at different depths with different qualities. Selecting a high quality image from these 2D images is the crucial task for further scoliosis measurement. However, adjacent images are similar and difficult to distinguish. To learn the nuances between these images, we propose selecting the best image automatically, based on their quality rankings. Here, the ranking algorithm we use is a pairwise learning-to-ranking network, RankNet. Then, to extract more efficient features of input images and to improve the discriminative ability of the model, we adopt the convolutional neural network as the backbone due to its high power of image exploration. Finally, by inputting the images in pairs into the proposed convolutional RankNet, we can select the best images from each case based on the output ranking orders. The experimental result shows that convolutional RankNet achieves better than 95.5% top-3 accuracy, and we prove that this performance is beyond the experience of a human expert.
Lyu, X, Ren, C, Ni, W, Tian, H, Cui, Q & Liu, RP 2021, 'Online Learning of Optimal Proactive Schedule Based on Outdated Knowledge for Energy Harvesting Powered Internet-of-Things', IEEE Transactions on Wireless Communications, vol. 20, no. 2, pp. 1248-1262.
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Lyu, X, Ren, C, Ni, W, Tian, H, Liu, RP & Tao, X 2021, 'Distributed Online Learning of Cooperative Caching in Edge Cloud', IEEE Transactions on Mobile Computing, vol. 20, no. 8, pp. 2550-2562.
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Ma, C, Zhang, J, Wang, J, Yang, N, Liu, Q, Zuo, S, Wu, X, Wang, P, Li, J & Fang, J 2021, 'Analytical Model of Open-Circuit Air-Gap Field Distribution in Interior Permanent Magnet Machines Based on Magnetic Equivalent Circuit Method and Boundary Conditions of Macroscopic Equations', IEEE Transactions on Magnetics, vol. 57, no. 3, pp. 1-9.
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To accurately and quickly predict the open-circuit air-gap magnetic field in interior permanent magnet synchronous machines (IPMSMs), an analytical model based on magnetic equivalent circuit (MEC) method is proposed. The analytical model can provide radial and tangential components of open-circuit magnetic field in the slotless air gap. The radial component is obtained from the MEC model, and the tangential component is obtained from boundary conditions of macroscopic equations. Then, a complex relative air-gap permeance is applied to take the effect of slots into account. As a result, an accurate solution of both radial and tangential components of the flux density in the slotted air gap is obtained. Additionally, the no-load back electromotive force (EMF) and cogging torque are calculated based on the open-circuit air-gap magnetic field. All the analytical results are verified by the finite element (FE) analysis and they are well matched. In the end, a direct measurement experiment of open-circuit air-gap magnetic field is proposed to verify the validity of both radial and tangential open-circuit air-gap magnetic fields.
Ma, M, Liu, Y, Wei, Y, Hao, D, Wei, W & Ni, B-J 2021, 'A facile oxygen vacancy and bandgap control of Bi(OH)SO4·H2O for achieving enhanced photocatalytic remediation', Journal of Environmental Management, vol. 294, pp. 113046-113046.
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The development of highly efficient photocatalysts is crucial for the remediation of organic pollutants. Herein, we reported a facile synthesis of oxygen vacancy rich Bi(OH)SO4·H2O photocatalyst by the control of precursor. The samples were characterized by XRD, scanning electron microscope, electron paramagnetic resonance, X-ray photoelectron spectroscopy etc. With more oxygen vacancies introduced, the photocatalytic activity on the degradation of RhB and tetracycline was significantly boosted. Density functional theory calculation was used to further reveal the influence of oxygen vacancy on the band structure of Bi(OH)SO4·H2O. The results and finding of this work are helpful for the development of sustainable environmental protection.
Ma, Y, Wu, N, Zhang, JA, Li, B & Hanzo, L 2021, 'Parametric Bilinear Iterative Generalized Approximate Message Passing Reception of FTN Multi-Carrier Signaling', IEEE Transactions on Communications, vol. 69, no. 12, pp. 8443-8458.
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A low-complexity parametric bilinear generalized approximate message passing (PBiGAMP)-based receiver is conceived for multi-carrier faster-than-Nyquist (MFTN) signaling over frequency-selective fading channels. To mitigate the inherent ill-conditioning problem of MFTN signaling, we construct a segment-based frequency-domain received signal model in the form of a block circulant linear transition matrix, which can be efficiently calculated by applying a two dimensional fast Fourier transform. Based on the eigenvalue decomposition of the block circulant matrices, we can diagonalize the covariance matrix of the complex-valued colored noise process imposed by the associated two dimensional non-orthogonal matched filtering. Building on this model, a PBiGAMP-based parametric joint channel estimation and equalization (JCEE) algorithm is proposed for MFTN systems. In this algorithm, we introduce a pair of additive terms for characterizing the interferences arising from adjacent segments and employ the exact discrete a priori probabilities of the transmitted symbols for improving the bit error rate (BER) performance. To further enhance the system's robustness in the presence of ill-conditioned matrices, we develop a refined PBiGAMP-based JCEE algorithm by introducing a series of scaled identity matrices. Moreover, the proposed PBiGAMP-based JCEE algorithms may be readily decomposed into GAMP-based equalization algorithms, when the channel state information is perfectly known. The overall complexity of the proposed algorithms only increases logarithmically with the total number of transmitted symbols. Our simulation results demonstrate the benefits of the proposed PBiGAMP-based iterative message passing receiver conceived for MFTN signaling.
Ma, Z, Xuan, J, Wang, YG, Li, M & Liò, P 2021, 'Path integral based convolution and pooling for graph neural networks*', Journal of Statistical Mechanics: Theory and Experiment, vol. 2021, no. 12, pp. 124011-124011.
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AbstractGraph neural networks (GNNs) extend the functionality of traditional neural networks to graph-structured data. Similar to CNNs, an optimized design of graph convolution and pooling is key to success. Borrowing ideas from physics, we propose path integral-based GNNs (PAN) for classification and regression tasks on graphs. Specifically, we consider a convolution operation that involves every path linking the message sender and receiver with learnable weights depending on the path length, which corresponds to the maximal entropy random walk. It generalizes the graph Laplacian to a new transition matrix that we call the maximal entropy transition (MET) matrix derived from a path integral formalism. Importantly, the diagonal entries of the MET matrix are directly related to the subgraph centrality, thus leading to a natural and adaptive pooling mechanism. PAN provides a versatile framework that can be tailored for different graph data with varying sizes and structures. We can view most existing GNN architectures as special cases of PAN. Experimental results show that PAN achieves state-of-the-art performance on various graph classification/regression tasks, including a new benchmark dataset from statistical mechanics that we propose to boost applications of GNN in physical sciences.
Maeda, EE, Haapasaari, P, Helle, I, Lehikoinen, A, Voinov, A & Kuikka, S 2021, 'Black Boxes and the Role of Modeling in Environmental Policy Making', Frontiers in Environmental Science, vol. 9.
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Modeling is essential for modern science, and science-based policies are directly affected by the reliability of model outputs. Artificial intelligence has improved the accuracy and capability of model simulations, but often at the expense of a rational understanding of the systems involved. The lack of transparency in black box models, artificial intelligence based ones among them, can potentially affect the trust in science driven policy making. Here, we suggest that a broader discussion is needed to address the implications of black box approaches on the reliability of scientific advice used for policy making. We argue that participatory methods can bridge the gap between increasingly complex scientific methods and the people affected by their interpretations
Mafi, R, Javankhoshdel, S, Cami, B, Jamshidi Chenari, R & Gandomi, AH 2021, 'Surface altering optimisation in slope stability analysis with non-circular failure for random limit equilibrium method', Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, vol. 15, no. 4, pp. 260-286.
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In limit equilibrium slope stability analysis, surface altering optimisation (SAO) is a novel approach to minimise the factor of safety for a given slip surface using spline curves in 2D. It is a local search algorithm that when combined with a global search method, can form a powerful hybrid optimisation technique used in slope stability analysis. Probabilistic analysis of a slope with spatial variability is a computationally intensive example that would demonstrate the accuracy and speed of optimisation techniques. In this paper, the probabilistic analysis results of three different slopes with both complicated and straightforward geometries are presented, and the application of SAO in spatial variability analysis using random limit equilibrium method (RLEM) is demonstrated. It was found that SAO combined with a global search method provides fairly accurate results and yields curtailed computational effort.
Mahanty, C, Kumar, R, Asteris, PG & Gandomi, AH 2021, 'COVID-19 Patient Detection Based on Fusion of Transfer Learning and Fuzzy Ensemble Models Using CXR Images', Applied Sciences, vol. 11, no. 23, pp. 11423-11423.
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The COVID-19 pandemic has claimed the lives of millions of people and put a significant strain on healthcare facilities. To combat this disease, it is necessary to monitor affected patients in a timely and cost-effective manner. In this work, CXR images were used to identify COVID-19 patients. We compiled a CXR dataset with equal number of 2313 COVID positive, pneumonia and normal CXR images and utilized various transfer learning models as base classifiers, including VGG16, GoogleNet, and Xception. The proposed methodology combines fuzzy ensemble techniques, such as Majority Voting, Sugeno Integral, and Choquet Fuzzy, and adaptively combines the decision scores of the transfer learning models to identify coronavirus infection from CXR images. The proposed fuzzy ensemble methods outperformed each individual transfer learning technique and several state-of-the-art ensemble techniques in terms of accuracy and prediction. Specifically, VGG16 + Choquet Fuzzy, GoogleNet + Choquet Fuzzy, and Xception + Choquet Fuzzy achieved accuracies of 97.04%, 98.48%, and 99.57%, respectively. The results of this work are intended to help medical practitioners achieve an earlier detection of coronavirus compared to other detection strategies, which can further save millions of lives and advantageously influence society.
Mahlia, TMI & Fattah, IMR 2021, 'Energy for Sustainable Future', Energies, vol. 14, no. 23, pp. 7962-7962.
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Energy and the environment are interrelated, and they are critical factors that influence the development of societies [...]
Mahmood, AH, Babaee, M, Foster, SJ & Castel, A 2021, 'Continuous Monitoring of the Early-Age Properties of Activated GGBFS with Alkaline Solutions of Different Concentrations', Journal of Materials in Civil Engineering, vol. 33, no. 12.
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Mahmood, AH, Foster, SJ & Castel, A 2021, 'Effects of mixing duration on engineering properties of geopolymer concrete', Construction and Building Materials, vol. 303, pp. 124449-124449.
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Mahmoudi, T, Pourhassan-Moghaddam, M, Shirdel, B, Baradaran, B, Morales-Narváez, E & Golmohammadi, H 2021, 'On-Site Detection of Carcinoembryonic Antigen in Human Serum', Biosensors, vol. 11, no. 10, pp. 392-392.
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Real-time connectivity and employment of sustainable materials empowers point-of-care diagnostics with the capability to send clinically relevant data to health care providers even in low-resource settings. In this study, we developed an advantageous kit for the on-site detection of carcinoembryonic antigen (CEA) in human serum. CEA sensing was performed using cellulose-based lateral flow strips, and colorimetric signals were read, processed, and measured using a smartphone-based system. The corresponding immunoreaction was reported by polydopamine-modified gold nanoparticles in order to boost the signal intensity and improve the surface blocking and signal-to-noise relationship, thereby enhancing detection sensitivity when compared with bare gold nanoparticles (up to 20-fold in terms of visual limit of detection). Such lateral flow strips showed a linear range from 0.05 to 50 ng/mL, with a visual limit of detection of 0.05 ng/mL and an assay time of 15 min. Twenty-six clinical samples were also tested using the proposed kit and compared with the gold standard of immunoassays (enzyme linked immunosorbent assay), demonstrating an excellent correlation (R = 0.99). This approach can potentially be utilized for the monitoring of cancer treatment, particularly at locations far from centralized laboratory facilities.
Mai, L, Ding, Y, Zhang, X, Fan, L, Yu, S & Xu, Z 2021, 'Energy efficiency with service availability guarantee for Network Function Virtualization', Future Generation Computer Systems, vol. 119, pp. 140-153.
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Following the trend of Network Function Virtualization (NFV), dedicated hardware middleboxes are replaced with innovative and flexible software middleboxes also known as Virtual Network Functions (VNFs). An ordered sequence of VNFs composing a logical service is called Service Function Chain (SFC). VNFs are generally run on commodity servers. In this way, the capital and operational expenditures of buying and maintaining dedicated hardware for telecom operators can be greatly reduced. One of the key issues in NFV is the optimal VNF placement and service chaining to achieve energy efficiency. However, the current NFV energy saving approaches seem to consider energy minimization as the only objective to be optimized. Little or no attention is given to other important aspects, e.g., service availability, which is paramountly important to fulfill Service Level Agreement (SLA) for telecom operators. This paper investigates the energy efficiency optimization with service availability guarantee in NFV-enabled networks. We firstly propose a novel green orchestration NFV architecture. Then, an energy-efficient VNF placement framework guaranteeing service availability is presented under the proposed architecture, and evaluated by extensive simulations. Open research issues and technical challenges in this emerging area are also presented.
Maina, JW, Merenda, A, Weber, M, Pringle, JM, Bechelany, M, Hyde, L & Dumée, LF 2021, 'Atomic layer deposition of transition metal films and nanostructures for electronic and catalytic applications', Critical Reviews in Solid State and Materials Sciences, vol. 46, no. 5, pp. 468-489.
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Atomic layer deposition (ALD) has emerged as the technique of choice in the microelectronics industry, owing to its self-limiting nature, that allows conformal film deposition in highly confined spaces. However, while the ALD of metal oxide has developed dramatically over the past decade, ALD of pure metal, particularly the transition metals has been developing at a very slow pace. This article reviews the latest development in the ALD of pure transition metals and alloys, for electronic and catalytic applications. In particular, the article analyzes how different factors, such as the substrate properties, deposition conditions, precursor and co-reactant properties, influence the deposition of the metal films and nanostructures, as well as the emerging applications of the ALD derived transition metal nanostructures. The challenges facing the field are highlighted, and suggestions are made for future research directions.
Makki Alamdari, M, Chang, KC, Kim, CW, Kildashti, K & Kalhori, H 2021, 'Transmissibility performance assessment for drive-by bridge inspection', Engineering Structures, vol. 242, pp. 112485-112485.
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Malik, M, Malik, MK, Mehmood, K & Makhdoom, I 2021, 'Automatic speech recognition: a survey', Multimedia Tools and Applications, vol. 80, no. 6, pp. 9411-9457.
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Recently great strides have been made in the field of automatic speech recognition (ASR) by using various deep learning techniques. In this study, we present a thorough comparison between cutting-edged techniques currently being used in this area, with a special focus on the various deep learning methods. This study explores different feature extraction methods, state-of-the-art classification models, and vis-a-vis their impact on an ASR. As deep learning techniques are very data-dependent different speech datasets that are available online are also discussed in detail. In the end, the various online toolkits, resources, and language models that can be helpful in the formulation of an ASR are also proffered. In this study, we captured every aspect that can impact the performance of an ASR. Hence, we speculate that this work is a good starting point for academics interested in ASR research.
Mallick, SK, Das, P, Maity, B, Rudra, S, Pramanik, M, Pradhan, B & Sahana, M 2021, 'Understanding future urban growth, urban resilience and sustainable development of small cities using prediction-adaptation-resilience (PAR) approach', Sustainable Cities and Society, vol. 74, pp. 103196-103196.
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Rapid urban proliferation is an indispensable and reciprocal issue in contemporary urban planning and development. This study envisages the prediction-adaptation-resilience (PAR) approach to analyze the future urban landscape resilience and sustainable development goals (SDGs). We have selected a small, unplanned growing up city, namely, Krishnanagar urban agglomeration (KUA), in India, to apply the PAR approach. Therefore, land use land cover map has been prepared for 2000, 2010, and 2020. The result shows the built-up area has been increased most in past 20 years, from 6.36 km2 to 13.23 km2. Then, the cellular automata-Markov chain model is applied to predict the future potential urban development surface for 2030 and 2040. The receiver operating characteristic (ROC) curve shows 83.6% success rate between the predicted and actual map of CA-Markov. The prediction map of 2030 and 2040 shows that the built-up area continuously expands (13.23 km2 to 16.52 km2) towards KUA's surrounding regions. Consequently, other decreasing land classes will be a threat to SDGs and urban resilience. So, people of KUA are adopting the changing hostile nature of urbanisation and urban vulnerability. Hence, this study will help the local administration to make a proper urban planning and adaptation strategies by maintaining good urban governance to achieve 8 SDGs of UN's 2030 Agenda in future.
Man, X, Xia, B, Luo, Z, Liu, J, Li, K & Nie, Y 2021, 'Engineering three-dimensional labyrinthine fractal acoustic metamaterials with low-frequency multi-band sound suppression', The Journal of the Acoustical Society of America, vol. 149, no. 1, pp. 308-319.
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Acoustic metamaterials are a class of artificially periodic structures with extraordinary elastic properties that cannot be easily found in naturally occurring materials and can be applied to regulate the sound propagation behavior. The fractal configuration can be widely found in the acoustic system, like characterizing the broadband or multi-band sound propagation. This work will engineer three-dimensional (3D) labyrinthine fractal acoustic metamaterials (LFAMs) to regulate the sound propagation on subwavelength scales. The dispersion relations of LFAMs are systematically analyzed by the Bloch theory and the finite element method (FEM). The multi-bands, acoustic modes, and isotropic properties characterize their acoustic wave properties in the low-frequency regime. The effective bulk modulus and mass density of the LFAMs are numerically calculated to explain the low-frequency bandgap behaviors in specific frequencies. The transmissions and pressure field distributions of 3D LFAMs have been used to measure the ability for sound suppression. Furthermore, when considering the thermo-viscous loss on the transmission properties, the high absorptions occur within the multi-band range for low-frequency sound. Hence, this research contributes to potential applications on 3D LFAMs for multi-bands blocking and/or absorption on deep-subwavelength scales.
Mann, L, Chang, R, Chandrasekaran, S, Coddington, A, Daniel, S, Cook, E, Crossin, E, Cosson, B, Turner, J, Mazzurco, A, Dohaney, J, O’Hanlon, T, Pickering, J, Walker, S, Maclean, F & Smith, TD 2021, 'From problem-based learning to practice-based education: a framework for shaping future engineers', European Journal of Engineering Education, vol. 46, no. 1, pp. 27-47.
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© 2020 SEFI. Problem-based learning (PBL) has a history of producing strong educational results in engineering; however, global society is challenged by highly complex environmental, socio-political and technical problems summarised in the UN Sustainable Development Goals (SDGs). This obliges us to explore educational approaches that address complexity. Yet, confronting complexity is sometimes constrained within PBL structures. This conceptual paper posits practice-based education (PBE) as a whole-of-education approach embracing complexity. We present a PBE framework with three elements: (1) the context of an authentic engineering practice, (2) supporting learners’ agency in the process of becoming professionals, and (3) opportunities to work and learn simultaneously. We make the case for innovative engineering education through the implementation of PBE using the case of the Engineering Practice Academy at Swinburne University of Technology. We detail innovations in student experience as a process of becoming, curriculum and assessment, and provide advice on the application of PBE elsewhere.
Mann, RL 2021, 'Simulating quantum computations with Tutte polynomials', npj Quantum Information, vol. 7, no. 1, pp. 1-8.
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AbstractWe establish a classical heuristic algorithm for exactly computing quantum probability amplitudes. Our algorithm is based on mapping output probability amplitudes of quantum circuits to evaluations of the Tutte polynomial of graphic matroids. The algorithm evaluates the Tutte polynomial recursively using the deletion–contraction property while attempting to exploit structural properties of the matroid. We consider several variations of our algorithm and present experimental results comparing their performance on two classes of random quantum circuits. Further, we obtain an explicit form for Clifford circuit amplitudes in terms of matroid invariants and an alternative efficient classical algorithm for computing the output probability amplitudes of Clifford circuits.
Mann, RL & Helmuth, T 2021, 'Efficient algorithms for approximating quantum partition functions', Journal of Mathematical Physics, vol. 62, no. 2, pp. 022201-022201.
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We establish a polynomial-time approximation algorithm for partition functions of quantum spin models at high temperature. Our algorithm is based on the quantum cluster expansion of Netočný and Redig and the cluster expansion approach to designing algorithms due to Helmuth, Perkins, and Regts. Similar results have previously been obtained by related methods, and our main contribution is a simple and slightly sharper analysis for the case of pairwise interactions on bounded-degree graphs.
Mannina, G, Alliet, M, Brepols, C, Comas, J, Harmand, J, Heran, M, Kalboussi, N, Makinia, J, Robles, Á, Rebouças, TF, Ni, B-J, Rodriguez-Roda, I, Victoria Ruano, M, Bertanza, G & Smets, I 2021, 'Integrated membrane bioreactors modelling: A review on new comprehensive modelling framework', Bioresource Technology, vol. 329, pp. 124828-124828.
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Integrated Membrane Bioreactor (MBR) models, combination of biological and physical models, have been representing powerful tools for the accomplishment of high environmental sustainability. This paper, produced by the International Water Association (IWA) Task Group on Membrane Modelling and Control, reviews the state-of-the-art, identifying gaps for future researches, and proposes a new integrated MBR modelling framework. In particular, the framework aims to guide researchers and managers in pursuing good performances of MBRs in terms of effluent quality, operating costs (such as membrane fouling, energy consumption due to aeration) and mitigation of greenhouse gas emissions.
Mao, Y-Z, Yang, J-X & Ji, J-C 2021, 'Theoretical and experimental study of surface texturing with laser machining', Advances in Manufacturing, vol. 9, no. 4, pp. 538-557.
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To explore the forming process and mechanism of the surface texture of laser micropits, this paper presents the thermal model of laser machining based on the Neumann boundary conditions and an investigation on the effects of various parameters on the processing. The surface profile and quality of the formed micropits were analyzed using NanoFocus 3D equipment through a design of experiment (DOE). The results showed that more intense melting and splashing occurred with higher power density and narrower pulse widths. Moreover, the compressive stress is an important indicator of the damage effects, and the circumferential thermal stress is the primary factor influencing the diameter expansion. During the process of laser machining, not only did oxides such as CuO and ZnO generate, the energy distribution also tended to decrease gradually from region #1 to region #3 based on an energy dispersive spectrometer (EDS) analysis. The factors significantly affecting the surface quality of the micropit surface texture are the energy and pulse width. The relationship between taper angle and energy is appropriately linear. Research on the formation process and mechanism of the surface texture of laser micropits provides important guidance for precision machining.
Mao, Z, Zhao, L, Huang, S, Fan, Y & Pui-Wai Lee, A 2021, 'Direct 3D ultrasound fusion for transesophageal echocardiography', Computers in Biology and Medicine, vol. 134, pp. 104502-104502.
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BACKGROUND: Real-time three-dimensional transesophageal echocardiography (3D TEE) has been increasingly used in clinic for fast 3D analysis of cardiac anatomy and function. However, 3D TEE still suffers from the limited field of view (FoV). It is challenging to adopt conventional multi-view fusion methods to 3D TEE images because feature-based registration methods tend to fail in the ultrasound scenario, and conventional intensity-based methods have poor convergence properties and require an iterative coarse-to-fine strategy. METHODS: A novel multi-view registration and fusion method is proposed to enlarge the FoV of 3D TEE images efficiently. A direct method is proposed to solve the registration problem in the Lie algebra space. Fast implementation is realized by searching voxels on three orthogonal planes between two volumes. Besides, a weighted-average 3D fusion method is proposed to fuse the aligned images seamlessly. For a sequence of 3D TEE images, they are fused incrementally. RESULTS: Qualitative and quantitative results of in-vivo experiments indicate that the proposed registration algorithm outperforms a state-of-the-art PCA-based registration method in terms of accuracy and efficiency. Image registration and fusion performed on 76 in-vivo 3D TEE volumes from nine patients show apparent enlargement of FoV (enlarged around two times) in the obtained fused images. CONCLUSIONS: The proposed methods can fuse 3D TEE images efficiently and accurately so that the whole Region of Interest (ROI) can be seen in a single frame. This research shows good potential to assist clinical diagnosis, preoperative planning, and future intraoperative guidance with 3D TEE.
Marchi, R, Neerman-Arbez, M, Gay, V, Mourey, G, Fiore, M, Mouton, C, Gautier, P, De Moerloose, P & Casini, A 2021, 'Comparison of different activators of coagulation by turbidity analysis of hereditary dysfibrinogenemia and controls', Blood Coagulation & Fibrinolysis, vol. 32, no. 2, pp. 108-114.
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Turbidity analysis is widely used as a quantitative technique in hereditary dysfibrinogenemia. We aimed to compare several coagulation triggers in hereditary dysfibrinogenemia and control plasmas. We included 20 patients with hereditary dysfibrinogenemia, 19 with hotspot mutations Aα Arg35His (n = 9), Aα Arg35Cys (n = 2), γ Arg301His (n = 6), γ Arg301Cys (n = 2), and one with Aα Phe27Tyr, and a commercial pooled normal plasma. Fibrin polymerization was activated by bovine or human thrombin or tissue factor (TF), in the presence or absence of tissue type plasminogen activator. The lag time (min), slope (mOD/s), maximum absorbance (MaxAbs, mOD), and area under the curve (AUCp, OD s) were calculated from the fibrin polymerization curves and the time for 50% clot degradation (T50, min), AUCf (OD s) and the overall fibrinolytic potential from fibrinolysis curves. The lag time was significantly shorter and AUC increased in Aα Arg35His patients with bovine thrombin as compared with human thrombin. The MaxAbs and AUCp were significantly higher in γArg301His patients with bovine thrombin compared with human thrombin. Fibrin polymerization parameters of patients’ samples were closer to those of control when assessed with TF compared with both human and bovine thrombin. T50 and overall fibrinolytic potential were similar in all samples regardless of the coagulation trigger used, however, with TF the AUCf of Aα Arg35His and γ Arg301His groups were significantly decreased compared with control. Bovine and human thrombin cannot be used equally for studying fibrin polymerization in hotspot hereditary dysfibrinogenemia or control plasmas.
