Abd Elaziz, M, Almodfer, R, Ahmadianfar, I, Ibrahim, IA, Mudhsh, M, Abualigah, L, Lu, S, Abd El-Latif, AA & Yousri, D 2022, 'Static models for implementing photovoltaic panels characteristics under various environmental conditions using improved gradient-based optimizer', Sustainable Energy Technologies and Assessments, vol. 52, pp. 102150-102150.
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Abdollahi Lorestani, M, Ashtarinakhaei, S, Abolhasan, M, Shariati, N, Lipman, J, Jamalipour, A & Ni, W 2022, 'Dynamic Routing Protocol Selection in Multi-hop Device-to-Device Wireless Networks', IEEE Transactions on Vehicular Technology, pp. 1-1.
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Abdollahi, A, Pradhan, B & Alamri, A 2022, 'SC-RoadDeepNet: A New Shape and Connectivity-Preserving Road Extraction Deep Learning-Based Network From Remote Sensing Data', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15.
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Abedin, B 2022, 'Managing the tension between opposing effects of explainability of artificial intelligence: a contingency theory perspective', Internet Research, vol. 32, no. 2, pp. 425-453.
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PurposeResearch into the interpretability and explainability of data analytics and artificial intelligence (AI) systems is on the rise. However, most recent studies either solely promote the benefits of explainability or criticize it due to its counterproductive effects. This study addresses this polarized space and aims to identify opposing effects of the explainability of AI and the tensions between them and propose how to manage this tension to optimize AI system performance and trustworthiness.Design/methodology/approachThe author systematically reviews the literature and synthesizes it using a contingency theory lens to develop a framework for managing the opposing effects of AI explainability.FindingsThe author finds five opposing effects of explainability: comprehensibility, conduct, confidentiality, completeness and confidence in AI (5Cs). The author also proposes six perspectives on managing the tensions between the 5Cs: pragmatism in explanation, contextualization of the explanation, cohabitation of human agency and AI agency, metrics and standardization, regulatory and ethical principles, and other emerging solutions (i.e. AI enveloping, blockchain and AI fuzzy systems).Research limitations/implicationsAs in other systematic literature review studies, the results are limited by the content of the selected papers.Practical implicationsThe findings show how AI owners and developers can manage tensions between profitability, prediction accuracy and system performance via visibility, accountabil...
Aboutorab, H, Hussain, OK, Saberi, M & Hussain, FK 2022, 'A reinforcement learning-based framework for disruption risk identification in supply chains', Future Generation Computer Systems, vol. 126, pp. 110-122.
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Abraham, MT, Satyam, N, Pradhan, B & Tian, H 2022, 'Debris flow simulation 2D (DFS 2D): Numerical modelling of debris flows and calibration of friction parameters', Journal of Rock Mechanics and Geotechnical Engineering.
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Abraham, MT, Satyam, N, Pradhan, B, Segoni, S & Alamri, A 2022, 'Developing a prototype landslide early warning system for Darjeeling Himalayas using SIGMA model and real-time field monitoring', Geosciences Journal, vol. 26, no. 2, pp. 289-301.
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Abualigah, L, Elaziz, MA, Khasawneh, AM, Alshinwan, M, Ibrahim, RA, Al-qaness, MAA, Mirjalili, S, Sumari, P & Gandomi, AH 2022, 'Meta-heuristic optimization algorithms for solving real-world mechanical engineering design problems: a comprehensive survey, applications, comparative analysis, and results', Neural Computing and Applications, vol. 34, no. 6, pp. 4081-4110.
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Abualigah, L, Elaziz, MA, Sumari, P, Geem, ZW & Gandomi, AH 2022, 'Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer', Expert Systems with Applications, vol. 191, pp. 116158-116158.
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Abualigah, L, Elaziz, MA, Sumari, P, Khasawneh, AM, Alshinwan, M, Mirjalili, S, Shehab, M, Abuaddous, HY & Gandomi, AH 2022, 'Black hole algorithm: A comprehensive survey', Applied Intelligence, vol. 52, no. 10, pp. 11892-11915.
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Adhikari, S, Thapa, S, Naseem, U, Singh, P, Huo, H, Bharathy, G & Prasad, M 2022, 'Exploiting linguistic information from Nepali transcripts for early detection of Alzheimer's disease using natural language processing and machine learning techniques', International Journal of Human-Computer Studies, vol. 160, pp. 102761-102761.
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Adibi, T, Razavi, SE, Ahmed, SF, Amrikachi, A & Saha, SC 2022, 'Characteristic-Based Fluid Flow Modeling between Two Eccentric Cylinders in Laminar and Turbulent Regimes', Geofluids, vol. 2022, pp. 1-9.
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Determining the flow between eccentric cylinders is crucial in a wide range of industries. The governing equations for the flow between eccentric cylinders cannot be solved analytically. Therefore, three-dimensional incompressible viscous fluid flow between eccentric and concentric cylinders has numerically been simulated in this paper to investigate them using a characteristic-based approach. The first-order characteristic-based scheme is used to calculate convective terms, whereas the second-order averaging technique is used to calculate viscous fluxes. The Taylor number, eccentricity distance, Reynolds number, and radius ratio are considered the controlling parameters of fluid flow between the cylinders. The influence of flow between cylinders on flow patterns is presented in terms of velocity, pressure, and flow contours. It is found that at a constant Taylor number, the asymmetric centrifugal forces produce the Taylor vortices on the right of the internal rotating cylinder as the eccentric distance increases. When the eccentric distance increases, the magnitude of shear stress and its fluctuation on the cylinder wall, as well as the pressure on the cylinder wall, rise. The numerical results obtained were validated by comparing them to previously published experimental results, which showed a high level of agreement.
Adibi, T, Sojoudi, A & Saha, SC 2022, 'Modeling of thermal performance of a commercial alkaline electrolyzer supplied with various electrical currents', International Journal of Thermofluids, vol. 13, pp. 100126-100126.
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Aditya, L, Mahlia, TMI, Nguyen, LN, Vu, HP & Nghiem, LD 2022, 'Microalgae-bacteria consortium for wastewater treatment and biomass production', Science of The Total Environment, vol. 838, pp. 155871-155871.
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Afroz, F, Braun, R & Chaczko, Z 2022, 'XX-MAC and EX-MAC: Two Variants of X-MAC Protocol for Low Power Wireless Sensor Networks', Ad-Hoc and Sensor Wireless Networks, vol. 51, no. 4, pp. 285-314.
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The strobed preamble approach introduced in the X-MAC protocol minimises long preamble duration, overhearing, and per-hop latency in conventional wireless sensor networks (WSNs). However, it incurs high per-packet overhead and extra delay under high traffic scenarios as it operates only in the unsynchronised state. In this paper, we model a variant of X-MAC, namely XX-MAC, which employs an adaptive dutycycling algorithm to address this issue in low data rate WSNs with short, fixed inter-packet arrival time. Furthermore, we identify the shortcoming of XX-MAC as well as propose a request-based MAC protocol, namely EX-MAC, targeting WSNs in dynamic traffic scenarios. Simulations show that at optimum slot duration, XX-MAC reduces the per-packet delay by 13.53% and 48.86% than the delay experienced by X-MAC and B-MAC, respectively. XX-MAC, on average, can deliver 92.5% of packets to the receiver, whereas X-MAC and B-MAC respectively support 91.66% and 82.91% packet delivery. XX-MAC also reduces the energy consumption per received packet by 2.61% than X-MAC, and by 65.31% than the B-MAC protocol. Experimental results also demonstrate that under variable traffic conditions, EX-MAC offers the lowest packet loss (8.55%), whilst XX-MAC and X-MAC experience 13.1% and 18.3% packet loss, respectively. EX-MAC decreases per-packet network energy consumption (3.056mJ/packet) compared with XX-MAC (3.107mJ/ packet) and X-MAC (3.424mJ/packet). Furthermore, EX-MAC minimises the mean delay per received packet by 5.758% and 10.457% (approximately) than that of XX-MAC and X-MAC, respectively.
Afroz, S, Zhang, Y, Nguyen, QD, Kim, T & Castel, A 2022, 'Effect of limestone in General Purpose cement on autogenous shrinkage of high strength GGBFS concrete and pastes', Construction and Building Materials, vol. 327, pp. 126949-126949.
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Afsari, M, Ghorbani, AH, Asghari, M, Shon, HK & Tijing, LD 2022, 'Computational fluid dynamics simulation study of hypersaline water desalination via membrane distillation: Effect of membrane characteristics and operational parameters', Chemosphere, vol. 305, pp. 135294-135294.
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Afzal, MU, Esselle, KP & Koli, MNY 2022, 'A Beam-Steering Solution With Highly Transmitting Hybrid Metasurfaces and Circularly Polarized High-Gain Radial-Line Slot Array Antennas', IEEE Transactions on Antennas and Propagation, vol. 70, no. 1, pp. 365-377.
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Agarwal, A, Leslie, WD, Nguyen, TV, Morin, SN, Lix, LM & Eisman, JA 2022, 'Performance of the Garvan Fracture Risk Calculator in Individuals with Diabetes: A Registry-Based Cohort Study', Calcified Tissue International, vol. 110, no. 6, pp. 658-665.
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Agarwal, A, Leslie, WD, Nguyen, TV, Morin, SN, Lix, LM & Eisman, JA 2022, 'Predictive performance of the Garvan Fracture Risk Calculator: a registry-based cohort study', Osteoporosis International, vol. 33, no. 3, pp. 541-548.
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Agrawal, D, Minocha, S, Namasudra, S & Gandomi, AH 2022, 'A robust drug recall supply chain management system using hyperledger blockchain ecosystem', Computers in Biology and Medicine, vol. 140, pp. 105100-105100.
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Ahmad, FB, Kalam, MA, Zhang, Z & Masjuki, HH 2022, 'Sustainable production of furan-based oxygenated fuel additives from pentose-rich biomass residues', Energy Conversion and Management: X, vol. 14, pp. 100222-100222.
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Ahmadianfar, I, Heidari, AA, Noshadian, S, Chen, H & Gandomi, AH 2022, 'INFO: An efficient optimization algorithm based on weighted mean of vectors', Expert Systems with Applications, vol. 195, pp. 116516-116516.
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Ahmed, SF, Kumar, PS, Rozbu, MR, Chowdhury, AT, Nuzhat, S, Rafa, N, Mahlia, TMI, Ong, HC & Mofijur, M 2022, 'Heavy metal toxicity, sources, and remediation techniques for contaminated water and soil', Environmental Technology & Innovation, vol. 25, pp. 102114-102114.
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Ahmed, SF, Mofijur, M, Chowdhury, SN, Nahrin, M, Rafa, N, Chowdhury, AT, Nuzhat, S & Ong, HC 2022, 'Pathways of lignocellulosic biomass deconstruction for biofuel and value-added products production', Fuel, vol. 318, pp. 123618-123618.
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Ahmed, SF, Mofijur, M, Islam, N, Parisa, TA, Rafa, N, Bokhari, A, Klemeš, JJ & Indra Mahlia, TM 2022, 'Insights into the development of microbial fuel cells for generating biohydrogen, bioelectricity, and treating wastewater', Energy, vol. 254, pp. 124163-124163.
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Ahmed, SF, Mofijur, M, Parisa, TA, Islam, N, Kusumo, F, Inayat, A, Le, VG, Badruddin, IA, Khan, TMY & Ong, HC 2022, 'Progress and challenges of contaminate removal from wastewater using microalgae biomass', Chemosphere, vol. 286, pp. 131656-131656.
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Ahmed, SF, Mofijur, M, Rafa, N, Chowdhury, AT, Chowdhury, S, Nahrin, M, Islam, ABMS & Ong, HC 2022, 'Green approaches in synthesising nanomaterials for environmental nanobioremediation: Technological advancements, applications, benefits and challenges', Environmental Research, vol. 204, pp. 111967-111967.
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Akbarzadeh, M, Oberst, S, Sepehrirahnama, S & Halkon, B 2022, 'Acoustic radiation force-induced push-pull particle oscillations', Journal of the Acoustical Society of America.
Akter, N, Fletcher, J, Perry, S, Simunovic, MP, Briggs, N & Roy, M 2022, 'Glaucoma diagnosis using multi-feature analysis and a deep learning technique', Scientific Reports, vol. 12, no. 1.
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AbstractIn this study, we aimed to facilitate the current diagnostic assessment of glaucoma by analyzing multiple features and introducing a new cross-sectional optic nerve head (ONH) feature from optical coherence tomography (OCT) images. The data (n = 100 for both glaucoma and control) were collected based on structural, functional, demographic and risk factors. The features were statistically analyzed, and the most significant four features were used to train machine learning (ML) algorithms. Two ML algorithms: deep learning (DL) and logistic regression (LR) were compared in terms of the classification accuracy for automated glaucoma detection. The performance of the ML models was evaluated on unseen test data, n = 55. An image segmentation pilot study was then performed on cross-sectional OCT scans. The ONH cup area was extracted, analyzed, and a new DL model was trained for glaucoma prediction. The DL model was estimated using five-fold cross-validation and compared with two pre-trained models. The DL model trained from the optimal features achieved significantly higher diagnostic performance (area under the receiver operating characteristic curve (AUC) 0.98 and accuracy of 97% on validation data and 96% on test data) compared to previous studies for automated glaucoma detection. The second DL model used in the pilot study also showed promising outcomes (AUC 0.99 and accuracy of 98.6%) to detect glaucoma compared to two pre-trained models. In combination, the result of the two studies strongly suggests the four features and the cross-sectional ONH cup area trained using deep learning have a great potential for use as an initial screening tool for glaucoma which will assist clinicians in making a precise decision.
Akter, S, Zakia, MA, Mofijur, M, Ahmed, SF, Vo, D-VN, Khandaker, G & Mahlia, TMI 2022, 'SARS-CoV-2 variants and environmental effects of lockdowns, masks and vaccination: a review', Environmental Chemistry Letters, vol. 20, no. 1, pp. 141-152.
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AL Hunaity, SA, Far, H & Saleh, A 2022, 'Vibration behaviour of cold-formed steel and particleboard composite flooring systems', STEEL AND COMPOSITE STRUCTURES, vol. 43, no. 3, pp. 403-417.
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Alajlouni, D, Tran, T, Bliuc, D, Blank, RD, Cawthon, PM, Orwoll, ES & Center, JR 2022, 'Muscle Strength and Physical Performance Improve Fracture Risk Prediction Beyond Garvan and FRAX : The Osteoporotic Fractures in Men ( MrOS ) Study', Journal of Bone and Mineral Research, vol. 37, no. 3, pp. 411-419.
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Alam, M, Lu, DD & Siwakoti, YP 2022, 'Time‐multiplexed hysteretic control for single‐inductor dual‐input single‐output DC‐DC power converter', International Journal of Circuit Theory and Applications, vol. 50, no. 4, pp. 1235-1249.
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Alam, MA, Wan, C, Tran, DT, Mofijur, M, Ahmed, SF, Mehmood, MA, Shaik, F, Vo, D-VN & Xu, J 2022, 'Microalgae binary culture for higher biomass production, nutrients recycling, and efficient harvesting: a review', Environmental Chemistry Letters, vol. 20, no. 2, pp. 1153-1168.
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Alam, S, Zardari, S & Bano, M 2022, 'Software engineering and 12 prominent sub‐areas: Comprehensive bibliometric assessment on 13 years (2007–2019)', IET Software, vol. 16, no. 2, pp. 125-145.
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Al-Doghman, F, Moustafa, N, Khalil, I, Tari, Z & Zomaya, A 2022, 'AI-enabled Secure Microservices in Edge Computing: Opportunities and Challenges', IEEE Transactions on Services Computing, pp. 1-1.
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Alharbi, SK, Ansari, AJ, Nghiem, LD & Price, WE 2022, 'New transformation products from ozonation and photolysis of diclofenac in the aqueous phase', Process Safety and Environmental Protection, vol. 157, pp. 106-114.
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Alibeikloo, M, Khabbaz, H & Fatahi, B 2022, 'Random Field Reliability Analysis for Time-Dependent Behaviour of Soft Soils Considering Spatial Variability of Elastic Visco-Plastic Parameters', Reliability Engineering & System Safety, vol. 219, pp. 108254-108254.
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Aljarajreh, H, Lu, DD-C, Siwakoti, YP & Tse, CK 2022, 'A Nonisolated Three-Port DC–DC Converter With Two Bidirectional Ports and Fewer Components', IEEE Transactions on Power Electronics, vol. 37, no. 7, pp. 8207-8216.
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Al-Juboori, RA, Bakly, S, Bowtell, L, Alkurdi, SSA & Altaee, A 2022, 'Innovative capacitive deionization-degaussing approach for improving adsorption/desorption for macadamia nutshell biochar', Journal of Water Process Engineering, vol. 47, pp. 102786-102786.
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Almuntashiri, A, Hosseinzadeh, A, Badeti, U, Shon, H, Freguia, S, Dorji, U & Phuntsho, S 2022, 'Removal of pharmaceutical compounds from synthetic hydrolysed urine using granular activated carbon: Column study and predictive modelling', Journal of Water Process Engineering, vol. 45, pp. 102480-102480.
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Alotaibi, AA, Maerz, NH, Boyko, KJ, Youssef, AM & Pradhan, B 2022, 'Temporal LiDAR scanning in quantifying cumulative rockfall volume and hazard assessment: A case study at southwestern Saudi Arabia', The Egyptian Journal of Remote Sensing and Space Science, vol. 25, no. 2, pp. 435-443.
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Alsahafi, YA, Gay, V & Khwaji, AA 2022, 'Factors affecting the acceptance of integrated electronic personal health records in Saudi Arabia: The impact of e-health literacy', Health Information Management Journal, vol. 51, no. 2, pp. 98-109.
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Background: National implementation of electronic personal health record (ePHR) systems is of vital importance to governments worldwide because this type of technology promises to promote and enhance healthcare. Although there is widespread agreement as to the advantages of ePHRs, the level of awareness and acceptance of this technology among healthcare consumers has been low. Objective: The aim of this study was to identify the factors that can influence the acceptance and use of an integrated ePHR system in Saudi Arabia. Method: The unified theory of acceptance and use of technology model was extended in this study to include e-health literacy (e-HL) and tested using structural equation modelling. Data were collected via a questionnaire survey, resulting in 794 valid responses. Results: The proposed model explained 56% of the variance in behavioural intention (BI) to use the integrated ePHR system. Findings also highlighted the significance of performance expectancy, effort expectancy, social influence (SI) and e-HL as determinants of Saudi healthcare consumers’ intentions to accept and use the integrated ePHR system. Additionally, assessment of the research model moderators revealed that only gender had a moderating influence on the relationship between SI and BI. Finally, findings showed a low level of awareness among Saudi citizens about the national implementation of an integrated ePHR system, suggesting the need to promote a greater and more widespread awareness of the system and to demonstrate its usefulness. Conclusion: Findings from this study can assist governments, policymakers and developers of health information technologies and systems by identifying importan...
Alsawwaf, M, Chaczko, Z, Kulbacki, M & Sarathy, N 2022, 'In Your Face: Person Identification Through Ratios and Distances Between Facial Features', Vietnam Journal of Computer Science, vol. 09, no. 02, pp. 187-202.
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These days identification of a person is an integral part of many computer-based solutions. It is a key characteristic for access control, customized services, and a proof of identity. Over the last couple of decades, many new techniques were introduced for how to identify human faces. This approach investigates the human face identification based on frontal images by producing ratios from distances between the different features and their locations. Moreover, this extended version includes an investigation of identification based on side profile by extracting and diagnosing the feature sets with geometric ratio expressions which are calculated into feature vectors. The last stage involves using weighted means to calculate the resemblance. The approach considers an explainable Artificial Intelligence (XAI) approach. Findings, based on a small dataset, achieve that the used approach offers promising results. Further research could have a great influence on how faces and face-profiles can be identified. Performance of the proposed system is validated using metrics such as Precision, False Acceptance Rate, False Rejection Rate, and True Positive Rate. Multiple simulations indicate an Equal Error Rate of 0.89. This work is an extended version of the paper submitted in ACIIDS 2020.
Alsufyani, N & Gill, AQ 2022, 'Digitalisation performance assessment: A systematic review', Technology in Society, vol. 68, pp. 101894-101894.
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AlZainati, N, Yadav, S, Altaee, A, Subbiah, S, Zaidi, SJ, Zhou, J, Al-Juboori, RA, Chen, Y & Shaheed, MH 2022, 'Impact of hydrodynamic conditions on optimum power generation in dual stage pressure retarded osmosis using spiral-wound membrane', Energy Nexus, vol. 5, pp. 100030-100030.
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Alzoubi, YI & Gill, AQ 2022, 'Can Agile Enterprise Architecture be Implemented Successfully in Distributed Agile Development? Empirical Findings', Global Journal of Flexible Systems Management, vol. 23, no. 2, pp. 221-235.
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Al-Zubi, MM, Mohan, AS, Plapper, P & Ling, SH 2022, 'Intra-Body Molecular Communication via Blood-Tissue Barrier for Internet of Bio-Nano Things', IEEE Internet of Things Journal, pp. 1-1.
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Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 2022, 'Remote Water Salinity Sensor Using Metamaterial Perfect Absorber', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Controlling water salinity plays a key role in farming efficiency. Current sensors are mostly expensive and need regular maintenance. In addition, they require electrical connections or extra power supply that leads to difficult and costly implementation in remote sensing scenarios. In this paper, an accurate and low-profile sensor is developed using a metamaterial perfect absorber (MPA) structure. The proposed sensor works based on the level and frequency of absorbed signals. Hence, there is no need for electrical connections, which enables remote sensing applications. Square shape channels have been created in a regular FR-4 substrate to facilitate sensing of water salinity level. A 7×7 array with a total size of 140mm×160mm has been fabricated that shows a resolution of 10 MHz per percentage of water salinity. The absorption frequency shifts from f=3.12 GHz to f=3.59 GHz for salinity level from 0% to 50%. A strong correlation between measurement and simulation results validates the design procedure.
Anand, U, Li, X, Sunita, K, Lokhandwala, S, Gautam, P, Suresh, S, Sarma, H, Vellingiri, B, Dey, A, Bontempi, E & Jiang, G 2022, 'SARS-CoV-2 and other pathogens in municipal wastewater, landfill leachate, and solid waste: A review about virus surveillance, infectivity, and inactivation', Environmental Research, vol. 203, pp. 111839-111839.
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Andaryani, S, Nourani, V, Pradhan, B, Jalali Ansarudi, T, Ershadfath, F & Torabi Haghighi, A 2022, 'Spatiotemporal evaluation of future groundwater recharge in arid and semi-arid regions under climate change scenarios', Hydrological Sciences Journal, vol. 67, no. 6, pp. 979-995.
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Angeloski, A, Price, JR, Ennis, C, Smith, K, McDonagh, AM, Dowd, A, Thomas, P, Cortie, M, Appadoo, D & Bhadbhade, M 2022, 'Thermosalience Revealed on the Atomic Scale: Rapid Synchrotron Techniques Uncover Molecular Motion Preceding Crystal Jumping', Crystal Growth & Design, vol. 22, no. 3, pp. 1951-1959.
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Anirban, S, Wang, J, Islam, MS, Kayesh, H, Li, J & Huang, ML 2022, 'Compression techniques for 2-hop labeling for shortest distance queries', World Wide Web, vol. 25, no. 1, pp. 151-174.
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Ansari, M, Jones, B & Guo, YJ 2022, 'Spherical Luneburg Lens of Layered Structure With Low Anisotropy and Low Cost', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4307-4318.
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Antoniadis, J, Arzoumanian, Z, Babak, S, Bailes, M, Bak Nielsen, A-S, Baker, PT, Bassa, CG, Bécsy, B, Berthereau, A, Bonetti, M, Brazier, A, Brook, PR, Burgay, M, Burke-Spolaor, S, Caballero, RN, Casey-Clyde, JA, Chalumeau, A, Champion, DJ, Charisi, M, Chatterjee, S, Chen, S, Cognard, I, Cordes, JM, Cornish, NJ, Crawford, F, Cromartie, HT, Crowter, K, Dai, S, DeCesar, ME, Demorest, PB, Desvignes, G, Dolch, T, Drachler, B, Falxa, M, Ferrara, EC, Fiore, W, Fonseca, E, Gair, JR, Garver-Daniels, N, Goncharov, B, Good, DC, Graikou, E, Guillemot, L, Guo, YJ, Hazboun, JS, Hobbs, G, Hu, H, Islo, K, Janssen, GH, Jennings, RJ, Johnson, AD, Jones, ML, Kaiser, AR, Kaplan, DL, Karuppusamy, R, Keith, MJ, Kelley, LZ, Kerr, M, Key, JS, Kramer, M, Lam, MT, Lamb, WG, Lazio, TJW, Lee, KJ, Lentati, L, Liu, K, Luo, J, Lynch, RS, Lyne, AG, Madison, DR, Main, RA, Manchester, RN, McEwen, A, McKee, JW, McLaughlin, MA, Mickaliger, MB, Mingarelli, CMF, Ng, C, Nice, DJ, Osłowski, S, Parthasarathy, A, Pennucci, TT, Perera, BBP, Perrodin, D, Petiteau, A, Pol, NS, Porayko, NK, Possenti, A, Ransom, SM, Ray, PS, Reardon, DJ, Russell, CJ, Samajdar, A, Sampson, LM, Sanidas, S, Sarkissian, JM, Schmitz, K, Schult, L, Sesana, A, Shaifullah, G, Shannon, RM, Shapiro-Albert, BJ, Siemens, X, Simon, J, Smith, TL, Speri, L, Spiewak, R, Stairs, IH, Stappers, BW, Stinebring, DR, Swiggum, JK, Taylor, SR, Theureau, G, Tiburzi, C, Vallisneri, M, van der Wateren, E, Vecchio, A, Verbiest, JPW, Vigeland, SJ, Wahl, H, Wang, JB, Wang, J, Wang, L, Witt, CA, Zhang, S & Zhu, XJ 2022, 'The International Pulsar Timing Array second data release: Search for an isotropic gravitational wave background', Monthly Notices of the Royal Astronomical Society, vol. 510, no. 4, pp. 4873-4887.
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ABSTRACT
We searched for an isotropic stochastic gravitational wave background in the second data release of the International Pulsar Timing Array, a global collaboration synthesizing decadal-length pulsar-timing campaigns in North America, Europe, and Australia. In our reference search for a power-law strain spectrum of the form $h_c = A(f/1\, \mathrm{yr}^{-1})^{\alpha }$, we found strong evidence for a spectrally similar low-frequency stochastic process of amplitude $A = 3.8^{+6.3}_{-2.5}\times 10^{-15}$ and spectral index α = −0.5 ± 0.5, where the uncertainties represent 95 per cent credible regions, using information from the auto- and cross-correlation terms between the pulsars in the array. For a spectral index of α = −2/3, as expected from a population of inspiralling supermassive black hole binaries, the recovered amplitude is $A = 2.8^{+1.2}_{-0.8}\times 10^{-15}$. None the less, no significant evidence of the Hellings–Downs correlations that would indicate a gravitational-wave origin was found. We also analysed the constituent data from the individual pulsar timing arrays in a consistent way, and clearly demonstrate that the combined international data set is more sensitive. Furthermore, we demonstrate that this combined data set produces comparable constraints to recent single-array data sets which have more data than the constituent parts of the combination. Future international data releases will deliver increased sensitivity to gravitational wave radiation, and significantly increase the detection probability.
Anwar, MJ, Gill, AQ, Fitzgibbon, AD & Gull, I 2022, 'PESTLE+ risk analysis model to assess pandemic preparedness of digital ecosystems', SECURITY AND PRIVACY, vol. 5, no. 1.
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Arachchige, CMK, Indraratna, B, Qi, Y, Vinod, JS & Rujikiatkamjorn, C 2022, 'Geotechnical characteristics of a Rubber Intermixed Ballast System', Acta Geotechnica, vol. 17, no. 5, pp. 1847-1858.
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Araujo, AM, Abaurrea, A, Azcoaga, P, López-Velazco, JI, Manzano, S, Rodriguez, J, Rezola, R, Egia-Mendikute, L, Valdés-Mora, F, Flores, JM, Jenkins, L, Pulido, L, Osorio-Querejeta, I, Fernández-Nogueira, P, Ferrari, N, Viera, C, Martín-Martín, N, Tzankov, A, Eppenberger-Castori, S, Alvarez-Lopez, I, Urruticoechea, A, Bragado, P, Coleman, N, Palazón, A, Carracedo, A, Gallego-Ortega, D, Calvo, F, Isacke, CM, Caffarel, MM & Lawrie, CH 2022, 'Stromal oncostatin M cytokine promotes breast cancer progression by reprogramming the tumor microenvironment', Journal of Clinical Investigation, vol. 132, no. 7.
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Argha, A, Celler, BG & Lovell, NH 2022, 'Artificial Intelligence Based Blood Pressure Estimation From Auscultatory and Oscillometric Waveforms: A Methodological Review', IEEE Reviews in Biomedical Engineering, vol. 15, pp. 152-168.
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Arivalagan, J, Indraratna, B, Rujikiatkamjorn, C & Warwick, A 2022, 'Effectiveness of a Geocomposite-PVD system in preventing subgrade instability and fluidisation under cyclic loading', Geotextiles and Geomembranes.
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Arnaz, A, Lipman, J, Abolhasan, M & Hiltunen, M 2022, 'Toward Integrating Intelligence and Programmability in Open Radio Access Networks: A Comprehensive Survey', IEEE Access, vol. 10, pp. 67747-67770.
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Arora, S, Nag, A, Kalra, A, Sinha, V, Meena, E, Saxena, S, Sutaria, D, Kaur, M, Pamnani, T, Sharma, K, Saxena, S, Shrivastava, SK, Gupta, AB, Li, X & Jiang, G 2022, 'Successful application of wastewater-based epidemiology in prediction and monitoring of the second wave of COVID-19 with fragmented sewerage systems–a case study of Jaipur (India)', Environmental Monitoring and Assessment, vol. 194, no. 5.
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Arsalanloo, A, Abbasalizadeh, M, Khalilian, M, Saniee, Y, Ramezanpour, A & Islam, MS 2022, 'A computational approach to understand the breathing dynamics and pharmaceutical aerosol transport in a realistic airways', Advanced Powder Technology, vol. 33, no. 7, pp. 103635-103635.
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Ashtari, S, Abdollahi, M, Abolhasan, M, Shariati, N & Lipman, J 2022, 'Performance analysis of multi-hop routing protocols in SDN-based wireless networks', Computers & Electrical Engineering, vol. 97, pp. 107393-107393.
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Ashtari, S, Zhou, I, Abolhasan, M, Shariati, N, Lipman, J & Ni, W 2022, 'Knowledge-defined networking: Applications, challenges and future work', Array, vol. 14, pp. 100136-100136.
