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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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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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, Rosi, A, Pradhan, B & Segoni, S 2021, 'Usage of antecedent soil moisture for improving the performance of rainfall thresholds for landslide early warning', CATENA, vol. 200, pp. 105147-105147.
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Landslides triggered by heavy rains are increasing in number and creating severe losses in hilly regions across the world. Rainfall thresholds on regional and local-scales are being used for forecasting such events, for efficient early warning. Empirical and probabilistic approaches for defining rainfall thresholds are traditional tools which are being used as part of the forecasting system for rainfall induced landslides. Such methods are easy-to-use and are based on statistical analyses. They can be derived without looking into the complex hydro-geological processes involved in slope failures, but are often associated with the disadvantage of higher false alarms, limiting their applications in a regional landslide early warning system (LEWS). This study is an attempt to improve the performance of conventional meteorological thresholds by considering the effect of soil moisture, using a probabilistic approach. Idukki district in southern part of India is highly susceptible to landslides and has witnessed major socio-economical setbacks in the recent disasters happened in 2018 and 2019. This tourist hub is now in need of a landslide forecasting system, which can help in landslide risk reduction. This study attempts to understand the effect of averaged soil moisture estimates derived from passive microwave remote sensing data, for improving the performance of conventional empirical and probabilistic thresholds. For defining empirical thresholds, an algorithm-based approach such as Calculation of Thresholds for Rainfall-induced Landslides Tool (CTRL-T) has been used. Probabilistic thresholds were defined using a Bayesian approach, finding the posterior probability of occurrence using the marginal and conditional probabilities of the control parameters along with the prior probability of occurrence of landslide. The derived rainfall thresholds were quantitatively compared with the Bayesian probabilistic threshold derived using rainfall severity and soil we...
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.
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.
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, Hafez, MG, Chu, Y-M & Mofijur, M 2021, 'Turbulent energy motion of fiber suspensions in a rotating frame', Alexandria Engineering Journal, vol. 60, no. 3, pp. 3345-3352.
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Turbulent flows play a major role in many fields of science and industry. Noticeable attention is seen on turbulent flows of suspending fibers because of the sensitivity of the electrical, thermal, and mechanical properties of the connecting fiber composites to the spatial configuration and orientation of fibers. The involvement of fibers in the turbulent flow greatly affects the turbulent energy. It is more influenced when the turbulent flow occurs in a rotating system. The effect of fibers on the turbulent energy in the rotating frame must therefore be investigated. For turbulent energy with fiber suspension, a mathematical model can be built in a rotating system that is very important to enhance the quality of industrial goods. This paper, therefore, develops a mathematical model for turbulent energy motion in a rotating frame with a fiber suspension. The model was formulated using the averaging procedure. The momentum equation for incompressible and viscous fluid turbulent flow was considered to develop the model. The turbulent energy motion of the fiber suspensions was presented in the rotating frame in second-order correlation tensors,, and, where all the tensors are the function of time, distance, and space coordinates.
Ahmed, SF, Liu, G, Mofijur, M, Azad, AK, Hazrat, MA & Chu, Y-M 2021, 'Physical and hybrid modelling techniques for earth-air heat exchangers in reducing building energy consumption: Performance, applications, progress, and challenges', Solar Energy, vol. 216, pp. 274-294.
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Noteworthy advancements are seen in developing the earth-air heat exchanger (EAHE) models in the past several decades to reduce building energy consumption. However, it is still an ongoing challenge in selecting and implementing the most suitable and appropriate EAHE modelling technique in buildings based on the climates, performance, and limitations of the techniques. Therefore, this paper aims to review the published research related to the physical, and hybrid EAHE modelling techniques used in buildings, and highlight the prospects, benefits, progress, and challenges of these techniques. This is the first study that comprehensively evidences the prospects and technical challenges caused by unmeasured disturbances, assumptions, or the uncertainties generated in experimental and numerical works of all EAHE modelling techniques. Nevertheless, this study found that hybrid modelling is more effective than physical models for accurate prediction. On the contrary, the hybrid models suffer from high complexity if EAHE operating conditions and all key parameters are considered during the model development. Regarding the generalization capability, the physical models offer improved performance followed by the hybrid models. A minimum number of training data is needed for developing physical models, whereas medium training data is required for the hybrid models. The outcome of this study also provides valuable information regarding the physical and hybrid EAHE modelling techniques to the scientists, researchers, and so on in adopting the most appropriate EAHE modelling technique for their climates.