Marjanovic, O, Cecez-Kecmanovic, D & Vidgen, R 2021, 'Algorithmic pollution: Making the invisible visible', Journal of Information Technology, vol. 36, no. 4, pp. 391-408.
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In this article, we focus on the growing evidence of unintended harmful societal effects of automated algorithmic decision-making in transformative services (e.g. social welfare, healthcare, education, policing and criminal justice), for individuals, communities and society at large. Drawing from the long-established research on social pollution, in particular its contemporary ‘pollution-as-harm’ notion, we put forward a claim – and provide evidence – that these harmful effects constitute a new type of digital social pollution, which we name ‘algorithmic pollution’. Words do matter, and by using the term ‘pollution’, not as a metaphor or an analogy, but as a transformative redefinition of the digital harm performed by automated algorithmic decision-making, we seek to make it visible and recognized. By adopting a critical performative perspective, we explain how the execution of automated algorithmic decision-making produces harm and thus performs algorithmic pollution. Recognition of the potential for unintended harmful effects of algorithmic pollution, and their examination as such, leads us to articulate the need for transformative actions to prevent, detect, redress, mitigate and educate about algorithmic harm. These actions, in turn, open up new research challenges for the information systems community.
Marks, NA, Stewart, MG, Netherton, MD & Stirling, CG 2021, 'Airblast variability and fatality risks from a VBIED in a complex urban environment', Reliability Engineering & System Safety, vol. 209, pp. 107459-107459.
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Explosive blasts and prediction of fatality risks in urban environments is a complicated task due to the variability in blast wave reflection and propagation. The terrorist threats considered in this paper are vehicle-borne improvised explosive devices (VBIED) containing 225 kg or 450 kg of TNT or ammonium nitrate fuel oil (ANFO) detonated in an open street. This paper uses Viper::Blast CFD software to estimate the variability of explosive blast loads using Monte-Carlo sampling. To probabilistically model the blast wave, the paper takes into consideration the variability of explosive charge mass, detonation location, height of detonation, net equivalent quantity, atmospheric pressure and temperature, and model errors. The fatality risk assessment combines lung-rupture, whole-body displacement and skull fracture dependant on the pressure and impulse. It was found that the mean fatality risk for a 450 kg home-made ANFO explosive device detonated at a road T-intersection is 16% for people exposed in the street. If bollards were placed 10 m from the main street then fatality risk for people in the main street is reduced by over 90%. It was found that a deterministic analysis yielded fatality risks 10–60% higher than a probabilistic analysis, leading to an overly conservative assessment of safety risks.
Masouros, C, Heath, R, Zhang, JA, Feng, Z, Zheng, L & Petropulu, A 2021, 'Editorial: Introduction to the Issue on Joint Communication and Radar Sensing for Emerging Applications', IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 6, pp. 1290-1294.
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Mastio, E & Dovey, K 2021, 'Contextual insight as an antecedent to strategic foresight', Futures, vol. 128, pp. 102715-102715.
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Mas-Tur, A, Roig-Tierno, N, Sarin, S, Haon, C, Sego, T, Belkhouja, M, Porter, A & Merigó, JM 2021, 'Co-citation, bibliographic coupling and leading authors, institutions and countries in the 50 years of Technological Forecasting and Social Change', Technological Forecasting and Social Change, vol. 165, pp. 120487-120487.
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Maus Esfahani, N, Catchpoole, D & Kennedy, PJ 2021, 'SMCKAT, a Sequential Multi-Dimensional CNV Kernel-Based Association Test', Life, vol. 11, no. 12, pp. 1302-1302.
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Copy number variants (CNVs) are the most common form of structural genetic variation, reflecting the gain or loss of DNA segments compared with a reference genome. Studies have identified CNV association with different diseases. However, the association between the sequential order of CNVs and disease-related traits has not been studied, to our knowledge, and it is still unclear that CNVs function individually or whether they work in coordination with other CNVs to manifest a disease or trait. Consequently, we propose the first such method to test the association between the sequential order of CNVs and diseases. Our sequential multi-dimensional CNV kernel-based association test (SMCKAT) consists of three parts: (1) a single CNV group kernel measuring the similarity between two groups of CNVs; (2) a whole genome group kernel that aggregates several single group kernels to summarize the similarity between CNV groups in a single chromosome or the whole genome; and (3) an association test between the CNV sequential order and disease-related traits using a random effect model. We evaluate SMCKAT on CNV data sets exhibiting rare or common CNVs, demonstrating that it can detect specific biologically relevant chromosomal regions supported by the biomedical literature. We compare the performance of SMCKAT with MCKAT, a multi-dimensional kernel association test. Based on the results, SMCKAT can detect more specific chromosomal regions compared with MCKAT that not only have CNV characteristics, but the CNV order on them are significantly associated with the disease-related trait.
Maus Esfahani, N, Catchpoole, D, Khan, J & Kennedy, PJ 2021, 'MCKAT: a multi-dimensional copy number variant kernel association test', BMC Bioinformatics, vol. 22, no. 1, p. 588.
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AbstractBackgroundCopy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various disorders, including autism, intellectual disability, and schizophrenia. Consequently, the interest in studying a possible association of CNVs to specific disease traits is growing. However, due to the specific multi-dimensional characteristics of the CNVs, methods for testing the association between CNVs and the disease-related traits are still underdeveloped. We propose a novel multi-dimensional CNV kernel association test (MCKAT) in this paper. We aim to find significant associations between CNVs and disease-related traits using kernel-based methods.ResultsWe address the multi-dimensionality in CNV characteristics. We first design a single pair CNV kernel, which contains three sub-kernels to summarize the similarity between two CNVs considering all CNV characteristics. Then, aggregate single pair CNV kernel to the whole chromosome CNV kernel, which summarizes the similarity between CNVs in two or more chromosomes. Finally, the association between the CNVs and disease-related traits is evaluated by comparing the similarity in the trait with kernel-based similarity using a score test in a random effect model. We apply MCKAT on genome-wide CNV datasets to examine the association between CNVs and disease-related traits, which demonstrates the potential usefulness the proposed method has for the CNV association tests. We compare the performance of MCKAT with CKAT, a uni-dimensional kernel method. Based on the results, MCKAT indicates stronger evidence, smallerp-value, in detecting significant associations between CNVs and disease-related traits in both rare and common CNV datasets.ConclusionA...
Maxit, L, Karimi, M & Guasch, O 2021, 'Spatial coherence of pipe vibrations induced by an internal turbulent flow', Journal of Sound and Vibration, vol. 493, pp. 115841-115841.
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Mayer, JU, Hilligan, KL, Chandler, JS, Eccles, DA, Old, SI, Domingues, RG, Yang, J, Webb, GR, Munoz-Erazo, L, Hyde, EJ, Wakelin, KA, Tang, S-C, Chappell, SC, von Daake, S, Brombacher, F, Mackay, CR, Sher, A, Tussiwand, R, Connor, LM, Gallego-Ortega, D, Jankovic, D, Le Gros, G, Hepworth, MR, Lamiable, O & Ronchese, F 2021, 'Homeostatic IL-13 in healthy skin directs dendritic cell differentiation to promote TH2 and inhibit TH17 cell polarization', Nature Immunology, vol. 22, no. 12, pp. 1538-1550.
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Mayya, V, Kamath Shevgoor, S, Kulkarni, U, Hazarika, M, Barua, PD & Acharya, UR 2021, 'Multi-Scale Convolutional Neural Network for Accurate Corneal Segmentation in Early Detection of Fungal Keratitis', Journal of Fungi, vol. 7, no. 10, pp. 850-850.
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Microbial keratitis is an infection of the cornea of the eye that is commonly caused by prolonged contact lens wear, corneal trauma, pre-existing systemic disorders and other ocular surface disorders. It can result in severe visual impairment if improperly managed. According to the latest World Vision Report, at least 4.2 million people worldwide suffer from corneal opacities caused by infectious agents such as fungi, bacteria, protozoa and viruses. In patients with fungal keratitis (FK), often overt symptoms are not evident, until an advanced stage. Furthermore, it has been reported that clear discrimination between bacterial keratitis and FK is a challenging process even for trained corneal experts and is often misdiagnosed in more than 30% of the cases. However, if diagnosed early, vision impairment can be prevented through early cost-effective interventions. In this work, we propose a multi-scale convolutional neural network (MS-CNN) for accurate segmentation of the corneal region to enable early FK diagnosis. The proposed approach consists of a deep neural pipeline for corneal region segmentation followed by a ResNeXt model to differentiate between FK and non-FK classes. The model trained on the segmented images in the region of interest, achieved a diagnostic accuracy of 88.96%. The features learnt by the model emphasize that it can correctly identify dominant corneal lesions for detecting FK.
Mazaheri, H, Ong, HC, Amini, Z, Masjuki, HH, Mofijur, M, Su, CH, Anjum Badruddin, I & Khan, TMY 2021, 'An Overview of Biodiesel Production via Calcium Oxide Based Catalysts: Current State and Perspective', Energies, vol. 14, no. 13, pp. 3950-3950.
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Biodiesel is a clean, renewable, liquid fuel that can be used in existing diesel engines without modification as pure or blend. Transesterification (the primary process for biodiesel generation) via heterogeneous catalysis using low-cost waste feedstocks for catalyst synthesis improves the economics of biodiesel production. Heterogeneous catalysts are preferred for the industrial generation of biodiesel due to their robustness and low costs due to the easy separation and relatively higher reusability. Calcium oxides found in abundance in nature, e.g., in seashells and eggshells, are promising candidates for the synthesis of heterogeneous catalysts. However, process improvements are required to design productive calcium oxide-based catalysts at an industrial scale. The current work presents an overview of the biodiesel production advancements using calcium oxide-based catalysts (e.g., pure, supported, and mixed with metal oxides). The review discusses different factors involved in the synthesis of calcium oxide-based catalysts, and the effect of reaction parameters on the biodiesel yield of calcium oxide-based catalysis are studied. Further, the common reactor designs used for the heterogeneous catalysis using calcium oxide-based catalysts are explained. Moreover, the catalytic activity mechanism, challenges and prospects of the application of calcium oxide-based catalysts in biodiesel generation are discussed. The study of calcium oxide-based catalyst should continue to be evaluated for the potential of their application in the commercial sector as they remain the pivotal goal of these studies.
Mazzurco, A, Crossin, E, Chandrasekaran, S, Daniel, S & Sadewo, GRP 2021, 'Empirical research studies of practicing engineers: a mapping review of journal articles 2000–2018', European Journal of Engineering Education, vol. 46, no. 4, pp. 479-502.
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© 2020 SEFI. Empirical research on practicing engineers can inform engineering education as it provides evidence for what engineering students ought to learn. Whilst there has been a growing interest and scholarship in this area, there has not been any systematic attempt to map the empirical research on practicing engineers and develop an agenda for research on engineering practice. To address this gap, we conducted a mapping review of empirical research studies of practicing engineers. We limited our search to studies published in peer-reviewed journal articles since 2000 and identified 187 papers. We used inductive content analysis to categorise the papers intofive research themes: 1) learning in the workplace, 2) competencies and attributes needed for practice, 3) activities undertaken by engineers, 4) diversity, and 5) engineers’ identity. For each theme, we report common findings and gaps that can inform future research.
McCammon, S, Marcon dos Santos, G, Frantz, M, Welch, TP, Best, G, Shearman, RK, Nash, JD, Barth, JA, Adams, JA & Hollinger, GA 2021, 'Ocean front detection and tracking using a team of heterogeneous marine vehicles', Journal of Field Robotics, vol. 38, no. 6, pp. 854-881.
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AbstractOcean monitoring is an expensive and time consuming endeavor, but it can be made more efficient through the use of teams of autonomous robots. In this paper, we present a system for the autonomous identification and tracking of ocean fronts by coordinating the sampling efforts of a heterogeneous team of autonomous surface vehicles (ASVs) and autonomous underwater vehicles (AUVs). The primary contributions of this study are (1) our algorithm for performing autonomous coordination using general autonomy principles: Sequential Allocation Monte Carlo Tree Search (SA‐MCTS) which incorporates domain knowledge into the environmental estimation through both augmenting a standard Gaussian process with a nearest neighbors prior and planning in a drifting reference frame, (2) our decision support user interface to help human operators oversee the autonomous system, and (3) the demonstration of the system's operation in a 2‐week long deployment in the Gulf of Mexico using a heterogeneous team of four Slocum gliders and two robotic ocean surface samplers. With these contributions, we aim to bridge the gap between state of the art autonomy algorithms and marine vehicle planning methods that have been tested in large‐scale field trials. This paper presents the first deployment of a general, heuristic‐based, multi‐robot coordination algorithm for an extended sampling mission.
Medeiros, P, Chen, X, Gunda, T, van Oel, P, Vico, G, Marston, L, O’Keeffe, J, Yang, E, Liu, S, Roobavannan, M, Gopalakrishnan, S, Gonzalez-Piedra, JI, Castilla-Rho, J, Cudennec, C & Sivapalan, M 2021, 'Agricultural human-water systems: challenges, advances, and knowledge gaps'.
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<p>Dynamic interactions between humans and water have produced unanticipated feedbacks, leading to unsustainability. Current water management practices are unable to capture the relevant spatial and temporal detail of the processes that drive the coupled human-water system. Whereas natural and socioeconomic processes occur slowly, local communities and individuals rapidly respond to ensure supply-demand balance. In this context, agricultural human-water systems stand out, as roughly 70% of global water demand is for agricultural uses. Additionally, interactions between humans and agricultural water systems involve many actors and occur at multiple spatial and temporal scales. For example, farmers are influenced by risk perceptions, and decisions made at the farm level influence regional hydrologic and socioeconomic systems, such as degradation and depletion of water sources as well as prices of crops. Regional behaviors, in turn, affect national and international dynamics associated with crop production and trade of associated investments. On the other hand, global and national priorities can also percolate down to the regional and local levels, influencing farmer decision-making through policies and programs supporting production of certain crops and local investments. Over the last decade, relevant phenomena in the coupled agricultural human-water systems have been described, as the irrigation efficiency paradox, reservoir effect, and river basin closure. Along with the globalization in the food market, attempts have been taken to developing and applying benchmarks for water-efficient food production, focusing on water productivities, water footprints and yield gaps for agricultural products. Furthermore, significant advancements have been achieved by incorporating social dimensions of agricultural human-water systems behavior. Fusion of quantitative datasets via observations, remote sensing retrieval, and physically-based models...
Mehr, AD & Gandomi, AH 2021, 'MSGP-LASSO: An improved multi-stage genetic programming model for streamflow prediction', Information Sciences, vol. 561, pp. 181-195.
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This paper presents the development and verification of a new multi-stage genetic programming (MSGP) technique, called MSGP-LASSO, which was applied for univariate streamflow forecasting in the Sedre River, an intermittent river in Turkey. The MSGP-LASSO is a practical and cost-neutral improvement over classic genetic programming (GP) that increases modelling accuracy, while decreasing its complexity by coupling the MSGP and multiple regression LASSO methods. The new model uses average mutual information to identify the optimum lags, and root mean-square technique to minimize forecasting error. Based on Nash-Sutcliffe efficiency and bias-corrected Akaike information criterion, MSGP-LASSO is superior to GP, multigene GP, MSGP, and hybrid MSGP-least-square models. It is explicit and promising for real-life applications.
Mehrabi, P, Shariati, M, Kabirifar, K, Jarrah, M, Rasekh, H, Trung, NT, Shariati, A & Jahandari, S 2021, 'Effect of pumice powder and nano-clay on the strength and permeability of fiber-reinforced pervious concrete incorporating recycled concrete aggregate', Construction and Building Materials, vol. 287, pp. 122652-122652.
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Mehta, M, Paudel, KR, Shukla, SD, Allam, VSRR, Kannaujiya, VK, Panth, N, Das, A, Parihar, VK, Chakraborty, A, Ali, MK, Jha, NK, Xenaki, D, Su, QP, Wich, PR, Adams, J, Hansbro, PM, Chellappan, DK, Oliver, BGG & Dua, K 2021, 'Recent trends of NFκB decoy oligodeoxynucleotide-based nanotherapeutics in lung diseases', Journal of Controlled Release, vol. 337, pp. 629-644.
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Mei, C, Zhang, Y, Wang, D, Wu, C & Xu, Y 2021, 'Parameter optimal investigation of modular prefabricated two-side connected buckling-restrained steel plate shear wall', Structures, vol. 29, pp. 2028-2043.
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This paper research the optimal scope of width-to-thickness ratio α1 of connection steel plate (CSP), thickness ratio α2 between CSP and inner steel plates (ISP), and width-to-thickness ratio α3 between the width of CSP and the thickness of the ISP of modular fabricated two-side connected buckling-restrained steel plate (MTBSP) shear wall. The finite element model (FEM) of the MTBSP shear wall is established based on the ABAQUS platform, which, considering the plastic-damage constitutive model of both concrete and steel materials. Through the result of a comparison between the test and FEM, it can be concluded that the FEM can effectively reflect the mechanical behavior of the MTBSP shear wall. Based on the verified FEM, the mechanical behavior of 93 computational cases with different ratios of α1, α2, and α3 of MTBSP shear wall is investigated and the corresponding optimal scope of α1, α2, and α3 are explored in detail. The results show that the mechanical performance of the MTBSP shear wall can be ensured effectively when the ratio α1, α2, and α3 is in the scope from 10 to 20, from 3 to 5, and from 60 to 80, respectively. Research of this study can be provided supporting the reliable design of the MTBSP shear wall.
Mei, C, Zhao, Z, Zhang, Y, Wang, D & Wu, C 2021, 'Performance Evaluation and Shear Resistance of Modular Prefabricated Two-Side Connected Composite Shear Walls', KSCE Journal of Civil Engineering, vol. 25, no. 8, pp. 2936-2950.
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This paper investigates the impact of configuration parameters on the seismic performance of the modular prefabricated two-side connected composite shear wall. Firstly, the finite element model of the modular prefabricated two-side connected composite shear wall was established and validated by Xu’s experimental results. Secondly, three aspects of parameter investigations were discussed in detail based on the validated numerical technology, namely the design parameter of ISP, design parameter of the stud, and design parameter of reinforced concrete faceplate (RCF). Then, the computation formula of shear capacity is deduced on the basis of the finite element model for reference to structural design. The results of parameter analysis displayed that the seismic performance of the modular prefabricated two-side connected composite shear wall has excellent seismic performance with the array studs of the length-diameter ratio of 4 which the center distance of 150 mm, and RCF with the thickness of 75 mm. A satisfactory but unadventurous estimation of the shear capacity of a modular prefabricated two-side connected composite shear wall is supplied by the advised technique.
Meilianda, E, Lavigne, F, Pradhan, B, Wassmer, P, Darusman, D & Dohmen-Janssen, M 2021, 'Barrier Islands Resilience to Extreme Events: Do Earthquake and Tsunami Play a Role?', Water, vol. 13, no. 2, pp. 178-178.
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Barrier islands are indicators of coastal resilience. Previous studies have proven that barrier islands are surprisingly resilient to extreme storm events. At present, little is known about barrier systems’ resilience to seismic events triggering tsunamis, co-seismic subsidence, and liquefaction. The objective of this study is, therefore, to investigate the morphological resilience of the barrier islands in responding to those secondary effects of seismic activity of the Sumatra–Andaman subduction zone and the Great Sumatran Fault system. Spatial analysis in Geographical Information Systems (GIS) was utilized to detect shoreline changes from the multi-source datasets of centennial time scale, including old topographic maps and satellite images from 1898 until 2017. Additionally, the earthquake and tsunami records and established conceptual models of storm effects to barrier systems, are corroborated to support possible forcing factors analysis. Two selected coastal sections possess different geomorphic settings are investigated: (1) Lambadeuk, the coast overlying the Sumatran Fault system, (2) Kuala Gigieng, located in between two segments of the Sumatran Fault System. Seven consecutive pairs of comparable old topographic maps and satellite images reveal remarkable morphological changes in the form of breaching, landward migrating, sinking, and complete disappearing in different periods of observation. While semi-protected embayed Lambadeuk is not resilient to repeated co-seismic land subsidence, the wave-dominated Kuala Gigieng coast is not resilient to the combination of tsunami and liquefaction events. The mega-tsunami triggered by the 2004 earthquake led to irreversible changes in the barrier islands on both coasts.
Mejia, C, Wu, M, Zhang, Y & Kajikawa, Y 2021, 'Exploring Topics in Bibliometric Research Through Citation Networks and Semantic Analysis', Frontiers in Research Metrics and Analytics, vol. 6, p. 742311.
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This article surveys topic distributions of the academic literature that employs the terms bibliometrics, scientometrics, and informetrics. This exploration allows informing on the adoption of those terms and publication patterns of the authors acknowledging their work to be part of bibliometric research. We retrieved 20,268 articles related to bibliometrics and applied methodologies that exploit various features of the dataset to surface different topic representations. Across them, we observe major trends including discussions on theory, regional publication patterns, databases, and tools. There is a great increase in the application of bibliometrics as science mapping and decision-making tools in management, public health, sustainability, and medical fields. It is also observed that the term bibliometrics has reached an overall generality, while the terms scientometrics and informetrics may be more accurate in representing the core of bibliometric research as understood by the information and library science field. This article contributes by providing multiple snapshots of a field that has grown too quickly beyond the confines of library science.
Mekhilef, S, Yang, Y, Siwakoti, Y, Lam, C & Sathik, J 2021, 'Guest editorial: Modelling, methodologies and control techniques of DC/AC power conversion topologies for small‐ and large‐scale photovoltaic power systems', IET Power Electronics, vol. 14, no. 12, pp. 2027-2030.
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Menegaki, AN, Ahmad, N, Aghdam, RF & Naz, A 2021, 'The convergence in various dimensions of energy-economy-environment linkages: A comprehensive citation-based systematic literature review', Energy Economics, vol. 104, pp. 105653-105653.
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Meng, Q, Wu, C, Li, J, Wu, P, Xu, S & Wang, Z 2021, 'A study of pressure characteristics of methane explosion in a 20 m buried tunnel and influence on structural behaviour of concrete elements', Engineering Failure Analysis, vol. 122, pp. 105273-105273.
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With increasing use of natural gas in urban metropolitan areas, utility tunnels that contain utility and gas lines have become critical infrastructure. A leak within a gas pipe may cause methane-air explosions in the tunnel, leading to structural damage and casualties. Thus, it is necessary to investigate the response of structural members against explosion loads in the tunnel. Few studies in the open literature have studied the effects of a methane-air explosion in a full-scale concrete tunnel. This study presents two 9.5% methane-air explosions in a tunnel with a dimension of 20000 mm × 1800 mm × 600 mm. The pressure characteristics are summarized and compared with existing pressure–time curve of the methane-air explosion in typical vented containers. Similarities of pressure characteristics between the current study and previous studies avaliable from the literature are identified. Apart from explosion pressure characteristics, concrete structural specimens with a dimension of 1800 mm × 400 mm × 90 mm are also investigated. Geopolymer concrete, ultra high performance concrete (UHPC) and conventional concrete with compressive strength of approximately 70 MPa, 150 MPa and 30 MPa, respectively, were used to manufacture the testing specimens subjected to the methane-air explosions in the tunnel. In this study, the cracks were observed on all specimens. Due to large size of the tunnel and gas leakage during blast, the pressure distribution in the tunnel is not as uniform as observed in methane-air explosion chamber from the previous literatures. The conventional concrete specimen positioned between two pressure sensors is selected to validate the numerical model in the present study. The calibrated numerical model is then used to study the structural responses of concrete specimen subjected to the captured pressure.
Merenda, A & Dumée, LF 2021, 'Virus remediation in water engineering: Are our current technologies up to the challenge?', Journal of Water Process Engineering, vol. 44, pp. 102370-102370.
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Merenda, A, Bortolassi, ACC, Rodriguez-Andres, J, Al-Attabi, R, Schütz, JA, Kujawski, W, Shon, HK & Dumée, LF 2021, 'Hybrid polymer/ionic liquid electrospun membranes with tunable surface charge for virus capture in aqueous environments', Journal of Water Process Engineering, vol. 43, pp. 102278-102278.
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Metia, S, Nguyen, HAD & Ha, QP 2021, 'IoT-Enabled Wireless Sensor Networks for Air Pollution Monitoring with Extended Fractional-Order Kalman Filtering', Sensors, vol. 21, no. 16, pp. 5313-5313.