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Asteris, PG, Gavriilaki, E, Touloumenidou, T, Koravou, E, Koutra, M, Papayanni, PG, Pouleres, A, Karali, V, Lemonis, ME, Mamou, A, Skentou, AD, Papalexandri, A, Varelas, C, Chatzopoulou, F, Chatzidimitriou, M, Chatzidimitriou, D, Veleni, A, Rapti, E, Kioumis, I, Kaimakamis, E, Bitzani, M, Boumpas, D, Tsantes, A, Sotiropoulos, D, Papadopoulou, A, Kalantzis, IG, Vallianatou, LA, Armaghani, DJ, Cavaleri, L, Gandomi, AH, Hajihassani, M, Hasanipanah, M, Koopialipoor, M, Lourenço, PB, Samui, P, Zhou, J, Sakellari, I, Valsami, S, Politou, M, Kokoris, S & Anagnostopoulos, A 2022, 'Genetic prediction of ICU hospitalization and mortality in COVID‐19 patients using artificial neural networks', Journal of Cellular and Molecular Medicine, vol. 26, no. 5, pp. 1445-1455.
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Atique, MN, Imran, S, Razzaq, L, Mujtaba, MA, Nawaz, S, Kalam, MA, Soudagar, MEM, Hussain, A, Veza, I & Arshad, A 2022, 'Hydraulic characterization of Diesel, B50 and B100 using momentum flux', Alexandria Engineering Journal, vol. 61, no. 6, pp. 4371-4388.
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Augustine, R, S, A, Nayeem, A, Salam, SA, Augustine, P, Dan, P, Maureira, P, Mraiche, F, Gentile, C, Hansbro, PM, McClements, L & Hasan, A 2022, 'Increased complications of COVID-19 in people with cardiovascular disease: Role of the renin–angiotensin-aldosterone system (RAAS) dysregulation', Chemico-Biological Interactions, vol. 351, pp. 109738-109738.
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Aung, TWW, Wan, Y, Huo, H & Sui, Y 2022, 'Multi-triage: A multi-task learning framework for bug triage', Journal of Systems and Software, vol. 184, pp. 111133-111133.
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Awang, MSN, Zulkifli, NWM, Abbas, MM, Zulkifli, SA, Kalam, MA, Yusoff, MNAM, Daud, WMAW & Ahmad, MH 2022, 'Effect of diesel-palm biodiesel fuel with plastic pyrolysis oil and waste cooking biodiesel on tribological characteristics of lubricating oil', Alexandria Engineering Journal, vol. 61, no. 9, pp. 7221-7231.
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Azadi, M, Emrouznejad, A, Ramezani, F & Hussain, FK 2022, 'Efficiency Measurement of Cloud Service Providers Using Network Data Envelopment Analysis', IEEE Transactions on Cloud Computing, vol. 10, no. 1, pp. 348-355.
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IEEE An increasing number of organizations and businesses around the world use cloud computing services to improve their performance in the competitive marketplace. However, one of the biggest challenges in using cloud computing services is performance measurement and the selection of the best cloud service providers (CSPs) based on quality of service (QoS) requirements (Duan, 2017). To address this shortcoming in this article we propose a network data envelopment analysis (DEA) method in measuring the efficiency of CSPs. When network dimensions are taken into consideration, a more comprehensive analysis is enabled where divisional efficiency is reflected in overall efficiency estimates. This helps managers and decision makers in organizations to make accurate decisions in selecting cloud services. In the current study, variable returns to scale (VRS), the non-oriented network slacks-based measure (SBM) model and input-oriented and output-oriented SBM models are applied to measure the performance of 18 CSPs. The obtained results show the superiority of the network DEA model and they also demonstrate that the proposed model can evaluate and rank CSPs much better than compared to traditional DEA models.
Azam, MA, Khan, KB, Salahuddin, S, Rehman, E, Khan, SA, Khan, MA, Kadry, S & Gandomi, AH 2022, 'A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics', Computers in Biology and Medicine, vol. 144, pp. 105253-105253.
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Azizi, N, Moradi CheshmehBeigi, H & Rouzbehi, K 2022, 'A modified droop control structure for simultaneous power‐sharing and DC voltage oscillations damping in MT‐HVDC grids', IET Generation, Transmission & Distribution, vol. 16, no. 9, pp. 1890-1900.
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Azizi, N, Moradi CheshmehBeigi, H & Rouzbehi, K 2022, 'HVDC grids stability improvement by direct current power system stabilizer', IET Generation, Transmission & Distribution, vol. 16, no. 3, pp. 492-502.
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High-voltage direct current breaker is among the essential components of high-voltage direct current grids. Such a breaker generally needs a direct current reactor to reduce the fault currents rate. However, direct current reactors have destructive effects on the multi-terminal high-voltage direct current grid dynamic stability, and in such a system, despite the variety of controllers, the system dynamics are highly sensitive to the operating point. Therefore, additional damping control will be needed. This paper proposes a modification to be applied to the traditional droop controller of high-voltage direct current grids to cope with the influence of these large reactors, improving the direct voltage stability and decreasing power variations in the transient events by introducing a direct current power system stabilizer. The proposed method for direct voltage control has been investigated through the analytical model of the system. Stability improvement has been studied following the application of the proposed method by investigating zeros, poles, and frequency response analysis. Moreover, a method is proposed for optimal design and optimal placement of direct current power system stabilizer. The system analysis and time-domain simulations demonstrate a decent damping improvement attained by the proposed method. All simulations and analytical studies are conducted on Cigré DCS3 test high-voltage direct current grid in MATLAB/Simulink.
Baba, AA, Hashmi, RM, Attygalle, M, Esselle, KP & Borg, D 2022, 'Ultrawideband Beam Steering at mm-Wave Frequency With Planar Dielectric Phase Transformers', IEEE Transactions on Antennas and Propagation, vol. 70, no. 3, pp. 1719-1728.
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Bachosz, K, Vu, MT, Nghiem, LD, Zdarta, J, Nguyen, LN & Jesionowski, T 2022, 'Enzyme-based control of membrane biofouling for water and wastewater purification: A comprehensive review', Environmental Technology & Innovation, vol. 25, pp. 102106-102106.
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Badeti, U, Jiang, J, Almuntashiri, A, Pathak, N, Dorji, U, Volpin, F, Freguia, S, Ang, WL, Chanan, A, Kumarasingham, S, Shon, HK & Phuntsho, S 2022, 'Impact of source-separation of urine on treatment capacity, process design, and capital expenditure of a decentralised wastewater treatment plant', Chemosphere, vol. 300, pp. 134489-134489.
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Ball, JE 2022, 'Modelling accuracy for urban design flood estimation', Urban Water Journal, vol. 19, no. 1, pp. 87-96.
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Management of flood risk remains a major problem in many urban environments. To generate the data needed for estimation of the flood risk, catchment models have been used with the reliability of the predicted catchment response for design flood estimation dependent upon the model calibration. However, the level of calibration required to achieve reliable design flood estimation remains unspecified. The purpose of this paper is to assess the event modelling accuracy needed if data from the calibrated model are to be used for continuous simulation of data for flood frequency analysis. For this purpose, a SWMM-based catchment model was investigated using 25 monitored events, while the assessment of the calibration was based on a normalised peak flow error. Alternative sets of parameter values were used to obtain estimates of the peak flow for each of the selected events. The best performing sets of these sets of parameter values were used with SWMM in a continuous simulation mode to predict flow sequences for extraction of Annual Maxima Series for an At-Site Flood Frequency Analysis. From the analysis of these At-Site Flood Frequency Analyses, it was concluded that the normalised peak flow error needed to be less than 10% if reliable design flood quantile estimates were to be obtained.
Banerjee, S, Lyu, J, Huang, Z, Leung, FHF, Lee, T, Yang, D, Su, S, Zheng, Y & Ling, SH 2022, 'Ultrasound spine image segmentation using multi-scale feature fusion Skip-Inception U-Net (SIU-Net)', Biocybernetics and Biomedical Engineering, vol. 42, no. 1, pp. 341-361.
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Bardhan, A, GuhaRay, A, Gupta, S, Pradhan, B & Gokceoglu, C 2022, 'A novel integrated approach of ELM and modified equilibrium optimizer for predicting soil compression index of subgrade layer of Dedicated Freight Corridor', Transportation Geotechnics, vol. 32, pp. 100678-100678.
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Bardhan, A, Kardani, N, Alzo'ubi, AK, Roy, B, Samui, P & Gandomi, AH 2022, 'Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil consolidation parameter', Journal of Rock Mechanics and Geotechnical Engineering.
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Barzegarkhoo, R, Farhangi, M, Aguilera, RP, Lee, SS, Blaabjerg, F & Siwakoti, YP 2022, 'Common-Ground Grid-Connected Five-Level Transformerless Inverter With Integrated Dynamic Voltage Boosting Feature', IEEE Journal of Emerging and Selected Topics in Power Electronics, pp. 1-1.
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Barzegarkhoo, R, Farhangi, M, Lee, SS, Aguilera, RP, Siwakoti, YP & Pou, J 2022, 'Nine-Level Nine-Switch Common-Ground Switched-Capacitor Inverter Suitable for High-Frequency AC-Microgrid Applications', IEEE Transactions on Power Electronics, vol. 37, no. 5, pp. 6132-6143.
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Barzegarkhoo, R, Forouzesh, M, Lee, SS, Blaabjerg, F & Siwakoti, YP 2022, 'Switched-Capacitor Multilevel Inverters: A Comprehensive Review', IEEE Transactions on Power Electronics, vol. 37, no. 9, pp. 11209-11243.
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Barzegarkhoo, R, Khan, SA, Siwakoti, YP, Aguilera, RP, Lee, SS & Khan, MNH 2022, 'Implementation and Analysis of a Novel Switched-Boost Common-Ground Five-Level Inverter Modulated With Model Predictive Control Strategy', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 1, pp. 731-744.
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Basack, S, Nimbalkar, S, Karakouzian, M, Bharadwaj, S, Xie, Z & Krause, N 2022, 'Field Installation Effects of Stone Columns on Load Settlement Characteristics of Reinforced Soft Ground', International Journal of Geomechanics, vol. 22, no. 4.
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Bendoy, AP, Zeweldi, HG, Park, MJ, Shon, HK, Kim, H, Chung, W-J & Nisola, GM 2022, 'Silicene nanosheets as support fillers for thin film composite forward osmosis membranes', Desalination, vol. 536, pp. 115817-115817.
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Beni, HM, Mortazavi, H & Islam, MS 2022, 'Biomedical and biophysical limits to mathematical modeling of pulmonary system mechanics: a scoping review on aerosol and drug delivery', Biomechanics and Modeling in Mechanobiology, vol. 21, no. 1, pp. 79-87.
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Beyhan, B, Akçomak, S & Cetindamar, D 2022, 'The Startup Selection Process in Accelerators: Qualitative Evidence from Turkey', Entrepreneurship Research Journal, vol. 0, no. 0, pp. 1-31.
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Abstract
Startup selection is an essential mechanism of how accelerators create value. Through in-depth case studies of 10 accelerators in Turkey, our research explores the selection process in accelerators. Our findings indicate that accelerators overcome their context’s extreme uncertainty by involving various actors in the selection process and reducing the information asymmetries for investors and startups. Accelerators tend to select effortlessly coachable startups, willing to collaborate with accelerators, mentors, or other actors, and passionate enough to overcome the pressure of creating a business at a fast pace. Our research also exhibits that the selection process serves startups by directing and training them to transmit the right signals to receivers, primarily investors. Accelerators prefer to work with entrepreneurial teams that are coachable, passionate, and collaborative to vibrate the right signals. Similarly, the accelerators’ selection process helps investors by decreasing signaling noise and mitigate information asymmetry. By doing so, accelerators contribute to a well-functioning and more effective entrepreneurship ecosystem.
Bhatnagar, P, Singh, AK, Gupta, KK & Siwakoti, YP 2022, 'A Switched-Capacitors-Based 13-Level Inverter', IEEE Transactions on Power Electronics, vol. 37, no. 1, pp. 644-658.
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Bhol, P, Swain, S, Altaee, A, Saxena, M & Samal, AK 2022, 'Cobalt–iron decorated tellurium nanotubes for high energy density supercapacitor', Materials Today Chemistry, vol. 24, pp. 100871-100871.
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We report the synthesis of cobalt-iron (Co-Fe) decorated tellurium nanotubes (Te NTs) using semiconductive Te NTs as a sacrificial template using the wet chemical method. The Co and Fe precursor concentration incorporated into Te NT plays a significant role in obtaining various bimetallic telluride structures. The one-dimensional (1-D) structure of Co-Fe decorated Te NTs with Te NTs in the backbone provides superior conductivity and exhibits high electrochemical performance with battery type electrode behaviour. The Co-Fe decorated Te NTs electrode is combined with the electric double-layer capacitors (EDLC) type electrode activated carbon (AC) to tune the energy density performance. The asymmetric assembly shows an excellent specific capacitance of 179.2 F g-1 (48.7 mAh g-1) at a current density of 0.9 A g-1 in 4 M KOH electrolyte. More importantly, it exhibits a maximum energy density of 62.1 Wh Kg-1 at a power density of 1138.2 W Kg-1 under a potential window of 1.58 V. This potential finding shows the significant applicability of Te NTs as a template for the synthesis of bimetallic tellurides with unique morphologies. The synergistic effect from multimetals and anisotropic morphology is beneficial for energy storage applications.
Bhowmick, S, Xu, F, Molla, MM & Saha, SC 2022, 'Chaotic phenomena of natural convection for water in a V-shaped enclosure', International Journal of Thermal Sciences, vol. 176, pp. 107526-107526.
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Bin Sawad, A, Narayan, B, Alnefaie, A, Maqbool, A, Mckie, I, Smith, J, Yuksel, B, Puthal, D, Prasad, M & Kocaballi, AB 2022, 'A Systematic Review on Healthcare Artificial Intelligent Conversational Agents for Chronic Conditions', Sensors, vol. 22, no. 7, pp. 2625-2625.
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This paper reviews different types of conversational agents used in health care for chronic conditions, examining their underlying communication technology, evaluation measures, and AI methods. A systematic search was performed in February 2021 on PubMed Medline, EMBASE, PsycINFO, CINAHL, Web of Science, and ACM Digital Library. Studies were included if they focused on consumers, caregivers, or healthcare professionals in the prevention, treatment, or rehabilitation of chronic diseases, involved conversational agents, and tested the system with human users. The search retrieved 1087 articles. Twenty-six studies met the inclusion criteria. Out of 26 conversational agents (CAs), 16 were chatbots, seven were embodied conversational agents (ECA), one was a conversational agent in a robot, and another was a relational agent. One agent was not specified. Based on this review, the overall acceptance of CAs by users for the self-management of their chronic conditions is promising. Users’ feedback shows helpfulness, satisfaction, and ease of use in more than half of included studies. Although many users in the studies appear to feel more comfortable with CAs, there is still a lack of reliable and comparable evidence to determine the efficacy of AI-enabled CAs for chronic health conditions due to the insufficient reporting of technical implementation details.
Binsawad, M, Abbasi, GA & Sohaib, O 2022, 'People’s expectations and experiences of big data collection in the Saudi context', PeerJ Computer Science, vol. 8, pp. e926-e926.
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Big data and machine learning technologies facilitate various business intelligence activities for businesses. However, personal data collection can generate adverse effects on consumers. Big data collection can compromise people’s sense of autonomy, harming digital privacy, transparency and trust. This research investigates personal data collection, control, awareness, and privacy regulation on people’s autonomy in Saudi. This study used a hybrid analytical model that incorporates symmetrical and asymmetrical analysis via fuzzy set qualitative comparative analysis (fsQCA) to analyze consumer sense of autonomy regarding big data collection. The symmetrical shows that ‘Control’ had the most significant influence on people’s autonomy, followed by ‘Big data collection’ and ‘Awareness’. The fsQCA shows 84% of the variation, explaining the people’s autonomy.
Bliuc, D, Tran, T, Adachi, JD, Atkins, GJ, Berger, C, Bergh, J, Cappai, R, Eisman, JA, 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 2022, 'Reply to: The Association Between Cognitive Decline and Bone Loss and Fracture Risk Is Not Affected by Medication With Anticholinergic Effect', Journal of Bone and Mineral Research, vol. 37, no. 5, pp. 1075-1076.
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Bo, L, Li, Q, Tian, Y, Wu, D, Yu, Y, Chen, X & Gao, W 2022, 'Nonlinear dynamic investigation of the perovskite solar cell with GPLR-FGP stiffeners under blast impact', International Journal of Mechanical Sciences, vol. 213, pp. 106866-106866.
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Bordbar, M, Neshat, A, Javadi, S, Pradhan, B, Dixon, B & Paryani, S 2022, 'Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches', Natural Hazards, vol. 110, no. 3, pp. 1799-1820.
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The main objective of this study is to integrate adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural network (ANN) to design an integrated supervised committee machine artificial intelligence (SCMAI) model to spatially predict the groundwater vulnerability to seawater intrusion in Gharesoo-Gorgan Rood coastal aquifer placed in the northern part of Iran. Six hydrological GALDIT parameters (i.e., G groundwater occurrence, A aquifer hydraulic conductivity, L level of groundwater above sea level, D distance from the shore, I impact of the existing status of seawater intrusion in the region, and T thickness of the aquifer) were considered as inputs for each model. In the training step, the values of GALDIT’s vulnerability index were conditioned by using the values of TDS concentration in order to obtain the conditioned vulnerability index (CVI). The CVI was considered as the target for each model. After training the models, each model was tested using a separate TDS dataset. The results indicated that the ANN and ANFIS algorithms performed better than the SVM algorithm. The values of correlation were obtained as 88, 87, and 80% for ANN, ANFIS, and SVM models, respectively. In the testing step of the SCMAI model, the values of RMSE, R2, and r were obtained as 6.4, 0.95, and 97%, respectively. Overall, SCMAI model outperformed other models to spatially predicting vulnerable zones. The result of the SCMAI model confirmed that the western zones along the shoreline had the highest vulnerability to seawater intrusion; therefore, it seems critical to consider emergency protection plans for study area. Graphic abstract: [Figure not available: see fulltext.]
Bour, H, Abolhasan, M, Jafarizadeh, S, Lipman, J & Makhdoom, I 2022, 'A multi-layered intrusion detection system for software defined networking', Computers and Electrical Engineering, vol. 101, pp. 108042-108042.
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Braytee, A, Naji, M & Kennedy, PJ 2022, 'Unsupervised Domain-Adaptation-Based Tensor Feature Learning With Structure Preservation', IEEE Transactions on Artificial Intelligence, vol. 3, no. 3, pp. 370-380.
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Bryant, L, Sedlarevic, N, Stubbs, P, Bailey, B, Nguyen, V, Bluff, A, Barnett, D, Estela, M, Hayes, C, Jacobs, C, Kneebone, I, Lucas, C, Mehta, P, Power, E & Hemsley, B 2022, 'Collaborative co-design and evaluation of an immersive virtual reality application prototype for communication rehabilitation (DISCOVR prototype)', Disability and Rehabilitation: Assistive Technology, pp. 1-10.
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PURPOSE: Virtual reality (VR) lends itself to communication rehabilitation by creating safe, replicable, and authentic simulated environments in which users learn and practice communication skills. The aim of this research was to obtain the views of health professionals and technology specialists on the design characteristics and usability of a prototype VR application for communication rehabilitation. MATERIALS AND METHODS: Nine professionals from different health and technology disciplines participated in an online focus group or individual online interview to evaluate the application and use of the VR prototype. Data sources were analysed using a content thematic analysis. RESULTS: Four main themes relating to VR design and implementation in rehabilitation were identified: (i) designing rehabilitation-focused virtual worlds; (ii) understanding and using VR hardware; (iii) making room for VR in rehabilitation and training; and (iv) implementing VR will not replace the health professional's role. DISCUSSION: Health professionals and technology specialists engaged in co-design while evaluating the VR prototype. They identified software features requiring careful consideration to ensure improved usability, client safety, and success in communication rehabilitation outcomes. Continuing inclusive co-design, engaging health professionals, clients with communication disability, and their families will be essential to creating useable VR applications and integrating these successfully into rehabilitation. Implications for rehabilitationHealth and technology professionals, along with clients, are integral to the co-design of new VR technology applications.Design of VR applications needs to consider the client's communication, physical, cognitive, sensory, psychosocial, and emotional needs for greater usability of these programs.Realism and authenticity of interactions, characters, and environments are considered important factors to allow users to be fully immersed in v...
Buchlak, QD, Clair, J, Esmaili, N, Barmare, A & Chandrasekaran, S 2022, 'Clinical outcomes associated with robotic and computer-navigated total knee arthroplasty: a machine learning-augmented systematic review', European Journal of Orthopaedic Surgery & Traumatology, vol. 32, no. 5, pp. 915-931.
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Bui, HT, Hussain, OK, Prior, D, Hussain, FK & Saberi, M 2022, 'Proof by Earnestness (PoE) to determine the authenticity of subjective information in blockchains - application in supply chain risk management', Knowledge-Based Systems, vol. 250, pp. 108972-108972.
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Bui, VG, Tu Bui, TM, Ong, HC, Nižetić, S, Bui, VH, Xuan Nguyen, TT, Atabani, AE, Štěpanec, L, Phu Pham, LH & Hoang, AT 2022, 'Optimizing operation parameters of a spark-ignition engine fueled with biogas-hydrogen blend integrated into biomass-solar hybrid renewable energy system', Energy, vol. 252, pp. 124052-124052.
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Bukhari, A, Hussain, FK & Hussain, OK 2022, 'Fog node discovery and selection: A Systematic literature review', Future Generation Computer Systems, vol. 135, pp. 114-128.
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Cai, B, Li, X, Kong, W, Yuan, J & Yu, S 2022, 'A Reliable and Lightweight Trust Inference Model for Service Recommendation in SIoT', IEEE Internet of Things Journal, vol. 9, no. 13, pp. 10988-11003.
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Cai, Y, Lu, Z, Pan, Y, He, L, Guo, X & Zhang, J 2022, 'Optimal scheduling of a hybrid AC/DC multi-energy microgrid considering uncertainties and Stackelberg game-based integrated demand response', International Journal of Electrical Power & Energy Systems, vol. 142, pp. 108341-108341.
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Cai, Y, Zhu, M, Meng, X, Zhou, JL, Zhang, H & Shen, X 2022, 'The role of biochar on alleviating ammonia toxicity in anaerobic digestion of nitrogen-rich wastes: A review', Bioresource Technology, vol. 351, pp. 126924-126924.
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Cao, L 2022, 'A New Age of AI: Features and Futures', IEEE Intelligent Systems, vol. 37, no. 1, pp. 25-37.
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Cao, L 2022, 'AI in Combating the COVID-19 Pandemic', IEEE Intelligent Systems, vol. 37, no. 2, pp. 3-13.
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Cao, L 2022, 'AI Science and Engineering', IEEE Intelligent Systems, vol. 37, no. 1, pp. 14-15.
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Cao, L 2022, 'AI Science and Engineering: A New Field', IEEE Intelligent Systems, vol. 37, no. 1, pp. 3-13.
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Cao, L 2022, 'Non-IID Federated Learning', IEEE Intelligent Systems, vol. 37, no. 2, pp. 14-15.
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Cao, X & Tsang, IW 2022, 'Shattering Distribution for Active Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 1, pp. 215-228.
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Active learning (AL) aims to maximize the learning performance of the current hypothesis by drawing as few labels as possible from an input distribution. Generally, most existing AL algorithms prune the hypothesis set via querying labels of unlabeled samples and could be deemed as a hypothesis-pruning strategy. However, this process critically depends on the initial hypothesis and its subsequent updates. This article presents a distribution-shattering strategy without an estimation of hypotheses by shattering the number density of the input distribution. For any hypothesis class, we halve the number density of an input distribution to obtain a shattered distribution, which characterizes any hypothesis with a lower bound on VC dimension. Our analysis shows that sampling in a shattered distribution reduces label complexity and error disagreement. With this paradigm guarantee, in an input distribution, a Shattered Distribution-based AL (SDAL) algorithm is derived to continuously split the shattered distribution into a number of representative samples. An empirical evaluation of benchmark data sets further verifies the effectiveness of the halving and querying abilities of SDAL in real-world AL tasks with limited labels. Experiments on active querying with adversarial examples and noisy labels further verify our theoretical insights on the performance disagreement of the hypothesis-pruning and distribution-shattering strategies. Our code is available at https://github.com/XiaofengCao-MachineLearning/Shattering-Distribution-for-Active-Learning.
Cao, Y, Zhao, L, Wen, S & Huang, T 2022, 'Lag H∞ synchronization of coupled neural networks with multiple state couplings and multiple delayed state couplings', Neural Networks, vol. 151, pp. 143-155.
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Cetindamar, D & Phaal, R 2022, 'Technology Management in the Age of Digital Technologies', IEEE Transactions on Engineering Management, pp. 1-9.
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Cetindamar, D, Abedin, B & Shirahada, K 2022, 'The Role of Employees in Digital Transformation: A Preliminary Study on How Employees’ Digital Literacy Impacts Use of Digital Technologies', IEEE Transactions on Engineering Management, pp. 1-12.
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Even though digital technologies such as cloud technologies are prevalent in transforming businesses, the role of employees and their digital skills in the process is, to a large extent, neglected. This study brings forward the novel concept of digital literacy to explore the role of employees in understanding the wide variety of opportunities of digital technologies and their actualization. By treating digital literacy as the antecedent of cognitive behavior of employees in utilizing cloud technology at companies, we apply the Theory of Planned Behavior (TPB) for analyzing preliminary empirical data collected from 124 Australian employees’ technology use intentionality and behavior. The quantitative analysis shows that the TPB holds for the utilization of cloud technology and there is a positive relationship between employees' digital literacy and the utilization of cloud technology at companies. Overall, the study contributes to the technology management literature by offering a workable construct to measure the digital skills of employees in the form of digital literacy. Further, it expands the TPB framework by introducing digital literacy as a perceived behavior control variable that helps to examine the role of employees in digital transformation. The paper ends with implications and limitations of our preliminary study, followed with suggestions for future studies.
Cetindamar, D, Kitto, K, Wu, M, Zhang, Y, Abedin, B & Knight, S 2022, 'Explicating AI Literacy of Employees at Digital Workplaces', IEEE Transactions on Engineering Management, pp. 1-14.
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This paper aims to understand the definition and dimensions of artificial intelligence (AI) literacy. Digital technologies, including AI, trigger organizational affordances in workplaces, yet few studies have investigated employees’ AI literacy. This paper uses a bibliometrics analysis of 270 articles to explore the meaning of AI literacy of employees in the extant literature. Descriptive statistics, keyword co-occurrence analysis, and a hierarchical topic tree are employed to profile the research landscape and identify the core research themes and relevant papers related to AI literacy’s definition, dimensions, challenges, and future directions. Findings highlight four sets of capabilities associated with AI literacy, namely technology-related, work-related, human-machine-related, and learning-related capabilities, pointing also to the importance of operationalizing AI literacy for non AI professionals. This result contributes to the literature associated with technology management studies by offering a novel conceptualization of AI literacy and link it to the employee’s role in digital workplaces. We conclude by inviting researchers to examine the effect of employee-technology interactions on employees’ AI literacy, which might improve the design and use of AI.
Cetindamar, D, Shdifat, B & Erfani, E 2022, 'Understanding Big Data Analytics Capability and Sustainable Supply Chains', Information Systems Management, vol. 39, no. 1, pp. 19-33.
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This paper presents the knowledge available in the literature regarding big data analytics capability (BDAC) and sustainable supply chain performance (SSCP). A detailed analysis of systematic literature reviews points out the lack of studies bridging these two separate streams of work. The paper puts forward a research agenda for researchers interested in understanding the impact of big data on sustainability.
Chacon, A, Kielly, M, Rutherford, H, Franklin, DR, Caracciolo, A, Buonanno, L, D’Adda, I, Rosenfeld, A, Guatelli, S, Carminati, M, Fiorini, C & Safavi-Naeini, M 2022, 'Detection and discrimination of neutron capture events for NCEPT dose quantification', Scientific Reports, vol. 12, no. 1.
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AbstractNeutron Capture Enhanced Particle Therapy (NCEPT) boosts the effectiveness of particle therapy by capturing thermal neutrons produced by beam-target nuclear interactions in and around the treatment site, using tumour-specific $$^{10}$$
10
B or $$^{157}$$
157
Gd-based neutron capture agents. Neutron captures release high-LET secondary particles together with gamma photons with energies of 478 keV or one of several energies up to 7.94 MeV, for $$^{10}$$
10
B and $$^{157}$$
157
Gd, respectively. A key requirement for NCEPT’s translation is the development of in vivo dosimetry techniques which can measure both the direct ion dose and the dose due t...
Chakraborty, S 2022, 'TOPSIS and Modified TOPSIS: A comparative analysis', Decision Analytics Journal, vol. 2, pp. 100021-100021.
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Chakraborty, SC, Qamruzzaman, M, Zaman, MWU, Alam, MM, Hossain, MD, Pramanik, BK, Nguyen, LN, Nghiem, LD, Ahmed, MF, Zhou, JL, Mondal, MIH, Hossain, MA, Johir, MAH, Ahmed, MB, Sithi, JA, Zargar, M & Moni, MA 2022, 'Metals in e-waste: Occurrence, fate, impacts and remediation technologies', Process Safety and Environmental Protection, vol. 162, pp. 230-252.
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Chakraborty, SC, Zaman, MWU, Hoque, M, Qamruzzaman, M, Zaman, JU, Hossain, D, Pramanik, BK, Nguyen, LN, Nghiem, LD, Mofijur, M, Mondal, MIH, Sithi, JA, Shahriar, SMS, Johir, MAH & Ahmed, MB 2022, 'Metals extraction processes from electronic waste: constraints and opportunities', Environmental Science and Pollution Research, vol. 29, no. 22, pp. 32651-32669.
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Chalmers, T & Lal, S 2022, 'Assessing cardiovascular links to depression and anxiety in Australian professional drivers', Journal of Integrative Neuroscience, vol. 21, no. 1, pp. 043-043.
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Chalmers, T, Eaves, S, Lees, T, Lin, C, Newton, PJ, Clifton‐Bligh, R, McLachlan, CS, Gustin, SM & Lal, S 2022, 'The relationship between neurocognitive performance and HRV parameters in nurses and non‐healthcare participants', Brain and Behavior, vol. 12, no. 3.
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Chandra Adhikari, S, Kumar Chanda, R, Bhowmick, S, Nath Mondal, R & Chandra Saha, S 2022, 'Pressure-Induced Instability Characteristics of a Transient Flow and Energy Distribution through a Loosely Bent Square Duct', Energy Engineering, vol. 119, no. 1, pp. 429-451.
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Chandra Shit, R, Sharma, S, Watters, P, Yelamarthi, K, Pradhan, B, Davison, R, Morgan, G & Puthal, D 2022, 'Privacy‐preserving cooperative localization in vehicular edge computing infrastructure', Concurrency and Computation: Practice and Experience, vol. 34, no. 14.
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Chandran, M, Ebeling, PR, Mitchell, PJ & Nguyen, TV 2022, 'Harmonization of Osteoporosis Guidelines: Paving the Way for Disrupting the Status Quo in Osteoporosis Management in the Asia Pacific', Journal of Bone and Mineral Research, vol. 37, no. 4, pp. 608-615.
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Chandrasekar, T, Raju, SK, Ramachandran, M, Patan, R & Gandomi, AH 2022, 'Lung cancer disease detection using service-oriented architectures and multivariate boosting classifier', Applied Soft Computing, vol. 122, pp. 108820-108820.
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Chang, X, Ren, P, Xu, P, Li, Z, Chen, X & Hauptmann, AG 2022, 'A Comprehensive Survey of Scene Graphs: Generation and Application', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1.