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, SF, Rafa, N, Mofijur, M, Badruddin, IA, Inayat, A, Ali, MS, Farrok, O & Yunus Khan, TM 2021, 'Biohydrogen Production From Biomass Sources: Metabolic Pathways and Economic Analysis', Frontiers in Energy Research, vol. 9.
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The commercialization of hydrogen as a fuel faces severe technological, economic, and environmental challenges. As a method to overcome these challenges, microalgal biohydrogen production has become the subject of growing research interest. Microalgal biohydrogen can be produced through different metabolic routes, the economic considerations of which are largely missing from recent reviews. Thus, this review briefly explains the techniques and economics associated with enhancing microalgae-based biohydrogen production. The cost of producing biohydrogen has been estimated to be between $10 GJ-1 and $20 GJ−1, which is not competitive with gasoline ($0.33 GJ−1). Even though direct biophotolysis has a sunlight conversion efficiency of over 80%, its productivity is sensitive to oxygen and sunlight availability. While the electrochemical processes produce the highest biohydrogen (>90%), fermentation and photobiological processes are more environmentally sustainable. Studies have revealed that the cost of producing biohydrogen is quite high, ranging between $2.13 kg−1 and 7.24 kg−1via direct biophotolysis, $1.42kg−1 through indirect biophotolysis, and between $7.54 kg−1 and 7.61 kg−1via fermentation. Therefore, low-cost hydrogen production technologies need to be developed to ensure long-term sustainability which requires the optimization of critical experimental parameters, microalgal metabolic engineering, and genetic modification.
Ahmed, SF, Saha, SC, Debnath, JC, Liu, G, Mofijur, M, Baniyounes, A, Chowdhury, SMEK & Vo, D-VN 2021, 'Data-driven modelling techniques for earth-air heat exchangers to reduce energy consumption in buildings: a review', Environmental Chemistry Letters, vol. 19, no. 6, pp. 4191-4210.
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Increasing population and urbanization call for smarter cities where the cycles of matter and energy are optimized, notably in buildings which are actually a source of pollution consuming a lot of energy. The efficiency of building energy has been improved by modelling earth-air heat exchangers, yet selecting the suitable models is challenging. Here we review data-driven earth-air heat exchanger models used for buildings. We discuss issues brought about by assumptions, unmeasured disruptions, and uncertainties in numerical and experimental works. We found that high accuracy can be reached if sufficient data is available. Models are appropriate for real-time activity due to their structure simplicity, yet they display a poor generalization capacity. Model development is limited by the constrained parameters and the complex boundary conditions of the heat exchangers.
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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Alam, MA, Muhammad, G, Khan, MN, Mofijur, M, Lv, Y, Xiong, W & Xu, J 2021, 'Choline chloride-based deep eutectic solvents as green extractants for the isolation of phenolic compounds from biomass', Journal of Cleaner Production, vol. 309, pp. 127445-127445.
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Bioresource valorization to obtain valuable phenolic compounds for medicinal, nutraceutical, food, and cosmetic applications are critical for a current and future sustainable and bio-based economy. Renewable, environmentally friendly, and non-toxic choline chloride-based deep eutectic solvents are the newest and utmost environmentally friendly alternatives to conventional organic solvents for the pretreatment and extraction of phenolic compounds. Recently, numerous studies have focused on phenolic compound extraction using choline chloride-based deep eutectic solvents as solvents or catalysts. Process variable optimization has been reported in terms of kinetic modeling and mechanisms involved in phenolic compounds extraction. This paper describes the cutting-edge methods used to extract phenolic compounds from different bio-based sources using choline chloride-based deep eutectic solvents. In addition, the factors affecting, kinetic models, and mechanisms involved in phenolic compound extraction using choline chloride-based deep eutectic solvents are thoroughly summarized. Moreover, future predictions, challenges, and anticipated growth in this field are addressed and can be used for biomass valorization.