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This paper presents the development of high-performance wireless sensor networks for local monitoring of air pollution. The proposed system, enabled by the Internet of Things (IoT), is based on low-cost sensors collocated in a redundant configuration for collecting and transferring air quality data. Reliability and accuracy of the monitoring system are enhanced by using extended fractional-order Kalman filtering (EFKF) for data assimilation and recovery of the missing information. Its effectiveness is verified through monitoring particulate matters at a suburban site during the wildfire season 2019–2020 and the Coronavirus disease 2019 (COVID-19) lockdown period. The proposed approach is of interest to achieve microclimate responsiveness in a local area.
Min, C, Bu, Y, Wu, D, Ding, Y & Zhang, Y 2021, 'Identifying citation patterns of scientific breakthroughs: A perspective of dynamic citation process', Information Processing & Management, vol. 58, no. 1, pp. 102428-102428.
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This paper introduces the perspective of dynamic citation process to identify citation patterns of scientific breakthroughs. We construct a series of citation metrics and apply them to over 100 pairs of Nobel and non-Nobel papers with millions of citations. As expected, we find that most metrics cannot distinguish the two groups under similar conditions of discipline, publication year, venue, and citation impact. Some metrics, however, not only show significant discriminative power, but also reflect scientific breakthroughs’ temporal and structural characteristics—namely, prematurity and fruitfulness. Breakthrough works, that is, have long-lasting impact, but recognition lags behind; they do not just solve a problem, but more importantly open up new questions. Three metrics—average clustering coefficient, connectivity, and density of citing literature networks—show particular promise for early identification of breakthrough works. Our findings bear significant implications for science and technology management practices: from a science policy standpoint, our work demonstrates the utility of this citation process-based approach and provides a new dimension for both innovation researchers and decision makers in search of emerging scientific breakthroughs.
Ming, Y & Lin, C-T 2021, 'The Coherence of the Working Memory Study Between Deep Neural Networks and Neurophysiology: Insights From Distinguishing Topographical Electroencephalogram Data Under Different Workloads', IEEE Systems, Man, and Cybernetics Magazine, vol. 7, no. 4, pp. 24-30.
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Ming, Y, Wu, D, Wang, Y-K, Shi, Y & Lin, C-T 2021, 'EEG-Based Drowsiness Estimation for Driving Safety Using Deep Q-Learning', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 5, no. 4, pp. 583-594.
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IEEE Fatigue is the most vital factor of road fatalities, and one manifestation of fatigue during driving is drowsiness. In this paper, we propose using deep Q-learning to study the correlation between drowsiness and driving performance. This study is carried out by analyzing an electroencephalogram (EEG) dataset captured during a simulated endurance driving test. Driving safety research using EEG data represents an important brain-computer interface (BCI) paradigm from an application perspective. To formulate the drowsiness estimation problem as an optimization of a Q-learning task, we adapt the terminologies in the driving test to fit the reinforcement learning framework. Based on that, a deep Q-network (DQN) is tailored by referring to the latest DQN technologies. The designed network merits the characteristics of the EEG data and can generate actions to indirectly estimate drowsiness. The results show that the trained model can trace the variations of mind state in a satisfactory way against the testing EEG data, which confirms the feasibility and practicability of this new computation paradigm. By comparison, it also reveals that our method outperforms the supervised learning counterpart and is superior for real applications. To the best of our knowledge, we are the first to introduce the deep reinforcement learning method to this BCI scenario, and our method can potentially be generalized to other BCI cases.
Mirkhalaf, M, Wang, X, Entezari, A, Dunstan, CR, Jiang, X & Zreiqat, H 2021, 'Redefining architectural effects in 3D printed scaffolds through rational design for optimal bone tissue regeneration', Applied Materials Today, vol. 25, pp. 101168-101168.
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Mirzaei, S, Vafakhah, M, Pradhan, B & Alavi, SJ 2021, 'Flood susceptibility assessment using extreme gradient boosting (EGB), Iran', Earth Science Informatics, vol. 14, no. 1, pp. 51-67.
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Flood occurs as a result of high intensity and long-term rainfalls accompanied by snowmelt which flow out of the main river channel onto the flood prone areas and damage the buildings, roads, and facilities and cause life losses. This study aims to implement extreme gradient boosting (EGB) method for the first time in flood susceptibility modelling and compare its performance with three advanced benchmark models including Frequency Ratio (FR), Random Forest (RF), and Generalized Additive Model (GAM). Flood susceptibility map is an efficient tool to make decision for flood control. To do this, the altitude, slope degree, profile curvature, topographic wetness index (TWI), distance from rivers, normalized difference vegetation index, plan curvature, rainfall, land use, stream power index, and lithology were fed to the models. To run the models, 243 flood locations were detected by field surveys and national reports. The same number of locations were randomly created in the study regions and considered as non-flood locations. The flood and non-flood locations were split in 70% ratio for the training dataset and 30% ratio for the testing dataset. Both flood and non-flood locations were fed into the models and output flood susceptibility maps were produced. In order to evaluate the performance of the algorithms, receiver operating characteristics (ROC) curve was implemented. The results of the current research show that the RF model and EGB have the best performances with the area under ROC curve (AUC) of 0.985, and 0.980, followed by the GAM and FR algorithms with AUC values of 0.97, and 0.953, respectively. The results of variable importance by the RF model show that distance from rivers has an important influence on flood susceptibility mapping (FSM), followed by profile curvature, slope, TWI, and altitude. Considering the high performances of the RF and EGB models in flood susceptibility modelling, application of these models is recommended for such studies.
Mirzaie, M, Lakzian, E, Khan, A, Warkiani, ME, Mahian, O & Ahmadi, G 2021, 'COVID-19 spread in a classroom equipped with partition – A CFD approach', Journal of Hazardous Materials, vol. 420, pp. 126587-126587.
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In this study, the motion and distribution of droplets containing coronaviruses emitted by coughing of an infected person in front of a classroom (e.g., a teacher) were investigated using CFD. A 3D turbulence model was used to simulate the airflow in the classroom, and a Lagrangian particle trajectory analysis method was used to track the droplets. The numerical model was validated and was used to study the effects of ventilation airflow speeds of 3, 5, and 7 m/s on the dispersion of droplets of different sizes. In particular, the effect of installing transparent barriers in front of the seats on reducing the average droplet concentration was examined. The results showed that using the seat partitions for individuals can prevent the infection to a certain extent. An increase in the ventilation air velocity increased the droplets’ velocities in the airflow direction, simultaneously reducing the trapping time of the droplets by solid barriers. As expected, in the absence of partitions, the closest seats to the infected person had the highest average droplet concentration (3.80 × 10−8 kg/m3 for the case of 3 m/s).
Mishra, DK, Ghadi, MJ, Azizivahed, A, Li, L & Zhang, J 2021, 'A review on resilience studies in active distribution systems', Renewable and Sustainable Energy Reviews, vol. 135, pp. 110201-110201.
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The world has been experiencing natural disasters and man-made attacks on power system networks over the past few decades. These occurrences directly affect electricity infrastructures, thereby resulting in immense economic loss. The electric infrastructure is the backbone and one of the most essential components of human life. Thus, a resilient infrastructure must be constructed to cope with events of high-impact, low-possibility. Moreover, achieving resilience in the active distribution system (ADS) has been a vital research field of planning and operation of electric power systems. The incorporation of recent breakthrough technologies, such as micro- and smart grids, can make the distribution system become considerably resilient through planning-operation activities prior, during, and after an extreme event. This study offers the concepts premised on a systematic review of available literature by distinguishing characteristics between reliability and resiliency. Thereafter, the most relevant proceedings in conformity with an overview of the major blackouts, hardening and its guidelines, weather-related scenarios, taxonomies, and remedial actions are discussed. In addition, this research presents the planning, operational, and planning-operational attributes in response to catastrophes. Furthermore, a case study is conducted to support the review work, where the reliability and resilience of the ADS (IEEE 33-bus test system) are evaluated as performance indices with and without the addition of PV units. The performed research is laying out the importance of the distributed generation, such as PV, in the context of resilience, with the inclusion of different faults.
Mishra, DK, Ghadi, MJ, Li, L, Hossain, MJ, Zhang, J, Ray, PK & Mohanty, A 2021, 'A review on solid-state transformer: A breakthrough technology for future smart distribution grids', International Journal of Electrical Power & Energy Systems, vol. 133, pp. 107255-107255.
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The modern power systems have now prompted the practice of power electronics-based converters for power conversion purposes, which has emerged a solid-state device named as solid-state transformer (SST). It provides the isolation between low/medium-voltage ports with a high-frequency transformer (HFT), and in addition, it facilitates controlling the active and reactive power automatically through power converters. With this objective, the SST is projected as an essential device for smart/microgrids, particularly in multi-microgrid systems, to enhance modern distribution systems' resiliency. This study explores how it is beneficial to the distribution systems in regards to the reduction of size, controllability, reliability, resiliency, and end-use applications. Moreover, the different component types and broad applications have also been discussed pertaining to their characteristics. Lastly, the conclusion gives a brief summary, and the possible direction of future research is presented, which will be useful for researchers and engineers working in future microgrids and smart grids. This review will guide to select appropriate components to develop an SST for a particular application.
Moallemi, EA, de Haan, FJ, Hadjikakou, M, Khatami, S, Malekpour, S, Smajgl, A, Smith, MS, Voinov, A, Bandari, R, Lamichhane, P, Miller, KK, Nicholson, E, Novalia, W, Ritchie, EG, Rojas, AM, Shaikh, MA, Szetey, K & Bryan, BA 2021, 'Evaluating Participatory Modeling Methods for Co‐creating Pathways to Sustainability', Earth's Future, vol. 9, no. 3.
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AbstractThe achievement of global sustainability agendas, such as the Sustainable Development Goals, relies on transformational change across society, economy, and environment that are co‐created in a transdisciplinary exercise by all stakeholders. Within this context, environmental and societal change is increasingly understood and represented via participatory modeling for genuine engagement with multiple collaborators in the modeling process. Despite the diversity of participatory modeling methods to promote engagement and co‐creation, it remains uncertain what the extent and modes of participation are in different contexts, and how to select the suitable methods to use in a given situation. Based on a review of available methods and specification of potential contextual requirements, we propose a unifying framework to guide how collaborators of different backgrounds can work together and evaluate the suitability of participatory modeling methods for co‐creating sustainability pathways. The evaluation of method suitability promises the integration of concepts and approaches necessary to address the complexities of problems at hand while ensuring robust methodologies based on well‐tested evidence and negotiated among participants. Using two illustrative case studies, we demonstrate how to explore and evaluate the choice of methods for participatory modeling in varying contexts. The insights gained can inform creative participatory approaches to pathway development through tailored combinations of methods that best serve the specific sustainability context of particular case studies.
Mofijur, M, Ahmed, SF, Rahman, SMA, Arafat Siddiki, SKY, Islam, ABMS, Shahabuddin, M, Ong, HC, Mahlia, TMI, Djavanroodi, F & Show, PL 2021, 'Source, distribution and emerging threat of micro- and nanoplastics to marine organism and human health: Socio-economic impact and management strategies', Environmental Research, vol. 195, pp. 110857-110857.
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The nature of micro- and nanoplastics and their harmful consequences has drawn significant attention in recent years in the context of environmental protection. Therefore, this paper aims to provide an overview of the existing literature related to this evolving subject, focusing on the documented human health and marine environment impacts of micro- and nanoplastics and including a discussion of the economic challenges and strategies to mitigate this waste problem. The study highlights the micro- and nanoplastics distribution across various trophic levels of the food web, and in different organs in infected animals which is possible due to their reduced size and their lightweight, multi-coloured and abundant features. Consequently, micro- and nanoplastics pose significant risks to marine organisms and human health in the form of cytotoxicity, acute reactions, and undesirable immune responses. They affect several sectors including aquaculture, agriculture, fisheries, transportation, industrial sectors, power generation, tourism, and local authorities causing considerable economic losses. This can be minimised by identifying key sources of environmental plastic contamination and educating the public, thus reducing the transfer of micro- and nanoplastics into the environment. Furthermore, the exploitation of the potential of microorganisms, particularly those from marine origins that can degrade plastics, could offer an enhanced and environmentally sound approach to mitigate micro- and nanoplastics pollution.
Mofijur, M, Fattah, IMR, Alam, MA, Islam, ABMS, Ong, HC, Rahman, SMA, Najafi, G, Ahmed, SF, Uddin, MA & Mahlia, TMI 2021, 'Impact of COVID-19 on the social, economic, environmental and energy domains: Lessons learnt from a global pandemic', Sustainable Production and Consumption, vol. 26, pp. 343-359.
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COVID-19 has heightened human suffering, undermined the economy, turned the lives of billions of people around the globe upside down, and significantly affected the health, economic, environmental and social domains. This study aims to provide a comprehensive analysis of the impact of the COVID-19 outbreak on the ecological domain, the energy sector, society and the economy and investigate the global preventive measures taken to reduce the transmission of COVID-19. This analysis unpacks the key responses to COVID-19, the efficacy of current initiatives, and summarises the lessons learnt as an update on the information available to authorities, business and industry. This review found that a 72-hour delay in the collection and disposal of waste from infected households and quarantine facilities is crucial to controlling the spread of the virus. Broad sector by sector plans for socio-economic growth as well as a robust entrepreneurship-friendly economy is needed for the business to be sustainable at the peak of the pandemic. The socio-economic crisis has reshaped investment in energy and affected the energy sector significantly with most investment activity facing disruption due to mobility restrictions. Delays in energy projects are expected to create uncertainty in the years ahead. This report will benefit governments, leaders, energy firms and customers in addressing a pandemic-like situation in the future.
Mofijur, M, Fattah, IMR, Kumar, PS, Siddiki, SYA, Rahman, SMA, Ahmed, SF, Ong, HC, Lam, SS, Badruddin, IA, Khan, TMY & Mahlia, TMI 2021, 'Bioenergy recovery potential through the treatment of the meat processing industry waste in Australia', Journal of Environmental Chemical Engineering, vol. 9, no. 4, pp. 105657-105657.
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The farm animal and meat processing industry generate waste, including manure, fat, blood, sludge, bones, and wastewater, which create environmental problems worldwide. The effluents generated by this industry are rich in proteins, lipids, fibres, and carbohydrates. All these pollutants have the potential to be used as a resource for energy recovery. The organic matters obtained from the farm animal and meat processing industry are critical sources for biogas production via anaerobic digestion. This process leads to the production of energy-rich biogas, reducing greenhouse gas emissions. This study attempts to determine biogas amount and the energy value produced from the farm animal and meat processing industry in Australia. Australia's livestock population mainly consists of dairy cattle, meat cattle, sheep and lambs, pigs, layers, and meat chickens. Results show a potential biogas amount of 23,874,165 million m3 (Mm3), 215,670 Mm3, 288,228 Mm3, 18,430 Mm3, and 392,284 Mm3 can be obtained from cattle, lamb, sheep, pig, and poultry annually, respectively. The methane generated from slaughterhouse waste and wastewater is estimated to provide 4.52E+ 14 MJ/yr of heat energy with total electricity generation potential from livestock wastes of 4.4E+ 13 kWh/yr. About half of the electricity can be generated in Queensland State. Finally, the present study suggests farm animal and meat processing industry effluent as a potential sustainable energy source in Australia.
Mofijur, M, Siddiki, SYA, Shuvho, MBA, Djavanroodi, F, Fattah, IMR, Ong, HC, Chowdhury, MA & Mahlia, TMI 2021, 'Effect of nanocatalysts on the transesterification reaction of first, second and third generation biodiesel sources- A mini-review', Chemosphere, vol. 270, pp. 128642-128642.
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Biodiesel is a fuel that has numerous benefits over traditional petrodiesel. The transesterification process is the most popular method for biodiesel production from various sources, categorized as first, second and third generation biodiesel depending on the source. The transesterification process is subject to a variety of factors that can be taken into account to improve biodiesel yield. One of the factors is catalyst type and concentration, which plays a significant role in the transesterification of biodiesel sources. At present, chemical and biological catalysts are being investigated and each catalyst has its advantages and disadvantages. Recently, nanocatalysts have drawn researchers' attention to the efficient production of biodiesel. This article discusses recent work on the role of several nanocatalysts in the transesterification reaction of various sources in the development of biodiesel. A large number of literature from highly rated journals in scientific indexes is reviewed, including the most recent publications. Most of the authors reported that nanocatalysts show an important influence regarding activity and selectivity. This study highlights that in contrast to conventional catalysts, the highly variable surface area of nanostructure materials favours interaction between catalysts and substrates that efficiently boost the performance of products. Finally, this analysis provides useful information to researchers in developing and processing cost-effective biodiesel.
Mohammed, A, Kurda, R, Armaghani, DJ & Hasanipanah, M 2021, 'Prediction of compressive strength of concrete modified with fly ash: Applications of neuro-swarm and neuro-imperialism models', Computers and Concrete, vol. 27, no. 5, pp. 489-512.
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In this study, two powerful techniques, namely particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) were selected and combined with a pre-developed ANN model aiming at improving its performance prediction of the compressive strength of concrete modified with fly ash. To achieve this study's aims, a comprehensive database with 379 data samples was collected from the available literature. The output of the database is the compressive strength (CS) of concrete samples, which are influenced by 9 parameters as model inputs, namely those related to mix composition. The modeling steps related to ICA-ANN (or neuro-imperialism) and PSO-ANN (or neuro-swarm) were conducted through the use of several parametric studies to design the most influential parameters on these hybrid models. A comparison of the CS values predicted by hybrid intelligence techniques with the experimental CS values confirmed that the neuro-swarm model could provide a higher degree of accuracy than another proposed hybrid model (i.e., neuro-imperialism). The train and test correlation coefficient values of (0.9042 and 0.9137) and (0.8383 and 0.8777) for neuro-swarm and neuro-imperialism models, respectively revealed that although both techniques are capable enough in prediction tasks, the developed neuro-swarm model can be considered as a better alternative technique in mapping the concrete strength behavior.
Mohammed, TU, Mahmood, AH, Apurbo, SM & Noor, MA 2021, 'Substituting brick aggregate with induction furnace slag for sustainable concrete', Sustainable Materials and Technologies, vol. 29, pp. e00303-e00303.
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Mohammed, TU, Mahmood, AH, Zunaied-Bin-Harun, M, Joy, JA & Ahmed, MA 2021, 'Destructive and non-destructive evaluation of concrete for optimum sand to aggregate volume ratio', Frontiers of Structural and Civil Engineering, vol. 15, no. 6, pp. 1400-1414.
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Mohan, HM, Anitha, S, Chai, R & Ling, SH 2021, 'Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano.', Adv. Hum. Comput. Interact., vol. 2021, pp. 6483003:1-6483003:1.
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The history of medicine shows that myocardial infarction is one of the significant causes of death in humans. The rapid evolution in autonomous technologies, the rise of computer vision, and edge computing offers intriguing possibilities in healthcare monitoring systems. The major motivation of the work is to improve the survival rate during a cardiac arrest through an automatic emergency recognition system under ambient intelligence. We present a novel approach to chest pain and fall posture-based vital sign detection using an intelligence surveillance camera to address the emergency during myocardial infarction. A real-time embedded solution persuaded from 'edge AI'is implemented using the state-of-the-art convolution neural networks: single shot detector Inception V2, single shot detector MobileNet V2, and Internet of Things embedded GPU platform NVIDIA's Jetson Nano. The deep learning algorithm is implemented for 3000 indoor color image datasets: Nanyang Technological University Red Blue Green and Depth, NTU RGB + D dataset, and private RMS dataset. The research mainly pivots on two key factors in creating and training a CNN model to detect the vital signs and evaluate its performance metrics. We propose a model, which is cost-effective and consumes low power for onboard detection of vital signs of myocardial infarction and evaluated the metrics to achieve a mean average precision of 76.4% and an average recall of 80%.
Mohanta, BK, Jena, D, Ramasubbareddy, S, Daneshmand, M & Gandomi, AH 2021, 'Addressing Security and Privacy Issues of IoT Using Blockchain Technology', IEEE Internet of Things Journal, vol. 8, no. 2, pp. 881-888.
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Internet of Things (IoT) has been the most emerging technology in the last decade because the number of smart devices and its associated technologies has rapidly grown in both industrial and research prospectives. The applications are developed using IoT techniques for real-time monitoring. Due to low processing power and storage capacity, smart things are vulnerable to the attacks as existing security or cryptography techniques are not suitable. In this study, we initially review and identify the security and privacy issues that exist in the IoT system. Second, as per blockchain technology, we provide some security solutions. The detailed analysis, including enabling technology and integration of IoT technologies, is explained. Finally, a case study is implemented using the Ethererum-based blockchain system in a smart IoT system and the results are discussed.
Mohanty, SS, Koul, Y, Varjani, S, Pandey, A, Ngo, HH, Chang, J-S, Wong, JWC & Bui, X-T 2021, 'A critical review on various feedstocks as sustainable substrates for biosurfactants production: a way towards cleaner production', Microbial Cell Factories, vol. 20, no. 1, p. 120.
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AbstractThe quest for a chemical surfactant substitute has been fuelled by increased environmental awareness. The benefits that biosurfactants present like biodegradability, and biocompatibility over their chemical and synthetic counterparts has contributed immensely to their popularity and use in various industries such as petrochemicals, mining, metallurgy, agrochemicals, fertilizers, beverages, cosmetics, etc. With the growing demand for biosurfactants, researchers are looking for low-cost waste materials to use them as substrates, which will lower the manufacturing costs while providing waste management services as an add-on benefit. The use of low-cost substrates will significantly reduce the cost of producing biosurfactants. This paper discusses the use of various feedstocks in the production of biosurfactants, which not only reduces the cost of waste treatment but also provides an opportunity to profit from the sale of the biosurfactant. Furthermore, it includes state-of-the-art information about employing municipal solid waste as a sustainable feedstock for biosurfactant production, which has not been simultaneously covered in many published literatures on biosurfactant production from different feedstocks. It also addresses the myriad of other issues associated with the processing of biosurfactants, as well as the methods used to address these issues and perspectives, which will move society towards cleaner production.
Mojiri, A, Zhou, JL, Ratnaweera, H, Ohashi, A, Ozaki, N, Kindaichi, T & Asakura, H 2021, 'Treatment of landfill leachate with different techniques: an overview', Journal of Water Reuse and Desalination, vol. 11, no. 1, pp. 66-96.
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AbstractLandfill leachate is characterised by high chemical and biological oxygen demand and generally consists of undesirable substances such as organic and inorganic contaminants. Landfill leachate may differ depending on the content and age of landfill contents, the degradation procedure, climate and hydrological conditions. We aimed to explain the characteristics of landfill leachate and define the practicality of using different techniques for treating landfill leachate. Different treatments comprising biological methods (e.g. bioreactors, bioremediation and phytoremediation) and physicochemical approaches (e.g. advanced oxidation processes, adsorption, coagulation/flocculation and membrane filtration) were investigated in this study. Membrane bioreactors and integrated biological techniques, including integrated anaerobic ammonium oxidation and nitrification/denitrification processes, have demonstrated high performance in ammonia and nitrogen elimination, with a removal effectiveness of more than 90%. Moreover, improved elimination efficiency for suspended solids and turbidity has been achieved by coagulation/flocculation techniques. In addition, improved elimination of metals can be attained by combining different treatment techniques, with a removal effectiveness of 40–100%. Furthermore, combined treatment techniques for treating landfill leachate, owing to its high chemical oxygen demand and concentrations of ammonia and low biodegradability, have been reported with good performance. However, further study is necessary to enhance treatment methods to achieve maximum removal efficiency.
Moldovan, D, Choi, J, Choo, Y, Kim, W-S & Hwa, Y 2021, 'Laser-based three-dimensional manufacturing technologies for rechargeable batteries', Nano Convergence, vol. 8, no. 1, p. 23.
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AbstractLaser three-dimensional (3D) manufacturing technologies have gained substantial attention to fabricate 3D structured electrochemical rechargeable batteries. Laser 3D manufacturing techniques offer excellent 3D microstructure controllability, good design flexibility, process simplicity, and high energy and cost efficiencies, which are beneficial for rechargeable battery cell manufacturing. In this review, notable progress in development of the rechargeable battery cells via laser 3D manufacturing techniques is introduced and discussed. The basic concepts and remarkable achievements of four representative laser 3D manufacturing techniques such as selective laser sintering (or melting) techniques, direct laser writing for graphene-based electrodes, laser-induced forward transfer technique and laser ablation subtractive manufacturing are highlighted. Finally, major challenges and prospects of the laser 3D manufacturing technologies for battery cell manufacturing will be provided.
Momeni, E, Yarivand, A, Dowlatshahi, MB & Armaghani, DJ 2021, 'An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures', Transportation Geotechnics, vol. 26, pp. 100446-100446.
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Mondal, BK, Sahoo, S, Paria, P, Chakraborty, S & Alamri, AM 2021, 'Multi-sectoral impact assessment during the 1st wave of COVID-19 pandemic in West Bengal (India) for sustainable planning and management', Arabian Journal of Geosciences, vol. 14, no. 23.
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Mondal, RN, Hasan, MS, Islam, MS, Islam, MZ & Saha, SC 2021, 'A Computational Study on Fluid Flow and Heat Transfer Through a Rotating Curved Duct with Rectangular Cross Section', International Journal of Heat and Technology, vol. 39, no. 4, pp. 1213-1224.