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Chang, Z, Long, G, Xie, Y & Zhou, JL 2022, 'Chemical effect of sewage sludge ash on early-age hydration of cement used as supplementary cementitious material', Construction and Building Materials, vol. 322, pp. 126116-126116.
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Chang, Z, Long, G, Xie, Y & Zhou, JL 2022, 'Pozzolanic reactivity of aluminum-rich sewage sludge ash: Influence of calcination process and effect of calcination products on cement hydration', Construction and Building Materials, vol. 318, pp. 126096-126096.
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Chang, Z, Long, G, Xie, Y & Zhou, JL 2022, 'Recycling sewage sludge ash and limestone for sustainable cementitious material production', Journal of Building Engineering, vol. 49, pp. 104035-104035.
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Chen, D, Liu, Y, Li, M, Guo, P, Zeng, Z, Hu, J & Jay Guo, Y 2022, 'A polarization programmable antenna array', Engineering.
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Chen, D, Wu, C, Li, J & Liao, K 2022, 'A numerical study of gas explosion with progressive venting in a utility tunnel', Process Safety and Environmental Protection, vol. 162, pp. 1124-1138.
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Chen, G, Zhao, L, Gao, D, Yuan, J, Bai, J & Wang, W 2022, 'Flexural Tensile Behavior of Single and Novel Multiple Hooked-End Steel Fiber–Reinforced Notched Concrete Beams', Journal of Materials in Civil Engineering, vol. 34, no. 6.
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Chen, H, Demerdash, NAO, EL-Refaie, AM, Guo, Y, Hua, W & Lee, CHT 2022, 'Investigation of a 3D-Magnetic Flux PMSM With High Torque Density for Electric Vehicles', IEEE Transactions on Energy Conversion, vol. 37, no. 2, pp. 1442-1454.
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Chen, H, Yin, H, Chen, T, Wang, W, Li, X & Hu, X 2022, 'Social Boosted Recommendation With Folded Bipartite Network Embedding', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 2, pp. 914-926.
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Chen, J, Wu, Y, Yang, Y, Wen, S, Shi, K, Bermak, A & Huang, T 2022, 'An Efficient Memristor-Based Circuit Implementation of Squeeze-and-Excitation Fully Convolutional Neural Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 4, pp. 1779-1790.
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Recently, there has been a surge of interest in applying memristors to hardware implementations of deep neural networks due to various desirable properties of the memristor, such as nonvolativity, multivalue, and nanosize. Most existing neural network circuit designs, however, are based on generic frameworks that are not optimized for memristors. Furthermore, to the best of our knowledge, there are no existing efficient memristor-based implementations of complex neural network operators, such as deconvolutions and squeeze-and-excitation (SE) blocks, which are critical for achieving high accuracy in common medical image analysis applications, such as semantic segmentation. This article proposes convolution-kernel first (CKF), an efficient scheme for designing memristor-based fully convolutional neural networks (FCNs). Compared with existing neural network circuits, CKF enables effective parameter pruning, which significantly reduces circuit power consumption. Furthermore, CKF includes the novel, memristor-optimized implementations of deconvolution layers and SE blocks. Simulation results on real medical image segmentation tasks confirm that CKF obtains up to 56.2% reduction in terms of computations and 33.62-W reduction in terms of power consumption in the circuit after weight pruning while retaining high accuracy on the test set. Moreover, the pruning results can be applied directly to existing circuits without any modification for the corresponding system.
Chen, L, Chen, L, Ge, Z, Sun, Y, Hamilton, T & Zhu, X 2022, 'A W-Band SPDT Switch With 15-dBm P1dB in 55-nm Bulk CMOS', IEEE Microwave and Wireless Components Letters, pp. 1-4.
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Chen, Q, Guo, D, Ke, W, Xu, C & Nimbalkar, S 2022, 'Novel Open Trench Techniques in Mitigating Ground-Borne Vibrations due to Traffic under a Wide Range of Ground Conditions', International Journal of Geomechanics, vol. 22, no. 6.
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Chen, S, Eager, D & Zhao, L 2022, 'Enhanced frequency synchronization for concurrent aeroelastic and base vibratory energy harvesting using a softening nonlinear galloping energy harvester', Journal of Intelligent Material Systems and Structures, vol. 33, no. 5, pp. 687-702.
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This paper proposes a softening nonlinear aeroelastic galloping energy harvester for enhanced energy harvesting from concurrent wind flow and base vibration. Traditional linear aeroelastic energy harvesters have poor performance with quasi-periodic oscillations when the base vibration frequency deviates from the aeroelastic frequency. The softening nonlinearity in the proposed harvester alters the self-excited galloping frequency and simultaneously extends the large-amplitude base-excited oscillation to a wider frequency range, achieving frequency synchronization over a remarkably broadened bandwidth with periodic oscillations for efficient energy conversion from dual sources. A fully coupled aero-electro-mechanical model is built and validated with measurements on a devised prototype. At a wind speed of 5.5 m/s and base acceleration of 0.1 g, the proposed harvester improves the performance by widening the effective bandwidth by 300% compared to the linear counterpart without sacrificing the voltage level. The influences of nonlinearity configuration, excitation magnitude, and electromechanical coupling strength on the mechanical and electrical behavior are examined. The results of this paper form a baseline for future efficiency enhancement of energy harvesting from concurrent wind and base vibration utilizing monostable stiffness nonlinearities.
Chen, S-L, Liu, Y, Zhu, H, Chen, D & Guo, YJ 2022, 'Millimeter-Wave Cavity-Backed Multi-Linear Polarization Reconfigurable Antenna', IEEE Transactions on Antennas and Propagation, vol. 70, no. 4, pp. 2531-2542.
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Chen, S-L, Liu, Y, Ziolkowski, RW, Li, Z & Guo, YJ 2022, 'High-Gain Single-Feed Overmoded Cavity Antenna With Closely Spaced Phased Patch Surface', IEEE Transactions on Antennas and Propagation, vol. 70, no. 1, pp. 229-239.
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Chen, S-L, Wu, G-B, Wong, H, Chen, B-J, Chan, CH & Jay Guo, Y 2022, 'Millimeter-Wave Slot-Based Cavity Antennas with Flexibly-Chosen Linear Polarization', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Slot-based cavity antennas are hailed as promising candidates for millimeter-wave applications. Nevertheless, the linear-polarization (LP) angle of their broadside main beam is limited by the slots etched on the cavity’s top surface. In this work, an innovative technique is developed to significantly improve the selection flexibility of their LP inclination angle. It is attained by an integration of a single-layer, closely-spaced C-shaped patch surface. A TE710-mode slot-based cavity antenna is employed as the base configuration, which radiates a broadside beam with its LP along ϕ=90°. To effectively predict and monitor the polarization conversion of the surface-integrated TE710-mode cavity antenna, an analysis method using a unit cavity extracted from its original cavity antenna is presented. A subsequent surface-integrated system with the specified 45°-LP was then simulated, fabricated, and measured. The measured results validate that a 45°-LP state is achieved with an operating bandwidth from 33.3 to 36.5 GHz. Further investigation is conducted to flexibly choose the LP direction from ϕ=15° to 165°. Two more examples with the fabricated antenna prototypes successfully radiate the specified ϕ=15° and 75° LP beam, respectively. This near-field polarization conversion surface can be generalized to cavities with different resonant modes.
Chen, T, Xie, G-S, Yao, Y, Wang, Q, Shen, F, Tang, Z & Zhang, J 2022, 'Semantically Meaningful Class Prototype Learning for One-Shot Image Segmentation', IEEE Transactions on Multimedia, vol. 24, pp. 968-980.
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Chen, W-H, Chen, K-H, Chein, R-Y, Ong, HC & Arunachalam, KD 2022, 'Optimization of hydrogen enrichment via palladium membrane in vacuum environments using Taguchi method and normalized regression analysis', International Journal of Hydrogen Energy.
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Chen, W-H, Hoang, AT, Nižetić, S, Pandey, A, Cheng, CK, Luque, R, Ong, HC, Thomas, S & Nguyen, XP 2022, 'Biomass-derived biochar: From production to application in removing heavy metal-contaminated water', Process Safety and Environmental Protection, vol. 160, pp. 704-733.
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Chen, X, Chen, H, Yang, L, Wei, W & Ni, B-J 2022, 'A comprehensive analysis of evolution and underlying connections of water research themes in the 21st century', Science of The Total Environment, vol. 835, pp. 155411-155411.
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Chen, X, Chen, S, Yao, J, Zheng, H, Zhang, Y & Tsang, IW 2022, 'Learning on Attribute-Missing Graphs', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 2, pp. 740-757.
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Chen, X, Gao, C, Li, C, Yang, Y & Meng, D 2022, 'Infrared Action Detection in the Dark via Cross-Stream Attention Mechanism', IEEE Transactions on Multimedia, vol. 24, pp. 288-300.
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Action detection plays an important role in the field of video understanding and attracts considerable attention in the last decade. However, current action detection methods are mainly based on visible videos, and few of them consider scenes with low-light, where actions are difficult to be detected by existing methods, or even by human eyes. Compared with visible videos, infrared videos are more suitable for the dark environment and resistant to background clutter. In this paper, we investigate the temporal action detection problem in the dark by using infrared videos, which is, to the best of our knowledge, the first attempt in the action detection community. Our model takes the whole video as input, a Flow Estimation Network (FEN) is employed to generate the optical flow for infrared data, and it is optimized with the whole network to obtain action-related motion representations. After feature extraction, the infrared stream and flow stream are fed into a Selective Cross-stream Attention (SCA) module to narrow the performance gap between infrared and visible videos. The SCA emphasizes informative snippets and focuses on the more discriminative stream automatically. Then we adopt a snippet-level classifier to obtain action scores for all snippets and link continuous snippets into final detection results. All these modules are trained in an end-to-end manner. We collect an Infrared action Detection (InfDet) dataset obtained in the dark and conduct extensive experiments to verify the effectiveness of the proposed method. Experimental results show that our proposed method surpasses the state-of-the-art temporal action detection methods designed for visible videos, and it also achieves the best performance compared with other infrared action recognition methods on both InfAR and Infrared-Visible datasets.
Chen, X, Li, F, Huo, P, Liu, J, Yang, L, Li, X, Wei, W & Ni, B-J 2022, 'Influences of longitudinal gradients on methane-driven membrane biofilm reactor for complete nitrogen removal: A model-based investigation', Water Research, vol. 220, pp. 118665-118665.
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Chen, X, Li, Y, Yao, L, Adeli, E, Zhang, Y & Wang, X 2022, 'Generative adversarial U-Net for domain-free few-shot medical diagnosis', Pattern Recognition Letters, vol. 157, pp. 112-118.
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Chen, X, Wen, H, Ni, W, Zhang, S, Wang, X, Xu, S & Pei, Q 2022, 'Distributed Online Optimization of Edge Computing With Mixed Power Supply of Renewable Energy and Smart Grid', IEEE Transactions on Communications, vol. 70, no. 1, pp. 389-403.
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Chen, X, Yao, L, Wang, X, Sun, A, Zhang, W & Sheng, QZ 2022, 'Generative adversarial reward learning for generalized behavior tendency inference', IEEE Transactions on Knowledge and Data Engineering.
Chen, Y, Shimoni, O, Huang, G, Wen, S, Liao, J, Duong, HTT, Maddahfar, M, Su, QP, Ortega, DG, Lu, Y, Campbell, DH, Walsh, BJ & Jin, D 2022, 'Upconversion nanoparticle‐assisted single‐molecule assay for detecting circulating antigens of aggressive prostate cancer', Cytometry Part A, vol. 101, no. 5, pp. 400-410.
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Chen, Y, Zhao, L, Zhang, Y, Huang, S & Dissanayake, G 2022, 'Anchor Selection for SLAM Based on Graph Topology and Submodular Optimization', IEEE Transactions on Robotics, vol. 38, no. 1, pp. 329-350.
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Chen, Z, Ren, Z, Zheng, R, Gao, H & Ni, B-J 2022, 'Migration behavior of impurities during the purification of waste graphite powders', Journal of Environmental Management, vol. 315, pp. 115150-115150.
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Chen, Z, Tang, M-C, Li, M, Yi, D, Mu, D & Ziolkowski, RW 2022, 'Wideband, High-Density Circularly Polarized Array With Reduced Mutual Coupling and Enhanced Realized Gain', IEEE Transactions on Antennas and Propagation, vol. 70, no. 2, pp. 1132-1143.
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Chen, Z, Wang, S, Fu, A, Gao, Y, Yu, S & Deng, RH 2022, 'LinkBreaker: Breaking the Backdoor-Trigger Link in DNNs via Neurons Consistency Check', IEEE Transactions on Information Forensics and Security, vol. 17, pp. 2000-2014.
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Chen, Z, Wei, W & Ni, B-J 2022, 'Transition metal chalcogenides as emerging electrocatalysts for urea electrolysis', Current Opinion in Electrochemistry, vol. 31, pp. 100888-100888.
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Chen, Z, Wei, W, Chen, H & Ni, B-J 2022, 'Eco-designed electrocatalysts for water splitting: A path toward carbon neutrality', International Journal of Hydrogen Energy.
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Chen, Z, Wei, W, Song, L & Ni, B-J 2022, 'Hybrid Water Electrolysis: A New Sustainable Avenue for Energy-Saving Hydrogen Production', Sustainable Horizons, vol. 1, pp. 100002-100002.
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Chen, Z, Zheng, R, Li, S, Wang, R, Wei, W, Wei, W, Ni, B-J & Chen, H 2022, 'Dual-anion etching induced in situ interfacial engineering for high-efficiency oxygen evolution', Chemical Engineering Journal, vol. 431, pp. 134304-134304.
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Chen, Z, Zheng, R, Wei, W, Wei, W, Ni, B-J & Chen, H 2022, 'Unlocking the electrocatalytic activity of natural chalcopyrite using mechanochemistry', Journal of Energy Chemistry, vol. 68, pp. 275-283.
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Chen, Z, Zheng, R, Wei, W, Wei, W, Zou, W, Li, J, Ni, B-J & Chen, H 2022, 'Recycling spent water treatment adsorbents for efficient electrocatalytic water oxidation reaction', Resources, Conservation and Recycling, vol. 178, pp. 106037-106037.
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Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Bui, XT, Wei, W, Ni, B, Varjani, S & Hoang, NB 2022, 'Enhanced photo-fermentative biohydrogen production from biowastes: An overview', Bioresource Technology, vol. 357, pp. 127341-127341.
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Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Deng, L, Chen, Z, Ye, Y, Bui, XT & Hoang, NB 2022, 'Advanced strategies for enhancing dark fermentative biohydrogen production from biowaste towards sustainable environment', Bioresource Technology, vol. 351, pp. 127045-127045.
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Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Zhang, S, Deng, S, An, D & Hoang, NB 2022, 'Impact factors and novel strategies for improving biohydrogen production in microbial electrolysis cells', Bioresource Technology, vol. 346, pp. 126588-126588.
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Cheng, H-C, Hanson, EP, Datta, N & Hsieh, M-H 2022, 'Duality between source coding with quantum side information and classical-quantum channel coding', IEEE Transactions on Information Theory, pp. 1-1.
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Cheng, Z, Ye, D, Zhu, T, Zhou, W, Yu, PS & Zhu, C 2022, 'Multi‐agent reinforcement learning via knowledge transfer with differentially private noise', International Journal of Intelligent Systems, vol. 37, no. 1, pp. 799-828.
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Chepurin, D, Chamoli, U & Diwan, AD 2022, 'Bony Stress and Its Association With Intervertebral Disc Degeneration in the Lumbar Spine: A Systematic Review of Clinical and Basic Science Studies', Global Spine Journal, vol. 12, no. 5, pp. 964-979.
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Study Design: Translational review encompassing basic science and clinical evidence. Objectives: Multiple components of the lumbar spine interact during its normal and pathological function. Bony stress in the lumbar spine is recognized as a factor in the development of pars interarticularis defect and stress fractures, but its relationship with intervertebral disc (IVD) degeneration is not well understood. Therefore, we conducted a systematic review to examine the relationship between bony stress and IVD degeneration. Methods: Online databases Scopus, PubMed and MEDLINE via OVID were searched for relevant studies published between January 1980-February 2020, using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. Two authors independently analyzed the data, noting characteristics and biases in various studies. Results: Thirty-two articles were included in the review: 8 clinical studies, 9 finite element modeling studies, 3 in-vivo biomechanical testing studies, and 12 in-vitro biomechanical testing studies. Of the 32 articles, 19 supported, 4 rejected and 9 made no conclusion on the hypothesis that there is a positive associative relationship between IVD degeneration and bony stress. However, sufficient evidence was not available to confirm or reject a causal relationship. Conclusions: Most studies suggest that the prevalence of IVD degeneration increases in the presence of bony stress; whether a causal relationship exists is unclear. The literature recommends early diagnosis and clinical suspicion of IVD degeneration and bony stress. Longitudinal studies are required to explore causal relationships between IVD degeneration and bony st...
Chiniforush, AA, Gharehchaei, M, Akbar Nezhad, A, Castel, A, Moghaddam, F, Keyte, L, Hocking, D & Foster, S 2022, 'Numerical simulation of risk mitigation strategies for early-age thermal cracking and DEF in concrete', Construction and Building Materials, vol. 322, pp. 126478-126478.
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Choi, PJ, Lim, S, Shon, H & An, AK 2022, 'Incorporation of negatively charged silver nanoparticles in outer-selective hollow fiber forward osmosis (OSHF-FO) membrane for wastewater dewatering', Desalination, vol. 522, pp. 115402-115402.
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Choo, Y, Snyder, RL, Shah, NJ, Abel, BA, Coates, GW & Balsara, NP 2022, 'Complete Electrochemical Characterization and Limiting Current of Polyacetal Electrolytes', Journal of The Electrochemical Society, vol. 169, no. 2, pp. 020538-020538.
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We investigate a polyacetal-based electrolyte, poly(1,3,6-trioxocane) (P(2EO-MO)) mixed with lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) salt, and report full electrochemical characterization of the transport parameters and a thermodynamic property in comparison to the previously reported poly(ethylene oxide) (PEO) electrolyte data [D. Gribble et al., J. Electrochem. Soc., 166, A3228 (2019)]. While the steady-state current fraction (ρ
+) of P(2EO-MO) electrolyte is greater than that of PEO electrolyte in the entire salt concentration window we explored, the rigorously defined transference number using Newman’s concentrated solution theory (
t
+
0
) appears to be similar to that of PEO electrolyte. On the basis of full electrochemical characterization, we calculate the salt concentration profile as a function of position in the cell and predict limiting current density (i
L
...
Chu, NH, Hoang, DT, Nguyen, DN, Van Huynh, N & Dutkiewicz, E 2022, 'Joint Speed Control and Energy Replenishment Optimization for UAV-assisted IoT Data Collection with Deep Reinforcement Transfer Learning', IEEE Internet of Things Journal, pp. 1-1.
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Clemon, LM 2022, 'Rapid estimation of viral emission source location via genetic algorithm', Computational Mechanics, vol. 69, no. 5, pp. 1213-1224.
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AbstractIndoor spread of infectious diseases is well-studied as a common transmission route. For highly infectious diseases, like Sars-CoV-2, considering poorly or semi ventilated areas outdoors is increasingly important. This is important in communities with high proportions of infected people, highly infectious variants, or where spread is difficult to manage. This work develops a simulation framework based on probabilistic distributions of viral particles, decay, and infection. The methodology reduces the computational cost of generating rapid estimations of a wide variety of scenarios compared to other simulation methods with high computational cost and more fidelity. Outdoor predictions are provided in example applications for a gathering of five people with oscillating wind and a public speaking event. The results indicate that infection is sensitive to population density and outdoor transmission is plausible and likely locations of a virtual super-spreader are identified. Outdoor gatherings should consider precautions to reduce infection spread.
Cui, H, Wang, W, Xu, F, Saha, S & Liu, Q 2022, 'Free convection flow and heat transfer within attics in cold climate', Thermal Science, no. 00, pp. 39-39.
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The transitional free convection flow and heat transfer within attics in cold
climate are investigated using three-dimensional numerical simulations for a
range of Rayleigh numbers from 103 to 106 and height-length ratios from 0.1
to 1.5. The development process of free convection in the attic could be
classified into three stages: an initial stage, a transitional stage and a
fully developed stage. Flow structures in different stages including
transverse and longitudinal rolls are critically analyzed in terms of the
location and strength of convection rolls and their impacts on the heat
transfer. The transition to unsteady flow and asymmetry flow in the fully
developed stage is discussed for the fixed height-length ratio 0.5. Various
flow regimes are given in a bifurcation diagram in the parameter space of
Rayleigh numbers (102<Ra<107) for height-length ratios (0.1<A<1.5). The time
series of heat transfer rate through the bottom wall is quantified for
different height-length ratios. The overall heat transfer rate for the low
Prandtl fluid (Pr=0.7) could be enhanced based on three-dimensional flow
structure.
Cui, L, Qu, Y, Xie, G, Zeng, D, Li, R, Shen, S & Yu, S 2022, 'Security and Privacy-Enhanced Federated Learning for Anomaly Detection in IoT Infrastructures', IEEE Transactions on Industrial Informatics, vol. 18, no. 5, pp. 3492-3500.
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Cui, Z, Chen, H, Cui, L, Liu, S, Liu, X, Xu, G & Yin, H 2022, 'Reinforced KGs reasoning for explainable sequential recommendation', World Wide Web, vol. 25, no. 2, pp. 631-654.
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da Rocha, CG, Saldanha, RB, Tonini de Araújo, M & Consoli, NC 2022, 'Social and environmental assessments of Eco-friendly Pavement alternatives', Construction and Building Materials, vol. 325, pp. 126736-126736.
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Dai, P, Hassan, M, Sun, X, Zhang, M, Bian, Z & Liu, D 2022, 'A framework for multi-robot coverage analysis of large and complex structures', Journal of Intelligent Manufacturing, vol. 33, no. 5, pp. 1545-1560.
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Dai, Y, Zhang, X, Liu, Y, Yu, H, Su, W, Zhou, J, Ye, Q & Huang, Z 2022, '1,6;2,3-Bis-BN Cyclohexane: Synthesis, Structure, and Hydrogen Release', Journal of the American Chemical Society, vol. 144, no. 19, pp. 8434-8438.
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Dang, B-T, Bui, X-T, Tran, DPH, Hao Ngo, H, Nghiem, LD, Hoang, T-K-D, Nguyen, P-T, Nguyen, HH, Vo, T-K-Q, Lin, C, Yi Andrew Lin, K & Varjani, S 2022, 'Current application of algae derivatives for bioplastic production: A review', Bioresource Technology, vol. 347, pp. 126698-126698.
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Dang, B-T, Nguyen, T-T, Ngo, HH, Pham, M-D-T, Le, LT, Nguyen, N-K-Q, Vo, T-D-H, Varjani, S, You, S-J, Lin, KA, Huynh, K-P-H & Bui, X-T 2022, 'Influence of C/N ratios on treatment performance and biomass production during co-culture of microalgae and activated sludge', Science of The Total Environment, vol. 837, pp. 155832-155832.
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Dang, TD, Hoang, D & Nguyen, DN 2022, 'Trust-Based Scheduling Framework for Big Data Processing with MapReduce', IEEE Transactions on Services Computing, vol. 15, no. 1, pp. 279-293.
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Security and privacy have become a great concern in cloud computing platforms in which users risk the leakage of their private data. The leakage can happen while the data is at rest (in storage), in processing, or on moving within a cloud or between different cloud infrastructures, e.g., from private to public clouds. This paper focuses on protecting data "in processing". For big data applications, the MapReduce framework has been proven as an efficient solution and has been widely deployed, e.g., in healthcare and business data analysis. In this article, we propose a trust-based framework for MapReduce in big data processing tasks. Specifically, we first quantify and propose to assign the sensitive values for data and trust values for map and reduce slots. We then compute the trust value of each resource employed in the big data processing tasks. Depending on the data's sensitivity level of a task, the task requires a given level of trust (i.e., higher sensitive data requires servers/slots with higher trust level). The MapReduce scheduling problem is then formulated as the maximum weighted matching problem of a bipartite graph that aims to maximize the total trust value over all possible assignments subject to various trust requirement of different tasks. The problem is known to be NP-hard. To tackle it, we observe that within a computing node (VM), slots share the same trust value granted from the secured transformation phase. This helps reduce the number of slot nodes of a weight bipartite graph. Leveraging this fact, we propose an efficient heuristic algorithm that achieves 94.7% of the optimal solution obtained via exhaustive search. Extensive simulations show that the trust-based scheduling scheme provides much higher protection for data sensitivity while ensuring good performance for big data applications.
Daniel, S 2022, 'A phenomenographic outcome space for ways of experiencing lecturing', Higher Education Research & Development, vol. 41, no. 3, pp. 681-698.
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Darwish, A, Halkon, B & Oberst, S 2022, 'Non-contact vibro-acoustic object recognition using laser Doppler vibrometry and convolutional neural networks'.
Darwish, A, Halkon, B, Rothberg, S, Oberst, S & Fitch, R 2022, 'A comparison of time and frequency domain-based approaches to laser Doppler vibrometer instrument vibration correction', Journal of Sound and Vibration, vol. 520, pp. 116607-116607.
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Dayarathne, HNP, Angove, MJ, Paudel, SR, Ngo, HH, Guo, W & Mainali, B 2022, 'Optimisation of dual coagulation process for the removal of turbidity in source water using streaming potential', Groundwater for Sustainable Development, vol. 16, pp. 100714-100714.
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De Carvalho Gomes, S, Zhou, JL, Zeng, X & Long, G 2022, 'Water treatment sludge conversion to biochar as cementitious material in cement composite', Journal of Environmental Management, vol. 306, pp. 114463-114463.
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Deady, M, Glozier, N, Calvo, R, Johnston, D, Mackinnon, A, Milne, D, Choi, I, Gayed, A, Peters, D, Bryant, R, Christensen, H & Harvey, SB 2022, 'Preventing depression using a smartphone app: a randomized controlled trial', Psychological Medicine, vol. 52, no. 3, pp. 457-466.
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AbstractBackgroundThere is evidence that depression can be prevented; however, traditional approaches face significant scalability issues. Digital technologies provide a potential solution, although this has not been adequately tested. The aim of this study was to evaluate the effectiveness of a new smartphone app designed to reduce depression symptoms and subsequent incident depression amongst a large group of Australian workers.MethodsA randomized controlled trial was conducted with follow-up assessments at 5 weeks and 3 and 12 months post-baseline. Participants were employed Australians reporting no clinically significant depression. The intervention group (N = 1128) was allocated to use HeadGear, a smartphone app which included a 30-day behavioural activation and mindfulness intervention. The attention-control group (N = 1143) used an app which included a 30-day mood monitoring component. The primary outcome was the level of depressive symptomatology (PHQ-9) at 3-month follow-up. Analyses were conducted within an intention-to-treat framework using mixed modelling.ResultsThose assigned to the HeadGear arm had fewer depressive symptoms over the course of the trial compared to those assigned to the control (F3,734.7 = 2.98, p = 0.031). Prevalence of depression over the 12-month period was 8.0% and 3.5% for controls and HeadGear recipients, respectively, with odds of depression caseness amongst the intervention group of 0.43 (p<...
Deng, J-Y, Luo, R-Q, Lin, W, Zhang, Y, Chen, Z & Guo, L-X 2022, 'Longitudinally Miniaturized H-Plane Horn Antenna With -30 dB Sidelobes Realized by Simple Blocks Redistributing the Aperture Field', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Deng, K, Zhu, S, Dai, W, Yang, C & Wen, S 2022, 'New Criteria on Stability of Dynamic Memristor Delayed Cellular Neural Networks', IEEE Transactions on Cybernetics, vol. 52, no. 6, pp. 5367-5379.
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IEEE Dynamic memristor (DM)-cellular neural networks (CNNs), which replace a linear resistor with flux-controlled memristor in the architecture of each cell of traditional CNNs, have attracted researchers' attention. Compared with common neural networks, the DM-CNNs have an outstanding merit: when a steady state is reached, all voltages, currents, and power consumption of DM-CNNs disappeared, in the meantime, the memristor can store the computation results by serving as nonvolatile memories. The previous study on stability of DM-CNNs rarely considered time delay, while delay is quite common and highly impacts the stability of the system. Thus, taking the time delay effect into consideration, we extend the original system to DM-D(delay)CNNs model. By using the Lyapunov method and the matrix theory, some new sufficient conditions for the global asymptotic stability and global exponential stability with a known convergence rate of DM-DCNNs are obtained. These criteria generalized some known conclusions and are easily verified. Moreover, we find DM-DCNNs have 3ⁿ equilibrium points (EPs) and 2ⁿ of them are locally asymptotically stable. These results are obtained via a given constitutive relation of memristor and the appropriate division of state space. Combine with these theoretical results, the applications of DM-DCNNs can be extended to other fields, such as associative memory, and its advantage can be used in a better way. Finally, numerical simulations are offered to illustrate the effectiveness of our theoretical results.
Deng, L, Guo, W, Ngo, HH, Zhang, X, Chen, C, Chen, Z, Cheng, D, Ni, S-Q & Wang, Q 2022, 'Recent advances in attached growth membrane bioreactor systems for wastewater treatment', Science of The Total Environment, vol. 808, pp. 152123-152123.
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Deng, L, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Pandey, A, Varjani, S & Hoang, NB 2022, 'Recent advances in circular bioeconomy based clean technologies for sustainable environment', Journal of Water Process Engineering, vol. 46, pp. 102534-102534.
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Deng, S, Ji, J, Wen, G & Xu, H 2022, 'Two-parameter dynamics of an autonomous mechanical governor system with time delay', Nonlinear Dynamics, vol. 107, no. 1, pp. 641-663.
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Deng, S, Peng, S, Ngo, HH, Oh, SJ-A, Hu, Z, Yao, H & Li, D 2022, 'Characterization of nitrous oxide and nitrite accumulation during iron (Fe(0))- and ferrous iron (Fe(II))-driven autotrophic denitrification: mechanisms, environmental impact factors and molecular microbial characterization', Chemical Engineering Journal, vol. 438, pp. 135627-135627.
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The iron (Fe(0))-/ferrous iron (Fe(II))-driven autotrophic denitrification processes have been alternative methods for nitrogen removal from low organic carbon (OC) wastewater, but the accumulation of nitrous oxide (N2O) and nitrite (NO2−) along with these processes remains unclear. This research aimed to systematically characterize the N2O/NO2− accumulation in Fe(0)-/Fe(II)-ADN processes through investigating the mechanisms, impact factors, and molecular biological characteristics. Results showed that Fe(II)-ADN was effective in NO3− reduction but was less efficient in N2O reduction (k = 0.50 h−1) than Fe(0)-ADN (k = 1.82 h−1). NO2−/N2O accumulation in Fe(II)-ADN (28.6%/30.7%) was much higher than that in Fe(0)-ADN (12.6%/1.5%). Introducing hydrogenotrophic denitrification (H-ADN) into Fe(II)-ADN system significantly (p < 0.05) reduced NO2−/N2O accumulation. Fe(0)-ADN was proved a coupled process of Fe(II)- and H-ADN by in-situ generating Fe(II)/H2, and Fe(II)-ADN and H-ADN mainly contributed to NO3− and NO2−/N2O reduction, respectively. Optimum pH (7.5) and temperature (30–35 °C) were confirmed with controlled NO2–/N2O accumulation and effective denitrification. Dosing inorganic carbon (IC) and OC enhanced denitrification and reduced NO2–/N2O accumulation, where OC was more efficient with an optimum dosage of 0.25 mmol C/mmol N. 16S rRNA high-throughput sequencing and Pearson Correlation Coefficients verified that Thiobacillus was the main contributor to NO3− reduction, whereas Thauera and Acidovorax possessed high NO2−/N2O reduction capability. Real-time quantitative polymerase chain reaction and enzyme activity assay demonstrated that the nitrite reductase encoded by gene nirK and the nitrous oxide reductase encoded by gene nosZ were efficient in catalyzing the further reduction of NO2− and N2O, respectively. This study could provide an in-depth understanding of NO2−/N2O accumulation in Fe(II)-/Fe(0)-ADN processes and contribute to their application, optimiza...