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-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, 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.
Alkalbani, AM & Hussain, W 2021, 'Cloud service discovery method: A framework for automatic derivation of cloud marketplace and cloud intelligence to assist consumers in finding cloud services', International Journal of Communication Systems, vol. 34, no. 8, pp. 1-17.
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SummaryThe increase in the number of cloud services advertisements, needs for cloud services marketplace to enable significant interaction with cloud consumers. Majority of the existing literature has focused on developing algorithms (such as matching algorithms) and assumed the availability of cloud service information. Furthermore, little attention is given to the efficient discovery of cloud services over the internet. Existing approaches unable to describe a user‐friendly method of harvesting related cloud services from the web. Moreover, the existing literature lacks a comprehensive ontology to represent cloud services and a registry for cloud services publication and discovery. The incomplete information prevents discovering accurate services and deriving intelligence from cloud reviews data. The paper presents a framework for automatic derivation of cloud marketplace and cloud intelligence (ADCM&CI) that assist cloud consumers for an effective and efficient cloud service discovery. The framework depends on the capabilities of the Harvester as a Service (HaaS) crawler that provides a user‐friendly interface to extract real‐time cloud dataset. The paper used Protégé OWL a domain‐specific ontology to extract meaningful data from a semi‐structured repository and transform to SaaS ads attribute. The framework conducts sentimental analysis to excerpt the polarity of reviews that assist potential consumers in service selection. The paper considers three measures—precision, recall and F Score as a benchmark and evaluates the accuracy of the proposed approach using machine learning methods—SVM, KNN, Decision Tree and Naïve Bayes algorithms. Through experiments, we validate and demonstrate the suitability of the proposed framework for an effective and efficient cloud service discovery.
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...
Altulyan, M, Yao, L, Huang, C, Wang, X & Kanhere, SS 2021, 'Context-Induced Activity Monitoring for On-Demand Things-of-Interest Recommendation in an Ambient Intelligent Environment', Future Internet, vol. 13, no. 12, pp. 305-305.
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Recommendation systems are crucial in the provision of services to the elderly with Alzheimer’s disease in IoT-based smart home environments. In this work, a Reminder Care System (RCS) is presented to help Alzheimer patients live in and operate their homes safely and independently. A contextual bandit approach is utilized in the formulation of the proposed recommendation system to tackle dynamicity in human activities and to construct accurate recommendations that meet user needs without their feedback. The system was evaluated based on three public datasets using a cumulative reward as a metric. Our experimental results demonstrate the feasibility and effectiveness of the proposed Reminder Care System for real-world IoT-based smart home applications.
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.
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.
Andriulli, F 2021, 'We Look With Hope Toward the New Year! [Editor’s Comments]', IEEE 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.
Arqam, M, Dao, DV, Mitchell, M & Woodfield, P 2021, 'Transient start-up of an electric swashplate refrigeration compressor', Applied Thermal Engineering, vol. 196, pp. 117351-117351.
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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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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.
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.
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.
Balogun, A-L, Yekeen, ST, Pradhan, B & Wan Yusof, KB 2021, 'Oil spill trajectory modelling and environmental vulnerability mapping using GNOME model and GIS', Environmental Pollution, vol. 268, no. Pt A, pp. 115812-115812.
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This study develops an oil spill environmental vulnerability model for predicting and mapping the oil slick trajectory pattern in Kota Tinggi, Malaysia. The impact of seasonal variations on the vulnerability of the coastal resources to oil spill was modelled by estimating the quantity of coastal resources affected across three climatic seasons (northeast monsoon, southwest monsoon and pre-monsoon). Twelve 100 m3 (10,000 splots) medium oil spill scenarios were simulated using General National Oceanic and Atmospheric Administration Operational Oil Modeling Environment (GNOME) model. The output was integrated with coastal resources, comprising biological, socio-economic and physical shoreline features. Results revealed that the speed of an oil slick (40.8 m per minute) is higher during the pre-monsoon period in a southwestern direction and lower during the northeast monsoon (36.9 m per minute). Evaporation, floating and spreading are the major weathering processes identified in this study, with approximately 70% of the slick reaching the shoreline or remaining in the water column during the first 24 h (h) of the spill. Oil spill impacts were most severe during the southwest monsoon, and physical shoreline resources are the most vulnerable to oil spill in the study area. The study concluded that variation in climatic seasons significantly influence the vulnerability of coastal resources to marine oil spill.