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The understanding of fluid flow and heat transfer (HT) through a rotating curved duct (RCD) is important for different engineering applications. The available literature improved the understanding of the fluid flow and HT through a large-curvature rotating duct. However, the comprehensive knowledge of fluid flow and HT through an RCD with small curvature is little known. This numerical study aims to perform fluid flow characterization and HT through an RCD with curvature ratio 0.001. The spectral based numerical approach investigates the effects of rotation on fluid flow and HT for the Taylor number −1000≤Tr≤1500. A constant pressure gradient force, the Dean number Dn = 100, and a constant buoyancy force parameter, the Grashof number Gr = 500 are used for the numerical simulation. Fortran code is developed for the numerical computations and Tecplot software is used for the post-processing purpose. The numerical study investigates steady solutions and a structure of two-branches of steady solutions is obtained for positive rotation. The transient solution reports the transitional flow patterns and HT through the rotating duct, and two- to four-vortex solutions are observed. In case of negative rotation, time-dependent solutions show that the Coriolis force exhibits an opposite effect to that of the curvature so that the flow characteristics exhibit various flow instabilities. The numerical result shows that convective HT is increased with the increase of rotation and highly complex secondary flow patterns influence the overall HT from the heated wall to the fluid. To validate the numerical results, a comparison with the experimental data is provided, which shows that a good agreement is attained between the numerical and experimental investigations.
Mong, GR, Chong, CT, Ng, J-H, Chong, WWF, Ong, HC & Tran, M-V 2021, 'Multivariate optimisation study and life cycle assessment of microwave-induced pyrolysis of horse manure for waste valorisation and management', Energy, vol. 216, pp. 119194-119194.
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Mora, A, Cardenas, R, Aguilera, RP, Angulo, A, Lezana, P & Lu, DD-C 2021, 'Predictive Optimal Switching Sequence Direct Power Control for Grid-Tied 3L-NPC Converters', IEEE Transactions on Industrial Electronics, vol. 68, no. 9, pp. 8561-8571.
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A model predictive control (MPC) strategy based on optimal switching sequence concepts is presented for direct power control of grid-connected three-level neutral-point clamped converters. The proposed control strategy explicitly considers the modulator in its formulation along with the model of the system. Through two well-formulated optimal control problems, the proposed strategy is shown to optimally achieve control of the average trajectory of the active and reactive powers as well as the dc-link capacitor voltages without using weighting factors to tradeoff both control targets. Experimental results demonstrate this strategy produces improved steady-state performance with a well-defined output voltage spectrum and fixed-switching frequency while maintaining the inherent fast dynamic responses of MPC strategies.
Moreira, C, Chou, Y-L, Velmurugan, M, Ouyang, C, Sindhgatta, R & Bruza, P 2021, 'LINDA-BN: An interpretable probabilistic approach for demystifying black-box predictive models', Decision Support Systems, vol. 150, pp. 113561-113561.
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Morshedi Rad, D, Alsadat Rad, M, Razavi Bazaz, S, Kashaninejad, N, Jin, D & Ebrahimi Warkiani, M 2021, 'A Comprehensive Review on Intracellular Delivery', Advanced Materials, vol. 33, no. 13, pp. e2005363-2005363.
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AbstractIntracellular delivery is considered an indispensable process for various studies, ranging from medical applications (cell‐based therapy) to fundamental (genome‐editing) and industrial (biomanufacture) approaches. Conventional macroscale delivery systems critically suffer from such issues as low cell viability, cytotoxicity, and inconsistent material delivery, which have opened up an interest in the development of more efficient intracellular delivery systems. In line with the advances in microfluidics and nanotechnology, intracellular delivery based on micro‐ and nanoengineered platforms has progressed rapidly and held great promises owing to their unique features. These approaches have been advanced to introduce a smorgasbord of diverse cargoes into various cell types with the maximum efficiency and the highest precision. This review differentiates macro‐, micro‐, and nanoengineered approaches for intracellular delivery. The macroengineered delivery platforms are first summarized and then each method is categorized based on whether it employs a carrier‐ or membrane‐disruption‐mediated mechanism to load cargoes inside the cells. Second, particular emphasis is placed on the micro‐ and nanoengineered advances in the delivery of biomolecules inside the cells. Furthermore, the applications and challenges of the established and emerging delivery approaches are summarized. The topic is concluded by evaluating the future perspective of intracellular delivery toward the micro‐ and nanoengineered approaches.
Moscato, P, Mathieson, L & Haque, MN 2021, 'Augmented intuition: a bridge between theory and practice', Journal of Heuristics, vol. 27, no. 4, pp. 497-547.
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Motivated by the celebrated paper of Hooker (J Heuristics 1(1): 33–42, 1995) published in the first issue of this journal, and by the relative lack of progress of both approximation algorithms and fixed-parameter algorithms for the classical decision and optimization problems related to covering edges by vertices, we aimed at developing an approach centered in augmenting our intuition about what is indeed needed. We present a case study of a novel design methodology by which algorithm weaknesses will be identified by computer-based and fixed-parameter tractable algorithmic challenges on their performance. Comprehensive benchmarkings on all instances of small size then become an integral part of the design process. Subsequent analyses of cases where human intuition “fails”, supported by computational testing, will then lead to the development of new methods by avoiding the traps of relying only on human perspicacity and ultimately will improve the quality of the results. Consequently, the computer-aided design process is seen as a tool to augment human intuition. It aims at accelerating and foster theory development in areas such as graph theory and combinatorial optimization since some safe reduction rules for pre-processing can be mathematically proved via theorems. This approach can also lead to the generation of new interesting heuristics. We test our ideas with a fundamental problem in graph theory that has attracted the attention of many researchers over decades, but for which seems it seems to be that a certain stagnation has occurred. The lessons learned are certainly beneficial, suggesting that we can bridge the increasing gap between theory and practice by a more concerted approach that would fuel human imagination from a data-driven discovery perspective.
Mosiman, DS, Chen, Y, Yang, L, Hawkett, B, Ringer, SP, Mariñas, BJ & Cairney, JM 2021, 'Atom Probe Tomography of Encapsulated Hydroxyapatite Nanoparticles', Small Methods, vol. 5, no. 2, pp. 2000692-2000692.
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AbstractHydroxyapatite nanoparticles (HAP NPs) are important for medicine, bioengineering, catalysis, and water treatment. However, current understanding of the nanoscale phenomena that confer HAP NPs their many useful properties is limited by a lack of information about the distribution of the atoms within the particles. Atom probe tomography (APT) has the spatial resolution and chemical sensitivity for HAP NP characterization, but difficulties in preparing the required needle‐shaped samples make the design of these experiments challenging. Herein, two techniques are developed to encapsulate HAP NPs and prepare them into APT tips. By sputter‐coating gold or the atomic layer deposition of alumina for encapsulation, partially fluoridated HAP NPs are successfully characterized by voltage‐ or laser‐pulsing APT, respectively. Analyses reveal that significant tradeoffs exist between encapsulant methods/materials for HAP characterization and that selection of a more robust approach will require additional technique development. This work serves as an essential starting point for advancing knowledge about the nanoscale spatiochemistry of HAP NPs.
Mosiman, DS, Chen, Y, Yang, L, Hawkett, B, Ringer, SP, Mariñas, BJ & Cairney, JM 2021, 'Inside Front Cover: Atom Probe Tomography of Encapsulated Hydroxyapatite Nanoparticles (Small Methods 2/2021)', Small Methods, vol. 5, no. 2.
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Motahari, R, Saeidi Sough, Y, Aboutorab, H & Saberi, M 2021, 'Joint optimization of maintenance and inventory policies for multi-unit systems', International Journal of System Assurance Engineering and Management, vol. 12, no. 3, pp. 587-607.
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Mousavi, M & Gandomi, AH 2021, 'Prediction error of Johansen cointegration residuals for structural health monitoring', Mechanical Systems and Signal Processing, vol. 160, pp. 107847-107847.
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Mousavi, M & Gandomi, AH 2021, 'Structural health monitoring under environmental and operational variations using MCD prediction error', Journal of Sound and Vibration, vol. 512, pp. 116370-116370.
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This paper proposes a novel technique that aims at detecting the effect of damage on structural frequency signals as “bad” outliers. To this end, a procedure is developed based on the Variational Mode Decomposition (VMD), Minimum Covariance Determinant (MCD), and Recurrent Neural Network (RNN) with Bi-directional Long-Short Term Memory (BiLSTM) cells. The VMD is first used in a pre-processing stage to denoise the signals and remove the seasonal patterns in them. Then, the proposed method seeks to learn the rules behind calculation of the Mahalanobis distances of the points from their distribution, using the parameters obtained from the MCD algorithm, through training an RNN on signals obtained from the inferior state of the structure (healthy state). It will be shown that, since the rule behind the effect of damage on the Mahalanobis distances has not been learnt by the trained RNN, the prediction errors of these values will increase significantly as soon as damage occurs using the data obtained from the posterior state of the structure (including damage). The performance of the proposed method is first tested on a numerical example and further validated through solving an experimental example of the Z24 bridge. Moreover, the proposed method is compared against a PCA-based method. The results demonstrate the superiority of the proposed method in long-term condition monitoring of civil infrastructures. The proposed method is an output-only condition monitoring method that requires only a couple of lowest structural natural frequency signals measured over a long-term monitoring of the structure. Therefore, it is recommended for cases when the measurements from the EOV are not available. Also the proposed method can be used along with other output-only or input-out methods to either improve or confirm the validity of their results.
Mousavi, M & Gandomi, AH 2021, 'Wood hole-damage detection and classification via contact ultrasonic testing', Construction and Building Materials, vol. 307, pp. 124999-124999.
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Damage detection in wood materials has numerous applications in different industries, such as construction and forestry. Wood is generally a complex medium due to its orthotropic and random properties, which increases the difficulty of non-destructive damage testing. However, machine learning algorithms can be employed to overcome this problem. In this paper, hole-defect classification problems of two common types of wood materials, namely hard (marbau) and soft (pine) wood, are studied using a naive Bayes classification technique. To this end, the results of contact ultrasonic tests conducted on these types of woods in different directions, i.e. tangential and radial to the growth rings of wood, were investigated. The various states of the intact, small defect, and large defect of each type of wood were considered in the testing regime. It is known that contact ultrasonic tests are highly sensitive to different aspects of the test, such as the amount of couplant gel applied to surfaces, the amount of pressure applied to the transducer and receiver, and misalignment of the transducer and receiver. Therefore, 50 replicates of each test were implemented. First, an advanced signal decomposition algorithm termed Variational Mode Decomposition (VMD) was exploited to derive some features from the recorded ultrasonic signals. Then, the derived features were used in a set of classification problems using a naive Bayes classifier to classify the damage state of the specimens. Different types of naive Bayes classifiers, namely Gaussian and kernel, along with combinations of different types of features were employed to improve the results, ultimately achieving nearly 100% 10-fold cross-validation accuracy in all cases individually. However, when cases from different types of wood and direction of the tests were mixed, 93.6% 10-fold cross-validation accuracy was achieved for the classification problem based on the health state of the cases, using kernel naive Bayes...
Mousavi, M, Holloway, D, Olivier, JC & Gandomi, AH 2021, 'A baseline-free damage detection method using VBI incomplete measurement data', Measurement, vol. 174, pp. 108957-108957.
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Mousavi, M, Holloway, D, Olivier, JC & Gandomi, AH 2021, 'Beam damage detection using synchronisation of peaks in instantaneous frequency and amplitude of vibration data', Measurement, vol. 168, pp. 108297-108297.
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This paper explores the advantages of Variational Mode Decomposition (VMD) in detecting local damage on beam type structures (bridge) subjected to a sprung mass (vehicle). VMD is used to decompose the acceleration time history of the bridge at its midspan into its constitutive intrinsic mode functions (IMFs). The instantaneous frequency (IF) and instantaneous amplitude (IA) of the first IMF show irregularities at the damage position. We demonstrate through computer simulation that VMD is superior for detecting damage when compared to the well-known Empirical Mode Decomposition (EMD) method. A new damage sensitive feature (DSF) is also introduced that considers synchronisation of peaks between the IA and IF signals. The results show that the new DSF can enhance the peak at the damage positions while suppressing peaks at other locations.
Moussa, L, Benrimoj, S, Musial, K, Kocbek, S & Garcia-Cardenas, V 2021, 'Data-driven approach for tailoring facilitation strategies to overcome implementation barriers in community pharmacy', Implementation Science, vol. 16, no. 1.
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Abstract Background Implementation research has delved into barriers to implementing change and interventions for the implementation of innovation in practice. There remains a gap, however, that fails to connect implementation barriers to the most effective implementation strategies and provide a more tailored approach during implementation. This study aimed to explore barriers for the implementation of professional services in community pharmacies and to predict the effectiveness of facilitation strategies to overcome implementation barriers using machine learning techniques. Methods Six change facilitators facilitated a 2-year change programme aimed at implementing professional services across community pharmacies in Australia. A mixed methods approach was used where barriers were identified by change facilitators during the implementation study. Change facilitators trialled and recorded tailored facilitation strategies delivered to overcome identified barriers. Barriers were coded according to implementation factors derived from the Consolidated Framework for Implementation Research and the Theoretical Domains Framework. Tailored facilitation strategies were coded into 16 facilitation categories. To predict the effectiveness of these strategies, data mining with random forest was used to provide the highest level of accuracy. A predictive resolution percentage was established for each implementation strategy in relation to the barriers that were resolved by that particular strategy. Results During the 2-year programme, 1131 barriers and facilitation strategies were recorded by change facilitators. The most frequently identified barriers...
Mueller, J & Stewart, MG 2021, 'Terrorism and Bathtubs: Comparing and Assessing the Risks', Terrorism and Political Violence, vol. 33, no. 1, pp. 138-163.
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The likelihood that anyone outside a war zone will be killed by an Islamist extremist terrorist is extremely small. In the United States, for example, some six people have perished each year since 9/11 at the hands of such terrorists—vastly smaller than the number of people who die in bathtub drownings. Some argue, however, that the incidence of terrorist destruction is low because counterterrorism measures are so effective. They also contend that terrorism may well become more frequent and destructive in the future as terrorists plot and plan and learn from experience, and that terrorism, unlike bathtubs, provides no benefit and exacts costs far beyond those in the event itself by damagingly sowing fear and anxiety and by requiring policy makers to adopt countermeasures that are costly and excessive. This article finds these arguments to be wanting. In the process, it concludes that terrorism is rare outside war zones because, to a substantial degree, terrorists don’t exist there. In general, as with rare diseases that kill few, it makes more policy sense to expend limited funds on hazards that inflict far more damage. It also discusses the issue of risk communication for this hazard.
Muhammad, G, Alam, MA, Mofijur, M, Jahirul, MI, Lv, Y, Xiong, W, Ong, HC & Xu, J 2021, 'Modern developmental aspects in the field of economical harvesting and biodiesel production from microalgae biomass', Renewable and Sustainable Energy Reviews, vol. 135, pp. 110209-110209.
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Microalgae have been widely explored because of the diverse number of their worthwhile applications and potential as a source biomass for the production of biofuels and value-added materials. However, downstream techniques have yet to be fully developed to overcome techno-economic barriers. Flocculation is a superior method for harvesting microalgae from growth medium because of its harvesting efficiency, economic feasibility. Various kind of bio-flocculation harvesting methods are consider as attractive low cost and environmentally friendly options and able to harvest >90% biomass. Lipid recovery from microalgal cells is a major barrier for the biofuel industry because of process complexity and algae cell structure. Thus, the pretreatment method is necessary to disrupt the cell walls of microalgae and enhance lipid extraction. Many techniques, including dry methods of extraction, are already being implemented but found out that they are not efficient and cost-effective. Various new wet harvesting strategies have been claimed to extract major lipids in cost-efficient (30% less than conventional) way as wet technologies can eliminate the cost of cell drying and associated instruments. It is necessary to develop new methods which are energy and cost-effective, and environmentally friendlier for the commercialization of biofuels. Therefore, this review presents the advances in the progress of various flocculation harvesting methods with special emphasis on innovative bio-flocculation, the underlying mechanism of microalgae and flocculation. In this study also summarize the recent progress on microalgal oil extraction processes, and comparison was made between the processes in terms of sustainability, technology readiness, and applications in larger scales.
Mujtaba, MA, Kalam, MA, Masjuki, HH, Razzaq, L, Khan, HM, Soudagar, MEM, Gul, M, Ahmed, W, Raju, VD, Kumar, R & Ong, HC 2021, 'Development of empirical correlations for density and viscosity estimation of ternary biodiesel blends', Renewable Energy, vol. 179, pp. 1447-1457.
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This study aims to investigate the density and viscosity of ternary biodiesel blends. Fuel density and viscosity play an important role in the fuel injection system, flame propagation, and combustion process in compression ignition engine. The density and viscosity of biodiesel are higher than high-speed diesel which is an implication in the commercialization of biodiesel. In the present study, palm oil has been used for the production of biodiesel through the ultrasound-assisted transesterification process. Three different types of fuel additives including butanol, dimethyl carbonate, and plastic oil have been used for the preparation of nine ternary biodiesel blends. The density and viscosity of individual fuels and ternary biodiesel were measured experimentally in a temperature range of 281.51 K–348.15 K. For the prediction of density and viscosity of ternary biodiesel blends, four density and viscosity models were developed. The prediction accuracy of these developed models was assessed by a statistical tool absolute percentage error (APE). Newly proposed exponential regression models predicted well compared to experimental data for density and viscosity values with high regression coefficient 0.9995 and 0.9841 and lower mean absolute percentage of error 0.012 % and – 0.516 % at (348.15 K) temperature respectively. These correlations are significant for the automobile industry in developing fuel pipeline and transport equipment where additives would be present in diesel-biodiesel fuel blends.
Mujtaba, MA, Kalam, MA, Masjuki, HH, Soudagar, MEM, Khan, HM, Fayaz, H, Farooq, M, Gul, M, Ahmed, W, Ahmad, M, Munir, M, Yaqoob, H, Samuel, OD & Razzaq, L 2021, 'Effect of palm-sesame biodiesel fuels with alcoholic and nanoparticle additives on tribological characteristics of lubricating oil by four ball tribo-tester', Alexandria Engineering Journal, vol. 60, no. 5, pp. 4537-4546.
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Mujtaba, MA, Muk Cho, H, Masjuki, HH, Kalam, MA, Farooq, M, Soudagar, MEM, Gul, M, Afzal, A, Ahmed, W, Raza, A, Khan, TMY, Bashir, S & Ahmad, Z 2021, 'Effect of primary and secondary alcohols as oxygenated additives on the performance and emission characteristics of diesel engine', Energy Reports, vol. 7, pp. 1116-1124.
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Mujtaba, MA, Muk Cho, H, Masjuki, HH, Kalam, MA, Farooq, M, Soudagar, MEM, Gul, M, Ahmed, W, Afzal, A, Bashir, S, Raju, VD, Yaqoob, H & Syahir, AZ 2021, 'Effect of alcoholic and nano-particles additives on tribological properties of diesel–palm–sesame–biodiesel blends', Energy Reports, vol. 7, pp. 1162-1171.
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Mulet-Forteza, C, Lunn, E, Merigó, JM & Horrach, P 2021, 'Research progress in tourism, leisure and hospitality in Europe (1969–2018)', International Journal of Contemporary Hospitality Management, vol. 33, no. 1, pp. 48-74.
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PurposeThis study aims to present a bibliometric overview of articles published in the field of tourism, leisure and hospitality and analyzed by researchers mainly affiliated with European institutions.Design/methodology/approachThe authors conducted a bibliometric study of journals included in the Web of Science related to the field of tourism, leisure and hospitality in 2019. The review incorporates various techniques to determine the field’s structure from a scientific and intellectual perspective.FindingsThe results are valuable for several reasons. First, they will support researchers in identifying those topics with the greatest potential for advancing research in this field. Second, they will constitute an important aid in the design of new policies for journal publishers.Practical implicationsThis study can lead to advances in the tourism, leisure and hospitality field, as it identifies the publication trends of researchers who are mainly affiliated with European institutions. It also offers useful information for practitioners and academics in their endeavor to identify gaps in the extant literature and future trends.Originality/valueNo other studies have analyzed this field for a period of this length.
Müller Bark, J, Kulasinghe, A, Hartel, G, Leo, P, Warkiani, ME, Jeffree, RL, Chua, B, Day, BW & Punyadeera, C 2021, 'Isolation of Circulating Tumour Cells in Patients With Glioblastoma Using Spiral Microfluidic Technology – A Pilot Study', Frontiers in Oncology, vol. 11, p. 681130.
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Glioblastoma (GBM) is the most common and aggressive type of tumour arising from the central nervous system. GBM remains an incurable disease despite advancement in therapies, with overall survival of approximately 15 months. Recent literature has highlighted that GBM releases tumoural content which crosses the blood-brain barrier (BBB) and is detected in patients’ blood, such as circulating tumour cells (CTCs). CTCs carry tumour information and have shown promise as prognostic and predictive biomarkers in different cancer types. Currently, there is limited data for the clinical utility of CTCs in GBM. Here, we report the use of spiral microfluidic technology to isolate CTCs from whole blood of newly diagnosed GBM patients before and after surgery, followed by characterization for GFAP, cell-surface vimentin protein expression and EGFR amplification. CTCs were found in 13 out of 20 patients (9/20 before surgery and 11/19 after surgery). Patients with CTC counts equal to 0 after surgery had a significantly longer recurrence-free survival (p=0.0370). This is the first investigation using the spiral microfluidics technology for the enrichment of CTCs from GBM patients and these results support the use of this technology to better understand the clinical value of CTCs in the management of GBM in future studies.
Munasinghe, N & Paul, G 2021, 'Radial slicing for helical-shaped advanced manufacturing applications', The International Journal of Advanced Manufacturing Technology, vol. 112, no. 3-4, pp. 1089-1100.
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The fourth industrial revolution (Industry 4.0) is transforming industries all around the world focusing on areas including advanced robotics and automation, sensor technology and data analytics. The authors are involved in a project developing a multi-robot material extrusion 3D printer to print a Gravity Separation Spiral (GSS), an instrument used in the mining industry to separate mineral slurry into different density components. Compared to traditional mould-based manufacturing, this new additive manufacturing method will significantly reduce manufacturing tooling costs, improve the customisation to enable the production of bespoke GSS that each process different minerals, and reduce worker exposure to hazardous materials. Slicing and printing large scale helical objects in conventional horizontal layer addition would result in an unreasonable amount of waste material from support structures, and poor surface quality due to step-wise bumps. This paper presents a novel slicing algorithm using concentric vertical ray lines to slice objects radially, enabling layers to be deposited progressively in the same fashion. This method can be applied in large scale additive manufacturing where objects are printed by a robot in a radial direction, which is different from layered vertical printing in conventional additive systems. An example GSS is sliced to generate motion plans for a print head affixed to the end effector of a robot arm. Then through simulations, it is shown how a robot's expected manipulability measure can be used to predict and ensure the successful completion of the print.
Murlidhar, BR, Nguyen, H, Rostami, J, Bui, X, Armaghani, DJ, Ragam, P & Mohamad, ET 2021, 'Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network', Journal of Rock Mechanics and Geotechnical Engineering, vol. 13, no. 6, pp. 1413-1427.
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N.Usefi, Sharafi, P, Mortazavi, M, Ronagh, H & Samali, B 2021, 'Structural performance and sustainability assessment of hybrid-cold formed modular steel frame', Journal of Building Engineering, vol. 34, pp. 101895-101895.
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© 2020 Elsevier Ltd Hybrid cold-formed steel (HCFS) structures are new structural systems in the light steel construction industry offering new possibilities, in particular with regard to the applications in mid-rise construction. The structural performance, sustainability as well as the economic and social costs of these structures are of great importance for decision-makers when it comes to deciding on employing these systems and comparing them with their conventional counterparts. In this study, the HCFS systems are evaluated with respect to sustainability, structural performance, economic cost, and social impacts. The results then are compared with those of Ordinary Moment Resisting Frames (OMRF), as the most popular conventional HRS framed system. The methodology consists of both qualitative and quantitative analyses that include the overview of the positive and negative points of each construction method in the form of a comparative study. The results of the structural analysis of the two construction systems show that the hybrid system exhibits better structural performance with regard to the storey shear and drift. It is also shown that in terms of most environmental performance indicators, HCFS framed structures can lead to less environmental impact than OMRF systems. Moreover, the economic assessment demonstrates that HCFS framed structures can save up to the 23% in framing costs, compared to OMRF systems, primarily owing to the fact that lightweight flooring system can be easily incorporated to the design of HCFS structure. Their great potential for prefabrication, on the other hand, makes HCFS a better option with respect to many social compact indicators such as noise, air, vibration and dust pollution and traffic.
Nadimi-Shahraki, MH, Mohammadi, S, Zamani, H, Gandomi, M & Gandomi, AH 2021, 'A Hybrid Imputation Method for Multi-Pattern Missing Data: A Case Study on Type II Diabetes Diagnosis', Electronics, vol. 10, no. 24, pp. 3167-3167.