Deng, Y & Feng, Y 2022, 'Formal semantics of a classical-quantum language', Theoretical Computer Science, vol. 913, pp. 73-93.
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Dhull, P, Guevara, AP, Ansari, M, Pollin, S, Shariati, N & Schreurs, D 2022, 'Internet of Things Networks: Enabling Simultaneous Wireless Information and Power Transfer', IEEE Microwave Magazine, vol. 23, no. 3, pp. 39-54.
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Dikshit, A, Pradhan, B & Santosh, M 2022, 'Artificial neural networks in drought prediction in the 21st century–A scientometric analysis', Applied Soft Computing, vol. 114, pp. 108080-108080.
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Dikshit, A, Pradhan, B, Assiri, ME, Almazroui, M & Park, H-J 2022, 'Solving transparency in drought forecasting using attention models', Science of The Total Environment, vol. 837, pp. 155856-155856.
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Ding, A, Lin, W, Chen, R, Ngo, HH, Zhang, R, He, X, Nan, J, Li, G & Ma, J 2022, 'Improvement of sludge dewaterability by energy uncoupling combined with chemical re-flocculation: Reconstruction of floc, distribution of extracellular polymeric substances, and structure change of proteins', Science of The Total Environment, vol. 816, pp. 151646-151646.
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Ding, L, Razavi Bazaz, S, Hall, T, Vesey, G & Ebrahimi Warkiani, M 2022, 'Giardia purification from fecal samples using rigid spiral inertial microfluidics', Biomicrofluidics, vol. 16, no. 1, pp. 014105-014105.
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Giardia is one of the most common waterborne pathogens causing around 200 × 106 diarrheal infections annually. It is of great interest to microbiological research as it is among the oldest known eukaryotic cells. Purifying Giardia from fecal samples for both research and diagnostic purposes presents one of the most difficult challenges. Traditional purification methods rely on density gradient centrifugation, membrane-based filtration, and sedimentation methods, which suffer from low recovery rates, high costs, and poor efficiency. Here, we report on the use of microfluidics to purify Giardia cysts from mouse feces. We propose a rigid spiral microfluidic device with a trapezoidal cross section to effectively separate Giardia from surrounding debris. Our characterizations reveal that the recovery rate is concentration-dependent, and our proposed device can achieve recovery rates as high as 75% with 0.75 ml/min throughput. Moreover, this device can purify Giardia from extremely turbid samples to a level where cysts are visually distinguishable with just one round of purification. This highly scalable and versatile 3D printed microfluidic device is then capable of further purifying or enhancing the recovery rate of the samples by recirculation. This device also has the potential to purify other gastrointestinal pathogens of similar size, and throughput can be significantly increased by parallelization.
Ding, W, Ming, Y, Cao, Z & Lin, C-T 2022, 'A Generalized Deep Neural Network Approach for Digital Watermarking Analysis', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 3, pp. 613-627.
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Technology advancement has facilitated digital content, such as images, being acquired in large volumes. However, requirement from the privacy or legislation perspective still demands the need for intellectual content protection. In this paper, we propose a deep neural network (DNN) based watermarking method to achieve this goal. Instead of training a neural network for protecting a specific image, we train the network on an image dataset and generalize the trained model to protect distinct test images in a bulk manner. Respective evaluations from both the subjective and objective aspects confirm the generality and practicality of our proposed method. To demonstrate the robustness of this general neural watermarking approach, commonly used attacks are applied to the watermarked images to examine the corresponding extracted watermarks, which still retain sufficient recognizable traits for some occasions. Testing on distinctive dataset shows the satisfying generalization of our proposed method, and practice such as loss function adjustment can cater to the capacity requirement of complicated watermark. We also discuss some traits of the trained model, which incur the vulnerability to JPEG compression attack. However, remedy seeking for this can potentially open a window to understand the underlying working principle of DNN in future work. Considering its performance and economy, it is concluded that subsequent studies that generalize our work on utilizing DNN for intellectual content protection might be a promising research trend.
Ding, Y, Wu, Y, Huang, C, Tang, S, Wu, F, Yang, Y, Zhu, W & Zhuang, Y 2022, 'NAP: Neural architecture search with pruning', Neurocomputing, vol. 477, pp. 85-95.
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Ding, Z, Chen, C, Wen, S, Li, S & Wang, L 2022, 'Lag projective synchronization of nonidentical fractional delayed memristive neural networks', Neurocomputing, vol. 469, pp. 138-150.
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Dinh, TH, Singh, AK, Linh Trung, N, Nguyen, DN & Lin, C-T 2022, 'EEG Peak Detection in Cognitive Conflict Processing Using Summit Navigator and Clustering-Based Ranking', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 1548-1556.
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Do, MH, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Pandey, A, Sharma, P, Varjani, S, Nguyen, TAH & Hoang, NB 2022, 'A dual chamber microbial fuel cell based biosensor for monitoring copper and arsenic in municipal wastewater', Science of The Total Environment, vol. 811, pp. 152261-152261.
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Do, NTT, Lin, C-T & Gramann, K 2022, 'Human brain dynamics in active spatial navigation', Scientific Reports, vol. 11, no. 1, pp. 1-12.
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Dobrzanski, J, Buswell, R, Cavalaro, S, Kinnell, P, Wang, W, Xu, J & Kolawole, J 2022, 'Milling a cement-based 3D printable mortar in its green state using a ball-nosed cutter', Cement and Concrete Composites, vol. 125, pp. 104266-104266.
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Dong, M, Yuan, F, Yao, L, Wang, X, Xu, X & Zhu, L 2022, 'A survey for trust-aware recommender systems: A deep learning perspective', Knowledge-Based Systems, vol. 249, pp. 108954-108954.
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Dong, W, Li, W, Guo, Y, Qu, F, Wang, K & Sheng, D 2022, 'Piezoresistive performance of hydrophobic cement-based sensors under moisture and chloride-rich environments', Cement and Concrete Composites, vol. 126, pp. 104379-104379.
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Dong, W, Li, W, Guo, Y, Wang, K & Sheng, D 2022, 'Mechanical properties and piezoresistive performances of intrinsic graphene nanoplate/cement-based sensors subjected to impact load', Construction and Building Materials, vol. 327, pp. 126978-126978.
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Dong, W, Li, W, Sun, Z, Ibrahim, I & Sheng, D 2022, 'Intrinsic graphene/cement-based sensors with piezoresistivity and superhydrophobicity capacities for smart concrete infrastructure', Automation in Construction, vol. 133, pp. 103983-103983.
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Dong, W, Li, W, Wang, K, Shah, SP & Sheng, D 2022, 'Multifunctional cementitious composites with integrated self-sensing and self-healing capacities using carbon black and slaked lime', Ceramics International, vol. 48, no. 14, pp. 19851-19863.
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Dong, W, Lu, Z, He, L, Geng, L, Guo, X & Zhang, J 2022, 'Low-carbon optimal planning of an integrated energy station considering combined power-to-gas and gas-fired units equipped with carbon capture systems', International Journal of Electrical Power & Energy Systems, vol. 138, pp. 107966-107966.
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Dong, Y, Guo, S, Wang, Q, Yu, S & Yang, Y 2022, 'Content Caching-Enhanced Computation Offloading in Mobile Edge Service Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 1, pp. 872-886.
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Dorji, P, Phuntsho, S, Kim, DI, Lim, S, Park, MJ, Hong, S & Shon, HK 2022, 'Electrode for selective bromide removal in membrane capacitive deionisation', Chemosphere, vol. 287, pp. 132169-132169.
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Dorji, U, Dorji, P, Shon, H, Badeti, U, Dorji, C, Wangmo, C, Tijing, L, Kandasamy, J, Vigneswaran, S, Chanan, A & Phuntsho, S 2022, 'On-site domestic wastewater treatment system using shredded waste plastic bottles as biofilter media: Pilot-scale study on effluent standards in Bhutan', Chemosphere, vol. 286, pp. 131729-131729.
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Dou, Y, Li, Y, Lin, Z, Yue, S & Zhu, J 2022, 'An Improved Cross-Yoke SST for Accurate 1-D and 2-D Magnetic Testing of Fe-Si Sheets', IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-10.
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Du, X, Sui, Y, Liu, Z & Ai, J 2022, 'An Empirical Study of Fault Triggers in Deep Learning Frameworks', IEEE Transactions on Dependable and Secure Computing, pp. 1-1.
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Du, Y, Huang, T, You, S, Hsieh, M-H & Tao, D 2022, 'Quantum circuit architecture search for variational quantum algorithms', npj Quantum Information, vol. 8, no. 1.
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AbstractVariational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a stronger expressivity, while the accumulated noise may render a poor trainability. To maximally improve the robustness and trainability of VQAs, here we devise a resource and runtime efficient scheme termed quantum architecture search (QAS). In particular, given a learning task, QAS automatically seeks a near-optimal ansatz (i.e., circuit architecture) to balance benefits and side-effects brought by adding more noisy quantum gates to achieve a good performance. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks. In the problems studied, numerical and experimental results show that QAS cannot only alleviate the influence of quantum noise and barren plateaus but also outperforms VQAs with pre-selected ansatze.
Duan, N, Xu, W, Ma, X, Wang, S & Zhu, J 2022, 'Magnetic Characteristic Analysis of High Temperature Superconductors by the Elemental Operator Model', IEEE Transactions on Magnetics, vol. 58, no. 2, pp. 1-4.
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In this paper, to accurately analysis the magnetic properties of high temperature superconductor, a superconductor hysteresis model is proposed. Based on the physical mechanism, the hysteresis loop of high temperature superconductor can be decomposed into an antimagnetic curve and a paramagnetic loop. The calculated and measured results of silver-sheathed Bi2223 superconducting tape samples under external magnetic field are compared to show the effectiveness and accuracy of the presented method.
Duong, HC, Nghiem, LD, Ansari, AJ, Vu, TD & Nguyen, KM 2022, 'Assessment of pilot direct contact membrane distillation regeneration of lithium chloride solution in liquid desiccant air-conditioning systems using computer simulation', Environmental Science and Pollution Research, vol. 29, no. 28, pp. 41941-41952.
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Duong, TD, Li, Q & Xu, G 2022, 'Stochastic intervention for causal inference via reinforcement learning', Neurocomputing, vol. 482, pp. 40-49.
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Dwivedi, KA, Kumar, V, Wang, C-T, Chong, WT & Ong, HC 2022, 'Design and feasibility study of novel swirler incorporated microbial fuel cell for enhancing power generation and domestic wastewater treatment', Journal of Cleaner Production, vol. 337, pp. 130382-130382.
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Ejegwa, PA, Wen, S, Feng, Y, Zhang, W & Tang, N 2022, 'Novel Pythagorean Fuzzy Correlation Measures Via Pythagorean Fuzzy Deviation, Variance, and Covariance With Applications to Pattern Recognition and Career Placement', IEEE Transactions on Fuzzy Systems, vol. 30, no. 6, pp. 1660-1668.
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El‐Hawat, O, Fatahi, B & Taciroglu, E 2022, 'Novel post‐tensioned rocking piles for enhancing the seismic resilience of bridges', Earthquake Engineering & Structural Dynamics, vol. 51, no. 2, pp. 393-417.
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Elsemary, MT, Maritz, MF, Smith, LE, Warkiani, M, Bandara, V, Napoli, S, Barry, SC, Coombs, JT & Thierry, B 2022, 'Inertial Microfluidic Purification of CAR‐T‐Cell Products', Advanced Biology, vol. 6, no. 1, pp. 2101018-2101018.
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Eslahi, H, Hamilton, TJ & Khandelwal, S 2022, 'Compact and Energy Efficient Neuron With Tunable Spiking Frequency in 22-nm FDSOI', IEEE Transactions on Nanotechnology, vol. 21, pp. 189-195.
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Fahmideh, M, Grundy, J, Beydoun, G, Zowghi, D, Susilo, W & Mougouei, D 2022, 'A model-driven approach to reengineering processes in cloud computing', Information and Software Technology, vol. 144, pp. 106795-106795.
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Fallahpoor, M, Chakraborty, S, Heshejin, MT, Chegeni, H, Horry, MJ & Pradhan, B 2022, 'Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection', Computers in Biology and Medicine, vol. 145, pp. 105464-105464.
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Fan, H, Yang, Y & Kankanhalli, M 2022, 'Point Spatio-Temporal Transformer Networks for Point Cloud Video Modeling', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1.
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Fan, H, Zhuo, T, Yu, X, Yang, Y & Kankanhalli, M 2022, 'Understanding Atomic Hand-Object Interaction With Human Intention', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 1, pp. 275-285.
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Hand-object interaction plays a very important role when humans manipulate objects. While existing methods focus on improving hand-object recognition with fully automatic methods, human intention has been largely neglected in the recognition process, thus leading to undesirable interaction descriptions. To better interpret human-object interaction that is aligned to human intention, we argue that a reference specifying human intention should be taken into account. Thus, we propose a new approach to represent interactions while reflecting human purpose with three key factors, i.e., hand, object and reference. Specifically, we design a pattern of <hand-object, object-reference, hand, object, reference> (HOR) to recognize intention based atomic hand-object interactions. This pattern aims to model interactions with the states of hand, object, reference and their relationships. Furthermore, we design a simple yet effective Spatially Part-based (3+1)D convolutional neural network, namely SP(3+1)D, which leverages 3D and 1D convolutions to model visual dynamics and object position changes based on our HOR, respectively. With the help of our SP(3+1)D network, the recognition results are able to indicate human purposes accurately. To evaluate the proposed method, we annotate a Something-1.3k dataset, which contains 10 atomic hand-object interactions and about 130 videos for each interaction. Experimental results on Something-1.3k demonstrate the effectiveness of our SP(3+1)D network.
Fan, S, Ni, W, Tian, H, Huang, Z & Zeng, R 2022, 'Carrier Phase-Based Synchronization and High-Accuracy Positioning in 5G New Radio Cellular Networks', IEEE Transactions on Communications, vol. 70, no. 1, pp. 564-577.
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Fan, X, Li, Y, Chen, L, Li, B & Sisson, S 2022, 'Hawkes processes with stochastic exogenous effects for continuous-time interaction modelling', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1.
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Fang, Z, Lu, J, Liu, F & Zhang, G 2022, 'Semi-supervised Heterogeneous Domain Adaptation: Theory and Algorithms', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1.
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Fani, A, Golroo, A, Ali Mirhassani, S & Gandomi, AH 2022, 'Pavement maintenance and rehabilitation planning optimisation under budget and pavement deterioration uncertainty', International Journal of Pavement Engineering, vol. 23, no. 2, pp. 414-424.
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Fani, A, Naseri, H, Golroo, A, Mirhassani, SA & Gandomi, AH 2022, 'A progressive hedging approach for large-scale pavement maintenance scheduling under uncertainty', International Journal of Pavement Engineering, vol. 23, no. 7, pp. 2460-2472.
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Farhangi, M, Barzegarkhoo, R, Aguilera, RP, Lee, SS, Lu, DD-C & Siwakoti, YP 2022, 'A Single-Source Single-Stage Switched-Boost Multilevel Inverter: Operation, Topological Extensions, and Experimental Validation', IEEE Transactions on Power Electronics, vol. 37, no. 9, pp. 11258-11271.
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Fathipour, H, Payan, M, Jamshidi Chenari, R & Fatahi, B 2022, 'General failure envelope of eccentrically and obliquely loaded strip footings resting on an inherently anisotropic granular medium', Computers and Geotechnics, vol. 146, pp. 104734-104734.
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Fattahi Nafchi, R, Raeisi Vanani, H, Noori Pashaee, K, Samadi Brojeni, H & Ostad-Ali-Askari, K 2022, 'Investigation on the effect of inclined crest step pool on scouring protection in erodible river beds', Natural Hazards, vol. 110, no. 3, pp. 1495-1505.
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Faust, O, Hong, W, Loh, HW, Xu, S, Tan, R-S, Chakraborty, S, Barua, PD, Molinari, F & Acharya, UR 2022, 'Heart rate variability for medical decision support systems: A review', Computers in Biology and Medicine, vol. 145, pp. 105407-105407.
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Fayaz, H, Khan, SA, Saleel, CA, Shaik, S, Yusuf, AA, Veza, I, Fattah, IMR, Rawi, NFM, Asyraf, MRM & Alarifi, IM 2022, 'Developments in Nanoparticles Enhanced Biofuels and Solar Energy in Malaysian Perspective: A Review of State of the Art', Journal of Nanomaterials, vol. 2022, pp. 1-22.
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The rapid rise in global oil prices, the scarcity of petroleum sources, and environmental concerns have all created severe issues. As a result of the country’s rapid expansion and financial affluence, Malaysia’s energy consumption has skyrocketed. Biodiesel and solar power are currently two of the most popular alternatives to fossil fuels in Malaysia. These two types of renewable energy sources appear to be viable options because of their abundant availability together with environmental and performance competence to highly polluting and fast depleting fossil fuels. The purpose of adopting renewable technology is to expand the nation’s accessibility to a reliable and secure power supply. The current review article investigates nonconventional energy sources added with nanosized metal particles called as nanomaterials including biodiesel and solar, as well as readily available renewable energy options. Concerning the nation’s energy policy agenda, the sources of energy demand are also investigated. The article evaluates Malaysia’s existing position in renewable energy industries, such as biodiesel and solar, as well as the impact of nanomaterials. This review article discusses biodiesel production, applications, and government policies in Malaysia, as well as biodiesel consumption and recent developments in the bioenergy sector, such as biodiesel property modifications utilizing nanoparticle additions. In addition, the current review study examines the scope of solar energy, different photovoltaic concentrators, types of solar energy harvesting systems, photovoltaic electricity potential in Malaysia, and the experimental setup of solar flat plate collectors (FPC) with nanotechnology.
Fazal, MAU, Ferguson, S & Saeed, Z 2022, 'Investigating cognitive workload in concurrent speech-based information communication', International Journal of Human-Computer Studies, vol. 157, pp. 102728-102728.
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Fazeli, A, Nguyen, HH, Tuan, HD & Poor, HV 2022, 'Non-Coherent Multi-Level Index Modulation', IEEE Transactions on Communications, vol. 70, no. 4, pp. 2240-2255.
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Feng, H, Li, L, Wang, W, Cheng, Z & Gao, D 2022, 'Mechanical properties of high ductility hybrid fibres reinforced magnesium phosphate cement-based composites', Composite Structures, vol. 284, pp. 115219-115219.
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Feng, K, Ji, JC, Li, Y, Ni, Q, Wu, H & Zheng, J 2022, 'A novel cyclic-correntropy based indicator for gear wear monitoring', Tribology International, vol. 171, pp. 107528-107528.
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Feng, L, Huang, Y, Tsang, IW, Gupta, A, Tang, K, Tan, KC & Ong, Y-S 2022, 'Towards Faster Vehicle Routing by Transferring Knowledge From Customer Representation', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 2, pp. 952-965.
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Ferguson, BM, Entezari, A, Fang, J & Li, Q 2022, 'Optimal placement of fixation system for scaffold-based mandibular reconstruction', Journal of the Mechanical Behavior of Biomedical Materials, vol. 126, pp. 104855-104855.
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Fleck, R, Gill, RL, Saadeh, S, Pettit, T, Wooster, E, Torpy, F & Irga, P 2022, 'Urban green roofs to manage rooftop microclimates: A case study from Sydney, Australia', Building and Environment, vol. 209, pp. 108673-108673.
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Fleck, R, Westerhausen, MT, Killingsworth, N, Ball, J, Torpy, FR & Irga, PJ 2022, 'The hydrological performance of a green roof in Sydney, Australia: A tale of two towers', Building and Environment, vol. 221, pp. 109274-109274.
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Flores‐Sosa, M, Avilés‐Ochoa, E & Merigó, JM 2022, 'Exchange rate and volatility: A bibliometric review', International Journal of Finance & Economics, vol. 27, no. 1, pp. 1419-1442.
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Flores-Sosa, M, Avilés-Ochoa, E, Merigó, JM & Kacprzyk, J 2022, 'The OWA operator in multiple linear regression', Applied Soft Computing, vol. 124, pp. 108985-108985.
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Fu, A, Yu, S, Zhang, Y, Wang, H & Huang, C 2022, 'NPP: A New Privacy-Aware Public Auditing Scheme for Cloud Data Sharing with Group Users', IEEE Transactions on Big Data, vol. 8, no. 1, pp. 14-24.
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Fu, D, Zhao, X & Zhu, J 2022, 'A Novel Robust Super-Twisting Nonsingular Terminal Sliding Mode Controller for Permanent Magnet Linear Synchronous Motors', IEEE Transactions on Power Electronics, vol. 37, no. 3, pp. 2936-2945.
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Fu, J, Huang, C-H, Dang, C & Wang, Q 2022, 'A review on treatment of disinfection byproduct precursors by biological activated carbon process', Chinese Chemical Letters, vol. 33, no. 10, pp. 4495-4504.
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Fumanal-Idocin, J, Takac, Z, Fernandez, J, Sanz, JA, Goyena, H, Lin, C-T, Wang, Y-K & Bustince, H 2022, 'Interval-Valued Aggregation Functions Based on Moderate Deviations Applied to Motor-Imagery-Based Brain–Computer Interface', IEEE Transactions on Fuzzy Systems, vol. 30, no. 7, pp. 2706-2720.
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Ganaie, MA, Tanveer, M & Lin, C-T 2022, 'Large scale fuzzy least squares twin SVMs for class imbalance learning', IEEE Transactions on Fuzzy Systems, pp. 1-1.
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Gandomi, AH & Roke, DA 2022, 'A Multiobjective Evolutionary Framework for Formulation of Nonlinear Structural Systems', IEEE Transactions on Industrial Informatics, vol. 18, no. 9, pp. 5795-5803.
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Gao, F, Zhang, S, He, X & Sheng, D 2022, 'Experimental Study on Migration Behavior of Sandy Silt under Cyclic Load', Journal of Geotechnical and Geoenvironmental Engineering, vol. 148, no. 5.
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Gao, H, Huang, J, Tao, Y, Hussain, W & Huang, Y 2022, 'The Joint Method of Triple Attention and Novel Loss Function for Entity Relation Extraction in Small Data-Driven Computational Social Systems', IEEE Transactions on Computational Social Systems, pp. 1-11.
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Gao, H, Qin, X, Barroso, RJD, Hussain, W, Xu, Y & Yin, Y 2022, 'Collaborative Learning-Based Industrial IoT API Recommendation for Software-Defined Devices: The Implicit Knowledge Discovery Perspective', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 1, pp. 66-76.
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Gao, H, Qiu, B, Duran Barroso, RJ, Hussain, W, Xu, Y & Wang, X 2022, 'TSMAE: A Novel Anomaly Detection Approach for Internet of Things Time Series Data Using Memory-Augmented Autoencoder', IEEE Transactions on Network Science and Engineering, pp. 1-1.
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Gao, L, Li, S, Xu, X, Zou, C & Zhang, G 2022, 'Highly Sensitive H2 Sensors Based on Co3O4/PEI-CNTs at Room Temperature', Journal of Nanomaterials, vol. 2022, pp. 1-8.
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The highly dispersed Co3O4 on the surface of CNTs modified with polyethylenimine (PEI) was synthesized using the hydrothermal method. In the CNT-Co3O4 composite materials, CNTs not only provide the substrate for the Co3O4 nanoparticles but also prevent their aggregation. Furthermore, the interaction between Co3O4 and CNTs modified with polyethylenimine (PEI) helps to improve the gas sensing performance. In particular, the CNT-Co3O4 composite synthesized at 190°C shows the outstanding sensitive characteristics to H2 with a lower detection limit of 30 ppm at room temperature. The obtained CNT-Co3O4 sensor displays excellent selectivity and stability to H2. The energy band model of the conductive mechanism has been built to explain the resistance change when the gas sensor is exposed to the H2. Hence, the CNT-Co3O4 composite material presents highly promising applications in H2 gas sensing.
Gao, W, Wu, J & Xu, G 2022, 'Detecting Duplicate Questions in Stack Overflow via Source Code Modeling', International Journal of Software Engineering and Knowledge Engineering, vol. 32, no. 02, pp. 227-255.
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Stack Overflow is one of the most popular Question-Answering sites for programmers. However, it faces the problem of question duplication, where newly created questions are identical to previous questions. Existing works on duplicate question detection in Stack Overflow extract a set of textual features on the question pairs and use supervised learning approaches to classify duplicate question pairs. However, they do not consider the source code information in the questions. While in some cases, the intention of a question is mainly represented by the source code. In this paper, we aim to learn the semantics of a question by combining both text features and source code features. We use word embedding and convolutional neural networks to extract textual features from questions to overcome the lexical gap issue. We use tree-based convolutional neural networks to extract structural and semantic features from source code. In addition, we perform multi-task learning by combining the duplication question detection task with a question tag prediction side task. We conduct extensive experiments on the Stack Overflow dataset and show that our approach can detect duplicate questions with higher recall and MRR compared with baseline approaches on Python and Java programming languages.
Gao, X & Zhang, Y 2022, 'What is behind the globalization of technology? Exploring the interplay of multi-level drivers of international patent extension in the solar photovoltaic industry', Renewable and Sustainable Energy Reviews, vol. 163, pp. 112510-112510.
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Gao, X, Yang, F, Yan, Z, Zhao, J, Li, S, Nghiem, L, Li, G & Luo, W 2022, 'Humification and maturation of kitchen waste during indoor composting by individual households', Science of The Total Environment, vol. 814, pp. 152509-152509.
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Garg, H, Gandomi, AH, Ali, Z & Mahmood, T 2022, 'Neutrality aggregation operators based on complex q‐rung orthopair fuzzy sets and their applications in multiattribute decision‐making problems', International Journal of Intelligent Systems, vol. 37, no. 1, pp. 1010-1051.
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Gaur, VK, Sharma, P, Gupta, S, Varjani, S, Srivastava, JK, Wong, JWC & Ngo, HH 2022, 'Opportunities and challenges in omics approaches for biosurfactant production and feasibility of site remediation: Strategies and advancements', Environmental Technology & Innovation, vol. 25, pp. 102132-102132.
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Gautam, S, Xiao, W, Lu, DD-C, Ahmed, H & Guerrero, JM 2022, 'Development of Frequency-Fixed All-Pass Filter-Based Single-Phase Phase-Locked Loop', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 1, pp. 506-517.
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Gill, AQ 2022, 'The digital ecosystem information framework: Insights from action design research', Journal of Information Science.
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Goh, BHH, Chong, CT, Ong, HC, Milano, J, Shamsuddin, AH, Lee, XJ & Ng, J-H 2022, 'Strategies for fuel property enhancement for second-generation multi-feedstock biodiesel', Fuel, vol. 315, pp. 123178-123178.
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Goh, BHH, Chong, CT, Ong, HC, Seljak, T, Katrašnik, T, Józsa, V, Ng, J-H, Tian, B, Karmarkar, S & Ashokkumar, V 2022, 'Recent advancements in catalytic conversion pathways for synthetic jet fuel produced from bioresources', Energy Conversion and Management, vol. 251, pp. 114974-114974.
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Gong, S, Ball, J & Surawski, N 2022, 'Urban land-use land-cover extraction for catchment modelling using deep learning techniques', Journal of Hydroinformatics, vol. 24, no. 2, pp. 388-405.
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Abstract
Throughout the world, the likelihood of floods and managing the associated risk are a concern to many catchment managers and the population residing in those catchments. Catchment modelling is a popular approach to predicting the design flood quantiles of a catchment with complex spatial characteristics and limited monitoring data to obtain the necessary information for preparing the flood risk management plan. As an important indicator of urbanisation, land use land cover (LULC) plays a critical role in catchment parameterisation and modelling the rainfall–runoff process. Digitising LULC from remote sensing imagery of urban catchment is becoming increasingly difficult and time-consuming as the variability and diversity of land uses occur during urban development. In recent years, deep learning neural networks (DNNs) have achieved remarkable image classification and segmentation outcomes with the powerful capacity to process complex workflow and features, learn sophisticated relationships and produce superior results. This paper describes end-to-end data assimilation and processing path using U-net and DeepLabV3+, also proposes a novel approach integrated with the clustering algorithm MeanShift. These methods were developed to generate pixel-based LULC semantic segmentation from high-resolution satellite imagery of the Alexandria Canal catchment, Sydney, Australia, and assess the applicability of their outputs as inputs to different catchment modelling systems. A significant innovation is using the MeanShift clustering algorithm to reduce the spatial noise in the raw image and propagate it to the deep learning network to improve prediction. All three methods achieved excellent classification performance, where the MeanShift+U-net has the highest accuracy and consistency on the test imagery. The final suitability assessment illustrates that all three methods are more suitable for the parameterisation of semi...
Gong, S, Guo, Z, Wen, S & Huang, T 2022, 'Stabilization Analysis for Linear Disturbed Event-Triggered Control System with Packet Losses', IEEE Transactions on Control of Network Systems, pp. 1-1.
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Gong, Y, Bai, Y, Zhao, D & Wang, Q 2022, 'Aggregation of carboxyl-modified polystyrene nanoplastics in water with aluminum chloride: Structural characterization and theoretical calculation', Water Research, vol. 208, pp. 117884-117884.
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Gong, Y, Li, Z, Zhang, J, Liu, W & Zheng, Y 2022, 'Online Spatio-Temporal Crowd Flow Distribution Prediction for Complex Metro System', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 2, pp. 865-880.
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Goudarzi, S, Ahmad Soleymani, S, Hossein Anisi, M, Ciuonzo, D, Kama, N, Abdullah, S, Abdollahi Azgomi, M, Chaczko, Z & Azmi, A 2022, 'Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network', Computers, Materials & Continua, vol. 70, no. 1, pp. 715-738.
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Grigorev, A, Mihaita, A-S, Lee, S & Chen, F 2022, 'Incident duration prediction using a bi-level machine learning framework with outlier removal and intra–extra joint optimisation', Transportation Research Part C: Emerging Technologies, vol. 141, pp. 103721-103721.
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Grosser, M, Lin, H, Wu, M, Zhang, Y, Tipper, S, Venter, D, Lu, J & dos Remedios, CG 2022, 'A bibliometric review of peripartum cardiomyopathy compared to other cardiomyopathies using artificial intelligence and machine learning', Biophysical Reviews, vol. 14, no. 1, pp. 381-401.
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Grzybowska, H, Wijayaratna, K, Shafiei, S, Amini, N & Travis Waller, S 2022, 'Ramp Metering Strategy Implementation: A Case Study Review', Journal of Transportation Engineering, Part A: Systems, vol. 148, no. 5.