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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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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Begum, M, Eskandari, M, Abuhilaleh, M, Li, L & Zhu, J 2021, 'Fuzzy-Based Distributed Cooperative Secondary Control with Stability Analysis for Microgrids', Electronics, vol. 10, no. 4, pp. 399-399.
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This research suggests a novel distributed cooperative control methodology for a secondary controller in islanded microgrids (MGs). The proposed control technique not only brings back the frequency/voltage to its reference values, but also maintains precise active and reactive power-sharing among distributed generation (DG) units by means of a sparse communication system. Due to the dynamic behaviour of distributed secondary control (DSC), stability issues are a great concern for a networked MG. To address this issue, the stability analysis is undertaken systematically, utilizing the small-signal state-space linearized model of considering DSC loops and parameters. As the dynamic behaviour of DSC creates new oscillatory modes, an intelligent fuzzy logic-based parameter-tuner is proposed for enhancing the system stability. Accurate tuning of the DSC parameters can develop the functioning of the control system, which increases MG stability to a greater extent. Moreover, the performance of the offered control method is proved by conducting a widespread simulation considering several case scenarios in MATLAB/Simscape platform. The proposed control method addresses the dynamic nature of the MG by supporting the plug-and-play functionality, and working even in fault conditions. Finally, the convergence and comparison study of the offered control system is shown.
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.
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.
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% (...
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...
C. Saha, S, M. Sefidan, A, Sojoudi, A & M. Molla, M 2021, 'Transient Free Convection and Heat Transfer in a Partitioned Attic-Shaped Space under Diurnal Thermal Forcing', Energy Engineering, vol. 118, no. 3, pp. 487-506.
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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, 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‐C 3 N 4 photocatalyst for enhancing photocatalytic activity', Micro & Nano Letters, vol. 16, no. 1, pp. 77-82.
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Graphitic 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, B, Guo, R, Yu, S & Yu, Y 2021, 'An active noise control method of non-stationary noise under time-variant secondary path', Mechanical Systems and Signal Processing, vol. 149, pp. 107193-107193.
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Active noise control (ANC) provides an efficient way for eliminating low-frequency noise, whereas accurate controlling non-stationary noise of rotating machinery under time-variant secondary path is still a major challenge. To address this, a novel ANC method based on signal decomposition and simultaneous perturbation is developed in this paper. In the proposed method, an indicator of periodic characteristic (IPC) with the degree of cyclostationarity is first presented to evaluate reference noise. Subsequently, a real-time strategy of the IPC combined with fast empirical mode decomposition is designed to adaptively decompose reference noise into a series of intrinsic mode functions, which are stationary and further controlled individually. Besides, the simultaneous perturbation is applied to update the controller weights for having a better tracking ability under the time-variant secondary path. Experiments were conducted to evaluate the capacity of the proposed method for controlling non-stationary reference noise under the time-variant secondary path. The results demonstrate that proposed method is capable of performing better than commonly used algorithms.
Chen, C, Liu, B, Liu, Y, Liao, J, Shan, X, Wang, F & Jin, D 2021, 'Heterochromatic Nonlinear Optical Responses in Upconversion Nanoparticles for Super‐Resolution Nanoscopy', Advanced Materials, vol. 33, no. 23, pp. e2008847-2008847.