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Real medical datasets usually consist of missing data with different patterns which decrease the performance of classifiers used in intelligent healthcare and disease diagnosis systems. Many methods have been proposed to impute missing data, however, they do not fulfill the need for data quality especially in real datasets with different missing data patterns. In this paper, a four-layer model is introduced, and then a hybrid imputation (HIMP) method using this model is proposed to impute multi-pattern missing data including non-random, random, and completely random patterns. In HIMP, first, non-random missing data patterns are imputed, and then the obtained dataset is decomposed into two datasets containing random and completely random missing data patterns. Then, concerning the missing data patterns in each dataset, different single or multiple imputation methods are used. Finally, the best-imputed datasets gained from random and completely random patterns are merged to form the final dataset. The experimental evaluation was conducted by a real dataset named IRDia including all three missing data patterns. The proposed method and comparative methods were compared using different classifiers in terms of accuracy, precision, recall, and F1-score. The classifiers’ performances show that the HIMP can impute multi-pattern missing values more effectively than other comparative methods.
Nagasubramanian, G, Sakthivel, RK, Patan, R, Sankayya, M, Daneshmand, M & Gandomi, AH 2021, 'Ensemble Classification and IoT-Based Pattern Recognition for Crop Disease Monitoring System', IEEE Internet of Things Journal, vol. 8, no. 16, pp. 12847-12854.
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Internet of Things (IoT) in the agriculture field provides crops-oriented data sharing and automatic farming solutions under single network coverage. The components of IoT collect the observable data from different plants at different points. The data gathered through IoT components, such as sensors and cameras, can be used to be manipulated for a better farming-oriented decision-making process. This work proposes a system that observes the crops' growth and leaf diseases continuously for advising farmers in need. To provide analytical statistics on plant growth and disease patterns, the proposed framework uses machine learning (ML) techniques, such as support vector machine (SVM) and convolutional neural network (CNN). This framework produces efficient crop condition notifications to terminal IoT components which are assisting in irrigation, nutrition planning, and environmental compliance related to the farming lands. In this regard, this work proposes ensemble classification and pattern recognition for crop monitoring system (ECPRC) to identify plant diseases at the early stages. The proposed ECPRC uses ensemble nonlinear SVM (ENSVM) for detecting leaf and crop diseases. In addition, this work performs comparative analysis between various ML techniques, such as SVM, CNN, naïve Bayes, and K -nearest neighbors. In this experimental section, the results show that the proposed ECPRC system works optimally compared to the other systems.
Naghibi, SA, Hashemi, H & Pradhan, B 2021, 'APG: A novel python-based ArcGIS toolbox to generate absence-datasets for geospatial studies', Geoscience Frontiers, vol. 12, no. 6, pp. 101232-101232.
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Nagy, Z, Seneviratne, JA, Kanikevich, M, Chang, W, Mayoh, C, Venkat, P, Du, Y, Jiang, C, Salib, A, Koach, J, Carter, DR, Mittra, R, Liu, T, Parker, MW, Cheung, BB & Marshall, GM 2021, 'An ALYREF-MYCN coactivator complex drives neuroblastoma tumorigenesis through effects on USP3 and MYCN stability', Nature Communications, vol. 12, no. 1, pp. 1-20.
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AbstractTo achieve the very high oncoprotein levels required to drive the malignant state cancer cells utilise the ubiquitin proteasome system to upregulate transcription factor levels. Here our analyses identify ALYREF, expressed from the most common genetic copy number variation in neuroblastoma, chromosome 17q21-ter gain as a key regulator of MYCN protein turnover. We show strong co-operativity between ALYREF and MYCN from transgenic models of neuroblastoma in vitro and in vivo. The two proteins form a nuclear coactivator complex which stimulates transcription of the ubiquitin specific peptidase 3, USP3. We show that increased USP3 levels reduce K-48- and K-63-linked ubiquitination of MYCN, thus driving up MYCN protein stability. In the MYCN-ALYREF-USP3 signal, ALYREF is required for MYCN effects on the malignant phenotype and that of USP3 on MYCN stability. This data defines a MYCN oncoprotein dependency state which provides a rationale for future pharmacological studies.
Nahar, K, Gill, AQ & Roach, T 2021, 'Developing an access control management metamodel for secure digital enterprise architecture modeling.', Secur. Priv., vol. 4, no. 4.
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AbstractThere is an increasing interest in embedding the security in the design of digital enterprise architecture (EA) modeling platform to secure the digital assets. Access control management (ACM) is one of the key aspects of a secure digital enterprise architecture modeling platform design. Typical enterprise architecture modeling approaches mainly focus on the modeling of business, information, and technology elements. This draws our attention to this important question: how to model ACM for a secure digital EA modeling platform to ensure secure access to digital assets? This article aims to address this important research question in collaboration with our industry partner and developed an ontology‐based ACM metamodel that can be used by enterprises to model their ACM for a particular situation. This research has been conducted using the well‐known action‐design research (ADR) method to develop and evaluate the ACM metamodel for the secure digital EA modeling platform.
Naji, O, Al-juboori, RA, Khan, A, Yadav, S, Altaee, A, Alpatova, A, Soukane, S & Ghaffour, N 2021, 'Ultrasound-assisted membrane technologies for fouling control and performance improvement: A review', Journal of Water Process Engineering, vol. 43, pp. 102268-102268.
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Namasudra, S, Deka, GC, Johri, P, Hosseinpour, M & Gandomi, AH 2021, 'The Revolution of Blockchain: State-of-the-Art and Research Challenges', Archives of Computational Methods in Engineering, vol. 28, no. 3, pp. 1497-1515.
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© 2020, CIMNE, Barcelona, Spain. With the rapid development of Information Technology (IT) industries, data or information security has become one of the critical issues. Nowadays, Blockchain technology is widely using for improving data security. It is a tool for the individual and organization to interchange the digital asset without the intervention of a trusted third party i.e. a central administrator. This technology has given the ability to create digital tokens for representing assets, innovation and likely reshaping the scenery of entrepreneurship. Blockchain has several key properties, such as decentralization, immutability and transparency without using a trusted third party. It can be used in several fields, such as healthcare, digital voting, Internet of Things (IoT) and many more. This study aims to discuss the fundamentals of Blockchain. In this paper, the technology or working procedure of Blockchain including many applications in several fields are discussed. Finally, future work directions and open research challenges in the domain of Blockchain have been also discussed in detail.
Namisango, F, Kang, K & Rehman, J 2021, 'Service co-creation on social media: varieties and measures among nonprofit organizations', Journal of Service Theory and Practice, vol. 31, no. 5, pp. 783-820.
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PurposeLittle is known about the variations in service co-creation on social media, despite the resource integrating capabilities and co-creator roles afforded by these platforms. The gap is even more troubling in the nonprofit sector, where leveraging public interaction on social media is prevalent and vital to charitable and philanthropic endeavors. Arguably, such interaction is embedded in resource integrating activities leading to nonprofit service co-creation. This paper reports the forms, dimensions or service co-creation measures enabled by social media use in the nonprofits' sector.Design/methodology/approachThe authors conducted a sequential exploratory mixed methods design. First, the authors interviewed 19 social media managers in education, health and social service nonprofit organizations to identify the varieties in service co-creation realized. Second, the authors surveyed 73 nonprofit organizations on social media and gathered 267 useable responses, which were used to analyze and validate the identified forms of service co-creation.FindingsThe authors found that nonprofit organizations realize up to seven forms of service co-creation using social media. These include co-ideating to tweak service ideas, co-diagnosing social needs and problems, co-assessing service events, co-transforming services to targeted communities, co-advocating for community and service reach, co-resourcing in service delivery, and co-experiencing through a pool of diverse service experiences.Originality/valueThis study develops a reliable and valid multidimensional measure for nonprofit service co-c...
Nan, Y, Huang, X, Gao, X & Guo, YJ 2021, '3-D Terahertz Imaging Based on Piecewise Constant Doppler Algorithm and Step- Frequency Continuous-Wave Signaling', IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 8, pp. 6771-6783.
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Nandanwar, L, Shivakumara, P, Kanchan, S, Basavaraja, V, Guru, DS, Pal, U, Lu, T & Blumenstein, M 2021, 'DCT-phase statistics for forged IMEI numbers and air ticket detection', Expert Systems with Applications, vol. 164, pp. 114014-114014.
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New tools have been developing with the intention of having more flexibility and greater user-friendliness for editing the images and documents in digital technologies, but, unfortunately, they are also being used for manipulating and tampering information. Examples of such crimes include creating forged International Mobile Equipment Identity (IMEI) numbers which are embedded on mobile packages and inside smart mobile cases for illicit activities. Another example of such crimes is altering the name or date on air tickets for breaching security at the airport. This paper presents a new expert system for detecting forged IMEI numbers as well as altered air ticket images. The proposed method derives the phase spectrum using the Discrete Cosine Transform (DCT) to highlight the suspicious regions; it is unlike the phase spectrum from a Fourier transform, which is ineffective due to power spectrum noise. From the phase spectrum, our method extracts phase statistics to study the effect of distortions introduced by forgery operations. This results in feature vectors, which are fed to a Support Vector Machine (SVM) classifier for detection of forged IMEI numbers and air ticket images. Experimental results on our dataset of forged IMEI numbers (which is created by us for this work), on altered air tickets, on benchmark datasets of video caption text (which is tampered text), and on altered receipts of the ICPR 2018 FDC dataset, show that the proposed method is robust across different datasets. Furthermore, comparative studies of the proposed method with the existing methods on the same datasets show that the proposed method outperforms the existing methods. The dataset created will be available freely on request to the authors.
Nasir, AA, Tuan, HD, Ngo, HQ, Duong, TQ & Poor, HV 2021, 'Cell-Free Massive MIMO in the Short Blocklength Regime for URLLC', IEEE Transactions on Wireless Communications, vol. 20, no. 9, pp. 5861-5871.
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This paper considers cell-free massive MIMO (cfm-MIMO) for downlink ultra reliable and low-latency communication (URLLC). At the time of writing, cfm-MIMO has only been considered for communication in the long blocklength regime (LBR), whose throughput is determined by the Shannon capacity with the interference treated as Gaussian noise. Conjugate beamforming (CB) is often used as it requires only local channel state information (CSI) for implementation but its design is based on a large-scale nonconvex problem, which is computationally intractable. The rate function in URLLC is much more complex than the Shannon rate function. The paper proposes a special class of CB, which admits a low-scale optimization formulation for computational tractability. Accordingly, a new path-following algorithm, which generates a sequence of better feasible points and converges at least to a locally optimal solution, is developed for optimizing URLLC rates and cfm-MIMO energy efficiency. Furthermore, the paper also develops improper Gaussian signaling to improve both the Shannon rate and URLLC rate.
Nasir, AA, Tuan, HD, Nguyen, HH, Debbah, M & Poor, HV 2021, 'Resource Allocation and Beamforming Design in the Short Blocklength Regime for URLLC', IEEE Transactions on Wireless Communications, vol. 20, no. 2, pp. 1321-1335.
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Providing ultra reliable and low-latency communication (URLLC) is considered one of the major challenges for wireless communication networks. This article considers a downlink URLLC system in which a base station (BS) serves multiple single-antenna users in the short blocklength regime. With the objective of maximizing the users' minimum rate, three different optimization problems are considered: (i) joint design of bandwidth and power allocation for the case of a single-antenna BS; (ii) beamforming design for the case of a multiple-antenna BS; and (iii) design of power allocation with regularized zero-forcing beamforming for the case of a multiple-antenna BS. In the short blocklength regime, the achievable rate is a complicated function of bandwidth and power allocation coefficients or beamforming vectors, which makes these max-min rate optimization problems challenging to solve. This work develops path-following algorithms, which generate a sequence of improved feasible points and converge at least to a locally optimal solution, to solve these three optimization problems. Performance of the proposed algorithms is analyzed through extensive simulations under various settings of transmit power budget, number of users, total bandwidth, transmission time, and number of transmit antennas at the BS. Simulation results clearly demonstrate the merits of the proposed algorithms.
Nasouri Gilvaei, M, Hosseini Imani, M, Jabbari Ghadi, M, Li, L & Golrang, A 2021, 'Profit-Based Unit Commitment for a GENCO Equipped with Compressed Air Energy Storage and Concentrating Solar Power Units', Energies, vol. 14, no. 3, pp. 576-576.
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With the advent of restructuring in the power industry, the conventional unit commitment problem in power systems, involving the minimization of operation costs in a traditional vertically integrated system structure, has been transformed to the profit-based unit commitment (PBUC) approach, whereby generation companies (GENCOs) perform scheduling of the available production units with the aim of profit maximization. Generally, a GENCO solves the PBUC problem for participation in the day-ahead market (DAM) through determining the commitment and scheduling of fossil-fuel-based units to maximize their own profit according to a set of forecasted price and load data. This study presents a methodology to achieve optimal offering curves for a price-taker GENCO owning compressed air energy storage (CAES) and concentrating solar power (CSP) units, in addition to conventional thermal power plants. Various technical and physical constraints regarding the generation units are considered in the provided model. The proposed framework is mathematically described as a mixed-integer linear programming (MILP) problem, which is solved by using commercial software packages. Meanwhile, several cases are analyzed to evaluate the impacts of CAES and CSP units on the optimal solution of the PBUC problem. The achieved results demonstrate that incorporating the CAES and CSP units into the self-scheduling problem faced by the GENCO would increase its profitability in the DAM to a great extent.
Naveed, M, Arslan, A, Javed, HMA, Manzoor, T, Quazi, MM, Imran, T, Zulfattah, ZM, Khurram, M & Fattah, IMR 2021, 'State-of-the-Art and Future Perspectives of Environmentally Friendly Machining Using Biodegradable Cutting Fluids', Energies, vol. 14, no. 16, pp. 4816-4816.
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The use of cutting fluids has played a vital role in machining operations in lubrication and cooling. Most cutting fluids are mineral oil-based products that are hazardous to the environment and the worker, cause severe diseases and pollute the environment. In addition, petroleum resources are becoming increasingly unsustainable. Due to environmental and health issues, legislations have been established to ensure that the consumption of mineral oil is reduced. Consequently, researchers are making efforts to replace these mineral oil-based products. Vegetable oils are grasping attention due to their better lubricating properties, ease of availability, biodegradability, low prices, and non-toxicity. In this study, a detailed review and critical analysis are conducted of the research works involving vegetable oils as cutting fluids keeping in view the shortcomings and possible solutions to overcome these drawbacks. The purpose of the review is to emphasise the benefits of vegetable oil-based cutting fluids exhibiting comparable performance to that of mineral oil-based products. In addition, an appropriate selection of non-edible vegetable oil-based cutting fluids along with optimum cutting parameters to avoid a scanty supply of edible oils is also discussed. According to this research, vegetable oils are capable of substituting synthetic cutting fluids, and this option might aid in the successful and cost-efficient implementation of green machining.
Nayak, A, Rayguru, MM, Mishra, S & Hossain, MJ 2021, 'A Quantitative Approach for Convergence Analysis of a Singularly Perturbed Inverter-Based Microgrid', IEEE Transactions on Energy Conversion, vol. 36, no. 4, pp. 3016-3030.
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Nazar, M, Alam, MM, Yafi, E & Su'ud, MM 2021, 'A Systematic Review of Human–Computer Interaction and Explainable Artificial Intelligence in Healthcare With Artificial Intelligence Techniques', IEEE Access, vol. 9, pp. 153316-153348.
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Artificial intelligence (AI) is one of the emerging technologies. In recent decades, artificial intelligence (AI) has gained widespread acceptance in a variety of fields, including virtual support, healthcare, and security. Human-Computer Interaction (HCI) is a field that has been combining AI and human-computer engagement over the past several years in order to create an interactive intelligent system for user interaction. AI, in conjunction with HCI, is being used in a variety of fields by employing various algorithms and employing HCI to provide transparency to the user, allowing them to trust the machine. The comprehensive examination of both the areas of AI and HCI, as well as their subfields, has been explored in this work. The main goal of this article was to discover a point of intersection between the two fields. The understanding of Explainable Artificial Intelligence (XAI), which is a linking point of HCI and XAI, was gained through a literature review conducted in this research. The literature survey encompassed themes identified in the literature (such as XAI and its areas, major XAI aims, and XAI problems and challenges). The study's other major focus was on the use of AI, HCI, and XAI in healthcare. The poll also addressed the shortcomings in XAI in healthcare, as well as the field's future potential. As a result, the literature indicates that XAI in healthcare is still a novel subject that has to be explored more in the future.
Nekoei, M, Moghaddas, SA, Mohammadi Golafshani, E & Gandomi, AH 2021, 'Introduction of ABCEP as an automatic programming method', Information Sciences, vol. 545, pp. 575-594.
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Automatic programming is a branch of artificial intelligence that presents each solution as a mathematical formula based on heuristic mechanisms. In this study, artificial bee colony expression programming (ABCEP) is presented, which is combined simultaneously with expression programming. By using expression sharing to generate new solutions, the proposed method can minimize certain deficiencies of artificial bee colony programming, such as weak convergence and high locality. A total number of 15 real-world regression benchmark functions was used to evaluate the performance of the proposed model. For comparison purposes, successful run percentage, mean best cost, convergence performance, and run time of ABCEP were compared to those of other tested automatic programming algorithms, including artificial bee colony programming, gene expression programming, genetic programming, and quick artificial bee colony programming. A Wilcoxon signed-rank test was also done to compare the behavior of the algorithms. Additionally, the accuracy of all algorithms was then evaluated using three real-world practical benchmarks. The results indicate that the predictions generated by ABCEP are better than those obtained by other control algorithms based on successful runs, mean fitness values, and convergence rate.
Neri, A, Cagno, E & Trianni, A 2021, 'Barriers and drivers for the adoption of industrial sustainability measures in European SMEs: Empirical evidence from chemical and metalworking sectors', Sustainable Production and Consumption, vol. 28, pp. 1433-1464.
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As industrial sustainability measures and interventions play a central role in enhancing the sustainability performance in industrial firms, it is of great importance to properly understand the factors that might influence the decision-making process leading to their adoption, namely barriers and drivers. However, there is scarce empirical literature discussing barriers and drivers to industrial sustainability as well as the effect of contextual factors or of the firm's approach towards sustainability issues. For this reason, we conducted an exploratory investigation in 26 small and medium enterprises operating in the chemical and metalworking manufacturing sectors across Germany and Italy. Our preliminary findings show that the sampled firms are mainly hindered by economic barriers and fostered by external drivers. The investigation highlighted the influence of the contextual factors sector, country, and size on the perception of barriers and drivers. Moreover, the presence of a dedicated manager for sustainability, the number of certifications held by a firm, and a holistic definition of sustainability, seem to affect the barriers and drivers perceived by the sampled industrial decision-makers. The paper concludes by offering insights to both theoretical and practical discussion over the adoption of industrial sustainability measures, while also providing additional knowledge to practitioners and policy makers on critical areas for the improvement of industrial sustainability.
Neri, A, Cagno, E, Lepri, M & Trianni, A 2021, 'A triple bottom line balanced set of key performance indicators to measure the sustainability performance of industrial supply chains', Sustainable Production and Consumption, vol. 26, pp. 648-691.
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The measurement of sustainability within industrial supply chains is becoming increasingly relevant, with both industry and academia calling for the development of a general and manageable set of key performance indicators (KPIs). With more than 2,000 performance measures already identified by the previous literature, the real challenge lays in the development of the right set of indicators. Stemming from a thorough literature review, we propose a novel set of KPIs, based on a Balance Score Card - Supply Chain Operations Reference integrated framework. Whilst including a limited number of KPIs, the proposed set: i) assures a balanced coverage of the sustainability pillars and related intersections; ii) addresses different decision-making levels, financial bases and components of performance; iii) simultaneously tackles the sustainability performance of an entire supply chain. We empirically validated the set in 3 supply chains and 7 focal firms, by assessing its completeness, usefulness and ease of use. The set resulted suitable for different contexts of application and appropriate for the evaluation of the sustainability performance of an overall supply chain. We conclude with remarks for academia, industry and policy-makers, also sketching directions for further research.
Neshat, M, Nezhad, MM, Abbasnejad, E, Mirjalili, S, Groppi, D, Heydari, A, Tjernberg, LB, Astiaso Garcia, D, Alexander, B, Shi, Q & Wagner, M 2021, 'Wind turbine power output prediction using a new hybrid neuro-evolutionary method', Energy, vol. 229, pp. 120617-120617.
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Neshat, M, Nezhad, MM, Abbasnejad, E, Mirjalili, S, Tjernberg, LB, Astiaso Garcia, D, Alexander, B & Wagner, M 2021, 'A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm', Energy Conversion and Management, vol. 236, pp. 114002-114002.
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Nezhad, MM, Neshat, M, Groppi, D, Marzialetti, P, Heydari, A, Sylaios, G & Garcia, DA 2021, 'A primary offshore wind farm site assessment using reanalysis data: a case study for Samothraki island', Renewable Energy, vol. 172, pp. 667-679.
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Nezhad, MM, Neshat, M, Heydari, A, Razmjoo, A, Piras, G & Garcia, DA 2021, 'A new methodology for offshore wind speed assessment integrating Sentinel-1, ERA-Interim and in-situ measurement', Renewable Energy, vol. 172, pp. 1301-1313.
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Ng, ECY, Huang, Y, Hong, G, Zhou, JL & Surawski, NC 2021, 'Reducing vehicle fuel consumption and exhaust emissions from the application of a green-safety device under real driving', Science of The Total Environment, vol. 793, pp. 148602-148602.
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Vehicle emissions have a significantly negative impact on climate change, air quality and human health. Drivers of vehicles are the last major and often overlooked factor that determines vehicle performance. Eco-driving is a relatively low-cost and immediate measure to reduce fuel consumption and emissions significantly. This paper reports investigation of the effects of an on-board green-safety device on fuel consumption and emissions for both experienced and inexperienced drivers. A portable emissions measurement system (PEMS) was installed on a diesel light goods vehicle (LGV) to measure real-driving emissions (RDE), including total hydrocarbons (THC), CO CO2, NO, NO2 and particulate matter (PM). In addition, driving parameters (e.g. vehicle speed and acceleration) and environmental parameters (e.g. ambient temperature, humidity and pressure) were recorded in the experiments. The experimental results were evaluated using the Vehicle Specific Power (VSP) methodology to understand the effects of driving behavior on fuel consumption and emissions. The results indicated that driving behavior was improved for both experienced and inexperienced drivers after activation of the on-board green-safety device. In addition, the average time spent was shifted from higher to lower VSP modes by avoiding excessive speed, and aggressive accelerations and decelerations. For experienced drivers, the average fuel consumption and NO, NO2 and soot emissions were reduced by 5%, 56%, 39% and 35%, respectively, with the on-board green-safety device. For inexperienced drivers, the average reductions were 6%, 65%, 50% and 19%, respectively. Moreover, the long-term formed habits of experienced drivers are harder to be changed to accept the assistance of the green-safety device, whereas inexperienced drivers are likely to be more receptive to change and improve their driving behaviors.
Ng, Y, Li, H & Kim, J 2021, 'Uncertainty Estimation of Dense Optical Flow for Robust Visual Navigation', Sensors, vol. 21, no. 22, pp. 7603-7603.
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This paper presents a novel dense optical-flow algorithm to solve the monocular simultaneous localisation and mapping (SLAM) problem for ground or aerial robots. Dense optical flow can effectively provide the ego-motion of the vehicle while enabling collision avoidance with the potential obstacles. Existing research has not fully utilised the uncertainty of the optical flow—at most, an isotropic Gaussian density model has been used. We estimate the full uncertainty of the optical flow and propose a new eight-point algorithm based on the statistical Mahalanobis distance. Combined with the pose-graph optimisation, the proposed method demonstrates enhanced robustness and accuracy for the public autonomous car dataset (KITTI) and aerial monocular dataset.
Nghiem, LD, Iqbal, HMN & Zdarta, J 2021, 'The shadow pandemic of single use personal protective equipment plastic waste: A blue print for suppression and eradication', Case Studies in Chemical and Environmental Engineering, vol. 4, pp. 100125-100125.
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Ngo, CQ, Chai, R, Jones, TW & Nguyen, HT 2021, 'The Effect of Hypoglycemia on Spectral Moments in EEG Epochs of Different Durations in Type 1 Diabetes Patients', IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 8, pp. 2857-2865.
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The potential of using an electroencephalogram (EEG) to detect hypoglycemia in patients with type 1 diabetes (T1D) has been investigated in both time and frequency domains. Under hyperinsulinemic hypoglycemic clamp conditions, we have shown that the brain's response to hypoglycemic episodes could be described by the centroid frequency and spectral gyration radius evaluated from spectral moments of EEG signals. The aim of this paper is to investigate the effect of hypoglycemia on spectral moments in EEG epochs of different durations and to propose the optimal time window for hypoglycemia detection without using clamp protocols. The incidence of hypoglycemic episodes at night time in five T1D adolescents was analyzed from selected data of ten days of observations in this study. We found that hypoglycemia is associated with significant changes (P < 0.05) in spectral moments of EEG segments in different lengths. Specifically, the changes were more pronounced on the occipital lobe. We used effect size as a measure to determine the best EEG epoch duration for the detection of hypoglycemic episodes. Using Bayesian neural networks, this study showed that 30 second segments provide the best detection rate of hypoglycemia. In addition, Clarke's error grid analysis confirms the correlation between hypoglycemia and EEG spectral moments of this optimal time window, with 86% of clinically acceptable estimated blood glucose values. These results confirm the potential of using EEG spectral moments to detect the occurrence of hypoglycemia.
Ngo, MTT, Ueyama, T, Makabe, R, Bui, X-T, Nghiem, LD, Nga, TTV & Fujioka, T 2021, 'Fouling behavior and performance of a submerged flat-sheet nanofiltration membrane system for direct treatment of secondary wastewater effluent', Journal of Water Process Engineering, vol. 41, pp. 101991-101991.