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Gul, M, Kalam, MA, Mohd Zulkifli, NW, Hj. Hassan, M, Abbas, MM, Yousuf, S, Al-Dahiree, OS, Gaffar Abbas, MK, Ahmed, W & Imran, S 2022, 'Enhancing AW/EP tribological characteristics of biolubricant synthesized from chemically modified cotton methyl-esters by using nanoparticle as additives', Industrial Lubrication and Tribology, vol. 74, no. 4, pp. 411-420.
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Purpose
The purpose of this study is to improve the tribological characteristics of cotton-biolubricant by adding nanoparticles at extreme pressure (EP) conditions in comparison with commercial lubricant SAE-40.
Design/methodology/approach
This research involved the synthesis of cotton-biolubricant by transesterification process and then the addition of nanoparticles in it to improve anti wear (AW)/EP tribological behavior. SAE-40 was studied as a reference commercial lubricant. AW/EP characteristics of all samples were estimated by the four-ball tribo-tester according to the American Society for Testing and Materials D2783 standard.
Findings
The addition of 1-Wt.% TiO2 and Al2O3 with oleic acid surfactant in cotton-biolubricant decreased wear scar diameter effectively and enhanced the lubricity, load-wear-index, weld-load and flash-temperature-parameters. This investigation revealed that cotton-biolubricant with TiO2 nano-particle additive is more effective and will help in developing new efficient biolubricant to replace petroleum-based lubricants.
Research limitations/implications
Cotton biolubricant with TiO2 nano-particles appeared as an optimistic solution for the global bio-lubricant market.
Originality/value
No one has not studied the cotton biolubricant with nanoparticles for internal combustion engine applications at high temperature and EP conditions.
Gunatilake, A, Kodagoda, S & Thiyagarajan, K 2022, 'A Novel UHF-RFID Dual Antenna Signals Combined With Gaussian Process and Particle Filter for In-Pipe Robot Localization', IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 6005-6011.
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Guo, B, Zhang, X, Su, M, Ma, H, Yang, Y & Siwakoti, YP 2022, 'A Single-Phase Common-Ground Five-Level Transformerless Inverter with Low Component Count for PV Applications', IEEE Transactions on Industrial Electronics, pp. 1-1.
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Guo, CA & Guo, Y 2022, 'A General Approach for Synthesizing Multibeam Antenna Arrays Employing Generalized Joined Coupler Matrix', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Guo, H, Dai, R, Xie, M, Peng, LE, Yao, Z, Yang, Z, Nghiem, LD, Snyder, SA, Wang, Z & Tang, CY 2022, 'Tweak in Puzzle: Tailoring Membrane Chemistry and Structure toward Targeted Removal of Organic Micropollutants for Water Reuse', Environmental Science & Technology Letters, vol. 9, no. 4, pp. 247-257.
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Guo, J, Cao, L & Gong, Z 2022, 'Recurrent Coupled Topic Modeling over Sequential Documents', ACM Transactions on Knowledge Discovery from Data, vol. 16, no. 1, pp. 1-32.
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The abundant sequential documents such as online archival, social media, and news feeds are streamingly updated, where each chunk of documents is incorporated with smoothly evolving yet dependent topics. Such digital texts have attracted extensive research on dynamic topic modeling to infer hidden evolving topics and their temporal dependencies. However, most of the existing approaches focus on single-topic-thread evolution and ignore the fact that a current topic may be coupled with multiple relevant prior topics. In addition, these approaches also incur the intractable inference problem when inferring latent parameters, resulting in a high computational cost and performance degradation. In this work, we assume that a current topic evolves from all prior topics with corresponding coupling weights, forming the
multi-topic-thread evolution
. Our method models the dependencies between evolving topics and thoroughly encodes their complex multi-couplings across time steps. To conquer the intractable inference challenge, a new solution with a set of novel data augmentation techniques is proposed, which successfully discomposes the multi-couplings between evolving topics. A fully conjugate model is thus obtained to guarantee the effectiveness and efficiency of the inference technique. A novel Gibbs sampler with a backward–forward filter algorithm efficiently learns latent time-evolving parameters in a closed-form. In addition, the latent Indian Buffet Process compound distribution is exploited to automatically infer the overall topic number and customize the sparse topic proportions for each sequential document without bias. The proposed method is evaluated on both synthetic and real-world datasets against the competitive baselines, demonstrating its superiority over the baselines in terms of the low per-word perplexity, high coherent topics, and better document time prediction.
Guo, K & Guo, Y 2022, 'Design and Analysis of an Outer Mover Linear-Rotary Vernier Machine', Journal of Electrical Engineering & Technology, vol. 17, no. 2, pp. 1087-1095.
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Guo, X, Zhang, H, Tang, W, Lu, Z, Hua, C, Siwakoti, YP, Malinowski, M & Blaabjerg, F 2022, 'Overview of Recent Advanced Topologies for Transformerless Dual-Grounded Inverters', IEEE Transactions on Power Electronics, vol. 37, no. 10, pp. 12679-12704.
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Guo, Z, Lian, M, Wen, S & Huang, T 2022, 'An Adaptive Multi-Agent System With Duplex Control Laws for Distributed Resource Allocation', IEEE Transactions on Network Science and Engineering, vol. 9, no. 2, pp. 389-400.
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Guo, Z, Xiao, F, Sheng, B, Sun, L & Yu, S 2022, 'TWCC: A Robust Through-the-Wall Crowd Counting System Using Ambient WiFi Signals', IEEE Transactions on Vehicular Technology, vol. 71, no. 4, pp. 4198-4211.
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Gupta, BB, Chaudhary, P, Chang, X & Nedjah, N 2022, 'Smart defense against distributed Denial of service attack in IoT networks using supervised learning classifiers', Computers & Electrical Engineering, vol. 98, pp. 107726-107726.
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Gupta, BB, Tewari, A, Cvitić, I, Peraković, D & Chang, X 2022, 'Artificial intelligence empowered emails classifier for Internet of Things based systems in industry 4.0', Wireless Networks, vol. 28, no. 1, pp. 493-503.
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Halkon, B, Perrin, R & Guo, Z 2022, 'Extensional and inextensional modes of axisymmetric structures', Experimental Techniques: a publication for the practicing engineer.
Hallad, SA, Ganachari, SV, Soudagar, MEM, Banapurmath, NR, Hunashyal, AM, Fattah, IMR, Hussain, F, Mujtaba, MA, Afzal, A, Kabir, MS & Elfasakhany, A 2022, 'Investigation of flexural properties of epoxy composite by utilizing graphene nanofillers and natural hemp fibre reinforcement', Polymers and Polymer Composites, vol. 30, pp. 096739112210936-096739112210936.
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This study aims to determine the optimum reinforcement required to attain the best combination of flexural strength of modified green composites (graphene oxide + hemp fibre reinforced epoxy composites) for potential use in structural applications. An attempt was also made for the combination of graphene and hemp fibres to enhance load-bearing ability. The infusion of hemp and graphene was made by the weight of the base matrix (epoxy composite). Results showed that graphene reinforcement at 0.4 wt.% of matrix showed load-sustaining capacity of 0.76 kN or 760 MPa. In the case of hemp fibre reinforcement at 0.2 wt.% of the matrix, infusion showed enhanced load-bearing ability (0.79 kN or 790 MPa). However, the combination of graphene (0.1 wt.% graphene nanofillers) and hemp (5 wt.% hemp fibre) indicated a load-sustaining ability of 0.425 kN or 425 MPa, whereas maximum deflection was observed for specimen with hemp 7.5 % + graphene 0.2 % with 1.9 mm. Graphene addition to the modified composites in combination with natural fibres showed promising results in enhancing the mechanical properties under study. Moreover, graphene-modified composites exhibited higher thermal resistance compared to natural fibre reinforced composites. However, when nanofiller reinforcement exceeded a threshold value, the composites exhibited reduced flexural strength as a result of nanofiller agglomeration.
Hamdi, AMA, Hussain, FK & Hussain, OK 2022, 'Task offloading in vehicular fog computing: State-of-the-art and open issues', Future Generation Computer Systems, vol. 133, pp. 201-212.
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Hamidi, BA, Hosseini, SA & Hayati, H 2022, 'Forced torsional vibration of nanobeam via nonlocal strain gradient theory and surface energy effects under moving harmonic torque', Waves in Random and Complex Media, vol. 32, no. 1, pp. 318-333.
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Hamidi, BA, Hosseini, SA, Hayati, H & Hassannejad, R 2022, 'Forced axial vibration of micro and nanobeam under axial harmonic moving and constant distributed forces via nonlocal strain gradient theory', Mechanics Based Design of Structures and Machines, vol. 50, no. 5, pp. 1491-1505.
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Han, C, Li, W, Wang, J & Huang, Z 2022, 'Boron leaching: Creating vacancy-rich Ni for enhanced hydrogen evolution', Nano Research, vol. 15, no. 3, pp. 1868-1873.
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Creating vacancy is often highly effective in enhancing the hydrogen evolution performance of transition metal-based catalysts. Vacancy-rich Ni nanosheets have been fabricated via topochemical formation of two-dimentional (2D) Ni2B on graphene precursor followed by boron leaching. Anchored on graphene, a few atomic layered Ni2B nanosheets are first obtained by reduction and annealing. Large number of atomic vacancies are then generated in the Ni2B layer via leaching boron atoms. When used for hydrogen evolution reaction (HER), the vacancy-rich Ni/Ni(OH)2 heterostructure nanosheets demonstrate remarkable performance with a low overpotential of 159 mV at a current density of 10 mA·cm−2 in alkaline solution, a dramatic improvement over 262 mV of its precursor. This enhancement is associated with the formation of vacancies which introduce more active sites for HER along Ni/Ni(OH)2 heterointerfaces. This work offers a facile and universal route to introduce vacancies and improve catalytic activity. [Figure not available: see fulltext.]
Han, Z, Huo, J, Zhang, X, Ngo, HH, Guo, W, Du, Q, Zhang, Y, Li, C & Zhang, D 2022, 'Characterization and flocculation performance of a newly green flocculant derived from natural bagasse cellulose', Chemosphere, vol. 301, pp. 134615-134615.
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Hao, D, Ma, T, Jia, B, Wei, Y, Bai, X, Wei, W & Ni, B-J 2022, 'Small molecule π-conjugated electron acceptor for highly enhanced photocatalytic nitrogen reduction of BiOBr', Journal of Materials Science & Technology, vol. 109, pp. 276-281.
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Hao, D, Wei, Y, Mao, L, Bai, X, Liu, Y, Xu, B, Wei, W & Ni, B-J 2022, 'Boosted selective catalytic nitrate reduction to ammonia on carbon/bismuth/bismuth oxide photocatalysts', Journal of Cleaner Production, vol. 331, pp. 129975-129975.
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Hao, Y, Zhang, X, Du, Q, Wang, H, Ngo, HH, Guo, W, Zhang, Y, Long, T & Qi, L 2022, 'A new integrated single-chamber air-cathode microbial fuel cell - Anaerobic membrane bioreactor system for improving methane production and membrane fouling mitigation', Journal of Membrane Science, vol. 655, pp. 120591-120591.
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Haribabu, K, Sivasubramanian, V, Deepanraj, B & Ong, HC 2022, 'Thematic issue: Bioenergy and biorefinery approaches for environmental sustainability', Biomass Conversion and Biorefinery, vol. 12, no. 5, pp. 1433-1433.
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Hasan, H 2022, 'NUMERICAL SIMULATION OF PERVIOUS CONCRETE PILE IN LOOSE AND SILTY SAND AFTER TREATING WITH MICROBIALLY INDUCED CALCITE PRECIPITATION', International Journal of GEOMATE, vol. 22, no. 90.
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Hasanpour, S, Nouri, T, Blaabjerg, F & Siwakoti, YP 2022, 'High Step-Up SEPIC-Based Trans-Inverse DC-DC Converter with Quasi-Resonance Operation for Renewable Energy Applications', IEEE Transactions on Industrial Electronics, pp. 1-1.
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Hasanpour, S, Siwakoti, YP & Blaabjerg, F 2022, 'A New High Efficiency High Step-Up DC/DC Converter for Renewable Energy Applications', IEEE Transactions on Industrial Electronics, pp. 1-1.
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Hassani, S, Mousavi, M & Gandomi, AH 2022, 'A mode shape sensitivity-based method for damage detection of structures with closely-spaced eigenvalues', Measurement, vol. 190, pp. 110644-110644.
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Hassani, S, Mousavi, M & Gandomi, AH 2022, 'Damage detection of composite laminate structures using VMD of FRF contaminated by high percentage of noise', Composite Structures, vol. 286, pp. 115243-115243.
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He, F, Parvez Mahmud, MA, Kouzani, AZ, Anwar, A, Jiang, F & Ling, SH 2022, 'An Improved SLIC Algorithm for Segmentation of Microscopic Cell Images', Biomedical Signal Processing and Control, vol. 73, pp. 103464-103464.
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He, HS, Teng, JD, Zhang, S & Sheng, DC 2022, 'Rationality of frost susceptibility of soils', Yantu Gongcheng Xuebao/Chinese Journal of Geotechnical Engineering, vol. 44, no. 2, pp. 224-234.
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The frost heave and thaw weakening are the critical issues for the infrastructures in cold regions. How to reasonably assess the frost susceptibility of soils has been a hotspot in cold-region geotechnics. The frost susceptibility has been studied for about one hundred years since Casagrande (1931) proposed fine content as a main index to evaluate the frost susceptibility of soils. In most cases, the frost characteristics defined by fines content are clear and very simple, and work well in guiding the engineering construction in cold regions. However, the recent studies show that: (1) The frost heave occurs frequently in the subgrade which is designed and constructed absolutely according to the existing frost susceptibility criteria. (2) The current frost susceptibility criteria vary greatly in different countries and regions with different accuracies. (3) The vapour flow can lead to considerable frost heave in coarse-grained soils, which is not considered in the existing frost susceptibility. The above issues challenge the existing frost susceptibility. It is worth to analyze whether the concept of frost susceptibility is reasonable or not as well as its evaluation system. This study tries to analyze the advantages and disadvantages of the existing frost susceptibility criteria. The main findings are: (1) The reliability of the existing frost susceptibility is generally low, within the range of 50% to 80%. (2) The existing frost susceptibility criteria are not suitable to the case that the frost heave in coarse-grained soils is caused by vapour transfer. The freezing environmental factors should be considered in defining the frost susceptibility. (3) The existing frost susceptibility may be suitable to indicate the thaw weakening property of soils. The outcome of this study is helpful to replenishing the classification of frost susceptibility criteria. It would be of great significance to frost disaster prevention in cold regions.
He, T, Wu, M, Aguilera, RP, Lu, DD-C, Liu, Q & Vazquez, S 2022, 'Low Computational Burden Model Predictive Control for Single-Phase Cascaded H-Bridge Converters Without Weighting Factor', IEEE Transactions on Industrial Electronics, pp. 1-1.
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He, T, Wu, M, Lu, DD-C, Song, K & Zhu, J 2022, 'Model Predictive Sliding Control for Cascaded H-Bridge Multilevel Converters With Dynamic Current Reference Tracking', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 2, pp. 1409-1421.
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He, X, Wang, F, Li, W & Sheng, D 2022, 'Deep learning for efficient stochastic analysis with spatial variability', Acta Geotechnica, vol. 17, no. 4, pp. 1031-1051.
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He, Y, Liu, P, Zhu, L & Yang, Y 2022, 'Filter Pruning by Switching to Neighboring CNNs With Good Attributes', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-13.
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He, Z, Luo, Q, Li, Q, Zheng, G & Sun, G 2022, 'Fatigue behavior of CFRP/Al adhesive joints — Failure mechanisms study using digital image correlation (DIC) technique', Thin-Walled Structures, vol. 174, pp. 109075-109075.
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Heidari, A, Bansal, RC, Hossain, J & Zhu, J 2022, 'Strategic risk aversion of smart energy hubs in the joined energy markets applying a stochastic game approach', Journal of Cleaner Production, vol. 349, pp. 131386-131386.
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Hesam‐Shariati, N, Chang, W, Wewege, MA, McAuley, JH, Booth, A, Trost, Z, Lin, C, Newton‐John, T & Gustin, SM 2022, 'The analgesic effect of electroencephalographic neurofeedback for people with chronic pain: A systematic review and meta‐analysis', European Journal of Neurology, vol. 29, no. 3, pp. 921-936.
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Hidayat, A, Cheema, MA, Lin, X, Zhang, W & Zhang, Y 2022, 'Continuous monitoring of moving skyline and top-k queries', The VLDB Journal, vol. 31, no. 3, pp. 459-482.
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Ho, W-HJ, Law, AMK, Masle-Farquhar, E, Castillo, LE, Mawson, A, O’Bryan, MK, Goodnow, CC, Gallego-Ortega, D, Oakes, SR & Ormandy, CJ 2022, 'Activation of the viral sensor oligoadenylate synthetase 2 (Oas2) prevents pregnancy-driven mammary cancer metastases', Breast Cancer Research, vol. 24, no. 1.
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Abstract
Background
The interferon response can influence the primary and metastatic activity of breast cancers and can interact with checkpoint immunotherapy to modulate its effects. Using N-ethyl-N-nitrosourea mutagenesis, we found a mouse with an activating mutation in oligoadenylate synthetase 2 (Oas2), a sensor of viral double stranded RNA, that resulted in an interferon response and prevented lactation in otherwise healthy mice.
Methods
To determine if sole activation of Oas2 could alter the course of mammary cancer, we combined the Oas2 mutation with the MMTV-PyMT oncogene model of breast cancer and examined disease progression and the effects of checkpoint immunotherapy using Kaplan–Meier survival analysis with immunohistochemistry and flow cytometry.
Results
Oas2 mutation prevented pregnancy from increasing metastases to lung. Checkpoint immunotherapy with antibodies against programmed death-ligand 1 was more effective when the Oas2 mutation was present.
Conclusions
These data establish OAS2 as a therapeutic target for agents designed to reduce metastases and increase the effectiveness of checkpoint immunotherapy.
Hoang, AT, Foley, AM, Nižetić, S, Huang, Z, Ong, HC, Ölçer, AI, Pham, VV & Nguyen, XP 2022, 'Energy-related approach for reduction of CO2 emissions: A critical strategy on the port-to-ship pathway', Journal of Cleaner Production, vol. 355, pp. 131772-131772.
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Hoang, AT, Huang, Z, Nižetić, S, Pandey, A, Nguyen, XP, Luque, R, Ong, HC, Said, Z, Le, TH & Pham, VV 2022, 'Characteristics of hydrogen production from steam gasification of plant-originated lignocellulosic biomass and its prospects in Vietnam', International Journal of Hydrogen Energy, vol. 47, no. 7, pp. 4394-4425.
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Holloway, CL, Prajapati, N, Artusio-Glimpse, AB, Berweger, S, Simons, MT, Kasahara, Y, Alù, A & Ziolkowski, RW 2022, 'Rydberg atom-based field sensing enhancement using a split-ring resonator', Applied Physics Letters, vol. 120, no. 20, pp. 204001-204001.
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We investigate the use of a split-ring resonator (SRR) incorporated with an atomic-vapor cell to improve the sensitivity and the minimal detectable electric (E) field of Rydberg atom-based sensors. In this approach, a sub-wavelength SRR is placed around an atomic vapor-cell filled with cesium atoms for E-field measurements at 1.3 GHz. The SRR provides a factor of 100 in the enhancement of the E-field measurement sensitivity. Using electromagnetically induced transparency (EIT) with Aulter–Townes splitting, E-field measurements down to 5 mV/m are demonstrated with the SRR, while in the absence of the SRR, the minimal detectable field is 500 mV/m. We demonstrate that by combining EIT with a heterodyne Rydberg atom-based mixer approach, the SRR allows for a sensitivity of 5.5 μV/m[Formula: see text], which is two-orders of magnitude improvement in sensitivity than when the SRR is not used.
Horng, S-J, Supardi, J, Zhou, W, Lin, C-T & Jiang, B 2022, 'Recognizing Very Small Face Images Using Convolution Neural Networks', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 2103-2115.
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Horng, S-J, Vu, D-T, Nguyen, T-V, Zhou, W & Lin, C-T 2022, 'Recognizing Palm Vein in Smartphones Using RGB Images', IEEE Transactions on Industrial Informatics, vol. 18, no. 9, pp. 5992-6002.
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Hossain, SM, Ibrahim, I, Choo, Y, Razmjou, A, Naidu, G, Tijing, L, Kim, J-H & Shon, HK 2022, 'Preparation of effective lithium-ion sieve from sludge-generated TiO2', Desalination, vol. 525, pp. 115491-115491.
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Hossain, SM, Tijing, L, Suzuki, N, Fujishima, A, Kim, J-H & Shon, HK 2022, 'Visible light activation of photocatalysts formed from the heterojunction of sludge-generated TiO2 and g-CN towards NO removal', Journal of Hazardous Materials, vol. 422, pp. 126919-126919.
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The feasibility of preparing TiO2/g-CN heterojunction from Ti-incorporated dried dye wastewater sludge is explored in this study. Two reaction routes of composite formation were evaluated. In the initial approach, one-step calcination of dried sludge and melamine mixture @600 °C was carried out. Detailed morphological and chemical characterizations showed that the one-step calcination route did not create TiO2/g-CN composites; instead, only N-doped anatase TiO2 composites were formed. Moreover, due to the non-uniform composition of organic content in the dried sludge, it was not easy to control the N doping level by varying melamine content (0-100%) in the precursor mix. However, successful formation of anatase TiO2 and g-CN was observed when a two-step calcination route was followed, i.e., via synthesis of anatase TiO2 from dried sludge, and later development of heterojunction by calcining (@550 °C) the TiO2 and melamine mixture. X-ray diffraction along with infrared and X-ray photoelectron spectroscopy verified the effective heterojunction. In addition, maximum atmospheric NO removal under UV and visible light were observed for the prepared composite when the melamine content in the precursor mixture was 70%. After 1 h of UV and visible light irradiation, the best TiO2/g-CN composite removed 25.71% and 13.50% of NO, respectively. Optical characterization suggested that the enhanced NO oxidation under UV/visible light was due to the bandgap narrowing and diminished photogenerated electron-hole recombination.
Hosseinzadeh, A, Zhou, JL, Altaee, A & Li, D 2022, 'Machine learning modeling and analysis of biohydrogen production from wastewater by dark fermentation process', Bioresource Technology, vol. 343, pp. 126111-126111.
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Hosseinzadeh, A, Zhou, JL, Li, X, Afsari, M & Altaee, A 2022, 'Techno-economic and environmental impact assessment of hydrogen production processes using bio-waste as renewable energy resource', Renewable and Sustainable Energy Reviews, vol. 156, pp. 111991-111991.
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Hosseinzadeh, A, Zhou, JL, Zyaie, J, AlZainati, N, Ibrar, I & Altaee, A 2022, 'Machine learning-based modeling and analysis of PFOS removal from contaminated water by nanofiltration process', Separation and Purification Technology, vol. 289, pp. 120775-120775.
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Hu, JY, Zhang, SS, Chen, E & Li, WG 2022, 'A review on corrosion detection and protection of existing reinforced concrete (RC) structures', Construction and Building Materials, vol. 325, pp. 126718-126718.
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Hu, S, Ni, W, Wang, X & Jamalipour, A 2022, 'Disguised Tailing and Video Surveillance With Solar-Powered Fixed-Wing Unmanned Aerial Vehicle', IEEE Transactions on Vehicular Technology, vol. 71, no. 5, pp. 5507-5518.
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Hu, X, Zhu, T, Zhai, X, Wang, H, Zhou, W & Zhao, W 2022, 'Privacy Data Diffusion Modeling and Preserving in Online Social Network', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Huang, C, Yao, L, Wang, X, Sheng, QZ, Dustdar, S, Wang, Z & Xu, X 2022, 'Intent-aware interactive internet of things for enhanced collaborative ambient intelligence', IEEE Internet Computing.
Huang, G, Zhu, Y, Wen, S, Mei, H, Liu, Y, Wang, D, Maddahfar, M, Su, QP, Lin, G, Chen, Y & Jin, D 2022, 'Single Small Extracellular Vesicle (sEV) Quantification by Upconversion Nanoparticles', Nano Letters, vol. 22, no. 9, pp. 3761-3769.
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Huang, H, Zhang, J, Yu, L, Zhang, J, Wu, Q & Xu, C 2022, 'TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization With Few Labeled Samples', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 2, pp. 853-866.
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Huang, J, Luo, K, Cao, L, Wen, Y & Zhong, S 2022, 'Learning Multiaspect Traffic Couplings by Multirelational Graph Attention Networks for Traffic Prediction', IEEE Transactions on Intelligent Transportation Systems, pp. 1-15.
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Huang, L, Liu, Z, Wu, C, Liang, J & Pei, Q 2022, 'A three-dimensional indirect boundary integral equation method for the scattering of seismic waves in a poroelastic layered half-space', Engineering Analysis with Boundary Elements, vol. 135, pp. 167-181.
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Huang, S & Zhao, L 2022, '2021 IEEE RAS Winter School on Simultaneous Localization and Mapping in Deformable Environments [Education]', IEEE Robotics & Automation Magazine, vol. 29, no. 1, pp. 120-122.
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Huang, S, Shi, W, Xu, Z, Tsang, IW & Lv, J 2022, 'Efficient Federated Multi-view Learning', Pattern Recognition, pp. 108817-108817.
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Huang, X, Clemon, LM, Islam, MS & C. Saha, S 2022, 'Optimization of fluid characteristics in the main nozzle of an air-jet loom', Textile Research Journal, vol. 92, no. 3-4, pp. 525-538.
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As part of the propulsion system, the fluid dynamic features of the main nozzle can immediately affect the stability and efficiency of an air-jet loom. This study aims to optimize the fluid characteristics in the main nozzle of an air-jet loom. To investigate ways of weakening the effect of airflow congestion and backflow phenomenon occurring in the sudden expansion region, the computational fluid dynamics method is employed. Three-dimensional turbulence flow models for a regular main nozzle and 12 prototypes with different nozzle core tip geometry are built, simulated, and analyzed to get the optimum performance. Furthermore, a set of modified equations that consider the direction of airflow are proposed for better estimation of the friction force applied by the nozzle. The result shows that the nozzle core tip's geometry has a significant influence on the internal airflow, affecting the acceleration tube airflow velocity, turbulence intensity, and backflow strength of the sudden expansion region, and other critical fluid characteristics as well. Several proposed models have succeeded in reducing the backflow and outperforming the original design in many different aspects. Models A-60 and C-P, in particular, manage to increase the propulsion force by 37.6% and 20.2% in the acceleration tube while reducing the maximum backflow by 57.1% and 52.2%, respectively. These simulation results can provide invaluable information for the future optimization of the main nozzle.
Huang, X, Li, S, Zuo, Y, Fang, Y, Zhang, J & Zhao, X 2022, 'Unsupervised Point Cloud Registration by Learning Unified Gaussian Mixture Models', IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7028-7035.
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Huang, X, Nan, Y & Guo, YJ 2022, 'Radio Frequency Camera: A Noncoherent Circular Array SAR With Uncoordinated Illuminations', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-14.
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Huang, Y, Lee, CKC, Yam, Y-S, Mok, W-C, Zhou, JL, Zhuang, Y, Surawski, NC, Organ, B & Chan, EFC 2022, 'Rapid detection of high-emitting vehicles by on-road remote sensing technology improves urban air quality', Science Advances, vol. 8, no. 5.
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Vehicle emissions are the most important source of air pollution in the urban environment worldwide, and their detection and control are critical for protecting public health. Here, we report the use of on-road remote sensing (RS) technology for fast, accurate, and cost-effective identification of high-emitting vehicles as an enforcement program for improving urban air quality. Using large emission datasets from chassis dynamometer testing, RS, and air quality monitoring, we found that significant percentages of in-use petrol and LPG vehicles failed the emission standards, particularly the high-mileage fleets. The RS enforcement program greatly cleaned these fleets, in terms of high-emitter percentages, fleet average emissions, roadside and ambient pollutant concentrations, and emission inventory. The challenges of the current enforcement program are conservative setting of cut points, single-lane measurement sites, and lack of application experience in diesel vehicles. Developing more accurate and vertical RS systems will improve and extend their applications.
Huang, Y, Ng, ECY, Surawski, NC, Zhou, JL, Wang, X, Gao, J, Lin, W & Brown, RJ 2022, 'Effect of diesel particulate filter regeneration on fuel consumption and emissions performance under real-driving conditions', Fuel, vol. 320, pp. 123937-123937.
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Huang, Y, Wu, Q, Xu, J, Zhong, Y, Zhang, P & Zhang, Z 2022, 'Alleviating Modality Bias Training for Infrared-Visible Person Re-Identification', IEEE Transactions on Multimedia, vol. 24, pp. 1570-1582.
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Huang, Z, Zhao, R, Leung, FHF, Banerjee, S, Lee, TT-Y, Yang, D, Lun, DPK, Lam, K-M, Zheng, Y-P & Ling, SH 2022, 'Joint Spine Segmentation and Noise Removal from Ultrasound Volume Projection Images with Selective Feature Sharing', IEEE Transactions on Medical Imaging, pp. 1-1.
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Huo, C, Wang, Y, Liu, C & Lei, G 2022, 'Study on the residual flux density measurement method for power transformer cores based on magnetising inductance', IET Electric Power Applications, vol. 16, no. 2, pp. 224-235.
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Huo, P, Chen, X, Yang, L, Wei, W & Ni, B-J 2022, 'Modeling of sulfur-driven autotrophic denitrification coupled with Anammox process', Bioresource Technology, vol. 349, pp. 126887-126887.
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Huo, X, Jiang, Z, Luo, Q, Li, Q & Sun, G 2022, 'Mechanical characterization and numerical modeling on the yield and fracture behaviors of polymethacrylimide (PMI) foam materials', International Journal of Mechanical Sciences, vol. 218, pp. 107033-107033.
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Huo, X, Luo, Q, Li, Q, Zheng, G & Sun, G 2022, 'On characterization of cohesive zone model (CZM) based upon digital image correlation (DIC) method', International Journal of Mechanical Sciences, vol. 215, pp. 106921-106921.
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Huq, T, Ong, HC, Chew, BT, Leong, KY & Kazi, SN 2022, 'Review on aqueous graphene nanoplatelet Nanofluids: Preparation, Stability, thermophysical Properties, and applications in heat exchangers and solar thermal collectors', Applied Thermal Engineering, vol. 210, pp. 118342-118342.
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Hussain, W, Merigó, JM, Raza, MR & Gao, H 2022, 'A new QoS prediction model using hybrid IOWA-ANFIS with fuzzy C-means, subtractive clustering and grid partitioning', Information Sciences, vol. 584, pp. 280-300.
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Hussain, W, Raza, MR, Ahmed Jan, M, Merigo, JM & Gao, H 2022, 'Cloud Risk Management with OWA- LSTM Predictive Intelligence and Fuzzy Linguistic Decision Making', IEEE Transactions on Fuzzy Systems, pp. 1-1.
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Huyen Vu, T, Dang, LC, Kang, G & Sirivivatnanon, V 2022, 'Chloride induced corrosion of steel reinforcement in alkali activated slag concretes: A critical review', Case Studies in Construction Materials, vol. 16, pp. e01112-e01112.