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AbstractPoint spread function (PSF) engineering by an emitter's response can code higher‐spatial‐frequency information of an image for microscopy to achieve super‐resolution. However, complexed excitation optics or repetitive scans are needed, which explains the issues of low speed, poor stability, and operational complexity associated with the current laser scanning microscopy approaches. Here, the diverse emission responses of upconversion nanoparticles (UCNPs) are reported for super‐resolution nanoscopy to improve the imaging quality and speed. The method only needs a doughnut‐shaped scanning excitation beam at an appropriate power density. By collecting the four‐photon emission of single UCNPs, the high‐frequency information of a super‐resolution image can be resolved through the doughnut‐emission PSF. Meanwhile, the two‐photon state of the same nanoparticle is oversaturated, so that the complementary lower‐frequency information of the super‐resolution image can be simultaneously collected by the Gaussian‐like emission PSF. This leads to a method of Fourier‐domain heterochromatic fusion, which allows the extended capability of the engineered PSFs to cover both low‐ and high‐frequency information to yield optimized image quality. This approach achieves a spatial resolution of 40 nm, 1/24th of the excitation wavelength. This work suggests a new scope for developing nonlinear multi‐color emitting probes in super‐resolution nanoscopy.
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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This 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 0.23λ 0 × 0.081λ 0 × 0.067λ 0. 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-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, 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, 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, X, Wu, K, Bai, A, Masuku, CM, Niederberger, J, Liporace, FS & Biegler, LT 2021, 'Real-time refinery optimization with reduced-order fluidized catalytic cracker model and surrogate-based trust region filter method', Computers & Chemical Engineering, vol. 153, pp. 107455-107455.
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Chen, X, Zhang, T, Shen, S, Zhu, T & Xiong, P 2021, 'An optimized differential privacy scheme with reinforcement learning in VANET', Computers & Security, vol. 110, pp. 102446-102446.
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The protection of vehicle trajectory in Vehicular ad hoc network is facing many challenges. Among these challenges, one of the most critical issues is to keep the balance between geographical location protection and semantic location protection. Traditional trajectory protection schemes either only focus on geographical location protection or only semantic location protection. Moreover, when trajectory privacy protection is carried out, each location is often given the same protection. This may lead to sensitive locations under insufficient protection and unimportant locations under overprotection. In this paper, based on differential privacy, we propose an optimized privacy differential privacy scheme with reinforcement learning in vehicular ad hoc network. The proposed scheme can dynamically optimize the privacy budget allocation for each location on the vehicle trajectory to reach a better balance between geolocation obfuscation and semantic security. Experiments results demonstrate that the proposed scheme can reduce the risk of geographical and semantic location leakage, and therefore ensure the balance between the utility and privacy.
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, 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.
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).
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.
Daviran, M, Maghsoudi, A, Ghezelbash, R & Pradhan, B 2021, 'A new strategy for spatial predictive mapping of mineral prospectivity: Automated hyperparameter tuning of random forest approach', Computers & Geosciences, vol. 148, pp. 104688-104688.
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Machine learning algorithms (e.g., random forest (RF)) have recently been performed in data-driven mineral prospectivity mapping. These methods are highly sensitive to hyperparameter values, since the predictive accuracy of them can significantly increase when the optimized hyperparameters are predefined and then adjusted to training procedure. The main goal of this contribution is to propose a hybrid genetic-based RF model, namely GRF, which is able to automatically adjust the optimized hyperparameters of RF with the excellent predictive accuracy. Therefore, three primary parameters of RF comprising N , N and d, were well-tuned employing genetic algorithm (GA) in establishing an efficient RF model. The proposed GRF model and also conventional RF were tested on mineralization-related geo-spatial dataset and the predictive models were generated for comparing the accuracy of the proposed GRF model with that of RF. The input dataset (e.g., multi-element geochemical signature, geological-structural layer and hydrothermal alteration evidences) which acquired from Feizabad district, NE Iran, were translated into mappable targeting criteria in the form of four predictor maps. In addition, the locations of 13 known Cu–Au deposits as prospect data and the locations of 13 randomly selected non-prospect data were used as target variables to train the models. Three authentic validation measures, K-fold cross-validation, confusion matrix and success-rate curves, were employed to evaluate the overall performance of two predictive models. Experimental results suggested the superiority of GRF model over the RF, as the favorable areas derived by GRF model occupy only 9% of the study area while predicting 100% of the known deposits. T S
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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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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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.