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Ngo, QT, Phan, KT, Xiang, W, Mahmood, A & Slay, J 2021, 'On Edge Caching in Satellite — IoT Networks', IEEE Internet of Things Magazine, vol. 4, no. 4, pp. 107-112.
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Nguyen, AQ, Vu, HP, Nguyen, LN, Wang, Q, Djordjevic, SP, Donner, E, Yin, H & Nghiem, LD 2021, 'Monitoring antibiotic resistance genes in wastewater treatment: Current strategies and future challenges', Science of The Total Environment, vol. 783, pp. 146964-146964.
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Antimicrobial resistance (AMR) is a growing threat to human and animal health. Progress in molecular biology has revealed new and significant challenges for AMR mitigation given the immense diversity of antibiotic resistance genes (ARGs), the complexity of ARG transfer, and the broad range of omnipresent factors contributing to AMR. Municipal, hospital and abattoir wastewater are collected and treated in wastewater treatment plants (WWTPs), where the presence of diverse selection pressures together with a highly concentrated consortium of pathogenic/commensal microbes create favourable conditions for the transfer of ARGs and proliferation of antibiotic resistant bacteria (ARB). The rapid emergence of antibiotic resistant pathogens of clinical and veterinary significance over the past 80 years has re-defined the role of WWTPs as a focal point in the fight against AMR. By reviewing the occurrence of ARGs in wastewater and sludge and the current technologies used to quantify ARGs and identify ARB, this paper provides a research roadmap to address existing challenges in AMR control via wastewater treatment. Wastewater treatment is a double-edged sword that can act as either a pathway for AMR spread or as a barrier to reduce the environmental release of anthropogenic AMR. State of the art ARB identification technologies, such as metagenomic sequencing and fluorescence-activated cell sorting, have enriched ARG/ARB databases, unveiled keystone species in AMR networks, and improved the resolution of AMR dissemination models. Data and information provided in this review highlight significant knowledge gaps. These include inconsistencies in ARG reporting units, lack of ARG/ARB monitoring surrogates, lack of a standardised protocol for determining ARG removal via wastewater treatments, and the inability to support appropriate risk assessment. This is due to a lack of standard monitoring targets and agreed threshold values, and paucity of information on the ARG-pat...
Nguyen, C, Nguyen, D, Dinh, HT, Pham, AH, Huynh, NT, Xiao, Y & Dutkiewicz, E 2021, 'BlockRoam: Blockchain-based Roaming Management System for Future Mobile Networks', IEEE Transactions on Mobile Computing, vol. PP, no. 99, pp. 1-1.
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Mobile service providers (MSPs) are particularly vulnerable to roaming frauds, especially ones that exploit the long delay in the data exchange process of the contemporary roaming management systems, causing multi-billion dollars loss each year. In this paper, we introduce BlockRoam, a novel blockchain-based roaming management system that provides an efficient data exchange platform among MSPs and mobile subscribers. Utilizing the Proof-of-Stake (PoS) consensus mechanism and smart contracts, BlockRoam can significantly shorten the information exchanging delay, thereby addressing the roaming fraud problems. Through intensive analysis, we show that the security and performance of such PoS-based blockchain network can be further enhanced by incentivizing more users (e.g., subscribers) to participate in the network. Moreover, users in such networks often join stake pools (e.g., formed by MSPs) to increase their profits. Therefore, we develop an economic model based on Stackelberg game to jointly maximize the profits of the network users and the stake pool, thereby encouraging user participation. We also propose an effective method to guarantee the uniqueness of this game's equilibrium. The performance evaluations show that the proposed economic model helps the MSPs to earn additional profits, attracts more investment to the blockchain network, and enhances the network's security and performance.
Nguyen, H, Bui, X-N, Choi, Y, Lee, CW & Armaghani, DJ 2021, 'A Novel Combination of Whale Optimization Algorithm and Support Vector Machine with Different Kernel Functions for Prediction of Blasting-Induced Fly-Rock in Quarry Mines', Natural Resources Research, vol. 30, no. 1, pp. 191-207.
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Nguyen, HC, Pan, J, Su, C, Ong, HC, Chern, J & Lin, J 2021, 'Sol‐gel synthesized lithium orthosilicate as a reusable solid catalyst for biodiesel production', International Journal of Energy Research, vol. 45, no. 4, pp. 6239-6249.
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SummaryLithium orthosilicate (Li4SiO4) is a promising solid catalyst for biodiesel synthesis. However, Li4SiO4 is traditionally prepared by a solid‐state reaction, which results in the unstable activity for the reaction. In the present study, Li4SiO4 was successfully prepared using a simple sol‐gel method and employed as an efficient solid alkali catalyst for biodiesel synthesis. The molar ratio of precursors and calcination temperature were optimized for the synthesis of Li4SiO4 by using the sol‐gel method. The physical and chemical properties were determined using X‐ray diffraction, scanning electron microscopy, laser diffraction particle size, and thermogravimetric analysis. The as‐prepared Li4SiO4 catalyst had much smaller particle size, pore volume, and pore size, but higher surface area and basicity than Li4SiO4 catalyst prepared by the solid‐state reaction. It was then used to transesterify methanol and soybean oil into biodiesel. The effect of reaction factors (reaction time from 1 to 3 hours, catalyst concentration from 3 to 9%; molar ratio of methanol to oil from 6:1 to 18:1, and temperature from 55°C to 75°C) on the Li4SiO4‐catalyzed transesterification was systematically examined. The highest biodiesel conversion of 91% was reached under the following conditions: reaction time of 2 hours, Li4SiO4 concentration of 6%, 12:1 methanol:oil molar ratio, and temperature of 65°C. Notably, Li4SiO4 could be efficiently reused for at least 10 times without significant loss of its activity; this suggests that the sol‐gel synthesized Li4
Nguyen, HD, Azzi, M, White, S, Salter, D, Trieu, T, Morgan, G, Rahman, M, Watt, S, Riley, M, Chang, LT-C, Barthelemy, X, Fuchs, D, Lieschke, K & Nguyen, H 2021, 'The Summer 2019–2020 Wildfires in East Coast Australia and Their Impacts on Air Quality and Health in New South Wales, Australia', International Journal of Environmental Research and Public Health, vol. 18, no. 7, pp. 3538-3538.
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The 2019–2020 summer wildfire event on the east coast of Australia was a series of major wildfires occurring from November 2019 to end of January 2020 across the states of Queensland, New South Wales (NSW), Victoria and South Australia. The wildfires were unprecedent in scope and the extensive character of the wildfires caused smoke pollutants to be transported not only to New Zealand, but also across the Pacific Ocean to South America. At the peak of the wildfires, smoke plumes were injected into the stratosphere at a height of up to 25 km and hence transported across the globe. The meteorological and air quality Weather Research and Forecasting with Chemistry (WRF-Chem) model is used together with the air quality monitoring data collected during the bushfire period and remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites to determine the extent of the wildfires, the pollutant transport and their impacts on air quality and health of the exposed population in NSW. The results showed that the WRF-Chem model using Fire Emission Inventory (FINN) from National Center for Atmospheric Research (NCAR) to simulate the dispersion and transport of pollutants from wildfires predicted the daily concentration of PM2.5 having the correlation (R2) and index of agreement (IOA) from 0.6 to 0.75 and 0.61 to 0.86, respectively, when compared with the ground-based data. The impact on health endpoints such as mortality and respiratory and cardiovascular diseases hospitalizations across the modelling domain was then estimated. The estimated health impact on each of the Australian Bureau of Statistics (ABS) census districts (SA4) of New South Wales was calculated based on epidemiological assumptions of the impact function and incidence rate data from the 2016 ABS and NSW Department of Health statistical health records. Summing up all SA4 census dist...
Nguyen, HG, Le, NV, Nguyen-Duong, KH, Ho-Pham, LT & Nguyen, TV 2021, 'Reference values of body composition parameters for Vietnamese men and women', European Journal of Clinical Nutrition, vol. 75, no. 8, pp. 1283-1290.
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Nguyen, HG, Lieu, KB, Ho-Le, TP, Ho-Pham, LT & Nguyen, TV 2021, 'Discordance between quantitative ultrasound and dual-energy X-ray absorptiometry in bone mineral density: The Vietnam Osteoporosis Study', Osteoporosis and Sarcopenia, vol. 7, no. 1, pp. 6-10.
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Nguyen, HT, Hoang, DT, Luong, NC, Niyato, D & Kim, DI 2021, 'A Hierarchical Game Model for OFDM Integrated Radar and Communication Systems', IEEE Transactions on Vehicular Technology, vol. 70, no. 5, pp. 5077-5082.
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This paper studies the spectrum allocation problem between spectrum service providers (SSPs) and terminals equipped with orthogonal frequency division multiplexing (OFDM) integrated radar and communication (IRC) systems. In particular, IRC-equipped terminals such as autonomous vehicles need to buy spectrum for their radar functions, e.g., sensing and detecting distant vehicles, and communication functions, e.g., transmitting sensing data to road-side units. The terminals determine their spectrum demands from the SSPs subject to their IRC performance requirements, while the SSPs compete with each other on the service prices to attract terminals. Taking into account the complicated interactions, a hierarchical Stackelberg game is proposed to reconcile the spectrum demand and service price, where the SSPs are the leaders and the terminals are the followers. Due to the spectrum constraints of the SSPs, we model the lower-layer subgame among the terminals as a generalized Nash equilibrium problem. An iterative searching algorithm is then developed that guarantees the convergence to the Stackelberg equilibrium. Numerical results demonstrate the effectiveness of our proposed scheme in terms of social welfare compared to baseline schemes.
Nguyen, HT, Tuan, HD, Niyato, D, Kim, DI & Vincent Poor, H 2021, 'Improper Gaussian Signaling for D2D Communication Coexisting MISO Cellular Networks', IEEE Transactions on Wireless Communications, vol. 20, no. 8, pp. 5186-5198.
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Nguyen, HT, Yoon, Y, Ngo, HH & Jang, A 2021, 'The application of microalgae in removing organic micropollutants in wastewater', Critical Reviews in Environmental Science and Technology, vol. 51, no. 12, pp. 1187-1220.
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© 2020, © 2020 Taylor & Francis Group, LLC. Micropollutants have become a serious environmental problem with several negative outcomes for human health and ecosystems. Many efforts have been made to remove micropollutants using a variety of physical, chemical and biological methods. By far, the most attention has been paid to microalgae-based technologies for wastewater treatment in order to obtain high-quality effluents, recover algal biomass for fertilizers, protein-rich feed, biofuel, and put them to other practical use. This paper reviews the potential of microalgae-based systems for the removal of organic micropollutants from open ponds to closed photobioreactors coupled by suspended microalgal cells, immobilized cells, or microalgae-microbial consortia. The inhibition of micropollutants on microalgae growth as well as micropollutant removal mechanisms performed by microalgae-based systems are also discussed. Other treatment methods for the removal of micropollutants are analyzed to show the advantages and limitations of microalgae-based treatment strategies, from which some possible combined systems can be suggested. Finally, some recommendations for future studies on this topic are proposed. (Figure presented.).
Nguyen, KT, Ahmed, MB, Mojiri, A, Huang, Y, Zhou, JL & Li, D 2021, 'Advances in As contamination and adsorption in soil for effective management', Journal of Environmental Management, vol. 296, pp. 113274-113274.
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Arsenic (As) is a heavy metal that causes widespread contamination and toxicity in the soil environment. This article reviewed the levels of As contamination in soils worldwide, and evaluated how soil properties (pH, clay mineral, organic matter, texture) and environmental conditions (ionic strength, anions, bacteria) affected the adsorption of As species on soils. The application of the adsorption isotherm models for estimating the adsorption capacities of As(III) and As(V) on soils was assessed. The results indicated that As concentrations in contaminated soil varying significantly from 1 mg/kg to 116,000 mg/kg, with the highest concentrations being reported in Mexico with mining being the dominating source. Regarding the controlling factors of As adsorption, soil pH, clay mineral and texture had demonstrated the most significant impacts. Both Langmuir and Freundlich isotherm models can be well fitted with As(III) and As(V) adsorption on soils. The Langmuir adsorption capacity varied in the range of 22-42400 mg/kg for As(V), which is greater than 45-8901 mg/kg for As(III). The research findings have enhanced our knowledge of As contamination in soil and its underlying controls, which are critical for the effective management and remediation of As-contaminated soil.
Nguyen, L, Kodagoda, S, Ranasinghe, R & Dissanayake, G 2021, 'Mobile Robotic Sensors for Environmental Monitoring using Gaussian Markov Random Field', Robotica, vol. 39, no. 5, pp. 862-884.
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SUMMARYThis paper addresses the issue of monitoring spatial environmental phenomena of interest utilizing information collected by a network of mobile, wireless, and noisy sensors that can take discrete measurements as they navigate through the environment. It is proposed to employ Gaussian Markov random field (GMRF) represented on an irregular discrete lattice by using the stochastic partial differential equations method to model the physical spatial field. It then derives a GMRF-based approach to effectively predict the field at unmeasured locations, given available observations, in both centralized and distributed manners. Furthermore, a novel but efficient optimality criterion is then proposed to design centralized and distributed adaptive sampling strategies for the mobile robotic sensors to find the most informative sampling paths in taking future measurements. By taking advantage of conditional independence property in the GMRF, the adaptive sampling optimization problem is proven to be resolved in a deterministic time. The effectiveness of the proposed approach is compared and demonstrated using pre-published data sets with appealing results.
Nguyen, LD, Tuan, HD, Duong, TQ, Poor, HV & Hanzo, L 2021, 'Energy-Efficient Multi-Cell Massive MIMO Subject to Minimum User-Rate Constraints', IEEE Transactions on Communications, vol. 69, no. 2, pp. 914-928.
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The capability of massive multiple-input multiple-output (mMIMO) systems supporting the throughput requirement of as many users as possible is investigated. The bottleneck of serving small numbers of users by a large number of transmit antennas in conventional mMIMO is unblocked by a new time-fraction-wise beamforming technique, which focuses signal transmission in fractions of a time slot. Based on this time-fraction-wise signal transmission, a new user service scheduling scheme for multi-cell mMIMO, whose cell-edge users suffer not only poor channel conditions but also multi-cell interference, is proposed to support a large user-population. We demonstrate that the numbers of users served by our multi-cell mMIMO within a time-slot may be as high as twice the number of its transmit antennas.
Nguyen, LN, Kumar, J, Vu, MT, Mohammed, JAH, Pathak, N, Commault, AS, Sutherland, D, Zdarta, J, Tyagi, VK & Nghiem, LD 2021, 'Biomethane production from anaerobic co-digestion at wastewater treatment plants: A critical review on development and innovations in biogas upgrading techniques', Science of The Total Environment, vol. 765, pp. 142753-142753.
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Anaerobic co-digestion (AcoD) can utilise spare digestion capacity at existing wastewater treatment plants (WWTP) to generate surplus biogas beyond the plant's internal energy requirement. Data from industry reports and the peer-reviewed literature show that through AcoD, numerous examples of WWTPs have become net energy producers, necessitating other high-value applications for surplus biogas. A globally emerging trend is to upgrade biogas to biomethane, which can then be used as town gas or transport fuel. Water, organic solvent and chemical scrubbing, pressure swing adsorption, membrane separation, and cryogenic technology are commercially available CO2 removal technologies for biogas upgrade. Although water scrubbing is currently the most widely applied technology due to low capital and operation cost, significant market growth in membrane separation has been seen over the 2015-2019 period. Further progress in materials engineering and sciences is expected and will further enhance the membrane separation competitiveness for biogas upgrading. Several emerging biotechnologies to i) improve biogas quality from AcoD; ii) accelerate the absorption rate, and iii) captures CO2 in microalgal culture have also been examined and discussed in this review. Through a combination of AcoD and biogas upgrade, more WWTPs are expected to become net energy producers.
Nguyen, LN, Vu, MT, Abu Hasan Johir, M, Pernice, M, Ngo, HH, Zdarta, J, Jesionowski, T & Nghiem, LD 2021, 'Promotion of direct interspecies electron transfer and potential impact of conductive materials in anaerobic digestion and its downstream processing - a critical review', Bioresource Technology, vol. 341, pp. 125847-125847.
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Nguyen, LV, Phung, MD & Ha, QP 2021, 'Iterative Learning Sliding Mode Control for UAV Trajectory Tracking', Electronics, vol. 10, no. 20, pp. 2474-2474.
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This paper presents a novel iterative learning sliding mode controller (ILSMC) that can be applied to the trajectory tracking of quadrotor unmanned aerial vehicles (UAVs) subject to model uncertainties and external disturbances. Here, the proposed ILSMC is integrated in the outer loop of a controlled system. The control development, conducted in the discrete-time domain, does not require a priori information of the disturbance bound as with conventional SMC techniques. It only involves an equivalent control term for the desired dynamics in the closed loop and an iterative learning term to drive the system state toward the sliding surface to maintain robust performance. By learning from previous iterations, the ILSMC can yield very accurate tracking performance when a sliding mode is induced without control chattering. The design is then applied to the attitude control of a 3DR Solo UAV with a built-in PID controller. The simulation results and experimental validation with real-time data demonstrate the advantages of the proposed control scheme over existing techniques.
Nguyen, MK, Tran, VS, Pham, TT, Pham, HG, Hoang, BL, Nguyen, TH, Nguyen, TH, Tran, TH & Ngo, HH 2021, 'Fenton/ozone-based oxidation and coagulation processes for removing metals (Cu, Ni)-EDTA from plating wastewater', Journal of Water Process Engineering, vol. 39, pp. 101836-101836.
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Wastewater containing heavy metals has caused many serious problems to land and marine environments. These heavy metal-laden wastewaters containing organic complexing agents are the consequence of using large-scale industrial applications for dissolving metals. Ethylenediaminetetraacetate (EDTA) is a widely used complexing agent in plating, metal finishing and chemical cleaning industries. However, due to the dramatic increase in the solubility of metal ions, EDTA has negative impact on heavy metals removed in wastewaters by conventional precipitation processes. This study aims to find the optimal conditions of combined/hybrid process of advanced oxidation and coagulation to treat metals-EDTA containing Cu, Ni plating wastewater from an electroplating manufacturer in Vietnam. The effects of pH, H2O2 dose, Fe2+ dose, ozone, reaction time and poly acrylic acid (PAA) dose were investigated. Results indicated that the 3-stage treatment process at the optimal conditions could remove 99.7 % of Ni and 99.72 % of Cu. The effluent of wastewater after the whole treatment process met the Vietnamese national regulation on industrial wastewater (QCVN 40:2011/BTNMT) for NH4+, Cu and Ni at column A and COD at column B. In short, the combined advanced oxidation processes and coagulation/flocculation could successfully be applied for plating wastewater treatment.
Nguyen, M-T, Le, DT & Le, L 2021, 'Transformers-based information extraction with limited data for domain-specific business documents', Engineering Applications of Artificial Intelligence, vol. 97, pp. 104100-104100.
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Nguyen, NHT, Perry, S, Bone, D, Le, HT & Nguyen, TT 2021, 'Two-stage convolutional neural network for road crack detection and segmentation', Expert Systems with Applications, vol. 186, pp. 115718-115718.
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Nguyen, N-T, Nguyen, DN, Hoang, DT, Van Huynh, N, Dutkiewicz, E, Nguyen, N-H & Nguyen, Q-T 2021, 'Time Scheduling and Energy Trading for Heterogeneous Wireless-Powered and Backscattering-Based IoT Networks', IEEE Transactions on Wireless Communications, vol. 20, no. 10, pp. 6835-6851.
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This article studies the strategic interactions between an IoT service provider (IoTSP) which consists of heterogeneous IoT devices and its energy service provider (ESP). To that end, we propose an economic framework using the Stackelberg game to maximize the network throughput and energy efficiency of both the IoTSP and ESP. To obtain the Stackelberg equilibrium (SE), we apply a backward induction technique which first derives a closed-form solution for the ESP (follower). Then, to tackle the non-convex optimization problem for the IoTSP (leader), we leverage the block coordinate descent and convex-concave procedure techniques to design two partitioning schemes (i.e., partial adjustment (PA) and joint adjustment (JA)) to find the optimal energy price and service time that constitute local SEs. Numerical results reveal that by jointly optimizing the energy trading and time allocation for IoT devices, one can achieve significant improvements in terms of the IoTSP's profit compared with those of conventional transmission methods (up to 38.7 folds). Different tradeoffs between the ESP's and IoTSP's profits and complexities of the PA/JA schemes can also be numerically tuned. Simulations also show that the obtained local SEs approach the optimal social welfare when the benefit per transmitted bit exceeds a given threshold.
Nguyen, P-D, Tran, N-ST, Nguyen, T-T, Dang, B-T, Le, M-TT, Bui, X-T, Mukai, F, Kobayashi, H & Ngo, HH 2021, 'Long-term operation of the pilot scale two-stage anaerobic digestion of municipal biowaste in Ho Chi Minh City', Science of The Total Environment, vol. 766, pp. 142562-142562.
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A pilot-scale two-stage anaerobic digestion system, which includes a feed tank (0.4 m3), a hydrolysis reactor (1.2 m3) followed by a methane fermenter (4.0 m3) was set up and run at the municipal solid waste landfill located in Ho Chi Minh City (HCMC), Vietnam. The feed that was separated from urban organic solid waste was collected at households and restaurants in District 1, HCMC. This study aimed to investigate the resource recovery performance of the pilot two-stage anaerobic digestion system, in terms of carbon recovery via biogas production and nutrient recovery from digestate. The average organic loading rate (OLR) of the system was step increased from 1.6 kg volatile solids (VS)·m-3·d-1, 2.5 kg VS·m-3·d-1 and 3.8 kg VS·m-3·d-1 during 400 days of operation. During the long-term operation at three OLRs, pH values and alkalinity were stable at both hydrolysis and methanogenesis stages without any addition of alkalinity for the methanogenesis phase. High buildup of propanoic acid and total volatile fatty acid concentrations in the fermenter did not drop pH values and inhibit the methanogenic process at high OLRs (2.5-3.8 kg VS m-3·d-1). The obtained total chemical oxygen demand (tCOD) removal performance was 83-87% at the OLRs ranging from 2.5 kg VS·m-3·d-1 and 3.8 kg VS·m-3·d-1, respectively. The highest biogas yield of 263 ± 64 L·kg-1 tCOD removed obtained at OLR of 2.5 kg VS·m-3·d-1. It is expected that a full scale 2S-AD plant with capacity of 5200 tons day-1 of biowaste collected currently from municipal solid waste in HCMC may create daily electricity of 552 MWh, thermal energy of 630 MWh, and recovery of 16.1 tons of NH4+-N, 11.4 tons of organic-N, and 2.1 tons of TP as both organic liquid and solid fertilizers.
Nguyen, QD, Castel, A, Kim, T & Khan, MSH 2021, 'Performance of fly ash concrete with ferronickel slag fine aggregate against alkali-silica reaction and chloride diffusion', Cement and Concrete Research, vol. 139, pp. 106265-106265.
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Ferronickel slag (FNS) is an industrial by-product of ferronickel alloy production at a high temperature which can be a promising potential to be used as fine aggregate to produce more sustainable concrete. In this study, the performance of concrete containing ferronickel slag sand and fly ash relating to alkali-silica reaction (ASR) and chloride contamination was investigated. ASR-induced expansion, chloride diffusion resistance, and chloride binding capacity of FNS concrete were determined through concrete prism tests (CPT), accelerated diffusion test, and bulk diffusion test. Thermogravimetric analysis (TGA) was conducted to measure the amount of Portlandite and Friedel's salt in concrete. Concrete with 50 wt% FNS sand as fine natural aggregate replacement and 25 wt% of cement replacement by fly ash showed a remarkable potential to be used not only as a low-carbon concrete with comparable mechanical properties to conventional concrete but also with a better performance against ASR and chloride contamination.
Nguyen, T-D, Musial, K & Gabrys, B 2021, 'AutoWeka4MCPS-AVATAR: Accelerating automated machine learning pipeline composition and optimisation', Expert Systems with Applications, vol. 185, pp. 115643-115643.
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Automated machine learning pipeline (ML) composition and optimisation aim at automating the process of finding the most promising ML pipelines within allocated resources (i.e., time, CPU and memory). Existing methods, such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods frequently require a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid in the first place, and attempting to execute them is a waste of time and resources. To address this issue, we propose a novel method to evaluate the validity of ML pipelines, without their execution, using a surrogate model (AVATAR). The AVATAR generates a knowledge base by automatically learning the capabilities and effects of ML algorithms on datasets’ characteristics. This knowledge base is used for a simplified mapping from an original ML pipeline to a surrogate model which is a Petri net based pipeline. Instead of executing the original ML pipeline to evaluate its validity, the AVATAR evaluates its surrogate model constructed by capabilities and effects of the ML pipeline components and input/output simplified mappings. Evaluating this surrogate model is less resource-intensive than the execution of the original pipeline. As a result, the AVATAR enables the pipeline composition and optimisation methods to evaluate more pipelines by quickly rejecting invalid pipelines. We integrate the AVATAR into the sequential model-based algorithm configuration (SMAC). Our experiments show that when SMAC employs AVATAR, it finds better solutions than on its own. This is down to the fact that the AVATAR can evaluate more pipelines within the same time budget and allocated resources.