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Ibrahim, I, Hossain, SM, Seo, DH, McDonagh, A, Foster, T, Shon, HK & Tijing, L 2022, 'Insight into the role of polydopamine nanostructures on nickel foam-based photothermal materials for solar water evaporation', Separation and Purification Technology, vol. 293, pp. 121054-121054.
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Ibrahim, I, Seo, DH, Park, MJ, Angeloski, A, McDonagh, A, Bendavid, A, Shon, HK & Tijing, L 2022, 'Highly stable gold nanolayer membrane for efficient solar water evaporation under a harsh environment', Chemosphere, vol. 299, pp. 134394-134394.
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Ibrahim, IA & Hossain, MJ 2022, 'A benchmark model for low voltage distribution networks with PV systems and smart inverter control techniques', Renewable and Sustainable Energy Reviews, vol. 166, pp. 112571-112571.
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Ibrahim, IA, Hossain, MJ & Duck, BC 2022, 'A hybrid wind driven-based fruit fly optimization algorithm for identifying the parameters of a double-diode photovoltaic cell model considering degradation effects', Sustainable Energy Technologies and Assessments, vol. 50, pp. 101685-101685.
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Ibrar, I, Yadav, S, Braytee, A, Altaee, A, HosseinZadeh, A, Samal, AK, Zhou, JL, Khan, JA, Bartocci, P & Fantozzi, F 2022, 'Evaluation of machine learning algorithms to predict internal concentration polarization in forward osmosis', Journal of Membrane Science, vol. 646, pp. 120257-120257.
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Ibrar, I, Yadav, S, Naji, O, Alanezi, AA, Ghaffour, N, Déon, S, Subbiah, S & Altaee, A 2022, 'Development in forward Osmosis-Membrane distillation hybrid system for wastewater treatment', Separation and Purification Technology, vol. 286, pp. 120498-120498.
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Indraratna, B, Haq, S, Rujikiatkamjorn, C & Israr, J 2022, 'Microscale boundaries of internally stable and unstable soils', Acta Geotechnica, vol. 17, no. 5, pp. 2037-2046.
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This study presents a microscale approach for evaluating the internal instability of natural granular soils using the discrete element method. The coordination number and the stress reduction factor are combined to assess the internal instability of soil. Distinct boundaries are identified between various soils that are internally stable and unstable. The microscale investigations are then compared with constriction and particle size-based criteria. The findings reveal that the constriction-based criterion predicts internal instability with significantly better accuracy. The relationship between microscale parameters and the constriction-based retention ratio is also examined for practical purposes.
Indraratna, B, Qi, Y, Tawk, M, Heitor, A, Rujikiatkamjorn, C & Navaratnarajah, SK 2022, 'Advances in ground improvement using waste materials for transportation infrastructure', Proceedings of the Institution of Civil Engineers - Ground Improvement, vol. 175, no. 1, pp. 3-22.
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Recycling waste materials for transport infrastructure such as coal wash (CW), steel furnace slag (SFS), fly ash (FA) and recycled tyre products is an efficient way of minimising the stockpiles of waste materials while offering significant economic and environmental benefits, as well as improving the stability and longevity of infrastructure foundations. This paper presents some of the most recent state-of-the-art studies undertaken at the University of Wollongong, Australia on the use of waste materials such as (a) CW-based granular mixtures (i.e. SFS + CW, CW + FA) for port reclamation and road base/subbase and (b) using recycled tyre products (i.e. rubber crumbs, tyre cell, under-sleeper pads and under-ballast mats) to increase track stability and reduce ballast degradation. Typical methods of applying these waste materials for different infrastructure conditions are described and the results of comprehensive laboratory and field tests are presented and discussed.
Indraratna, B, Singh, M, Nguyen, T, Rujikiatkamjorn, C, Malisetty, R, Arivalagan, J, NAIR, L & Nguyen, T 2022, 'Internal instability and fluidisation of subgrade soil under cyclic loading', Indian Geotechnical Journal, vol. 52, no. 3.
Islam, MR, Lu, H, Hossain, MJ & Li, L 2022, 'Coordinating Electric Vehicles and Distributed Energy Sources Constrained by User’s Travel Commitment', IEEE Transactions on Industrial Informatics, vol. 18, no. 8, pp. 5307-5317.
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Islam, MZ, Hossain, SI, Deplazes, E & Saha, SC 2022, 'The steroid mometasone alters protein containing lung surfactant monolayers in a concentration-dependent manner', Journal of Molecular Graphics and Modelling, vol. 111, pp. 108084-108084.
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Islam, MZ, Krajewska, M, Hossain, SI, Prochaska, K, Anwar, A, Deplazes, E & Saha, SC 2022, 'Concentration-Dependent Effect of the Steroid Drug Prednisolone on a Lung Surfactant Monolayer', Langmuir, vol. 38, no. 14, pp. 4188-4199.
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Jaafari, A, Panahi, M, Mafi-Gholami, D, Rahmati, O, Shahabi, H, Shirzadi, A, Lee, S, Bui, DT & Pradhan, B 2022, 'Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides', Applied Soft Computing, vol. 116, pp. 108254-108254.
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Jamshaid, M, Masjuki, HH, Kalam, MA, Zulkifli, NWM, Arslan, A & Qureshi, AA 2022, 'Experimental investigation of performance, emissions and tribological characteristics of B20 blend from cottonseed and palm oil biodiesels', Energy, vol. 239, pp. 121894-121894.
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Jayaraman, S, Ramachandran, M, Patan, R, Daneshmand, M & Gandomi, AH 2022, 'Fuzzy Deep Neural Learning Based on Goodman and Kruskal's Gamma for Search Engine Optimization', IEEE Transactions on Big Data, vol. 8, no. 1, pp. 268-277.
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IEEE Search engine optimization (SEO) is a significant problem for enhancing a website's visibility with search engine results. SEO issues, such as Site Popularity, Content Quality, Keyword Density, and Publicity, were not considered during the search engine optimization process. Therefore, the retrieval rate of the existing techniques is inadequate. In this study, Triangular Fuzzy Deep Structured Learning-Based Predictive Page Ranking (TFDSL-PPR) Technique is proposed to solve these limitations. First, the TFDSL-PPR technique takes a number of user queries as input in the input layer, and then it employs four hidden layers in order to deeply analyze the web pages based on an input query. The first hidden layer determines the keywords from the user query. The second hidden layer measures the site popularity, content quality, keyword density and publicity of all web pages in the search engine. It then accomplishes Goodman and Kruskal's Gamma Predictive Ranking process in the third hidden layer, where it ranks the web pages by considering their similarities. The proposed TFDSL-PPR technique is applied to the ClueWeb09 Dataset with respect to a variety of user queries. The results are benchmarked by existing methods based on several metrics such as retrieval rate, time, and false-positive rate.
Jennifer, JJ, Saravanan, S & Pradhan, B 2022, 'Persistent Scatterer Interferometry in the post-event monitoring of the Idukki Landslides', Geocarto International, vol. 37, no. 5, pp. 1514-1528.
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Jia, M, Gabrys, B & Musial, K 2022, 'Measuring Quadrangle Formation in Complex Networks', IEEE Transactions on Network Science and Engineering, vol. 9, no. 2, pp. 538-551.
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Jiang, G, Wu, J, Weidhaas, J, Li, X, Chen, Y, Mueller, J, Li, J, Kumar, M, Zhou, X, Arora, S, Haramoto, E, Sherchan, S, Orive, G, Lertxundi, U, Honda, R, Kitajima, M & Jackson, G 2022, 'Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology', Water Research, vol. 218, pp. 118451-118451.
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Jiang, M, Wu, T, Wang, Z, Gong, Y, Zhang, L & Liu, RP 2022, 'A Multi-Intersection Vehicular Cooperative Control Based on End-Edge-Cloud Computing', IEEE Transactions on Vehicular Technology, vol. 71, no. 3, pp. 2459-2471.
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Jiang, S, Li, K & Da Xu, RY 2022, 'Magnitude Bounded Matrix Factorisation for Recommender Systems', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 4, pp. 1856-1869.
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Jifroudi, HM, Mansor, SB, Pradhan, B, Halin, AA, Ahmad, N & Abdullah, AFB 2022, 'A new approach to derive buildings footprint from light detection and ranging data using rule-based learning techniques and decision tree', Measurement, vol. 192, pp. 110781-110781.
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Jin, JX, Zhou, Q, Yang, RH, Li, YJ, Li, H, Guo, YG & Zhu, JG 2022, 'A superconducting magnetic energy storage based current-type interline dynamic voltage restorer for transient power quality enhancement of composited data center and renewable energy source power system', Journal of Energy Storage, vol. 52, pp. 105003-105003.
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Jin, L, Ruckin, J, Kiss, SH, Vidal-Calleja, T & Popovic, M 2022, 'Adaptive-Resolution Field Mapping Using Gaussian Process Fusion With Integral Kernels', IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7471-7478.
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Jin, X, Kaw, HY, Liu, Y, Zhao, J, Piao, X, Jin, D, He, M, Yan, X-P, Zhou, JL & Li, D 2022, 'One-step integrated sample pretreatment technique by gas-liquid microextraction (GLME) to determine multi-class pesticide residues in plant-derived foods', Food Chemistry, vol. 367, pp. 130774-130774.
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Jin, Y, Jiang, W, Yang, Y & Mu, Y 2022, 'Zero-Shot Video Event Detection With High-Order Semantic Concept Discovery and Matching', IEEE Transactions on Multimedia, vol. 24, pp. 1896-1908.
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Jin, Z, Sun, X, Lei, G, Guo, Y & Zhu, J 2022, 'Sliding Mode Direct Torque Control of SPMSMs Based on a Hybrid Wolf Optimization Algorithm', IEEE Transactions on Industrial Electronics, vol. 69, no. 5, pp. 4534-4544.
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Direct torque control has been widely employed to control surface-mounted permanent magnet synchronous motors (SPMSMs). However, the large torque ripple and accuracy of the flux tracking affect its performance. As a promising improvement of conventional direct torque control, sliding mode direct torque control (SMDTC) can handle these problems to a certain extent. Nevertheless, the optimal performance is hardly obtained by trial and error tuning. Hence, in this paper, a hybrid wolf optimization algorithm (HWOA) is proposed to automatically adjust the parameters of the controllers of SMDTC for SPMSMs. This algorithm combines the grey wolf optimization algorithm (GWOA) and coyote optimization algorithm (COA) and retains their respective advantages. A conversion probability is designed to realize the simultaneous use of these two algorithms. The convergence is relatively fast, and the locally optimal problem can be avoided effectively. Meanwhile, a special fitness index with penalty terms is designed to enhance flux tracking and reduce the torque ripple. Comparison among the GWOA-based, COA-based and HWOA-based controllers is implemented both in simulation and experiment to show the superiority of the proposed control method.
John, AR, Singh, AK, Do, T-TN, Eidels, A, Nalivaiko, E, Gavgani, AM, Brown, S, Bennett, M, Lal, S, Simpson, AM, Gustin, SM, Double, K, Walker, FR, Kleitman, S, Morley, J & Lin, C-T 2022, 'Unraveling the Physiological Correlates of Mental Workload Variations in Tracking and Collision Prediction Tasks', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 770-781.
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John, CB, Raja, SA, Deepanraj, B & Ong, HC 2022, 'Palm stearin biodiesel: preparation, characterization using spectrometric techniques and the assessment of fuel properties', Biomass Conversion and Biorefinery, vol. 12, no. 5, pp. 1679-1693.
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Joshi, S & Sharma, M 2022, 'Digital technologies (DT) adoption in agri-food supply chains amidst COVID-19: an approach towards food security concerns in developing countries', Journal of Global Operations and Strategic Sourcing, vol. 15, no. 2, pp. 262-282.
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Purpose
This study aims to explore the critical factors for digital technologies (DT) adoption to develop a sustainable agri-food supply chain (AFSC). As the developing countries are struggling to survive during COVID-19, DT adoption in AFSC can bring resilience and minimizes the food security concerns.
Design/methodology/approach
The study has used Fuzzy Delphi and fuzzy decision-making trial and evaluation laboratory (DEMATEL) methods for identifying the critical success factors (CSFs) for DT adoption and inter-relationship among them to explore the crucial factors for food security across AFSC.
Findings
The research reveals that “Digital Technologies, Logistics and infrastructure” is the most crucial CSF for managing food security in developing economy during the COVID-19 situation. This factor supports the decision-makers to manage data for demand and supply management and helps to survive and sustain in the disruptive environment. The findings of the study will help farmers and supply chain partners to manage the smooth flow of food items from source to end-users during a disruptive environment. The sourcing, manufacturing and delivery methods are needed to be changed with DT inclusion and may support to redesign their internal systems for improvisation. This shorter AFSC will enhance the resilience in AFSCs.
Research limitations/implications
The emergency situation raised by the COVID-19 pandemic has brought global food security concerns. Adoption of DT across AFSCs can strategically reduce food waste and optimize the demand and supply balance.
Joshi, S & Sharma, M 2022, 'Impact of sustainable Supply Chain Management on the Performance of SMEs during COVID-19 Pandemic: An Indian Perspective', International Journal of Logistics Economics and Globalisation.
Joshi, S & Sharma, M 2022, 'Prolonging retailer-supplier relationship: A study of retail firms during pandemic COVID-19', International Journal of Logistics Economics and Globalisation.
Joshua Tapas, M, Thomas, P, Vessalas, K & Sirivivatnanon, V 2022, 'Mechanisms of Alkali-Silica Reaction Mitigation in AMBT Conditions: Comparative Study of Traditional Supplementary Cementitious Materials', Journal of Materials in Civil Engineering, vol. 34, no. 3.
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Jung, MC, Chai, R, Zheng, J & Nguyen, H 2022, 'Enhanced myoelectric control against arm position change with weighted recursive Gaussian process', Neural Computing and Applications, vol. 34, no. 7, pp. 5015-5028.
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Jupp, JR & Parkes, M 2022, 'Integrated Construction Enterprise Systems: A Strategic Approach to Model-based Data and Process Management', Automation in Construction, vol. (to appear).
Kabir, MM, Alam, F, Akter, MM, Gilroyed, BH, Didar-ul-Alam, M, Tijing, L & Shon, HK 2022, 'Highly effective water hyacinth (Eichhornia crassipes) waste-based functionalized sustainable green adsorbents for antibiotic remediation from wastewater', Chemosphere, vol. 304, pp. 135293-135293.
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Kacprzak, S & Tijing, LD 2022, 'Microplastics in indoor environment: Sources, mitigation and fate', Journal of Environmental Chemical Engineering, vol. 10, no. 2, pp. 107359-107359.
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Kalhori, H, Rafiee, R, Ye, L, Halkon, B & Bahmanpour, M 2022, 'Randomized Kaczmarz and Landweber Algorithms for Impact Force Identification on a Composite Panel'.
Kang, G, Jiang, L, Wei, Y, Yang, Y & Hauptmann, A 2022, 'Contrastive Adaptation Network for Single- and Multi-Source Domain Adaptation', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 4, pp. 1793-1804.
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Unsupervised Domain Adaptation (UDA) makes predictions for the target domain data while manual annotations are only available in the source domain. Previous methods minimize the domain discrepancy neglecting the class information, which may lead to misalignment and poor generalization performance. To tackle this issue, this paper proposes Contrastive Adaptation Network (CAN) that optimizes a new metric named Contrastive Domain Discrepancy explicitly modeling the intra-class domain discrepancy and the inter-class domain discrepancy. To optimize CAN, two technical issues need to be addressed: 1) the target labels are not available and 2) the conventional mini-batch sampling is imbalanced. Thus we design an alternating update strategy to optimize both the target label estimations and the feature representations. Moreover, we develop class-aware sampling to enable more efficient and effective training. Our framework can be generally applied to the single-source and multi-source domain adaptation scenarios. In particular, to deal with multiple source domain data, we propose 1) multi-source clustering ensemble which exploits the complementary knowledge of distinct source domains to make more accurate and robust target label estimations, and 2) boundary-sensitive alignment to make the decision boundary better fitted to the target. Experiments conducted on three real-world benchmarks, demonstrating CAN performs favorably against previous state-of-the-arts.
Kaplan, E, Ekinci, T, Kaplan, S, Barua, PD, Dogan, S, Tuncer, T, Tan, R-S, Arunkumar, N & Acharya, UR 2022, 'PFP-LHCINCA: Pyramidal Fixed-Size Patch-Based Feature Extraction and Chi-Square Iterative Neighborhood Component Analysis for Automated Fetal Sex Classification on Ultrasound Images', Contrast Media & Molecular Imaging, vol. 2022, pp. 1-10.
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Objectives. Fetal sex determination with ultrasound (US) examination is indicated in pregnancies at risk of X-linked genetic disorders or ambiguous genitalia. However, misdiagnoses often arise due to operator inexperience and technical difficulties while acquiring diagnostic images. We aimed to develop an efficient automated US-based fetal sex classification model that can facilitate efficient screening and reduce misclassification. Methods. We have developed a novel feature engineering model termed PFP-LHCINCA that employs pyramidal fixed-size patch generation with average pooling-based image decomposition, handcrafted feature extraction based on local phase quantization (LPQ), and histogram of oriented gradients (HOG) to extract directional and textural features and used Chi-square iterative neighborhood component analysis feature selection (CINCA), which iteratively selects the most informative feature vector for each image that minimizes calculated feature parameter-derived k-nearest neighbor-based misclassification rates. The model was trained and tested on a sizeable expert-labeled dataset comprising 339 males’ and 332 females’ fetal US images. One transverse fetal US image per subject zoomed to the genital area and standardized to 256 × 256 size was used for analysis. Fetal sex was annotated by experts on US images and confirmed postnatally. Results. Standard model performance metrics were compared using five shallow classifiers—k-nearest neighbor (kNN), decision tree, naïve Bayes, linear discriminant, and support vector machine (SVM)—with the hyperparameters tuned using a Bayesian optimizer. The PFP-LHCINCA model achieved a sex classification accuracy of ≥88% with all five classifiers and the best accuracy rates (>98%) with kNN and SVM classifiers. Conclusions. US-based fetal sex classification is feasible and accurate using the presented PFP-LHCINCA model. The salutary results support its clinical use for fetal US image screening for sex class...
Karimi, M, Kinns, R & Kessissoglou, N 2022, 'Radiated Sound Power from Near-Surface Acoustic Sources', Journal of Ship Research, vol. 66, no. 02, pp. 151-158.
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Abstract
This article investigates the radiated sound power from idealized propeller noise sources, characterized by elemental monopole and dipole acoustic sources near the sea surface. The free surface of the sea is modeled as a pressure-release surface. The ratio of sound power of the near surface sources to the sound power from the same sources in an unbounded fluid is presented as a function of source immersion relative to sound wavelength. We herein show that the sound power radiated by submerged monopole and horizontal dipole sources is greatly reduced by the effect of the free surface at typical blade passing frequencies. By contrast, the sound power from a submerged vertical dipole is doubled. A transition frequency for the submerged monopole and horizontal dipole is identified. Above this transition frequency, the radiated power is not significantly influenced by the sea surface. Directivity patterns for the acoustic sources are also presented.
Introduction
The principal sources contributing to underwater radiated noise (URN) over a wide frequency range are propellers and onboard machinery (Urick 1983; Ross 1987; Collier 1997; Carlton 2007). Propeller sources are highly complex, but simplification is possible at low frequencies where the wavelength of underwater sound is much larger than propeller dimensions. The propeller may then be regarded as a set of fluctuating forces at the propeller hub and a stationary monopole source that represents the growth and collapse of a cavitation region as each blade passes through the region of wake deficit. This type of model was used by Kinns and Bloor (2004) to examine the net fluctuating forces on a cruise ship hull due to defined propeller sources. The nature of the monopole source was considered by Gray a...
Karki, D, Al-Hunaity, S, Far, H & Saleh, A 2022, 'Composite connections between CFS beams and plywood panels for flooring systems: Testing and analysis', Structures, vol. 40, pp. 771-785.
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Karki, D, Far, H & AL HUNAITY, S 2022, 'Determination of Slip Modulus of Cold Formed Steel Composite Members Sheathed with Plywood Structural Panels', Steel and Composite Structures: an international journal, vol. 43, no. 4, pp. 511-522.
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Kashani, AR, Camp, CV, Rostamian, M, Azizi, K & Gandomi, AH 2022, 'Population-based optimization in structural engineering: a review', Artificial Intelligence Review, vol. 55, no. 1, pp. 345-452.
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Kashyap, PK, Kumar, S, Jaiswal, A, Kaiwartya, O, Kumar, M, Dohare, U & Gandomi, AH 2022, 'DECENT: Deep Learning Enabled Green Computation for Edge centric 6G Networks', IEEE Transactions on Network and Service Management, pp. 1-1.
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Katzmarek, DA, Pradeepkumar, A, Ziolkowski, RW & Iacopi, F 2022, 'Review of graphene for the generation, manipulation, and detection of electromagnetic fields from microwave to terahertz', 2D Materials, vol. 9, no. 2, pp. 022002-022002.
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Abstract
Graphene has attracted considerable attention ever since the discovery of its unprecedented properties, including its extraordinary and tunable electronic and optical properties. In particular, applications within the microwave to terahertz frequency spectrum can benefit from graphene’s high electrical conductivity, mechanical flexibility and robustness, transparency, support of surface-plasmon-polaritons, and the possibility of dynamic tunability with direct current to light sources. This review aims to provide an in-depth analysis of current trends, challenges, and prospects within the research areas of generating, manipulating, and detecting electromagnetic fields using graphene-based devices that operate from microwave to terahertz frequencies. The properties of and models describing graphene are reviewed first, notably those of importance to electromagnetic applications. State-of-the-art graphene-based antennas, such as resonant and leaky-wave antennas, are discussed next. A critical evaluation of the performance and limitations within each particular technology is given. Graphene-based metasurfaces and devices used to manipulate electromagnetic fields, e.g. wavefront engineering, are then examined. Lastly, the state-of-the-art of detecting electromagnetic fields using graphene-based devices is discussed.
Keshavarz, R & Shariati, N 2022, 'Highly Sensitive and Compact Quad-Band Ambient RF Energy Harvester', IEEE Transactions on Industrial Electronics, vol. 69, no. 4, pp. 3609-3621.
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Keshavarz, R & Shariati, N 2022, 'High-Sensitivity and Compact Time Domain Soil Moisture Sensor Using Dispersive Phase Shifter for Complex Permittivity Measurement', IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-10.
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Khan, HU, Aruya, JA & Gill, AQ 2022, 'Web 2.0 technologies adoption barriers for external contacts and participation: a case study of federal establishment of Africa', International Journal of Business Information Systems, vol. 39, no. 2, pp. 278-278.
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Khan, MNH, Barzegarkhoo, R, Siwakoti, YP, Khan, SA, Li, L & Blaabjerg, F 2022, 'A new switched-capacitor multilevel inverter with soft start and quasi resonant charging capabilities', International Journal of Electrical Power & Energy Systems, vol. 135, pp. 107412-107412.
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Khan, MNH, Hasan, SU & Siwakoti, YP 2022, 'New PWM Strategy to Enable Dual Mode Operation Capability in Common-Grounded Transformerless Inverters', IEEE Journal of Emerging and Selected Topics in Power Electronics, pp. 1-1.
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Khan, R, Tao, X, Anjum, A, Malik, SR, Yu, S, Khan, A, Rehman, W & Malik, H 2022, '( τ , m )‐ slicedBucket privacy model for sequential anonymization for improving privacy and utility', Transactions on Emerging Telecommunications Technologies, vol. 33, no. 6.
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Khodasevych, I, Rufangura, P & Iacopi, F 2022, 'Designing concentric nanoparticles for surface-enhanced light-matter interaction in the mid-infrared', Optics Express, vol. 30, no. 13, pp. 24118-24118.
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Kiani, M, Andreu-Perez, J, Hagras, H, Papageorgiou, EI, Prasad, M & Lin, C-T 2022, 'Effective Brain Connectivity for fNIRS With Fuzzy Cognitive Maps in Neuroergonomics', IEEE Transactions on Cognitive and Developmental Systems, vol. 14, no. 1, pp. 50-63.
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Kim, J, Charbel-Salloum, A, Perry, S & Palmisano, S 2022, 'Effects of display lag on vection and presence in the Oculus Rift HMD', Virtual Reality, vol. 26, no. 2, pp. 425-436.
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Kim, J, Kim, H-W, Tijing, LD, Shon, HK & Hong, S 2022, 'Elucidation of physicochemical scaling mechanisms in membrane distillation (MD): Implication to the control of inorganic fouling', Desalination, vol. 527, pp. 115573-115573.
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Kim, J, Yun, E-T, Tijing, L, Shon, HK & Hong, S 2022, 'Mitigation of fouling and wetting in membrane distillation by electrical repulsion using a multi-layered single-wall carbon nanotube/polyvinylidene fluoride membrane', Journal of Membrane Science, vol. 653, pp. 120519-120519.
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King, J-T, John, AR, Wang, Y-K, Shih, C-K, Zhang, D, Huang, K-C & Lin, C-T 2022, 'Brain Connectivity Changes During Bimanual and Rotated Motor Imagery', IEEE Journal of Translational Engineering in Health and Medicine, vol. 10, pp. 1-8.
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Kiss, SH, Katuwandeniya, K, Alempijevic, A & Vidal-Calleja, T 2022, 'Constrained Gaussian Processes with Integrated Kernels for Long-Horizon Prediction of Dense Pedestrian Crowd Flows', IEEE Robotics and Automation Letters, pp. 1-1.
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Koli, MNY, Afzal, MU & Esselle, KP 2022, 'Increasing the Gain of Beam-Tilted Circularly Polarized Radial Line Slot Array Antennas', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4392-4403.
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Korzekwa, K, Puchala, Z, Tomamichel, M & Zyczkowski, K 2022, 'Encoding Classical Information Into Quantum Resources', IEEE Transactions on Information Theory, vol. 68, no. 7, pp. 4518-4530.
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Koul, Y, Devda, V, Varjani, S, Guo, W, Ngo, HH, Taherzadeh, MJ, Chang, J-S, Wong, JWC, Bilal, M, Kim, S-H, Bui, X-T & Parra-Saldívar, R 2022, 'Microbial electrolysis: a promising approach for treatment and resource recovery from industrial wastewater', Bioengineered, vol. 13, no. 4, pp. 8115-8134.
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Kouretzis, G, Sheng, D & Thomas, HR 2022, 'In memory of Scott William Sloan (1954–2019)', Computers and Geotechnics, vol. 143, pp. 104593-104593.
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Kozanoglu, DC, Daim, TU & Contreras-Cruz, A 2022, 'Unraveling the Dynamics of Immigrant Engineers’ Full-Utilization in Australia', IEEE Transactions on Engineering Management, pp. 1-16.
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Kumar, AN, Ashok, B, Nanthagopal, K, Ong, HC, Geca, MJ, Victor, J, Vignesh, R, Jeevanantham, AK, Kannan, C & Kishore, PS 2022, 'Experimental analysis of higher alcohol–based ternary biodiesel blends in CI engine parameters through multivariate and desirability approaches', Biomass Conversion and Biorefinery, vol. 12, no. 5, pp. 1525-1540.
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The present work has enabled to aspire the enhancement of palm biodiesel viability for compression ignition (CI) engine applications using reformulation strategy by the addition of higher alcohols. In this work, 20% and 30% of 1-decanol and n-hexanol were used for ternary blend preparation along with palm biodiesel concentration as 20–30% and 50% diesel fuels for combustion, emission, and performance behaviour investigation in CI engine. Furthermore, the experimental results were also compared with 100% palm biodiesel (P100) and pure diesel (D100) and a binary blend of D50B50 fuels. The experimental study has revealed that the presence of higher alcohols in the ternary blends has improved the cylinder pressure and heat release rate whereas the same trend was not evident in binary biodiesel blend. All the ternary blends of higher alcohols-biodiesel and diesels have shown higher brake thermal efficiency and reduction in brake-specific fuel consumption. At the same time, decanol and hexanol addition in the palm biodiesel-diesel blends has favoured in all exhaust emission reductions with slight exemption in NO emission. The experimental results are optimized through multivariate and desirability analyses for identifying the effective composition of blend. Multivariate analysis has revealed that the higher alcohol proposition in the ternary blend was more influential than the type of higher alcohol. Furthermore, the desirability study has also validated the prescribed proportion with the maximum error of 6.17% for D50P22DC28 and 4.84% for D50P26HE24. Finally, the research concludes that decanol would be the preferable choice for ternary blend preparation than hexanol due to its overall better performance. x
Kumar, M, Jiang, G, Kumar Thakur, A, Chatterjee, S, Bhattacharya, T, Mohapatra, S, Chaminda, T, Kumar Tyagi, V, Vithanage, M, Bhattacharya, P, Nghiem, LD, Sarkar, D, Sonne, C & Mahlknecht, J 2022, 'Lead time of early warning by wastewater surveillance for COVID-19: Geographical variations and impacting factors', Chemical Engineering Journal, vol. 441, pp. 135936-135936.
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Kumar, S, Lyalin, A, Huang, Z & Taketsugu, T 2022, 'Catalytic Oxidative Dehydrogenation of Light Alkanes over Oxygen Functionalized Hexagonal Boron Nitride', ChemistrySelect, vol. 7, no. 1.
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Kurdkandi, NV, Husev, O, Matiushkin, O, Vinnikov, D, Siwakoti, Y & Lee, SS 2022, 'Novel Family of Flying Inductor-based Single-Stage Buck-Boost Inverters', IEEE Journal of Emerging and Selected Topics in Power Electronics, pp. 1-1.
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Kurdkandi, NV, Marangalu, MG, Mohammadsalehian, S, Tarzamni, H, Siwakoti, YP, Islam, MR & Muttaqi, KM 2022, 'A New Six-Level Transformer-Less Grid-Connected Solar Photovoltaic Inverter With Less Leakage Current', IEEE Access, vol. 10, pp. 63736-63753.
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Lai, Y, Paul, G, Cui, Y & Matsubara, T 2022, 'User intent estimation during robot learning using physical human robot interaction primitives', Autonomous Robots, vol. 46, no. 2, pp. 421-436.
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AbstractAs robotic systems transition from traditional setups to collaborative work spaces, the prevalence of physical Human Robot Interaction has risen in both industrial and domestic environments. A popular representation for robot behavior is movement primitives which learn, imitate, and generalize from expert demonstrations. While there are existing works in context-aware movement primitives, they are usually limited to contact-free human robot interactions. This paper presents physical Human Robot Interaction Primitives (pHRIP), which utilize only the interaction forces between the human user and robot to estimate user intent and generate the appropriate robot response during physical human robot interactions. The efficacy of pHRIP is evaluated through multiple experiments based on target-directed reaching and obstacle avoidance tasks using a real seven degree of freedom robot arm. The results are validated against Interaction Primitives which use observations of robotic trajectories, with discussions of future pHRI applications utilizing pHRIP.
Lakomy, K, Madonski, R, Dai, B, Yang, J, Kicki, P, Ansari, M & Li, S 2022, 'Active Disturbance Rejection Control Design With Suppression of Sensor Noise Effects in Application to DC–DC Buck Power Converter', IEEE Transactions on Industrial Electronics, vol. 69, no. 1, pp. 816-824.