Dikshit, A, Pradhan, B & Huete, A 2021, 'An improved SPEI drought forecasting approach using the long short-term memory neural network', Journal of Environmental Management, vol. 283, pp. 111979-111979.
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Droughts are slow-moving natural hazards that gradually spread over large areas and capable of extending to continental scales, leading to severe socio-economic damage. A key challenge is developing accurate drought forecast model and understanding a models' capability to examine different drought characteristics. Traditionally, forecasting techniques have used various time-series approaches and machine learning models. However, the use of deep learning methods have not been tested extensively despite its potential to improve our understanding of drought characteristics. The present study uses a deep learning approach, specifically the Long Short-Term Memory (LSTM) to predict a commonly used drought measure, the Standard Precipitation Evaporation Index (SPEI) at two different time scales (SPEI 1, SPEI 3). The model was compared with other common machine learning method, Random Forests, Artificial Neural Networks and applied over the New South Wales (NSW) region of Australia, using hydro-meteorological variables as predictors. The drought index and predictor data were collected from the Climatic Research Unit (CRU) dataset spanning from 1901 to 2018. We analysed the LSTM forecasted results in terms of several drought characteristics (drought intensity, drought category, or spatial variation) to better understand how drought forecasting was improved. Evaluation of the drought intensity forecasting capabilities of the model were based on three different statistical metrics, Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The model achieved R2 value of more than 0.99 for both SPEI 1 and SPEI 3 cases. The variation in drought category forecasted results were studied using a multi-class Receiver Operating Characteristic based Area under Curves (ROC-AUC) approach. The analysis revealed an AUC value of 0.83 and 0.82 for SPEI 1 and SPEI 3 respectively. The spatial variation between observed a...
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, 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.
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.
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.
Dong, X, Yang, Y, Wei, S-E, Weng, X, Sheikh, Y & Yu, S-I 2021, 'Supervision by Registration and Triangulation for Landmark Detection'.
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We present Supervision by Registration and Triangulation (SRT), anunsupervised approach that utilizes unlabeled multi-view video to improve theaccuracy and precision of landmark detectors. Being able to utilize unlabeleddata enables our detectors to learn from massive amounts of unlabeled datafreely available and not be limited by the quality and quantity of manual humanannotations. To utilize unlabeled data, there are two key observations: (1) thedetections of the same landmark in adjacent frames should be coherent withregistration, i.e., optical flow. (2) the detections of the same landmark inmultiple synchronized and geometrically calibrated views should correspond to asingle 3D point, i.e., multi-view consistency. Registration and multi-viewconsistency are sources of supervision that do not require manual labeling,thus it can be leveraged to augment existing training data during detectortraining. End-to-end training is made possible by differentiable registrationand 3D triangulation modules. Experiments with 11 datasets and a newly proposedmetric to measure precision demonstrate accuracy and precision improvements inlandmark detection on both images and video. Code is available athttps://github.com/D-X-Y/landmark-detection.
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.
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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Ejaz, A, Babar, H, Ali, HM, Jamil, F, Janjua, MM, Fattah, IMR, Said, Z & Li, C 2021, 'Concentrated photovoltaics as light harvesters: Outlook, recent progress, and challenges', Sustainable Energy Technologies and Assessments, vol. 46, pp. 101199-101199.