Nguyen, TH, Nguyen, AT, Loganathan, P, Nguyen, TV, Vigneswaran, S, Nguyen, THH & Tran, HN 2021, 'Low-cost laterite-laden household filters for removing arsenic from groundwater in Vietnam and waste management', Process Safety and Environmental Protection, vol. 152, pp. 154-163.
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Nguyen, TKL, Ngo, HH, Guo, W, Nghiem, LD, Qian, G, Liu, Q, Liu, J, Chen, Z, Bui, XT & Mainali, B 2021, 'Assessing the environmental impacts and greenhouse gas emissions from the common municipal wastewater treatment systems', Science of The Total Environment, vol. 801, pp. 149676-149676.
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This study measured the environmental impacts from three same-size wastewater treatment systems, specifically activated sludge, a constructed wetland, and a high rate algal pond. Detailed data inventories were employed using SimaPro 9 software to calculate the entire consequences by ReCiPe 2016 and Greenhouse Gas Protocol method. The environmental outcomes caused by substance emissions and resource extraction are presented in several impact categories at the endpoint level. For a better comparison, the single score tool was applied to aggregate all factors into three areas of protection: human health, ecosystem, and resource shortage. Results showed that concrete and steel are the main contributors to the construction phase, while electricity is responsible for the operation stage. The single score calculation indicates that the proportion of construction activities could be equal to or even higher than the operation stage for a small capacity plant. The total environmental impact of the conventional system was 2.3-fold and 3-fold higher than that of constructed wetland and high rate algal pond, respectively. High rate algal pond has the best environmental performance when generating the least burdens and greenhouse gas emissions of 0.72 kg CO2 equivalent per m3. Constructed wetland produces 5.69 kg CO2, higher than an algal pond but much lower than activated sludge plant, emitting 11.42 kg CO2 per m3.
Nguyen, TKL, Ngo, HH, Guo, W, Nguyen, TLH, Chang, SW, Nguyen, DD, Varjani, S, Lei, Z & Deng, L 2021, 'Environmental impacts and greenhouse gas emissions assessment for energy recovery and material recycle of the wastewater treatment plant', Science of The Total Environment, vol. 784, pp. 147135-147135.
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Nguyen, T-T, Bui, X-T, Ngo, HH, Nguyen, T-T-D, Nguyen, K-Q, Nguyen, H-H, Huynh, K-P-H, Némery, J, Fujioka, T, Duong, CH, Dang, B-T & Varjani, S 2021, 'Nutrient recovery and microalgae biomass production from urine by membrane photobioreactor at low biomass retention times', Science of The Total Environment, vol. 785, pp. 147423-147423.
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Urine has been considered as an ideal nutrient source for microalgae cultivation thanks to its composition containing the high concentrations of nitrogen and phosphorus. Herein, the microalgae growth in urine was evaluated in a lab-scale membrane photobioreactor (MPBR) system. This work aimed to validate the influence of low biomass retention times (BRT) (10, 7, 5, 3, 2 d) on nutrient remediation and biomass productivity. It revealed that BRT of 7 d resulted in synergistically high biomass production (biomass productivity of 313 mg/L.d) and removal rates (TN of 90.5 mg/L.d and TP of 4.7 mg/L.d). Notably, the short BRT of 2–5 d was not sufficient to trigger actively growing microalgae and thus reduced biomass production rate. In addition, as operated at a low flux of 2 L/m2.h, MPBR system required no physical cleaning for 100 days of operation. The BRT-dependent biomass concentration played a pivotal role in changing the fouling rate of MPBR; however, the fouling is reversible in the MPBR system under the low flux condition.
Nguyen, TT, Indraratna, B & Singh, M 2021, 'Dynamic parameters of subgrade soils prone to mud pumping considering the influence of kaolin content and the cyclic stress ratio', Transportation Geotechnics, vol. 29, pp. 100581-100581.
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Nguyen, TTQ, Loganathan, P, Dinh, BK, Nguyen, TV, Vigneswaran, S & Ngo, HH 2021, 'Removing arsenate from water using batch and continuous-flow electrocoagulation with diverse power sources', Journal of Water Process Engineering, vol. 41, pp. 102028-102028.
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Nguyen, TV 2021, 'Personalized fracture risk assessment: where are we at?', Expert Review of Endocrinology & Metabolism, vol. 16, no. 4, pp. 191-200.
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Introduction: Osteoporotic fracture imposes a significant health care burden globally. Personalized assessment of fracture risk can potentially guide treatment decisions. Over the past decade, a number of risk prediction models, including the Garvan Fracture Risk Calculator (Garvan) and FRAX®, have been developed and implemented in clinical practice. Areas covered: This article reviews recent development and validation results concerning the prognostic performance of the two tools. The main areas of review are the need for personalized fracture risk prediction, purposes of risk prediction, predictive performance in terms of discrimination and calibration, concordance between the Garvan and FRAX tools, genetic profiling for improving predictive performance, and treatment thresholds. In some validation studies, FRAX tended to underestimate fracture by as high as 50%. Studies have shown that the predicted risk from the Garvan tool is highly concordant with clinical decision. Expert opinion: Although there are some discrepancy in fracture risk prediction between Garvan and FRAX, both tools are valid and can aid patients and doctors communicate about risk and make informed decision. The ideal of personalized risk assessment for osteoporosis patients will be realized through the incorporation of genetic profiling into existing fracture risk assessment tools.
Nguyen, V-T, Tran, TTN, Van, T-K & Tran, T 2021, 'DNA-Templated Silver Nanoclusters Used as a Label-Free Fluorescent Probe for the Detection of O6-Methyltransferase Activity', Journal of Analytical Chemistry, vol. 76, no. 5, pp. 585-591.
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Nguyen, XC, Ly, QV, Li, J, Bae, H, Bui, X-T, Nguyen, TTH, Tran, QB, Vo, T-D-H & Nghiem, LD 2021, 'Nitrogen removal in subsurface constructed wetland: Assessment of the influence and prediction by data mining and machine learning', Environmental Technology & Innovation, vol. 23, pp. 101712-101712.
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Nguyen, XC, Ly, QV, Peng, W, Nguyen, V-H, Nguyen, DD, Tran, QB, Huyen Nguyen, TT, Sonne, C, Lam, SS, Ngo, HH, Goethals, P & Le, QV 2021, 'Vertical flow constructed wetlands using expanded clay and biochar for wastewater remediation: A comparative study and prediction of effluents using machine learning', Journal of Hazardous Materials, vol. 413, pp. 125426-125426.
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Nguyen, XC, Nguyen, TTH, Bui, X-T, Tran, XV, Tran, TCP, Hoang, NTT, La, DD, Chang, SW, Ngo, HH & Nguyen, DD 2021, 'Status of water use and potential of rainwater harvesting for replacing centralized supply system in remote mountainous areas: a case study', Environmental Science and Pollution Research, vol. 28, no. 45, pp. 63589-63598.
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The failure of the centralized water supply system forced XY community to become more dependent on uncertain and unstable water sources. The results of surveying 50 households showed that 89.18% of total households depended on water collected from rivers, which contributed 58.3% of the total water volume used for the domestic demands. The average water volume consumed was 19.5 liters/person/day (l/p/d), and 86.5% of households used more than one source; 13.5% of households collected water only from rivers, and 45.94% of families had rainwater harvesting (RWH) for their activities (domestic water demand); however, RWH only provided 9.9% of total water consumption. In this study, basic methods were applied to calculate the storage tanks necessary to balance the water deficit created by drought months. Three levels of water demand (14, 20, and 30 l/p/d) can be the best choices for RWH; for a higher demand (40 and 60 l/p/d), small roof area (30-40 m2), and many people (six to seven) per family, RWH might be impractical because of unsuitable rainfall or excessively large storage tanks.
Nguyen-Ky, T, Tuan, HD, Savkin, A, Do, MN & Van, NTT 2021, 'Real-Time EEG Signal Classification for Monitoring and Predicting the Transition Between Different Anaesthetic States', IEEE Transactions on Biomedical Engineering, vol. 68, no. 5, pp. 1450-1458.
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Quantitative identification of the transitions between anaesthetic states is very essential for optimizing patient safety and quality care during surgery but poses a very challenging task. The state-of-the-art monitors are still not capable of providing their manifest variables, so the practitioners must diagnose them based on their own experience. The present paper proposes a novel real-time method to identify these transitions. Firstly, the Hurst method is used to pre-process the de-noised electro-encephalograph (EEG) signals. The maximum of Hurst's ranges is then accepted as the EEG real-time response, which induces a new real-time feature under moving average framework. Its maximum power spectral density is found to be very differentiated into the distinct transitions of anaesthetic states and thus can be used as the quantitative index for their identification.
Ni, Q, Ji, JC, Feng, K & Halkon, B 2021, 'A novel correntropy-based band selection method for the fault diagnosis of bearings under fault-irrelevant impulsive and cyclostationary interferences', Mechanical Systems and Signal Processing, vol. 153, pp. 107498-107498.
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Demodulation analysis is one of the most effective methods for bearing fault diagnosis. However, in practical applications, the interferences from ambient noises or other rotating components may create great challenges to demodulation analysis and thus decrease its effectiveness. Generally, a selection procedure for the most informative frequency band (IFB) is usually implemented in advance to extract the fault features that are hidden by the interferences. The fast kurtogram (FK) has been utilized as a benchmark for the IFB selection. Although designed to identify the most impulsive part of the signal, the FK is inevitably affected by the fault-irrelevant impulsive and cyclostationary interferences due to the dual sensitiveness to the impulsiveness and cyclostationarity of the kurtosis, and thus it may produce a misleading band for demodulation. To address this issue, a novel and robust IFB selection method based on the fault energy of correntropy (named FECgram) is proposed in this paper to replace the FK, through which the IFB can capture the fault symptom without being influenced by the fault-irrelevant impulsive and cyclostationary interferences. The superiority of the FECgram in combination with the squared envelope spectrum (SES) is validated on both simulation data and three different challenging experimental datasets.
Ni, W, Song, S-P & Jiang, Y-D 2021, 'Association between routine hematological parameters and sudden sensorineural hearing loss: A meta-analysis', Journal of Otology, vol. 16, no. 1, pp. 47-54.
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Ni, W, Zhu, G, Liu, F, Li, Z, Xie, C & Han, Y 2021, 'Carboxylic Acids in Petroleum: Separation, Analysis, and Geochemical Significance', Energy & Fuels, vol. 35, no. 16, pp. 12828-12844.
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Ni, X, Wen, S, Wang, H, Guo, Z, Zhu, S & Huang, T 2021, 'Observer-Based Quasi-Synchronization of Delayed Dynamical Networks With Parameter Mismatch Under Impulsive Effect', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 7, pp. 3046-3055.
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This article focuses on the observer-based quasi-synchronization problem of delayed dynamical networks with parameter mismatch under impulsive effect. First, since the state of each node is unknown in the real situation, the state estimation strategy is proposed to estimate the state of each node, so as to design an appropriate synchronization controller. Then, the corresponding controller is constructed to synchronize the slave nodes with their leader node. In this article, we take the impulsive effect into consideration, which means that an impulsive signal will be applied to the system every so often. Due to the existence of parameter mismatch and time-varying delay, by constructing an appropriate Lyapunouv function, we will eventually obtain a differential equation with constant and time-varying delay terms. Then, we analyze its trajectory by introducing the Cauchy matrix and prove its boundedness by contradiction. Finally, a numerical simulation is presented to illustrate the validness of obtained results.
Ni, Z, Zhang, JA, Huang, X, Yang, K & Yuan, J 2021, 'Uplink Sensing in Perceptive Mobile Networks With Asynchronous Transceivers', IEEE Transactions on Signal Processing, vol. 69, no. 99, pp. 1287-1300.
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Nie, X, Takalkar, MA, Duan, M, Zhang, H & Xu, M 2021, 'GEME: Dual-stream multi-task GEnder-based micro-expression recognition', Neurocomputing, vol. 427, pp. 13-28.
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© 2020 Elsevier B.V. Recognition of micro-expressions remains a topic of concern considering its brief span and low intensity. This issue is addressed through convolutional neural networks (CNNs) by developing multi-task learning (MTL) method to effectively leverage a side task: gender detection. A dual-stream multi-task framework called GEME is introduced that recognises micro-expressions by incorporating unique gender characteristics and subsequently improves the micro-expression recognition accuracy. This research aims to examine how gender differences influence the way micro-expressions are displayed. The current study proves that selecting relevant features of micro-expressions distinctive to the gender and added to the micro-expression features improves the micro-expression recognition accuracy. This network learns gender-specific features and micro-expression features and adds them together to learn the combination of shared and task-specific representations. A multi-class focal loss is used to mitigate the class imbalance issue by down-weighing the easy samples and concentrate more on misclassified samples. The Class-Balanced (CB) focal loss is also implemented for a better class balancing during Leave-One-Subject-Out (LOSO) validations where CB loss re-balances and re-weights the loss. The experimental results on three widely used databases demonstrate the improved performance of the proposed network and achieve comparable results with the state-of-the-art methods.
Nikraftar, Z, Mostafaie, A, Sadegh, M, Afkueieh, JH & Pradhan, B 2021, 'Multi-type assessment of global droughts and teleconnections', Weather and Climate Extremes, vol. 34, pp. 100402-100402.
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Nikshad, A, Aghlmandi, A, Safaralizadeh, R, Aghebati-Maleki, L, Warkiani, ME, Khiavi, FM & Yousefi, M 2021, 'Advances of microfluidic technology in reproductive biology', Life Sciences, vol. 265, pp. 118767-118767.
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According to World Health Organization (WHO) reports about 70 million couples suffer from infertility all over the world. A lot of research groups are working on this issue and have made therapeutic approaches by integrating biology, medicine, genetics, chemistry, psychology, mechanic, and many other branches of science. However, these methods have their own pros and cons. Assisted Reproductive Technologies (ART) has appeared to solve infertility problems. In Vitro Fertilization (IVF), Intracytoplasmic Sperm Injection (ICSI), Intrauterine Insemination (IUI) are the most common and conventional technologies in this regard. There are at least two characteristics of microfluidics, mechanical and biochemical, which can be influential in the field of mammalian gamete and preimplantation embryo biology. These microfluidic characteristics can assist in basic biological studies on sperm, oocyte and preimplantation embryo structure, function and environment. Using microfluidics in sorting sperm, conducting different steps of oocyte selection and preparation, and transferring embryo by passing sub-microliter fluid through microchannels results in low cost and short time. The size and shape of microchannels and the volume of used fluid differs from non-human cells to human cells. The most progressions have been seen in animal models. Results suggest that microfluidic systems will lead to improved efficiencies in assisted reproduction.
Ninan, J, Clegg, S, Burdon, S & Clay, J 2021, 'Overt obstacles and covert causes: An exploratory study of poor performance in megaprojects', Project Leadership and Society, vol. 2, pp. 100011-100011.
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Nithya, S, Sangeetha, M, Apinaya Prethi, KN, Sagar Sahoo, K, Kumar Panda, S & Gandomi, AH 2021, 'Correction to “SDCF: A Software-Defined Cyber Foraging Framework for Cloudlet Environment”', IEEE Transactions on Network and Service Management, vol. 18, no. 2, pp. 2450-2450.
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In the above article [1], the corresponding author was incorrectly identified. The corresponding author is the first author, S. Nithya.
Niu, T, Wang, J, Lu, H, Yang, W & Du, P 2021, 'A Learning System Integrating Temporal Convolution and Deep Learning for Predictive Modeling of Crude Oil Price', IEEE Transactions on Industrial Informatics, vol. 17, no. 7, pp. 4602-4612.
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Nizami, MSH, Hossain, MJ & Mahmud, K 2021, 'A Coordinated Electric Vehicle Management System for Grid-Support Services in Residential Networks', IEEE Systems Journal, vol. 15, no. 2, pp. 2066-2077.
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Nizami, MSH, Hossain, MJ & Mahmud, K 2021, 'A Nested Transactive Energy Market Model to Trade Demand-Side Flexibility of Residential Consumers', IEEE Transactions on Smart Grid, vol. 12, no. 1, pp. 479-490.
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Nizami, S, McGregor AM, C & Green, JR 2021, 'Integrating Physiological Data Artifacts Detection With Clinical Decision Support Systems: Observational Study', JMIR Biomedical Engineering, vol. 6, no. 2, pp. e23495-e23495.
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Background Clinical decision support systems (CDSS) have the potential to lower the patient mortality and morbidity rates. However, signal artifacts present in physiological data affect the reliability and accuracy of the CDSS. Moreover, patient monitors and other medical devices generate false alarms while processing physiological data, further leading to alarm fatigue because of increased noise levels, staff disruption, and staff desensitization in busy critical care environments. This adversely affects the quality of care at the patient bedside. Hence, artifact detection (AD) algorithms play a crucial role in assessing the quality of physiological data and mitigating the impact of these artifacts. Objective The aim of this study is to evaluate a novel AD framework for integrating AD algorithms with CDSS. We designed the framework with features that support real-time implementation within critical care. In this study, we evaluated the framework and its features in a false alarm reduction study. We developed static framework component models, followed by dynamic framework compositions to formulate four CDSS. We evaluated these formulations using neonatal patient data and validated the six framework features: flexibility, reusability, signal quality indicator standardization, scalability, customizability, and real-time implementation support. Methods We developed four exemplar static AD components with standardized requirements and provisions interfaces that facilitate the interoperability of framework components. These AD components were mixed and matched into four different AD compositions to mitigate the artifacts’ effects. We developed a novel static clinical event det...
Nonahal, M, White, SJU, Regan, B, Li, C, Trycz, A, Kim, S, Aharonovich, I & Kianinia, M 2021, 'Bottom‐Up Synthesis of Single Crystal Diamond Pyramids Containing Germanium Vacancy Centers', Advanced Quantum Technologies, vol. 4, no. 7, pp. 1-6.
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AbstractDiamond resonators containing color‐centers are highly sought after for application in quantum technologies. Bottom‐up approaches are promising for the generation of single‐crystal diamond structures with purposely introduced color centers. Here the possibility of using a polycrystalline diamond to grow single‐crystal diamond structures by employing a pattern growth method is demonstrated. For, the possible mechanism of growing a single‐crystal structure with predefined shape and size from a polycrystalline substrate by controlling the growth condition is clarified. Then, by introducing germanium impurities during the growth, localized and enhanced emission from fabricated pyramid shaped single‐crystal diamonds containing germanium vacancy (GeV) color centers is demonstrated. Finally, linewidth of ∼500 MHz at 4 K from a single GeV center in the pyramid shaped diamonds is measured. The method is an important step toward fabrication of 3D structures for integrated diamond photonics.
Noushini, A, Nguyen, QD & Castel, A 2021, 'Assessing alkali-activated concrete performance in chloride environments using NT Build 492', Materials and Structures, vol. 54, no. 2.
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The production of Portland cement is responsible for approximately 5–7% of the worldwide CO2 emission. Geopolymer concrete (GPC) presents potential to become a more sustainable alternative to Portland cement and reduce the environment impact of the concrete industry. Marine environment is one of the most corrosion issues for reinforced concrete structures. Chloride migration test (Nordtest NT Build 492) and bulk diffusion chloride test (ASTM C1556) has been widely used to assess chloride diffusion resistance of Portland cement concrete. NT Build 492, involving externally applied electrical voltage, provides fast and adequate assessment of chloride penetration resistance of Portland cement concrete. However, the utilization of NT Build 492 for GPCs requires recalibration due to their different microstructure and pore solution composition compared to Portland cement concrete. This study aims to establish performance-based criteria for GPCs in marine environments using NT Build 492 and ASTM C1556 test protocols. Experimental results revealed a good correlation between chloride migration coefficients (NT Build 492) and chloride diffusion coefficients (ASTM C1556). In addition, chloride concentration at the colour change boundary used to calculate the chloride migration coefficients in NT Build 492 has been recalibrated for the chemistry of GPCs pore solution.
Nuvoli, S, Tola, A, Muntoni, A, Pietroni, N, Gobbetti, E & Scateni, R 2021, 'Automatic Surface Segmentation for Seamless Fabrication Using 4-axis Milling Machines.', Comput. Graph. Forum, vol. 40, pp. 191-203.
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Obeid, F, Van, TC, Guo, B, Surawski, NC, Hornung, U, Brown, RJ, Ramirez, JA, Thomas-Hall, SR, Stephens, E, Hankamer, B & Rainey, T 2021, 'The fate of nitrogen and sulphur during co-liquefaction of algae and bagasse: Experimental and multi-criterion decision analysis', Biomass and Bioenergy, vol. 151, pp. 106119-106119.
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The removal of nitrogen (N) and sulphur (S) from biocrude oil produced using hydrothermal liquefaction (HTL), is important for the production of high quality renewable fuels. Here the effect of co-liquefaction of bagasse and algae was analysed. Algae (Chlorella vulgaris and Cyanobacteria) were mixed with bagasse (1:1) subjected to HTL at 250–350 °C for 10–60 min. Higher HTL temperatures had a positive effect in increasing the biocrude yield and slightly reduced N content; S did not show a consistent trend. Most of the nitrogen (~66%) and sulphur (~80%) were recovered in the aqueous phase rather than in the biocrude phase, opening the opportunity to recycle these nutrients for algae cultivation. Co-liquefying bagasse with algae improved the biocrude yield (54 wt%) compared to pure Cyanobacteria (47.5 wt%). It also reduced N content from 7 wt% (Cyanobacteria biocrude) to 4.2 wt% (Cyanobacteria: Bagasse) and S from 0.7 wt% to 0.4 wt%. Principal Component Analysis (PCA) analysis identified that biocrude yield is positively correlated with the initial lipid content and anti-correlated with the carbohydrates fraction. Biocrude N content is closely related to the initial amount of proteins in the algae. The Preference Ranking Organization METHod for Enrichment of Evaluations and its descriptive complement Geometrical Analysis for Interactive Aid (PROMETHEE and GAIA) analysis ranked the co-liquefaction of Chlorella vulgaris and bagasse (1:1) at 350 °C and 60 min as one of the best overall combination in terms of biocrude yield, N and S content.
Oberst, S, Halkon, B, Ji, J & Brown, T 2021, 'Preface', Vibration Engineering for a Sustainable Future: Numerical and Analytical Methods to Study Dynamical Systems, Vol. 3, pp. v-vi.
Oberst, S, Halkon, B, Ji, J & Brown, T 2021, 'Preface', Vibration Engineering for a Sustainable Future: Experiments, Materials and Signal Processing, Vol. 2, vol. 2, pp. v-vi.
Oberst, S, Martin, R, Halkon, BJ, Lai, JCS, Evans, TA & Saadatfar, M 2021, 'Submillimetre mechanistic designs of termite-built structures', Journal of The Royal Society Interface, vol. 18, no. 178, pp. 1-10.
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Termites inhabit complex underground mounds of intricate stigmergic labyrinthine designs with multiple functions as nursery, food storage and refuge, while maintaining a homeostatic microclimate. Past research studied termite building activities rather than the actual material structure. Yet, prior to understanding how multi-functionality shaped termite building, a thorough grasp of submillimetre mechanistic architecture of mounds is required. Here, we identify for Nasutitermes exitiosus via granulometry and Fourier transform infrared spectroscopy analysis, preferential particle sizes related to coarse silts and unknown mixtures of organic/inorganic components. High-resolution micro-computed X-ray tomography and microindentation tests reveal wall patterns of filigree laminated layers and sub-millimetre porosity wrapped around a coarse-grained inner scaffold. The scaffold geometry, which is designed of a lignin-based composite and densely biocementitious stercoral mortar, resembles that of trabecula cancellous bones. Fractal dimension estimates indicate multi-scaled porosity, important for enhanced evaporative cooling and structural stability. The indentation moduli increase from the outer to the inner wall parts to values higher than those found in loose clays and which exceed locally the properties of anthropogenic cementitious materials. Termites engineer intricately layered biocementitious composites of high elasticity. The multiple-scales and porosity of the structure indicate a potential to pioneer bio-architected lightweight and high-strength materials.
Oey, O, Ghaffari, M, Li, JJ & Hosseini-Beheshti, E 2021, 'Application of extracellular vesicles in the diagnosis and treatment of prostate cancer: Implications for clinical practice', Critical Reviews in Oncology/Hematology, vol. 167, pp. 103495-103495.
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Extracellular vesicles (EV) are cell-derived lipid bilayer-delimited structures providing an important means of intercellular communication. Recent studies have shown that EV, particularly exosomes and large-oncosomes contain miRNA and proteins crucial in prostate cancer (PCa) progression, metastasis and treatment resistance. This includes not just EV released from PCa cells, but also from other cells in the tumor microenvironment. PCa patient derived EV have a unique composition compared to healthy and benign prostatic diseases. As such, EV show promise as diagnostic liquid biopsy biomarkers, both as an adjunct and alternative to the invasive current gold-standard. EV could also be utilized to stratify patients' risk and predict response to hormonal, chemo, immune- and targeted therapy, which will direct future treatment decisions in PCa. We present a summary of the current evidence on the role of EV in PCa and the application of EV in PCa diagnosis and treatment to optimize patient outcomes.
Ojo, O 2021, 'In Praise of JESTPE Associate Editors—Part IV', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 6, pp. 6455-6459.