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Lalbakhsh, A, Afzal, MU, Esselle, KP & Smith, SL 2022, 'All-Metal Wideband Frequency-Selective Surface Bandpass Filter for TE and TM Polarizations', IEEE Transactions on Antennas and Propagation, vol. 70, no. 4, pp. 2790-2800.
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Lalbakhsh, A, Pitcairn, A, Mandal, K, Alibakhshikenari, M, Esselle, KP & Reisenfeld, S 2022, 'Darkening Low-Earth Orbit Satellite Constellations: A Review', IEEE Access, vol. 10, pp. 24383-24394.
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Le, A, Nimbalkar, S, Zobeiry, N & Malek, S 2022, 'An efficient multi-scale approach for viscoelastic analysis of woven composites under bending', Composite Structures, vol. 292, pp. 115698-115698.
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Le, AT, Huang, X, Tran, LC & Guo, YJ 2022, 'On the Impacts of I/Q Imbalance in Analog Least Mean Square Adaptive Filter for Self-Interference Cancellation in Full-Duplex Radios', IEEE Transactions on Vehicular Technology, pp. 1-11.
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Lee, T, Min, C, Naidu, G, Huang, Y, Shon, HK & Kim, S-H 2022, 'Optimizing the performance of sweeping gas membrane distillation for treating naturally heated saline groundwater', Desalination, vol. 532, pp. 115736-115736.
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Lee, XJ, Ong, HC, Ooi, J, Yu, KL, Tham, TC, Chen, W-H & Ok, YS 2022, 'Engineered macroalgal and microalgal adsorbents: Synthesis routes and adsorptive performance on hazardous water contaminants', Journal of Hazardous Materials, vol. 423, pp. 126921-126921.
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Leng, D, Zhu, Z, Liu, G & Li, Y 2022, 'Neuro fuzzy logic control of magnetorheological elastomer isolation system for vibration mitigation of offshore jacket platforms', Ocean Engineering, vol. 253, pp. 111293-111293.
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Lezana, P, Norambuena, M & P. Aguilera, R 2022, 'Dual-Stage Control Strategy for a Flying Capacitor Converter Based on Model Predictive and Linear Controllers', IEEE Transactions on Industrial Informatics, vol. 18, no. 4, pp. 2203-2212.
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Li, A, Yang, B, Hussain, FK & Huo, H 2022, 'HSR: Hyperbolic Social Recommender', Information Sciences, vol. 585, pp. 275-288.
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With the prevalence of online social media, users’ social connections have been widely studied and utilized to enhance the performance of recommender systems. In this paper, we explore the use of hyperbolic geometry for social recommendation. We present the Hyperbolic Social Recommender (HSR), a novel social recommendation framework that utilizes hyperbolic geometry to boost the performance. With the help of hyperbolic space, HSR can learn high-quality user and item representations to better model user-item interaction and user-user social relations. Through extensive experiments on four real-world datasets, we show that our proposed HSR outperforms its Euclidean counterpart and state-of-the-art social recommenders in click-through rate prediction and top-K recommendation, demonstrating the effectiveness of social recommendation in the hyperbolic space.
Li, C, Fang, J, Wu, C, Sun, G, Steven, G & Li, Q 2022, 'Phase field fracture in elasto-plastic solids: Incorporating phenomenological failure criteria for ductile materials', Computer Methods in Applied Mechanics and Engineering, vol. 391, pp. 114580-114580.
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Li, D, Tang, M-C, Wang, Y, Hu, K-Z & Ziolkowski, RW 2022, 'Dual-Band, Differentially-Fed Filtenna With Wide Bandwidth, High Selectivity, and Low Cross-Polarization', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4872-4877.
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Li, G, Zhou, H, Feng, B, Zhang, Y & Yu, S 2022, 'Efficient Provision of Service Function Chains in Overlay Networks Using Reinforcement Learning', IEEE Transactions on Cloud Computing, vol. 10, no. 1, pp. 383-395.
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IEEE Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies facilitate deploying Service Function Chains (SFCs) at clouds in efficiency and flexibility. However, it is still challenging to efficiently chain Virtualized Network Functions (VNFs) in overlay networks without knowledge of underlying network configurations. Although there are many deterministic approaches for VNF placement and chaining, they have high complexity and depend on state information of substrate networks. Fortunately, Reinforcement Learning (RL) brings opportunities to alleviate this challenge as it can learn to make suitable decisions without prior knowledge. Therefore, in this paper, we propose an RL approach for efficient SFC provision in overlay networks, where the same VNFs provided by multiple vendors are with different performance. Specifically, we first formulate the problem into an Integer Linear Programming (ILP) model for benchmarking. Then, we present the online SFC path selection into a Markov Decision Process (MDP) and propose a corresponding policy-gradient-based solution. Finally, we evaluate our proposed approach with extensive simulations with randomly generated SFC requests and a real-world video streaming dataset, and implement an emulation system for feasibility verification. Related results demonstrate that performance of our approach is close to the ILP-based method and better than deep Q-learning, random, and load-least-greedy methods.
Li, H, Li, J & Bi, K 2022, 'A quasi-active negative stiffness damper for structural vibration control under earthquakes', Mechanical Systems and Signal Processing, vol. 173, pp. 109071-109071.
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Li, H, Yang, Y, Li, X, Zhou, Z, Feng, J, Dai, Y, Li, X & Ren, J 2022, 'Degradation of sulfamethazine by vacuum ultraviolet-activated sulfate radical-advanced oxidation: efficacy, mechanism and influences of water constituents', Separation and Purification Technology, vol. 282, pp. 120058-120058.
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Li, H, Yang, Y, Ren, J, Zhou, Z, Li, X, Liu, Y & Feng, J 2022, 'Fate of organic fractions of greywater in combined process of vacuum-ultraviolet (VUV/UV)/ozone pre-oxidation with enhanced coagulation', Journal of Environmental Chemical Engineering, vol. 10, no. 3, pp. 107417-107417.
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Li, J, Hlavka-Zhang, J, Shrimp, JH, Piper, C, Dupéré-Richér, D, Roth, JS, Jing, D, Casellas Román, HL, Troche, C, Swaroop, A, Kulis, M, Oyer, JA, Will, CM, Shen, M, Riva, A, Bennett, RL, Ferrando, AA, Hall, MD, Lock, RB & Licht, JD 2022, 'PRC2 Inhibitors Overcome Glucocorticoid Resistance Driven by NSD2 Mutation in Pediatric Acute Lymphoblastic Leukemia', Cancer Discovery, vol. 12, no. 1, pp. 186-203.
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Abstract
Mutations in epigenetic regulators are common in relapsed pediatric acute lymphoblastic leukemia (ALL). Here, we uncovered the mechanism underlying the relapse of ALL driven by an activating mutation of the NSD2 histone methyltransferase (p.E1099K). Using high-throughput drug screening, we found that NSD2-mutant cells were specifically resistant to glucocorticoids. Correction of this mutation restored glucocorticoid sensitivity. The transcriptional response to glucocorticoids was blocked in NSD2-mutant cells due to depressed glucocorticoid receptor (GR) levels and the failure of glucocorticoids to autoactivate GR expression. Although H3K27me3 was globally decreased by NSD2 p.E1099K, H3K27me3 accumulated at the NR3C1 (GR) promoter. Pretreatment of NSD2 p.E1099K cell lines and patient-derived xenograft samples with PRC2 inhibitors reversed glucocorticoid resistance in vitro and in vivo. PRC2 inhibitors restored NR3C1 autoactivation by glucocorticoids, increasing GR levels and allowing GR binding and activation of proapoptotic genes. These findings suggest a new therapeutic approach to relapsed ALL associated with NSD2 mutation.
Significance:
NSD2 histone methyltransferase mutations observed in relapsed pediatric ALL drove glucocorticoid resistance by repression of the GR and abrogation of GR gene autoactivation due to accumulation of K3K27me3 at its promoter. Pretreatment with PRC2 inhibitors reversed resistance, suggesting a new therapeutic approach to these patients with ALL.
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Li, J, Liang, W, Xu, W, Xu, Z, Jia, X, Zhou, W & Zhao, J 2022, 'Maximizing User Service Satisfaction for Delay-Sensitive IoT Applications in Edge Computing', IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 5, pp. 1199-1212.
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Li, J, Liang, W, Xu, Z, Jia, X & Zhou, W 2022, 'Service Provisioning for Multi-source IoT Applications in Mobile Edge Computing', ACM Transactions on Sensor Networks, vol. 18, no. 2, pp. 1-25.
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We are embracing an era of Internet of Things (IoT). The latency brought by unstable wireless networks caused by limited resources of IoT devices seriously impacts the quality of services of users, particularly the service delay they experienced. Mobile Edge Computing (MEC) technology provides promising solutions to delay-sensitive IoT applications, where cloudlets (edge servers) are co-located with wireless access points in the proximity of IoT devices. The service response latency for IoT applications can be significantly shortened due to that their data processing can be performed in a local MEC network. Meanwhile, most IoT applications usually impose Service Function Chain (SFC) enforcement on their data transmission, where each data packet from its source gateway of an IoT device to the destination (a cloudlet) of the IoT application must pass through each Virtual Network Function (VNF) in the SFC in an MEC network. However, little attention has been paid on such a service provisioning of multi-source IoT applications in an MEC network with SFC enforcement.
In this article, we study service provisioning in an MEC network for multi-source IoT applications with SFC requirements and aiming at minimizing the cost of such service provisioning, where each IoT application has multiple data streams from different sources to be uploaded to a location (cloudlet) in the MEC network for aggregation, processing, and storage purposes. To this end, we first formulate two novel optimization problems: the cost minimization problem of service provisioning for a single multi-source IoT application, and the service provisioning problem for a set of multi-source IoT applications, respectively, and show that both problems are NP-hard. Second, we propose a service provisioning framework in the MEC network for multi-source IoT applications that consists of uploading stream data from multiple sources of the IoT application to the MEC network, data ...
Li, J, Wang, W, Wu, C, Liu, Z & Wu, P 2022, 'Impact response of ultra-high performance fiber-reinforced concrete filled square double-skin steel tubular columns', Steel and Composite Structures, vol. 42, no. 3, pp. 325-351.
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This paper studies the lateral impact behavior of ultra-high performance fiber-reinforced concrete (UHPFRC) filled double-skin steel tubular (UHPFRCFDST) columns. The impact force, midspan deflection, and strain histories were recorded. Based on the test results, the influences of drop height, axial load, concrete type, and steel tube wall thickness on the impact resistance of UHPFRCFDST members were analyzed. LS-DYNA software was used to establish a finite element (FE) model of UHPFRC filled steel tubular members. The failure modes and histories of impact force and midspan deflection of specimens were obtained. The simulation results were compared to the test results, which demonstrated the accuracy of the finite element analysis (FEA) model. Finally, the effects of the steel tube thickness, impact energy, type of concrete and impact indenter shape, and void ratio on the lateral impact performances of the UHPFRCFDST columns were analyzed.
Li, J, Zhu, X & Guo, J 2022, 'Enhanced drive‐by bridge modal identification via dual Kalman filter and singular spectrum analysis', Structural Control and Health Monitoring, vol. 29, no. 5.
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Li, JJ, Liu, H, Zhu, Y, Yan, L, Liu, R, Wang, G, Wang, B & Zhao, B 2022, 'Animal Models for Treating Spinal Cord Injury Using Biomaterials-Based Tissue Engineering Strategies', Tissue Engineering Part B: Reviews, vol. 28, no. 1, pp. 79-100.
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Li, K, Duan, H, Liu, L, Qiu, R, van den Akker, B, Ni, B-J, Chen, T, Yin, H, Yuan, Z & Ye, L 2022, 'An Integrated First Principal and Deep Learning Approach for Modeling Nitrous Oxide Emissions from Wastewater Treatment Plants', Environmental Science & Technology, vol. 56, no. 4, pp. 2816-2826.
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Li, K, Lu, J, Zuo, H & Zhang, G 2022, 'Dynamic Classifier Alignment for Unsupervised Multi-Source Domain Adaptation', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Li, K, Ni, W & Dressler, F 2022, 'LSTM-Characterized Deep Reinforcement Learning for Continuous Flight Control and Resource Allocation in UAV-Assisted Sensor Network', IEEE Internet of Things Journal, vol. 9, no. 6, pp. 4179-4189.
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Li, M, Chen, S, Shen, Y, Liu, G, Tsang, IW & Zhang, Y 2022, 'Online Multi-Agent Forecasting With Interpretable Collaborative Graph Neural Networks', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Li, M, Huang, P-Y, Chang, X, Hu, J, Yang, Y & Hauptmann, A 2022, 'Video Pivoting Unsupervised Multi-Modal Machine Translation', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-15.
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Li, N, Asteris, PG, Tran, TT, Pradhan, B & Nguyen, H 2022, 'Modelling the deflection of reinforced concrete beams using the improved artificial neural network by imperialist competitive optimization', Steel and Composite Structures, vol. 42, no. 6, pp. 733-745.
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This study proposed a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling the deflection of reinforced concrete beams, abbreviated as ICA-ANN model. Accordingly, the ICA was used to adjust and optimize the parameters of an ANN model (i.e., weights and biases) aiming to improve the accuracy of the ANN model in modelling the deflection reinforced concrete beams. A total of 120 experimental datasets of reinforced concrete beams were employed for this aim. Therein, applied load, tensile reinforcement strength and the reinforcement percentage were used to simulate the deflection of reinforced concrete beams. Besides, five other AI models, such as ANN, SVM (support vector machine), GLMNET (lasso and elastic-net regularized generalized linear models), CART (classification and regression tree) and KNN (k-nearest neighbours), were also used for the comprehensive assessment of the proposed model (i.e., ICA-ANN). The comparison of the derived results with the experimental findings demonstrates that among the developed models the ICA-ANN model is that can approximate the reinforced concrete beams deflection in a more reliable and robust manner.
Li, N, Nguyen, H, Rostami, J, Zhang, W, Bui, X-N & Pradhan, B 2022, 'Predicting rock displacement in underground mines using improved machine learning-based models', Measurement, vol. 188, pp. 110552-110552.
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Li, P, Li, C, Bore, JC, Si, Y, Li, F, Cao, Z, Zhang, Y, Wang, G, Zhang, Z, Yao, D & Xu, P 2022, 'L1-norm based time-varying brain neural network and its application to dynamic analysis for motor imagery', Journal of Neural Engineering, vol. 19, no. 2, pp. 026019-026019.
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Abstract
Objective
. Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface offers a promising way to improve the efficiency of motor rehabilitation and motor skill learning. In recent years, the power of dynamic network analysis for MI classification has been proved. In fact, its usability mainly depends on the accurate estimation of brain connection. However, traditional dynamic network estimation strategies such as adaptive directed transfer function (ADTF) are designed in the L2-norm. Usually, they estimate a series of pseudo connections caused by outliers, which results in biased features and further limits its online application. Thus, how to accurately infer dynamic causal relationship under outlier influence is urgent. Approach
. In this work, we proposed a novel ADTF, which solves the dynamic system in the L1-norm space (L1-ADTF), so as to restrict the outlier influence. To enhance its convergence, we designed an iteration strategy with the alternating direction method of multipliers, which could be used for the solution of the dynamic state-space model restricted in the L1-norm space. Furthermore, we compared L1-ADTF to traditional ADTF and its dual extension across both simulation and real EEG experiments. Main results
. A quantitative comparison between L1-ADTF and other ADTFs in simulation studies demonstrates that fewer bias errors and more desirable dynamic state transformation patterns can be captured by the L1-ADTF. Application to real MI EEG datasets seriously noised by ocular artifacts also reveals the efficiency of the proposed L1-ADTF approach to extract the time-varying brain neural network patterns, even when more complex noises are involved.
Li, P, Xu, Y, Wei, Y & Yang, Y 2022, 'Self-Correction for Human Parsing', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 6, pp. 3260-3271.
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Li, Q, Yuan, X, Hu, X, Meers, E, Ong, HC, Chen, W-H, Duan, P, Zhang, S, Lee, KB & Ok, YS 2022, 'Co-liquefaction of mixed biomass feedstocks for bio-oil production: A critical review', Renewable and Sustainable Energy Reviews, vol. 154, pp. 111814-111814.
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Li, S, Show, PL, Ngo, HH & Ho, S-H 2022, 'Algae-mediated antibiotic wastewater treatment: A critical review', Environmental Science and Ecotechnology, vol. 9, pp. 100145-100145.
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Li, W, Cao, L, Yue, P, Wen, S, Liao, J, Xia, J & Feng, X 2022, 'VAVM: A Flexible Technique for Variable-Angle Around View Monitor System Towards Articulated Engineering Vehicle', IEEE Transactions on Vehicular Technology, vol. 71, no. 4, pp. 3556-3568.
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Li, W, Dong, W, Guo, Y, Wang, K & Shah, SP 2022, 'Advances in multifunctional cementitious composites with conductive carbon nanomaterials for smart infrastructure', Cement and Concrete Composites, vol. 128, pp. 104454-104454.
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Li, W, Qiao, M, Qin, L, Zhang, Y, Chang, L & Lin, X 2022, 'Distance labeling: on parallelism, compression, and ordering', The VLDB Journal, vol. 31, no. 1, pp. 129-155.
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Distance labeling approaches are widely adopted to speed up the online performance of shortest-distance queries. The construction of the distance labeling, however, can be exhaustive, especially on big graphs. For a major category of large graphs, small-world networks, the state-of-the-art approach is pruned landmark labeling (PLL). PLL prunes distance labels based on a node order and directly constructs the pruned labels by performing breadth-first searches in the node order. The pruning technique, as well as the index construction, has a strong sequential nature which hinders PLL from being parallelized. It becomes an urgent issue on massive small-world networks whose index can hardly be constructed by a single thread within a reasonable time. This paper first scales distance labeling on small-world networks by proposing a parallel shortest-distance labeling (PSL) scheme. PSL insightfully converts the PLL’s node-order dependency to a shortest-distance dependence, which leads to a propagation-based parallel labeling in D rounds where D denotes the diameter of the graph. To further scale up PSL, it is critical to reduce the index size. This paper proposes effective index compression techniques based on graph properties as well as label properties; it also explores best practices in using betweenness-based node order to reduce the index size. The efficient betweenness estimation of the graph nodes proposed may be of independent interest to graph practitioners. Extensive experimental results verify our efficiency on billion-scale graphs, near-linear speedup in a multi-core environment, and up to 94 % reduction in the index size.
Li, W, Qu, F, Dong, W, Mishra, G & Shah, SP 2022, 'A comprehensive review on self-sensing graphene/cementitious composites: A pathway toward next-generation smart concrete', Construction and Building Materials, vol. 331, pp. 127284-127284.
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Li, W, Wen, S, Shi, K, Yang, Y & Huang, T 2022, 'Neural Architecture Search With a Lightweight Transformer for Text-to-Image Synthesis', IEEE Transactions on Network Science and Engineering, vol. 9, no. 3, pp. 1567-1576.
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Li, X, Johnson, I, Mueller, K, Wilkie, S, Hanzic, L, Bond, PL, O'Moore, L, Yuan, Z & Jiang, G 2022, 'Corrosion mitigation by nitrite spray on corroded concrete in a real sewer system', Science of The Total Environment, vol. 806, pp. 151328-151328.
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Li, X, Kulandaivelu, J, Guo, Y, Zhang, S, Shi, J, O’Brien, J, Arora, S, Kumar, M, Sherchan, SP, Honda, R, Jackson, G, Luby, SP & Jiang, G 2022, 'SARS-CoV-2 shedding sources in wastewater and implications for wastewater-based epidemiology', Journal of Hazardous Materials, vol. 432, pp. 128667-128667.
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Li, Y, Fan, X, Chen, L, Li, B & Sisson, SA 2022, 'Smoothing graphons for modelling exchangeable relational data', Machine Learning, vol. 111, no. 1, pp. 319-344.
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Li, Y, Liu, Z, Yao, L, Wang, X, McAuley, J & Chang, X 2022, 'An Entropy-guided Reinforced Partial Convolutional Network for Zero-Shot Learning', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Li, Y, Tan, VYF & Tomamichel, M 2022, 'Optimal Adaptive Strategies for Sequential Quantum Hypothesis Testing', Communications in Mathematical Physics, vol. 392, no. 3, pp. 993-1027.
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Li, Y, Wang, Q & Li, S 2022, 'On quotients of formal power series', Information and Computation, vol. 285, pp. 104874-104874.
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Li, Y, Zhu, J, Li, Y & Zhu, L 2022, 'A hybrid Jiles–Atherton and Preisach model of dynamic magnetic hysteresis based on backpropagation neural networks', Journal of Magnetism and Magnetic Materials, vol. 544, pp. 168655-168655.
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Li, Z, Tang, Y, Huang, T & Wen, S 2022, 'Formation Control of Multiagent Networks: Cooperative and Antagonistic Interactions', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 5, pp. 2809-2818.
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This article studies the formation control problem of second-order multiagent networks, in which cooperative and antagonistic interactions of the agents spontaneously coexist in the communication process. Based on the convex analysis theory, several convex polytopes that do not require some kinds of system constraints are constructed in the presence of these interactions. Then, the matrix perturbation theory and some mathematical techniques are utilized to analyze these convex polytopes. The obtained results show that the agents with cooperative interactions monotonously converge to their own specified formation shape while maintaining the desired relative position of the other agents with antagonistic interactions. Subsequently, two numerical examples are presented to illustrate the obtained results.
Liang, M, Huang, S, Pan, S, Gong, M & Liu, W 2022, 'Learning multi-level weight-centric features for few-shot learning', Pattern Recognition, vol. 128, pp. 108662-108662.
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Liang, Y, Liu, Y, Zhou, Y, Shi, Q, Zhang, M, Li, Y, Wen, W, Feng, L & Wu, J 2022, 'Efficient and stable electrorheological fluids based on chestnut-like cobalt hydroxide coupled with surface-functionalized carbon dots', Soft Matter, vol. 18, no. 20, pp. 3845-3855.
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The synergistic effect of the lipophilic groups on the surface of CDs and the biomimetic chestnut-like structure give Co(OH)2@CDs good wettability with silicone oil, great electrorheological efficiency and dynamic shear stress stability.
Liao, Q, Wang, D & Xu, M 2022, 'Category attention transfer for efficient fine-grained visual categorization', Pattern Recognition Letters, vol. 153, pp. 10-15.
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Liao, W, Zhang, Q, Yuan, B, Zhang, G & Lu, J 2022, 'Heterogeneous Multidomain Recommender System Through Adversarial Learning', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-13.
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Lidfors Lindqvist, A, Zhou, S & Walker, PD 2022, 'Direct yaw moment control of an ultra-lightweight solar-electric passenger vehicle with variation in loading conditions', Vehicle System Dynamics, vol. 60, no. 4, pp. 1393-1415.
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Large variations load-to-curb weight ratios are linked to significant changes in parameters critical to control design for vehicle stability control system. Unique and highly customised vehicles, such as the lightweight solar car in this paper, are more susceptible to the impact of such variations when developing control methods. The purpose of this study is to study the influence of variation in loading conditions, the effect of ignoring changes in inertial parameters, and develop and compare a number of alternative vehicle stability control methods that can be applied to rear-wheel driven vehicles via in-wheel motors. In this paper a Sliding Mode Control (SMC) both nominal and when including uncertainty, Dynamic Curvature Control (DCC) and a Proportional–Integral Control (PI) strategies are compared to the baseline open-loop control case. Each controller is implemented through co-simulation via MATLAB® Simulink® and Siemens Amesim™ using a 15-DOF non-linear vehicle model. The results show that SMC achieves the best performance, whilst DCC tends to overshoot target conditions prior to settling, indicating that SMC is the preferred control strategy. It is also demonstrated that by ignoring the change in the inertial parameters in simulation environments can produce an incorrect translation of the control performance.
Lin, A, Cheng, J, Rutkowski, L, Wen, S, Luo, M & Cao, J 2022, 'Asynchronous Fault Detection for Memristive Neural Networks With Dwell-Time-Based Communication Protocol', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12.
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Lin, C, Cheruiyot, NK, Bui, X-T & Ngo, HH 2022, 'Composting and its application in bioremediation of organic contaminants', Bioengineered, vol. 13, no. 1, pp. 1073-1089.
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Lin, C-T, Liu, J, Fang, C-N, Hsiao, S-Y, Chang, Y-C & Wang, Y-K 2022, 'Multi-stream 3D Convolution Neural Network with Parameter Sharing for Human State Estimation', IEEE Transactions on Cognitive and Developmental Systems, pp. 1-1.
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Lin, C-T, Tian, Y, Wang, Y-K, Do, T-TN, Chang, Y-L, King, J-T, Huang, K-C & Liao, L-D 2022, 'Effects of Multisensory Distractor Interference on Attentional Driving', IEEE Transactions on Intelligent Transportation Systems, pp. 1-9.
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Lin, J, Ma, J & Zhu, J 2022, 'A Privacy-Preserving Federated Learning Method for Probabilistic Community-Level Behind-the-Meter Solar Generation Disaggregation', IEEE Transactions on Smart Grid, vol. 13, no. 1, pp. 268-279.
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Lin, J, Ma, J & Zhu, J 2022, 'Privacy-Preserving Household Characteristic Identification With Federated Learning Method', IEEE Transactions on Smart Grid, vol. 13, no. 2, pp. 1088-1099.
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Lin, J, Ma, J, Zhu, J & Liang, H 2022, 'Deep Domain Adaptation for Non-Intrusive Load Monitoring Based on a Knowledge Transfer Learning Network', IEEE Transactions on Smart Grid, vol. 13, no. 1, pp. 280-292.
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Lin, J, Sun, G, Beydoun, G & Li, L 2022, 'Applying Machine Translation and Language Modelling Strategies for the Recommendation Task of Micro Learning Service', Educational Technology and Society, vol. 25, no. 1, pp. 205-212.
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A newly emerged micro learning service offers a flexible formal, informal, or non-formal online learning opportunity to worldwide users with different backgrounds in real-time. With the assist of big data technology and cloud computing service, online learners can access tremendous fine-grained learning resources through micro learning service. However, big data also causes serious information overload during online learning activities. Hence, an intelligent recommender system is required to filter out not-suitable learning resources and pick the one that matches the learner’s learning requirement and academic background. From the perspective of natural language processing (NLP), this study proposed a novel recommender system that utilises machine translation and language modelling. The proposed model aims to overcome the defects of conventional recommender systems and further enhance distinguish ability of the recommender system for different learning resources.
Lin, Q, Tang, M-C, Chen, X, Yi, D, Li, M & Ziolkowski, RW 2022, 'Low-Profile, Electrically Small, Ultra-Wideband Antenna Enabled with an Inductive Grid Array Metasurface', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Lin, W & Ziolkowski, RW 2022, 'High Performance Electrically Small Huygens Rectennas Enable Wirelessly Powered Internet of Things Sensing Applications: A Review', Engineering, vol. 11, pp. 42-59.
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Lin, W, Ding, A, Ngo, HH, Ren, Z, Nan, J, Li, G & Ma, J 2022, 'Effects of the metabolic uncoupler TCS on residual sludge treatment: Analyses of the microbial community and sludge dewaterability potential', Chemosphere, vol. 288, pp. 132473-132473.
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Lin, W, Liu, X, Ding, A, Ngo, HH, Zhang, R, Nan, J, Ma, J & Li, G 2022, 'Advanced oxidation processes (AOPs)-based sludge conditioning for enhanced sludge dewatering and micropollutants removal: A critical review', Journal of Water Process Engineering, vol. 45, pp. 102468-102468.
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Lin, W-T, Chen, G & Huang, Y 2022, 'Incentive edge-based federated learning for false data injection attack detection on power grid state estimation: A novel mechanism design approach', Applied Energy, vol. 314, pp. 118828-118828.
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Linghu, Q, Zhang, F, Lin, X, Zhang, W & Zhang, Y 2022, 'Anchored coreness: efficient reinforcement of social networks', The VLDB Journal, vol. 31, no. 2, pp. 227-252.
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Litov, N, Falkner, B, Zhou, H, Mehta, A, Gondwe, W, Thalakotuna, DN, Mirshekar-Syahkal, D, Esselle, K & Nakano, H 2022, 'Radar Cross Section Analysis of Two Wind Turbines via a Novel Millimeter-Wave Technique and Scale Model Measurements', IEEE Access, vol. 10, pp. 17897-17907.
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Liu, A, Lu, J, Song, Y, Xuan, J & Zhang, G 2022, 'Concept Drift Detection Delay Index', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Liu, B, Chang, X, Yuan, D & Yang, Y 2022, 'HCDC-SRCF tracker: Learning an adaptively multi-feature fuse tracker in spatial regularized correlation filters framework', Knowledge-Based Systems, vol. 238, pp. 107913-107913.
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Liu, B, Ding, M, Shaham, S, Rahayu, W, Farokhi, F & Lin, Z 2022, 'When Machine Learning Meets Privacy', ACM Computing Surveys, vol. 54, no. 2, pp. 1-36.
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The newly emerged machine learning (e.g., deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy and machine learning are still in an infancy stage, as most existing solutions only focus on privacy problems during the machine learning process. Therefore, a comprehensive study on the privacy preservation problems and machine learning is required. This article surveys the state of the art in privacy issues and solutions for machine learning. The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning-aided privacy protection, and (iii) machine learning-based privacy attack and corresponding protection schemes. The current research progress in each category is reviewed and the key challenges are identified. Finally, based on our in-depth analysis of the area of privacy and machine learning, we point out future research directions in this field.
Liu, C, Wang, X, Wang, S, Wang, Y, Lei, G & Zhu, J 2022, 'Magnetothermal Coupling Analysis of Permanent Magnet Claw Pole Machine Using Combined 3D Magnetic and Thermal Network Method', IEEE Transactions on Applied Superconductivity, vol. 32, no. 6, pp. 1-5.
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Liu, C, Wang, X, Wang, Y, Lei, G, Guo, Y & Zhu, J 2022, 'Comparative study of rotor PM transverse flux machine and stator PM transverse flux machine with SMC cores', Electrical Engineering, vol. 104, no. 3, pp. 1153-1161.
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Liu, H, Zhang, C, Yao, Y, Wei, X-S, Shen, F, Tang, Z & Zhang, J 2022, 'Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Open-Set Noise and Utilizing Hard Examples', IEEE Transactions on Multimedia, vol. 24, pp. 546-557.
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Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators. As such, learning directly from web images for fine-grained recognition has attracted broad attention. However, the presence of label noise and hard examples in web images are two obstacles for training robust fine-grained recognition models. To this end, in this paper, we propose a novel approach to remove irrelevant samples from real-world web images during training, while employing useful hard examples to update the network. Thus, our approach can alleviate the harmful effects of irrelevant noisy web images and hard examples to achieve better performance. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is far superior to current state-of-the-art web-supervised methods.
Liu, J, Li, J, Fang, J, Liu, K, Su, Y & Wu, C 2022, 'Investigation of ultra-high performance concrete slabs under contact explosions with a calibrated K&C model', Engineering Structures, vol. 255, pp. 113958-113958.
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Liu, J, Li, J, Fang, J, Su, Y & Wu, C 2022, 'Ultra-high performance concrete targets against high velocity projectile impact – a-state-of-the-art review', International Journal of Impact Engineering, vol. 160, pp. 104080-104080.