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Concentrated photovoltaics (CPV) is a dawn technology in the field of photovoltaic that helps in escalating the effective use of solar energy. Nowadays, applications of photovoltaic solar cells are catching attention due to the better utilization of solar energy. A huge amount of solar energy is received by the earth from the sun, but a barrier to the large-scale use of photovoltaic solar cells is their higher initial cost and lower conversion compared to other non-renewable energy systems. Concentrated Photovoltaics (CPV) is one of the vital tools that focus solar radiation on the small area of solar cells using optical devices to maximize solar to thermal conversion. Low cost, high efficiency, and climate-friendly are the main advantages of concentrated photovoltaics. The review study presents the outlook of work conducted worldwide on the different types of concentrated photovoltaics. In addition, the effect of various performance affecting parameters, challenges, and recent progress is also part of the study. Most of the CPV have efficiency up to 15% while some have an efficiency range of 25–28% which is still very low. It was found that the CPV gave maximum efficiency of up to 38.5% at optimal solar radiation. The focus of sunlight on a small area of solar cell increases the temperature of concentrated photovoltaic allegedly pernicious for electrical efficiency and the life of CPV. Factors like direct normal irradiance, high cell temperature, soiling, optical design, reliability, and durability are considered as challenges and a concise summary of various studies on these challenges is presented. In this regard, various cooling techniques have been investigated by different researchers for thermal management of CPV systems which are discussed in detail. As CPV technology is still in the development phase, various new optical designs emphasizing novel designs and materials are also summarized in the current study. Finally, some recommendations are o...
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.
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.
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.
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.
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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Gandomi, M, Kashani, AR, Farhadi, A, Akhani, M & Gandomi, AH 2021, 'Spectral acceleration prediction using genetic programming based approaches', Applied Soft Computing, vol. 106, pp. 107326-107326.
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Evolutionary computation (EC) is a widely used computational intelligence that facilitates the formulation of a range of complex engineering problems. This study tackled two hybrid EC techniques based on genetic programming (GP) for ground motion prediction equations (GMPEs). The first method coupled regression analysis with multi-objective genetic programming. In this way, the strategy was maximizing the accuracy and minimizing the models’ complexity simultaneously. The second approach incorporated mesh adaptive direct search (MADS) into gene expression programming to optimize the obtained coefficients. A big data set provided by the Pacific Earthquake Engineering Research Centre (PEER) was used for the model development. Two explicit formulations were developed during this effort. In those formulae, we correlated spectral acceleration to a set of seismological parameters, including the period of vibration, magnitude, the closest distance to the fault ruptured area, shear wave velocity averaged over the top 30 meters, and style of faulting. The GP-based models are verified by a comprehensive comparison with the most well-known methods for GMPEs. The results show that the proposed models are quite simple and straightforward. The high degrees of accuracy of the predictions are competitive with the NGA complex models. Correlations of the predicted data using GEP-MADs and MOGP-R models with the real observations seem to be better than those available in the literature. Three statistical measures for GMPEs, such as E (%), LLH, and EDR index, confirmed those observations.
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...
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, Wang, L, Luo, Z & Gao, L 2021, 'IgaTop: an implementation of topology optimization for structures using IGA in MATLAB', Structural and Multidisciplinary Optimization, vol. 64, no. 3, pp. 1669-1700.
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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, L, Liu, G, Zamyadi, A, Wang, Q & Li, M 2021, 'Life-cycle cost analysis of a hybrid algae-based biological desalination – low pressure reverse osmosis system', Water Research, vol. 195, pp. 116957-116957.
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To fully understand the economic viability and implementation strategy of the emerging algae-based desalination technology, this study investigates the economic aspects of algae-based desalination system by comparing the life-cycle costs of three different scenarios: (1) a multi-stage microalgae based desalination system; (2) a hybrid desalination system based on the combination of microalgae and low pressure reverse osmosis (LPRO) system; and (3) a seawater reverse osmosis (SWRO) desalination system. It is identified that the capital expenditure (CAPEX) and operational expenditure (OPEX) of scenario 1 are significantly higher than those of scenarios 2 and 3, when algal biomass reuse is not taken into consideration. If the revenues obtained from the algal biomass reuse are taken into account, the OPEX of scenario 1 will decrease significantly, and scenarios 2 and 3 will have the highest and lowest OPEX, respectively. However, due to the high CAPEX of scenario 1, the total expenditure (TOTEX) of scenario 1 is still 27% and 33% higher than those of scenarios 2 and 3, respectively. A sensitivity study is undertaken to understand the effects of six key parameters on water total cost for different scenarios. It is suggested that the electricity unit price plays the most important role in determining the water total cost for different scenarios. An uncertainty analysis is also conducted to investigate the effects and limitations of the key assumptions made in this study. It is suggested that the assumption of total dissolved solids (TDS) removal efficiency of microalgae results in a high uncertainty of life-cycle cost analysis (LCCA). Additionally, it is estimated that 1.58 megaton and 0.30 megaton CO2 can be captured by the algae-based desalination process for scenarios 1 and 2, respectively, over 20 years service period, which could result in approximately AU $18 million and AU $3 million indirect financial benefits for scenarios 1 and 2, respectively. When...