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Oliveira, CYB, Oliveira, CDL, Prasad, R, Ong, HC, Araujo, ES, Shabnam, N & Gálvez, AO 2021, 'A multidisciplinary review ofTetradesmus obliquus: a microalga suitable for large‐scale biomass production and emerging environmental applications', Reviews in Aquaculture, vol. 13, no. 3, pp. 1594-1618.
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AbstractMicroalgae biomass is among one of the most promising sustainable raw materials for many industrial sectors especially biodiesel production. Although a great diversity of microalgae species has been described and isolated, few have been used for large‐scale cultivation. This review presents a multidisciplinary overview of studies onTetradesmus obliquus– a freshwater microalga suitable for large‐scale production and emerging environmental applications. It reviews the taxonomic history ofT. obliquusand its potential commercial applications, including cultivations techniques and environmental parameters, production systems, harvesting and drying of biomass, and its biochemical composition. In addition, a model refinery forT. obliquusis proposed that combines the main productive bioprocesses. Finally, a bibliometric analysis is presented and opportunities for future research withT. obliquusare identified.
Ollerton, RL & Shannon, AG 2021, 'A note on brousseau’s summation problem', Fibonacci Quarterly, vol. 58, no. 5, pp. 190-199.
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This paper takes a historical view of some long-standing problems associated with the development of sums of Fibonacci numbers in which the latter have powers of integers as coefficients. The sequences of coefficients of these polynomials are arrayed in matrices with links to The On-Line Encyclopedia of Integer Sequences. This is an extension of previous work on the summation problem of Ledin because Brousseau introduced some elegant techniques for contracting the summations and the papers of both authors link with some interesting matrices.
Ollerton, RL & Shannon, AG 2021, 'Erratum: Some properties of generalized pascal squares and triangles (The Fibonacci Quarterly (1998) 36.2 (98-109))', Fibonacci Quarterly, vol. 59, no. 3, p. 272.
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On p. 106, the sequence near the bottom of the page should have a 9 between the 6 and 18 (as OEIS A038754), i.e., {1, 2, 3, 6, 9, 18, 27, 54, 81, 162, ...}.
Onasanya, BO, Wen, S, Feng, Y, Zhang, W, Tang, N & Ademola, AT 2021, 'Varying control intensity of synchronized chaotic system with time delay', Journal of Physics: Conference Series, vol. 1828, no. 1, pp. 012143-012143.
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Abstract Hyperchaotic system is a very useful tool in secure and encrypted communications. But situations arise when engineers and scientists seek to synchronize two hyperchaotic systems. This gives another (error) system. The goal is to minimize the error as much as can be in order to make one system look like the other by synchronization. This is a particularly challenging situation. In this paper, two hyperchaotic systems are synchronized by impulsive control. Also, the condition for uniform asymptotic stability of the synchronized error system was given. Finally, the simulation results to justify the reliability of this method is also presented.
Ong, HC, Tiong, YW, Goh, BHH, Gan, YY, Mofijur, M, Fattah, IMR, Chong, CT, Alam, MA, Lee, HV, Silitonga, AS & Mahlia, TMI 2021, 'Recent advances in biodiesel production from agricultural products and microalgae using ionic liquids: Opportunities and challenges', Energy Conversion and Management, vol. 228, pp. 113647-113647.
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© 2020 Elsevier Ltd Biodiesel is considered as a potential substitute for petroleum-based diesel fuel owing to its comparable properties to diesel. Biodiesel is generally produced from renewable sources such as agricultural products and microalgae in the presence of a suitable catalyst. Recently ionic liquid (IL) catalyzed synthesis of biodiesel has become a promising pathway to an eco-friendly production route for biodiesel. This review focuses on the use of ILs both as solvents as well as catalysts for sustainable biodiesel production from agricultural feedstocks and microalgae with high free fatty acid content. Reactions catalyzed by ILs are known to render high reactivity under the mild condition and high selectivity of ester product with simple separation steps. The article first discusses the state of the art of biodiesel production using ILs along with the physicochemical properties of the produced biodiesel. Then, current IL technologies were elucidated in terms of the categories such as acidic and basic ILs. The use of more advanced ILs such as supported ionic liquids and ionic liquid-enzyme catalysts on different biodiesel feedstocks were also discussed. Furthermore, the role of IL catalyst in intensified biodiesel production methods such as microwave and ultrasound technologies were also discussed. Finally, the prospects and challenges of IL catalyzed biodiesel production are discussed in this article. The review shows that ILs with brønsted acidity or basicity not only pose a low risk to the environment but also result in high biodiesel yields with mild reaction conditions in a short time. Brønsted acidic ILs can convert free fatty acids as well as triglycerides to biodiesel without the need for pretreatment, which facilitates in reducing the production cost of biodiesel. From the review, it can be concluded that ILs present great potential as catalysts for biodiesel production.
Ong, HC, Yu, KL, Chen, W-H, Pillejera, MK, Bi, X, Tran, K-Q, Pétrissans, A & Pétrissans, M 2021, 'Variation of lignocellulosic biomass structure from torrefaction: A critical review', Renewable and Sustainable Energy Reviews, vol. 152, pp. 111698-111698.
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Ooi, HK, Koh, XN, Ong, HC, Lee, HV, Mastuli, MS, Taufiq-Yap, YH, Alharthi, FA, Alghamdi, AA & Asikin Mijan, N 2021, 'Progress on Modified Calcium Oxide Derived Waste-Shell Catalysts for Biodiesel Production', Catalysts, vol. 11, no. 2, pp. 194-194.
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The dwindling of global petroleum deposits and worsening environmental issues have triggered researchers to find an alternative energy such as biodiesel. Biodiesel can be produced via transesterification of vegetable oil or animal fat with alcohol in the presence of a catalyst. A heterogeneous catalyst at an economical price has been studied widely for biodiesel production. It was noted that various types of natural waste shell are a potential calcium resource for generation of bio-based CaO, with comparable chemical characteristics, that greatly enhance the transesterification activity. However, CaO catalyzed transesterification is limited in its stability and studies have shown deterioration of catalytic reactivity when the catalyst is reused for several cycles. For this reason, different approaches are reviewed in the present study, which focuses on modification of waste-shell derived CaO based catalyst with the aim of better transesterification reactivity and high reusability of the catalyst for biodiesel production. The catalyst stability and leaching profile of the modified waste shell derived CaO is discussed. In addition, a critical discussion of the structure, composition of the waste shell, mechanism of CaO catalyzed reaction, recent progress in biodiesel reactor systems and challenges in the industrial sector are also included in this review.
Ortiz Marrero, C, Kieferová, M & Wiebe, N 2021, 'Entanglement-Induced Barren Plateaus', PRX Quantum, vol. 2, no. 4, p. 040316.
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Orwa, JO, Reiner, J, Juma, A, Stacey, A, Sears, K, Schütz, JA, Merenda, A, Hyde, L, Guijt, R, Adineh, VR, Li, Q, Naebe, M, Kouzani, AZ & Dumée, LF 2021, 'Growth of diamond coating on carbon fiber: Relationship between fiber microstructure and stability in hydrogen plasma', Diamond and Related Materials, vol. 115, pp. 108349-108349.
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Stability of carbon fiber in hydrogen plasma during chemical vapor deposition growth of diamond film was investigated for a range of carbon fiber samples with different physical properties. Morphological studies using scanning electron microscopy and focused ion beam showed that pre-growth seeding with nanodiamonds was necessary both to protect the carbon fiber from atomic hydrogen attack and to promote diamond growth. Microstructural studies using Raman spectroscopy indicated that carbon fibers with larger crystallite size, which correspond to high and ultra-high modulus fibers, were less susceptible to etching compared to carbon fibers with smaller crystallite size, corresponding to intermediate modulus fibers. A model was developed to predict the diamond film coverage, following pre-seeding of the carbon fibers with nanodiamonds. Compared to larger seeds, a dense seeding with smaller sized nanodiamonds resulted in faster coalescence, which provides significant benefits as the diamond layer protects the carbon fiber from hydrogen plasma attack. The results of this study will facilitate the integration of diamond and carbon fiber into a versatile hybrid material and, in particular, pave the way towards development of novel biocompatible diamond-coated carbon fiber micro-electrodes with long-term efficacy.
Osman, I, Pileggi, SF, Ben Yahia, S & Diallo, G 2021, 'An Alignment-Based Implementation of a Holistic Ontology Integration Method', MethodsX, vol. 8, pp. 101460-101460.
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Ottenhaus, L-M, Jockwer, R, van Drimmelen, D & Crews, K 2021, 'Designing timber connections for ductility – A review and discussion', Construction and Building Materials, vol. 304, pp. 124621-124621.
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Ouyang, D, Shao, J, Jiang, H, Wen, S & Nguang, SK 2021, 'Finite-time stability of coupled impulsive neural networks with time-varying delays and saturating actuators', Neurocomputing, vol. 453, pp. 590-598.
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The paper considers the stability of coupled impulsive neural networks with time-varying delays and saturating actuators in finite time. Based on a delayed state feedback controller, the stability of coupled impulsive neural networks with time-varying delays and saturating actuators can be achieved in finite time. Combined with Lyapunov-based finite-time stability theory, some sufficient conditions are obtained to ensure the stability of coupled impulsive neural networks with time-varying delays and saturating actuators in finite time by using polytopic representation approach and sector nonlinearity model approach, respectively. Moreover, the setting time of coupled impulsive neural networks with saturating actuators is given, and it is found to be related to both the initial state and impulse effect. Furthermore, as special cases, some finite-time stability results of coupled impulsive neural networks with saturating actuators are given under a memoryless controller. Finally, two simulation examples are used to test the effectiveness of the obtained results.
Ozdowska, A, Wyeth, P, Carrington, S & Ashburner, J 2021, 'Using assistive technology with SRSD to support students on the autism spectrum with persuasive writing', British Journal of Educational Technology, vol. 52, no. 2, pp. 934-959.
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AbstractChildren on the autism spectrum (AS) often struggle with writing tasks at school. They commonly experience difficulty with two key aspects of writing: the skills required for handwriting (fine motor and perceptual) and the conceptual and language skills required for written composition. Specialist intervention to assist with written expression is, therefore, often needed for students on the AS to succeed academically. This research evaluated the impact of using self‐regulated strategy development (SRSD) in combination with assistive technology on the quality and length of written compositions of students on the AS. It also investigated how students felt about using the SRSD writing strategy. Eight primary school students on the AS between the ages of 9 and 12 participated in this single‐subject study. An ABAC study design was used to evaluate student writing performance across three conditions. Baseline handwriting measurements were collected during condition A. During condition B students used assistive technology alone; in condition C, students applied their understanding of SRSD while using assistive technology. Results from this study show that, in many cases, the quality and/or length of written compositions and feelings of self‐efficacy towards persuasive writing of students on the AS improved when they received physical and/or conceptual writing supports. This paper presents the research design, methods and results from this single‐subject study followed by a discussion of the results and final thoughts and areas for future research.Practitioner NotesWhat is already known about this topic?There is limited research about how the persuasive writing of students on ...
Pachauri, RK, Kansal, I, Babu, TS & Alhelou, HH 2021, 'Power Losses Reduction of Solar PV Systems Under Partial Shading Conditions Using Re-Allocation of PV Module-Fixed Electrical Connections', IEEE Access, vol. 9, pp. 94789-94812.
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Pais-Roldán, P, Mateo, C, Pan, W-J, Acland, B, Kleinfeld, D, Snyder, LH, Yu, X & Keilholz, S 2021, 'Contribution of animal models toward understanding resting state functional connectivity', NeuroImage, vol. 245, pp. 118630-118630.
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Palanisamy, A, Siwakoti, YP, Mahajan, A, Long, T, Kashani, OF & Blaabjerg, F 2021, 'A transformerless three‐level three‐phase boost PWM inverter for PV applications', IET Power Electronics, vol. 14, no. 10, pp. 1768-1778.
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AbstractMultilevel converters have seen rising demands in the past decades, due to their increased power ratings, enhanced power quality, low switching losses and reduced electromagnetic interference. Prominent among them are the three‐level (3L) neutral point clamped and the flying capacitor inverter topologies along with their derivatives. Nevertheless, the main drawback of these topologies is the requirement of a front‐end boost DC–DC converter to compensate the high dc‐link voltage demand, which is usually twice the grid peak voltage. This multi‐stage power conversion further pulls down the overall system efficiency. A single‐stage dc–ac power converter with boost capability offer an interesting alternative compared to the two stage approach. Considering this aspect, a novel three‐level three‐phase boost type inverter is introduced in this paper for general‐purpose applications (prominently grid‐connected renewable energy). The proposed inverter would reduce the DC‐link voltage requirement to half using the same or even less number of active and passive components, compared to the conventional three‐level neutral point clamped and flying capacitor family. The principle of operation and theoretical analysis are discussed in detail. The design methodology along with simulation and experimental waveforms for a 5 kVA inverter are presented to prove the concept of the proposed inverter topology for practical applications.
Palpandian, M, Winston, DP, Kumar, BP, Kumar, CS, Babu, TS & Alhelou, HH 2021, 'A New Ken-Ken Puzzle Pattern Based Reconfiguration Technique for Maximum Power Extraction in Partial Shaded Solar PV Array', IEEE Access, vol. 9, pp. 65824-65837.
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Pan, S, Chen, X, Cao, C, Chen, Z, Hao Ngo, H, Shi, Q, Guo, W & Hu, H-Y 2021, 'Fluorescence analysis of centralized water supply systems: Indications for rapid cross-connection detection and water quality safety guarantee', Chemosphere, vol. 277, pp. 130290-130290.
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Pan, Y, Tsang, IW, Lyu, Y, Singh, AK & Lin, C-T 2021, 'Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation', Neural Computation, vol. 33, no. 6, pp. 1616-1655.
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Driver mental fatigue leads to thousands of traffic accidents. The increasing quality and availability of low-cost electroencephalogram (EEG) systems offer possibilities for practical fatigue monitoring. However, non-data-driven methods, designed for practical, complex situations, usually rely on handcrafted data statistics of EEG signals. To reduce human involvement, we introduce a data-driven methodology for online mental fatigue detection: self-weight ordinal regression (SWORE). Reaction time (RT), referring to the length of time people take to react to an emergency, is widely considered an objective behavioral measure for mental fatigue state. Since regression methods are sensitive to extreme RTs, we propose an indirect RT estimation based on preferences to explore the relationship between EEG and RT, which generalizes to any scenario when an objective fatigue indicator is available. In particular, SWORE evaluates the noisy EEG signals from multiple channels in terms of two states: shaking state and steady state. Modeling the shaking state can discriminate the reliable channels from the uninformative ones, while modeling the steady state can suppress the task-nonrelevant fluctuation within each channel. In addition, an online generalized Bayesian moment matching (online GBMM) algorithm is proposed to online-calibrate SWORE efficiently per participant. Experimental results with 40 participants show that SWORE can maximally achieve consistent with RT, demonstrating the feasibility and adaptability of our proposed framework in practical mental fatigue estimation.
Pan, Y, Xu, X, Ding, X, Huang, S, Wang, Y & Xiong, R 2021, 'GEM: Online Globally Consistent Dense Elevation Mapping for Unstructured Terrain', IEEE Transactions on Instrumentation and Measurement, vol. 70, no. 99, pp. 1-13.
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IEEE Online dense mapping gives a representation of the unstructured terrain, which is indispensable for safe robotic motion planning. In this paper, we propose such an elevation mapping system, namely GEM, to generate a dense local elevation map in constant real-time for fast responsive local planning, and maintain a globally consistent dense map for path routing at the same time. We model the global elevation map as a collection of submaps. When the trajectory estimation of the robot is corrected by SLAM, only relative poses between submaps are updated without re-building the submap. As a result, this deformable global dense map representation is able to keep the global consistency online. Besides, we accelerate the local mapping by integrating traversability analysis into the mapping system to save the computation cost by obstacle awareness. The system is implemented by CPU-GPU coordinated processing to guarantee constant real-time performance for in-time handling of dynamic obstacles. Substantial experimental results on both simulated and real-world dataset validate the efficiency and effectiveness of GEM.
Panda, SS, Jena, D, Mohanta, BK, Ramasubbareddy, S, Daneshmand, M & Gandomi, AH 2021, 'Authentication and Key Management in Distributed IoT Using Blockchain Technology', IEEE Internet of Things Journal, vol. 8, no. 16, pp. 12947-12954.
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Pandu, SB, Sundarabalan, CK, Srinath, NS, Krishnan, TS, Priya, GS, Balasundar, C, Sharma, J, Soundarya, G, Siano, P & Alhelou, HH 2021, 'Power Quality Enhancement in Sensitive Local Distribution Grid Using Interval Type-II Fuzzy Logic Controlled DSTATCOM', IEEE Access, vol. 9, pp. 59888-59899.
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Pang, G, Cao, L & Chen, L 2021, 'Homophily outlier detection in non-IID categorical data', Data Mining and Knowledge Discovery, vol. 35, no. 4, pp. 1163-1224.
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Most of existing outlier detection methods assume that the outlier factors (i.e., outlierness scoring measures) of data entities (e.g., feature values and data objects) are Independent and Identically Distributed (IID). This assumption does not hold in real-world applications where the outlierness of different entities is dependent on each other and/or taken from different probability distributions (non-IID). This may lead to the failure of detecting important outliers that are too subtle to be identified without considering the non-IID nature. The issue is even intensified in more challenging contexts, e.g., high-dimensional data with many noisy features. This work introduces a novel outlier detection framework and its two instances to identify outliers in categorical data by capturing non-IID outlier factors. Our approach first defines and incorporates distribution-sensitive outlier factors and their interdependence into a value-value graph-based representation. It then models an outlierness propagation process in the value graph to learn the outlierness of feature values. The learned value outlierness allows for either direct outlier detection or outlying feature selection. The graph representation and mining approach is employed here to well capture the rich non-IID characteristics. Our empirical results on 15 real-world data sets with different levels of data complexities show that (i) the proposed outlier detection methods significantly outperform five state-of-the-art methods at the 95%/99% confidence level, achieving 10–28% AUC improvement on the 10 most complex data sets; and (ii) the proposed feature selection methods significantly outperform three competing methods in enabling subsequent outlier detection of two different existing detectors.
Pare, S, Mittal, H, Sajid, M, Bansal, JC, Saxena, A, Jan, T, Pedrycz, W & Prasad, M 2021, 'Remote Sensing Imagery Segmentation: A Hybrid Approach', Remote Sensing, vol. 13, no. 22, pp. 4604-4604.
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In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi’s entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi’s entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi’s entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity.
Park, MJ, Nisola, GM, Seo, DH, Wang, C, Phuntsho, S, Choo, Y, Chung, W-J & Shon, HK 2021, 'Chemically Cross-Linked Graphene Oxide as a Selective Layer on Electrospun Polyvinyl Alcohol Nanofiber Membrane for Nanofiltration Application', Nanomaterials, vol. 11, no. 11, pp. 2867-2867.
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Graphene oxide (GO) nanosheets were utilized as a selective layer on a highly porous polyvinyl alcohol (PVA) nanofiber support via a pressure-assisted self-assembly technique to synthesize composite nanofiltration membranes. The GO layer was rendered stable by cross-linking the nanosheets (GO-to-GO) and by linking them onto the support surface (GO-to-PVA) using glutaraldehyde (GA). The amounts of GO and GA deposited on the PVA substrate were varied to determine the optimum nanofiltration membrane both in terms of water flux and salt rejection performances. The successful GA cross-linking of GO interlayers and GO-PVA via acetalization was confirmed by FTIR and XPS analyses, which corroborated with other characterization results from contact angle and zeta potential measurements. Morphologies of the most effective membrane (CGOPVA-50) featured a defect-free GA cross-linked GO layer with a thickness of ~67 nm. The best solute rejections of the CGOPVA-50 membrane were 91.01% for Na2SO4 (20 mM), 98.12% for Eosin Y (10 mg/L), 76.92% for Methylene blue (10 mg/L), and 49.62% for NaCl (20 mM). These findings may provide one of the promising approaches in synthesizing mechanically stable GO-based thin-film composite membranes that are effective for solute separation via nanofiltration.
Park, MJ, Wang, C, Seo, DH, Gonzales, RR, Matsuyama, H & Shon, HK 2021, 'Inkjet printed single walled carbon nanotube as an interlayer for high performance thin film composite nanofiltration membrane', Journal of Membrane Science, vol. 620, pp. 118901-118901.
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Inkjet printing process enables rapid deposition of inks with precise amount and location. Moreover, the process can be automated and provide control such as repetitive printing of the inks. Utilizing the advantageous features of the inkjet printing process, we demonstrate the synthesis of thin film composite (TFC) flat-sheet membrane for NF application where single walled carbon nanotube (SWCNT) was deposited via an inkjet printing process, acting as an interlayer between the polyamide (PA) selective layer and polyethersulfone (PES) MF membrane support. By controlling the number of SWCNT printings on the PES membrane, we investigated how the SWCNT interlayer thickness influences the formation of PA selective layer. The best membrane performance was achieved from the TFC membrane synthesized using 15 cycles of SWCNT printing, where both high water flux (18.24 ± 0.43 L m−2 h−1 bar−1) and the high Na2SO4 salt rejection (97.88 ± 0.33%) rates were demonstrated. SWCNT interlayer provided highly porous, interconnected structure with uniform pore size distribution which led to the formation of a defect-free ultrathin PA selective layer. Designing of TFC membrane using the SWCNT deposition via inkjet printing is the new approach and successfully demonstrated the significant improvement in the NF membrane performances.
Parsa, K, Hassall, M & Naderpour, M 2021, 'Process Alarm Modeling Using Graph Theory: Alarm Design Review and Rationalization', IEEE Systems Journal, vol. 15, no. 2, pp. 2257-2268.
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Investigations into process accidents have identified that flaws in alarm management systems are a major contributing factor to these accidents. Poor alarm system design can lead to alarm flooding, loss of situation awareness, and poor decision-making, causing unnecessary shutdowns, or further escalation of the abnormal situations. A review of research literature suggests that there appears to be limited methods available to help analysts evaluate alarm system design and to prioritize and rationalize alarms in a manner that promotes operators' situation awareness and correct decision-making. This article documents the first part of the research which aims to develop a means to rationalize defined alarms in operations through use of the causal modeling approach that is linked with the graph modeling technique that involves graph analytics to provide metrics to evaluate the alarm system performance. The article concludes by discussing the implications of the research findings and makes recommendations about further research required to fully develop an alarm system that use prioritization and rationalization to improve the operator's situation awareness and responses during abnormal operating situations.
Parsajoo, M, Armaghani, DJ, Mohammed, AS, Khari, M & Jahandari, S 2021, 'Tensile strength prediction of rock material using non-destructive tests: A comparative intelligent study', Transportation Geotechnics, vol. 31, pp. 100652-100652.
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Parsajoo, M, Mohammed, AS, Yagiz, S, Armaghani, DJ & Khandelwal, M 2021, 'An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass', Journal of Rock Mechanics and Geotechnical Engineering, vol. 13, no. 6, pp. 1290-1299.
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Parvin, K, Lipu, MSH, Hannan, MA, Abdullah, MA, Jern, KP, Begum, RA, Mansur, M, Muttaqi, KM, Mahlia, TMI & Dong, ZY 2021, 'Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects', IEEE Access, vol. 9, pp. 41577-41602.
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Paryani, S, Neshat, A & Pradhan, B 2021, 'Improvement of landslide spatial modeling using machine learning methods and two Harris hawks and bat algorithms', The Egyptian Journal of Remote Sensing and Space Science, vol. 24, no. 3, pp. 845-855.
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Paryani, S, Neshat, A & Pradhan, B 2021, 'Spatial landslide susceptibility mapping using integrating an adaptive neuro-fuzzy inference system (ANFIS) with two multi-criteria decision-making approaches', Theoretical and Applied Climatology, vol. 146, no. 1-2, pp. 489-509.
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Landslide is a type of slope process causing a plethora of economic damage and loss of lives worldwide every year. This study aimed to analyze spatial landslide susceptibility mapping in the Khalkhal-Tarom Basin by integrating an adaptive neuro-fuzzy inference system (ANFIS) with two multi-criteria decision-making approaches, i.e., the best-worst method (BWM) and the stepwise weight assessment ratio analysis (SWARA) techniques. For this purpose, the first step was to prepare a landslide inventory map, which was then divided randomly into the ratio of 70/30% for model training and validation. Thirteen conditioning factors were selected based on the previous studies and available data. In the next step, the BWM and the SWARA methods were utilized to determine the relationships between the sub-criteria and landslides. Finally, landslide susceptibility maps were generated by implementing ANFIS-BWM and ANFIS-SWARA ensemble models, and then several quantitative indices such as positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root-mean-square-error, and the ROC curve were employed to appraise the predictive accuracy of each model. The results indicated that the ANFIS-BWM ensemble model (AUC = 75%, RMSE = 0.443) has better performance than ANFIS-SWARA (AUC = 73.6%, RMSE = 0.477). At the same time, the ANFIS-BWM model had the maximum sensitivity, specificity, and accuracy with values of 87.1%, 54.3%, and 40.7%, respectively. As a result, the BWM method was more efficient in training the ANFIS. Evidently, the generated landslide susceptibility maps (LSMs) can be very efficient in managing land use and preventing the damage caused by the landslide phenomenon.