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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Garcia Marin, J, Biloria, N, Robertson, H & Fornes, M 2021, 'Urban Health and Wellbeing in the Contemporary City', HealthManagement, vol. 21, no. 6.
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This paper explores and debates the intricate connection between our built environment and an increasingly technocentric approach to distinguish health and wellbeing from a multidisciplinary perspective. The authors profess the dire need for rethinking the ‘smart’ within the city by reconsidering models of urban development and focusing on the democratisation of technology for the purpose of enhancing our lived urban experience and psychophysiological wellbeing.
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.
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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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.
Geng, L, Lu, Z, Guo, X, Zhang, J, Li, X & He, L 2021, 'Coordinated operation of coupled transportation and power distribution systems considering stochastic routing behaviour of electric vehicles and prediction error of travel demand', IET Generation, Transmission & Distribution, vol. 15, no. 14, pp. 2112-2126.
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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.
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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Gharehbaghi, S, Gandomi, M, Plevris, V & Gandomi, AH 2021, 'Prediction of seismic damage spectra using computational intelligence methods', Computers & Structures, vol. 253, pp. 106584-106584.
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Predicting seismic damage spectra, capturing both structural and earthquake features, is useful in performance-based seismic design and quantifying the potential seismic damage of structures. The objective of this paper is to accurately predict the seismic damage spectra using computational intelligence methods. For this purpose, an inelastic single-degree-of-freedom system subjected to a set of earthquake ground motion records is used to compute the (exact) spectral damage. The Park-Ang damage index is used to quantify the seismic damage. Both structural and earthquake features are involved in the prediction models where multi-gene genetic programming (MGGP) and artificial neural networks (ANNs) are applied. Common performance metrics were used to assess the models developed for seismic damage spectra, and indicated that their accuracy was higher than a corresponding model in the literature. Although the performance metrics revealed that the ANN model is more accurate than the MGGP model, the explicit MGGP-based mathematical model renders it more practical in quantifying the potential seismic damage of structures.
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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This 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.
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, 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, Q, Huang, Y, Luo, Y, Liu, P, Xu, M & Yang, Y 2021, 'Discriminative Feature Learning for Thorax Disease Classification in Chest X-ray Images', IEEE Transactions on Image Processing, vol. 30, pp. 2476-2487.
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This paper focuses on the thorax disease classification problem in chest X-ray (CXR) images. Different from the generic image classification task, a robust and stable CXR image analysis system should consider the unique characteristics of CXR images. Particularly, it should be able to: 1) automatically focus on the disease-critical regions, which usually are of small sizes; 2) adaptively capture the intrinsic relationships among different disease features and utilize them to boost the multi-label disease recognition rates jointly. In this paper, we propose to learn discriminative features with a two-branch architecture, named ConsultNet, to achieve those two purposes simultaneously. ConsultNet consists of two components. First, an information bottleneck constrained feature selector extracts critical disease-specific features according to the feature importance. Second, a spatial-and-channel encoding based feature integrator enhances the latent semantic dependencies in the feature space. ConsultNet fuses these discriminative features to improve the performance of thorax disease classification in CXRs. Experiments conducted on the ChestX-ray14 and CheXpert dataset demonstrate the effectiveness of the proposed method.
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.
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, 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, 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, 'Corrections to “Design Optimization of Linear-Rotary Motion Permanent Magnet Generator With E-Shaped Stator” [Nov 21 Art. no. 0600705]', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-1.
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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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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, Y, Gu, P, Gao, W, Xu, G & Wu, J 2021, 'Aspect-level sentiment capsule network for micro-video click-through rate prediction', World Wide Web, vol. 24, no. 4, pp. 1045-1064.
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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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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 Elevatio