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Liu, J, Liu, C, Qu, K, Li, J & Wu, C 2022, 'Calibration of Holmquist Johnson Cook (HJC) model for projectile penetration of geopolymer-based ultra-high performance concrete (G-UHPC)', Structures, vol. 43, pp. 149-163.
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Liu, J, Liu, C, Xu, S, Li, J, Fang, J, Su, Y & Wu, C 2022, 'G-UHPC slabs strengthened with high toughness and lightweight energy absorption materials under contact explosions', Journal of Building Engineering, vol. 50, pp. 104138-104138.
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Liu, J, Peng, Y, Xu, S, Yuan, P, Qu, K, Yu, X, Hu, F, Zhang, W & Su, Y 2022, 'Investigation of geopolymer-based ultra-high performance concrete slabs against contact explosions', Construction and Building Materials, vol. 315, pp. 125727-125727.
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Liu, J, Singh, AK & Lin, C-T 2022, 'Using virtual global landmark to improve incidental spatial learning', Scientific Reports, vol. 12, no. 1.
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AbstractTo reduce the decline of spatial cognitive skills caused by the increasing use of automated GPS navigation, the virtual global landmark (VGL) system is proposed to help people naturally improve their sense of direction. Designed to accompany a heads-up navigation system, VGL system constantly displays silhouette of global landmarks in the navigator’s vision as a notable frame of reference. This study exams how VGL system impacts incidental spatial learning, i.e., subconscious spatial knowledge acquisition. We asked 55 participants to explore a virtual environment and then draw a map of what they had explored while capturing electroencephalogram (EEG) signals and eye activity. The results suggest that, with the VGL system, participants paid more attention during exploration and performed significantly better at the map drawing task—a result that indicates substantially improved incidental spatial learning. This finding might kickstart a redesigning navigation aids, to teach users to learn a route rather than simply showing them the way.
Liu, J, Wang, X, Shen, S, Fang, Z, Yu, S, Yue, G & Li, M 2022, 'Intelligent Jamming Defense Using DNN Stackelberg Game in Sensor Edge Cloud', IEEE Internet of Things Journal, vol. 9, no. 6, pp. 4356-4370.
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Liu, K, Song, R, Li, J, Guo, T, Li, X, Yang, J & Yan, Z 2022, 'Effect of steel fiber type and content on the dynamic tensile properties of ultra-high performance cementitious composites (UHPCC)', Construction and Building Materials, vol. 342, pp. 127908-127908.
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Liu, K, Wu, C, Li, X, Tao, M, Li, J, Liu, J & Xu, S 2022, 'Fire damaged ultra-high performance concrete (UHPC) under coupled axial static and impact loading', Cement and Concrete Composites, vol. 126, pp. 104340-104340.
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Liu, L, Yang, R, Cui, J, Chen, P, Ri, HC, Sun, H, Piao, X, Li, M, Pu, Q, Quinto, M, Zhou, JL, Shang, H-B & Li, D 2022, 'Circular Nonuniform Electric Field Gel Electrophoresis for the Separation and Concentration of Nanoparticles', Analytical Chemistry, vol. 94, no. 23, pp. 8474-8482.
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Liu, L-Y, Xie, G-J, Ding, J, Liu, B-F, Xing, D-F, Ren, N-Q & Wang, Q 2022, 'Microbial methane emissions from the non-methanogenesis processes: A critical review', Science of The Total Environment, vol. 806, pp. 151362-151362.
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Liu, M, Nothling, MD, Zhang, S, Fu, Q & Qiao, GG 2022, 'Thin film composite membranes for postcombustion carbon capture: Polymers and beyond', Progress in Polymer Science, vol. 126, pp. 101504-101504.
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Liu, Q & Cao, L 2022, 'Modeling time evolving COVID-19 uncertainties with density dependent asymptomatic infections and social reinforcement', Scientific Reports, vol. 12, no. 1.
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AbstractThe COVID-19 pandemic has posed significant challenges in modeling its complex epidemic transmissions, infection and contagion, which are very different from known epidemics. The challenges in quantifying COVID-19 complexities include effectively modeling its process and data uncertainties. The uncertainties are embedded in implicit and high-proportional undocumented infections, asymptomatic contagion, social reinforcement of infections, and various quality issues in the reported data. These uncertainties become even more apparent in the first 2 months of the COVID-19 pandemic, when the relevant knowledge, case reporting and testing were all limited. Here we introduce a novel hybrid approach SUDR by expanding the foundational compartmental epidemic Susceptible-Infected-Recovered (SIR) model with two compartments to a Susceptible-Undocumented infected-Documented infected-Recovered (SUDR) model. First, SUDR (1) characterizes and distinguishes Undocumented (U) and Documented (D) infections commonly seen during COVID-19 incubation periods and asymptomatic infections. Second, SUDR characterizes the probabilistic density of infections by capturing exogenous processes like clustering contagion interactions, superspreading, and social reinforcement. Lastly, SUDR approximates the density likelihood of COVID-19 prevalence over time by incorporating Bayesian inference into SUDR. Different from existing COVID-19 models, SUDR characterizes the undocumented infections during unknown transmission processes. To capture the uncertainties of temporal transmission and social reinforcement during COVID-19 contagion, the transmission rate is modeled by a time-varying density function of undocumented infectious cases. By sampling from the mean-field posterior distribution with reasonable priors, SUDR handles the randomness, noise and sparsity of COVID-19 observations widely seen in the public COVID-19 case data. The results demonstrate a deeper...
Liu, Q, Zhang, Q, Jiang, S, Du, Z, Zhang, X, Chen, H, Cao, W, Nghiem, LD & Ngo, HH 2022, 'Enhancement of lead removal from soil by in-situ release of dissolved organic matters from biochar in electrokinetic remediation', Journal of Cleaner Production, vol. 361, pp. 132294-132294.
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Liu, S, Wang, S, Liu, X, Dai, J, Muhammad, K, Gandomi, AH, Ding, W, Hijji, M & de Albuquerque, VHC 2022, 'Human Inertial Thinking Strategy: A Novel Fuzzy Reasoning Mechanism for IoT-Assisted Visual Monitoring', IEEE Internet of Things Journal, pp. 1-1.
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Liu, T, Zhang, W, Li, J, Ueland, M, Forbes, SL, Zheng, WX & Su, SW 2022, 'A Multiscale Wavelet Kernel Regularization-Based Feature Extraction Method for Electronic Nose', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-12.
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Liu, X, Duan, X, Bao, T, Hao, D, Chen, Z, Wei, W, Wang, D, Wang, S & Ni, B-J 2022, 'High-performance photocatalytic decomposition of PFOA by BiOX/TiO2 heterojunctions: Self-induced inner electric fields and band alignment', Journal of Hazardous Materials, vol. 430, pp. 128195-128195.
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Liu, Y, Zhang, S, Fang, H, Wang, Q, Jiang, S, Zhang, C & Qiu, P 2022, 'Inactivation of antibiotic resistant bacterium Escherichia coli by electrochemical disinfection on molybdenum carbide electrode', Chemosphere, vol. 287, pp. 132398-132398.
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Liu, Z, Gao, Y, Yang, J, Xu, X, Fang, J & Xie, F 2022, 'Multi-objective optimization framework of a vehicle door design in the slamming event for optimal dynamic performances', Applied Acoustics, vol. 187, pp. 108526-108526.
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Liu, Z, Wang, X, Li, Y, Yao, L, An, J, Bai, L & Lim, E-P 2022, 'Face to purchase: Predicting consumer choices with structured facial and behavioral traits embedding', Knowledge-Based Systems, vol. 235, pp. 107665-107665.
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Lloret-Cabot, M & Sheng, D 2022, 'Assessing the accuracy and efficiency of different order implicit and explicit integration schemes', Computers and Geotechnics, vol. 141, pp. 104531-104531.
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Loengbudnark, W, Khalilpour, K, Bharathy, G, Voinov, A & Thomas, L 2022, 'Impact of occupant autonomy on satisfaction and building energy efficiency', Energy and Built Environment.
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Loganathan, P, Kandasamy, J, Jamil, S, Ratnaweera, H & Vigneswaran, S 2022, 'Ozonation/adsorption hybrid treatment system for improved removal of natural organic matter and organic micropollutants from water – A mini review and future perspectives', Chemosphere, vol. 296, pp. 133961-133961.
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Loh, HW, Xu, S, Faust, O, Ooi, CP, Barua, PD, Chakraborty, S, Tan, R-S, Molinari, F & Acharya, UR 2022, 'Application of photoplethysmography signals for healthcare systems: An in-depth review', Computer Methods and Programs in Biomedicine, vol. 216, pp. 106677-106677.
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Lou, B, Barbieri, DM, Passavanti, M, Hui, C, Gupta, A, Hoff, I, Lessa, DA, Sikka, G, Chang, K, Fang, K, Lam, L, Maharaj, B, Ghasemi, N, Qiao, Y, Adomako, S, Foroutan Mirhosseini, A, Naik, B, Banerjee, A, Wang, F, Tucker, A, Liu, Z, Wijayaratna, K, Naseri, S, Yu, L, Chen, H, Shu, B, Goswami, S, Peprah, P, Hessami, A, Abbas, M & Agarwal, N 2022, 'Air pollution perception in ten countries during the COVID-19 pandemic', Ambio, vol. 51, no. 3, pp. 531-545.
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AbstractAs largely documented in the literature, the stark restrictions enforced worldwide in 2020 to curb the COVID-19 pandemic also curtailed the production of air pollutants to some extent. This study investigates the perception of the air pollution as assessed by individuals located in ten countries: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the USA. The perceptions towards air quality were evaluated by employing an online survey administered in May 2020. Participants (N = 9394) in the ten countries expressed their opinions according to a Likert-scale response. A reduction in pollutant concentration was clearly perceived, albeit to a different extent, by all populations. The survey participants located in India and Italy perceived the largest drop in the air pollution concentration; conversely, the smallest variation was perceived among Chinese and Norwegian respondents. Among all the demographic indicators considered, only gender proved to be statistically significant.
Lu, L, Ren, X, Cui, C, Luo, Y & Huang, M 2022, 'Tensor mutual information and its applications', Concurrency and Computation: Practice and Experience, vol. 34, no. 14.
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© 2020 John Wiley & Sons, Ltd. Correlation analysis has long been a question of great interest in measuring the relationship among different variables and has been applied in many fields, such as dimension reduction, classification, and so on. However, current methods of correlation analysis take into account the linear relationship between multiple variables and only few works on nonlinear interaction of two variables have been considered. In this article, we first present a nonlinear analysis method of multiple (two or more) variables based on mutual information for tensor analysis (MITA). In addition, we extend the mutual-information matrix analysis directly to MITA and show the multivariable mutual information formula based on Venn diagram. Experiments on multiview dimension reduction, including attacking internet traffic prediction, advertisement classification, and biometric structure prediction illustrate the effectiveness of the proposed method, especially in the case of low-dimensional subspace.
Lu, W, Wang, R, Wang, S, Peng, X, Wu, H & Zhang, Q 2022, 'Aspect-Driven User Preference and News Representation Learning for News Recommendation', IEEE Transactions on Intelligent Transportation Systems, pp. 1-11.
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Lu, X, Cong Luong, N, Hoang, DT, Niyato, D, Xiao, Y & Wang, P 2022, 'Secure Wirelessly Powered Networks at the Physical Layer: Challenges, Countermeasures, and Road Ahead', Proceedings of the IEEE, vol. 110, no. 1, pp. 193-209.
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Lu, X, Qiu, J, Lei, G & Zhu, J 2022, 'Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia', Applied Energy, vol. 308, pp. 118296-118296.
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Lu, Z, Qi, S, Zhang, J, Cai, Y, Guo, X & Luo, S 2022, 'An improved multi-objective bacterial colony chemotaxis algorithm based on Pareto dominance', Soft Computing, vol. 26, no. 1, pp. 69-87.
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Lu, Z, Xu, Y, Peng, L, Liang, C, Liu, Y & Ni, B-J 2022, 'A two-stage degradation coupling photocatalysis to microalgae enhances the mineralization of enrofloxacin', Chemosphere, vol. 293, pp. 133523-133523.
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Lu, Z-H, Wang, J, Tang, Z, Zhao, Y-G & Li, W 2022, 'A novel cohesive zone model for predicting the interface bonding behaviours of the ballastless track of high-speed railway', Structures, vol. 41, pp. 1-14.
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Luo, Q, Tong, L, Khezri, M, Rasmussen, KJR & Bambach, MR 2022, 'Optimal design of thin laminate plates for efficient airflow in ventilation via buckling', Thin-Walled Structures, vol. 170, pp. 108582-108582.
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Luo, T, Xu, Q, Wei, W, Sun, J, Dai, X & Ni, B-J 2022, 'Performance and Mechanism of Fe3O4 Improving Biotransformation of Waste Activated Sludge into Liquid High-Value Products', Environmental Science & Technology, vol. 56, no. 6, pp. 3658-3668.
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Luo, Z, Li, W, Wang, K, Shah, SP & Sheng, D 2022, 'Nano/micromechanical characterisation and image analysis on the properties and heterogeneity of ITZs in geopolymer concrete', Cement and Concrete Research, vol. 152, pp. 106677-106677.
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Luthra, S, Sharma, M, Kumar, A, Joshi, S, Collins, E & Mangla, S 2022, 'Overcoming barriers to cross-sector collaboration in circular supply chain management: a multi-method approach', Transportation Research Part E: Logistics and Transportation Review, vol. 157, pp. 102582-102582.
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Luu, MH, Walsum, TV, Mai, HS, Franklin, D, Nguyen, TTT, Le, TM, Moelker, A, Le, VK, Vu, DL, Le, NH, Tran, QL, Chu, DT & Trung, NL 2022, 'Automatic scan range for dose-reduced multiphase CT imaging of the liver utilizing CNNs and Gaussian models', Medical Image Analysis, vol. 78, pp. 102422-102422.
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Ly, QV, Truong, VH, Ji, B, Nguyen, XC, Cho, KH, Ngo, HH & Zhang, Z 2022, 'Exploring potential machine learning application based on big data for prediction of wastewater quality from different full-scale wastewater treatment plants', Science of The Total Environment, vol. 832, pp. 154930-154930.
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Lyu, C, Shi, Y, Sun, L & Lin, C-T 2022, 'Community detection in multiplex networks based on evolutionary multi-task optimization and evolutionary clustering ensemble', IEEE Transactions on Evolutionary Computation, pp. 1-1.
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Ma, B, Wang, X, Ni, W & Liu, RP 2022, 'Personalized Location Privacy With Road Network-Indistinguishability', IEEE Transactions on Intelligent Transportation Systems, pp. 1-13.
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Ma, F, Zhu, L & Yang, Y 2022, 'Weakly Supervised Moment Localization with Decoupled Consistent Concept Prediction', International Journal of Computer Vision, vol. 130, no. 5, pp. 1244-1258.
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Ma, Y, Huang, Y, Wu, J, E, J, Zhang, B, Han, D & Ong, HC 2022, 'A review of atmospheric fine particulate matters: chemical composition, source identification and their variations in Beijing', Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 44, no. 2, pp. 4783-4807.
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Ma, Y, Wu, N, Zhang, JA, Li, B & Hanzo, L 2022, 'Generalized Approximate Message Passing Equalization for Multi-Carrier Faster-Than-Nyquist Signaling', IEEE Transactions on Vehicular Technology, vol. 71, no. 3, pp. 3309-3314.
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Mądzik, MT, Asaad, S, Youssry, A, Joecker, B, Rudinger, KM, Nielsen, E, Young, KC, Proctor, TJ, Baczewski, AD, Laucht, A, Schmitt, V, Hudson, FE, Itoh, KM, Jakob, AM, Johnson, BC, Jamieson, DN, Dzurak, AS, Ferrie, C, Blume-Kohout, R & Morello, A 2022, 'Precision tomography of a three-qubit donor quantum processor in silicon', Nature, vol. 601, no. 7893, pp. 348-353.
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Mahmudul, HM, Akbar, D, Rasul, MG, Narayanan, R & Mofijur, M 2022, 'Estimation of the sustainable production of gaseous biofuels, generation of electricity, and reduction of greenhouse gas emissions using food waste in anaerobic digesters', Fuel, vol. 310, pp. 122346-122346.
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Makhanbet, M, Lv, T, Ni, W & Orynbet, M 2022, 'Energy-Delay-Aware Power Control for Reliable Transmission of Dynamic Cell-Free Massive MIMO', IEEE Transactions on Communications, vol. 70, no. 1, pp. 276-290.
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Makhdoom, I, Abolhasan, M & Lipman, J 2022, 'A comprehensive survey of covert communication techniques, limitations and future challenges', Computers & Security, vol. 120, pp. 102784-102784.
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Makhdoom, I, Lipman, J, Abolhasan, M & Challen, D 2022, 'Science and Technology Parks: A Futuristic Approach', IEEE Access, vol. 10, pp. 31981-32021.
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Malik, K, Kumar, D, Perissin, D & Pradhan, B 2022, 'Estimation of ground subsidence of New Delhi, India using PS-InSAR technique and Multi-sensor Radar data', Advances in Space Research, vol. 69, no. 4, pp. 1863-1882.
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Marjanovic, O 2022, 'A novel mechanism for business analytics value creation: improvement of knowledge-intensive business processes', Journal of Knowledge Management, vol. 26, no. 1, pp. 17-44.
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Purpose
This paper focuses on the “how” of business analytics (BA) value creation, which remains an open research problem and a practical challenge. The main purpose of this paper is to propose a novel BA value creation mechanism that is BA-enabled improvement of Knowledge-intensive Business Processes (KIBPs), with experiential knowledge of decision makers as the key to a more sustainable BA-enabled competitive differentiation.
Design/methodology/approach
This research uses a qualitative research case study, conducted in a large retail distribution company. The research insights were observed through a combined lens of work systems theory and the knowledge-based view (KBV) of the firm, using an interpretive approach.
Findings
The proposed theoretical model identifies three stages of KIBP improvement through BA and explains how they lead to a sustainable BA-enabled competitive differentiation. Stage 1 focusses on BA support for individual knowledge-intensive tasks, Stage 2 focusses on individual decision makers and their ability to gain KIBP-related analytical insights and turn them into action; and Stage 3 on sharing of the acquired experiential knowledge amongst decision makers using BA.
Originality/value
In addition to proposing a novel mechanism for BA value creation, this research demonstrates the importance of leveraging experiential knowledge of decision makers as a pathway to a more sustainable competitive differentiation through BA. This, in turn, creates new opportunities for knowledge management researchers to engage in BA-related research. It also opens a new approach for BA resea...
Marjanovic, O, Cecez-Kecmanovic, D & Vidgen, R 2022, 'Theorising Algorithmic Justice', European Journal of Information Systems, vol. 31, no. 3, pp. 269-287.
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Martin, K, Arbour, S, McGregor, C & Rice, M 2022, 'Silver linings: Observed reductions in aggression and use of restraints and seclusion in psychiatric inpatient care during COVID‐19', Journal of Psychiatric and Mental Health Nursing, vol. 29, no. 2, pp. 381-385.
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Masangkay, J, Munasinghe, N, Watterson, P & Paul, G 2022, 'Simulation and experimental characterisation of a 3D-printed electromagnetic vibration sensor', Sensors and Actuators A: Physical, vol. 338, pp. 113470-113470.
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Additive manufacturing, also known as 3D printing has already transformed from a rapid prototyping tool to a final end-product manufacturing technique. 3D printing can be used to develop various types of sensors. This paper investigates the ability to use the electromagnetic induction properties of 3D printed carbon-based filament for developing sensors. The paper presents a novel prototype vibration sensor which is 3D-printable, except for an included NdFeB magnet. Motion is detected from the voltage induced by the relative motion of the magnet. The devised vibration sensor is simulated using ANSYS, and a novel prototype is 3D-printed for physical testing to characterise and understand its electromagnetic properties. Simulation helped establish constraints for the design. Two types of experimental setups were physically tested, one setup with a magnet freely sliding inside a cylindrical cavity within an oscillating coil, and the other setup with a stationary coil and oscillating magnet. At a frequency of 10 Hz and a motion travel of about 12 mm, the induced voltage for the moving coil case varied from 5.4 mV RMS for pure sliding motion of the internal magnet to 22.1 mV RMS. The findings of this paper suggest that future sensors can be developed using the electromagnetic induction properties of the carbon-based filament.
Mathew, M, Rad, MA, Mata, JP, Mahmodi, H, Kabakova, IV, Raston, CL, Tang, Y, Tipper, JL & Tavakoli, J 2022, 'Hyperbranched polymers tune the physicochemical, mechanical, and biomedical properties of alginate hydrogels', Materials Today Chemistry, vol. 23, pp. 100656-100656.
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Matta, SM, Selam, MA, Manzoor, H, Adham, S, Shon, HK, Castier, M & Abdel-Wahab, A 2022, 'Predicting the performance of spiral-wound membranes in pressure-retarded osmosis processes', Renewable Energy, vol. 189, pp. 66-77.
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Maxit, L, Karimi, M, Guasch, O & Michel, F 2022, 'Numerical analysis of vibroacoustic beamforming gains for acoustic source detection inside a pipe conveying turbulent flow', Mechanical Systems and Signal Processing, vol. 171, pp. 108888-108888.
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Medawela, S, Indraratna, B, Athuraliya, S, Lugg, G & Nghiem, LD 2022, 'Monitoring the performance of permeable reactive barriers constructed in acid sulfate soils', Engineering Geology, vol. 296, pp. 106465-106465.
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Mehrabi, N & Khabbaz, H 2022, 'A trustful transition zone for high-speed rail using stone columns', Australian Journal of Civil Engineering, vol. 20, no. 1, pp. 56-66.
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The high-speed railway projects have encountered several geotechnical challenges. One of the most important challenges is the differential settlement control in transition zones. Cement-treated soil is a common method to prevent the differential settlement at transition zones. An alternative method uses stone columns for controlling the differential settlement in approaching embankment of bridges. In this study, numerical modelling using PLAXIS 2D is selected for the assessment of stone columns in the reduction of total and differential settlements. One of the overpass bridges of the track constructed for the Tehran–Isfahan railway, the first high-speed railway in the country, is chosen as the case study. Three models are created based on the properties of the selected case study. The first one is a typical approaching embankment. The second one is the bridge abutment section, and the last one is a typical reinforced approaching embankment with stone columns.
Meng, X, Li, X, Nghiem, LD, Ruiz, E, Johir, MA, Gao, L & Wang, Q 2022, 'Improved stormwater management through the combination of the conventional water sensitive urban design and stormwater pipeline network', Process Safety and Environmental Protection, vol. 159, pp. 1164-1173.
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Milano, J, Shamsuddin, AH, Silitonga, AS, Sebayang, AH, Siregar, MA, Masjuki, HH, Pulungan, MA, Chia, SR & Zamri, MFMA 2022, 'Tribological study on the biodiesel produced from waste cooking oil, waste cooking oil blend with Calophyllum inophyllum and its diesel blends on lubricant oil', Energy Reports, vol. 8, pp. 1578-1590.
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Mishra, DK, Ghadi, MJ, Li, L, Zhang, J & Hossain, MJ 2022, 'Active distribution system resilience quantification and enhancement through multi-microgrid and mobile energy storage', Applied Energy, vol. 311, pp. 118665-118665.
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Mishra, DK, Ray, PK, Li, L, Zhang, J, Hossain, MJ & Mohanty, A 2022, 'Resilient control based frequency regulation scheme of isolated microgrids considering cyber attack and parameter uncertainties', Applied Energy, vol. 306, pp. 118054-118054.
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Cyber-physical attacks and parameter uncertainties are becoming a compelling issue on load frequency control, directly affecting the resilience (i.e., reliability plus security) of the microgrid and multi-microgrid systems enabled by internet of things and the fifth generation communication system. A resilient system aims to endure and quickly restore a system's transients during extreme events. Therefore, it is critically important to have a resilient system to evade the total system failure or blackout in order to make them attack-resilient. With this objective, this paper presents a resilience-based frequency regulation scheme in a microgrid under different operating conditions, such as, step and random change in load and different wind speed patterns. Furthermore, a cyber-attack model is considered in the problem formulation to make the system robust against external attacks. To protect against the cyber-attack and parameter uncertainties in the system, different control schemes are employed, and their robustness characteristics are compared through various performance indices. Besides, the proposed control schemes are validated through a real-time software synchronisation environment, i.e., OPAL-RT. As noted, the proposed type-2 fuzzy proportional-integral-derivative based controller provides the most significant improvement in the dynamic performance for frequency regulation compared to that of the others under the cyber-attack and uncertainties.
Mishra, M, Chaudhuri, S, Kshetrimayum, RS, Alphones, A & Esselle, KP 2022, 'Space Efficient Meta-Grid Lines for Mutual Coupling Reduction in Two-Port Planar Monopole and DRA Array', IEEE Access, vol. 10, pp. 49829-49838.
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Mittal, A, Shivakumara, P, Pal, U, Lu, T & Blumenstein, M 2022, 'A new method for detection and prediction of occluded text in natural scene images', Signal Processing: Image Communication, vol. 100, pp. 116512-116512.
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Text detection from natural scene images is an active research area for computer vision, signal, and image processing because of several real-time applications such as driving vehicles automatically and tracing person behaviors during sports or marathon events. In these situations, there is a high probability of missing text information due to the occlusion of different objects/persons while capturing images. Unlike most of the existing methods, which focus only on text detection by ignoring the effect of missing texts, this work detects and predicts missing texts so that the performance of the OCR improves. The proposed method exploits the property of DCT for finding significant information in images by selecting multiple channels. For chosen DCT channels, the proposed method studies texture distribution based on statistical measurement to extract features. We propose to adopt Bayesian classifier for categorizing text pixels using extracted features. Then a deep learning model is proposed for eliminating false positives to improve text detection performance. Further, the proposed method employs a Natural Language Processing (NLP) model for predicting missing text information by using detected and recognition texts. Experimental results on our dataset, which contains texts occluded by objects, show that the proposed method is effective in predicting missing text information. To demonstrate the effectiveness and objectiveness of the proposed method, we also tested it on the standard datasets of natural scene images, namely, ICDAR 2017-MLT, Total-Text, and CTW1500.
Mofijur, M, Ashrafur Rahman, SM, Nguyen, LN, Mahlia, TMI & Nghiem, LD 2022, 'Selection of microalgae strains for sustainable production of aviation biofuel', Bioresource Technology, vol. 345, pp. 126408-126408.
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Mohamed, BA, Bilal, M, Salama, E-S, Periyasamy, S, Fattah, IMR, Ruan, R, Awasthi, MK & Leng, L 2022, 'Phenolic-rich bio-oil production by microwave catalytic pyrolysis of switchgrass: Experimental study, life cycle assessment, and economic analysis', Journal of Cleaner Production, pp. 132668-132668.
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Mohamed, BA, Fattah, IMR, Yousaf, B & Periyasamy, S 2022, 'Effects of the COVID-19 pandemic on the environment, waste management, and energy sectors: a deeper look into the long-term impacts', Environmental Science and Pollution Research, vol. 29, no. 31, pp. 46438-46457.
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Mohammadi, E, Jahanandish, M, Ghahramani, A, Nikoo, MR, Javankhoshdel, S & Gandomi, AH 2022, 'Stochastic optimization model for determining support system parameters of a subway station', Expert Systems with Applications, vol. 203, pp. 117509-117509.
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Mojiri, A, Zhou, JL, Nazari V, M, Rezania, S, Farraji, H & Vakili, M 2022, 'Biochar enhanced the performance of microalgae/bacteria consortium for insecticides removal from synthetic wastewater', Process Safety and Environmental Protection, vol. 157, pp. 284-296.
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Mojiri, A, Zhou, JL, Ratnaweera, H, Rezania, S & Nazari V, M 2022, 'Pharmaceuticals and personal care products in aquatic environments and their removal by algae-based systems', Chemosphere, vol. 288, pp. 132580-132580.
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Monadi, M, Farzin, H, Salehizadeh, MR & Rouzbehi, K 2022, 'Integrated control and monitoring of a smart charging station with a proposed data exchange protocol', IET Renewable Power Generation, vol. 16, no. 3, pp. 532-546.
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Monjurul Hasan, ASM, Trianni, A, Shukla, N & Katic, M 2022, 'A novel characterization based framework to incorporate industrial energy management services', Applied Energy, vol. 313, pp. 118891-118891.
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Morgan, AL, Torpy, FR, Irga, PJ, Fleck, R, Gill, RL & Pettit, T 2022, 'The botanical biofiltration of volatile organic compounds and particulate matter derived from cigarette smoke', Chemosphere, vol. 295, pp. 133942-133942.
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Morshedi Rad, D, Rezaei, M, Radfar, P & Ebrahimi Warkiani, M 2022, 'Microengineered filters for efficient delivery of nanomaterials into mammalian cells', Scientific Reports, vol. 12, no. 1.
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AbstractIntracellular delivery of nanomaterials into the cells of interest has enabled cell manipulation for numerous applications ranging from cell-based therapies to biomedical research. To date, different carriers or membrane poration-based techniques have been developed to load nanomaterials to the cell interior. These biotools have shown promise to surpass the membrane barrier and provide access to the intracellular space followed by passive diffusion of exogenous cargoes. However, most of them suffer from inconsistent delivery, cytotoxicity, and expensive protocols, somewhat limiting their utility in a variety of delivery applications. Here, by leveraging the benefits of microengineered porous membranes with a suitable porosity, we demonstrated an efficient intracellular loading of diverse nanomaterials to different cell types based on inducing mechanical disruption to the cell membrane. In this work, for the first time, we used ultra-thin silicon nitride (SiN) filter membranes with uniform micropores smaller than the cell diameter to load impermeable nanomaterials into adherent and non-adherent cell types. The delivery performance using SiN microsieves has been validated through the loading of functional nanomaterials from a few nanometers to hundreds of nanometers into mammalian cells with minimal undesired impacts. Besides the high delivery efficiency and improved cell viability, this simple and low-cost approach offers less clogging and higher throughput (107 cell min−1). Therefore, it yields to the efficient introduction of exogenous nanomaterials into the large population of cells, illustrating the potential of these microengineered filters to be widely used in the microfiltroporation (MFP) setup.
Mortazavi, H, Mortazavy Beni, H & Islam, MS 2022, 'Thermal/fluid characteristics of the inline stacked plain‐weave screen as solar‐powered Stirling engine heat regenerators', IET Renewable Power Generation, vol. 16, no. 5, pp. 956-965.
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Mousavi, M, Gandomi, AH, Holloway, D, Berry, A & Chen, F 2022, 'Machine learning analysis of features extracted from time–frequency domain of ultrasonic testing results for wood material assessment', Construction and Building Materials, vol. 342, pp. 127761-127761.
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Mousavi, M, Holloway, D, Olivier, JC & Gandomi, AH 2022, 'Quaternion analysis of beam multi‐type vibration data for damage detection', Structural Control and Health Monitoring, vol. 29, no. 2.
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Nahar, K & Gill, AQ 2022, 'Integrated identity and access management metamodel and pattern system for secure enterprise architecture', Data & Knowledge Engineering, vol. 140, pp. 102038-102038.
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Naik, D, Ramesh, D, Gandomi, AH & Babu Gorojanam, N 2022, 'Parallel and distributed paradigms for community detection in social networks: A methodological review', Expert Systems with Applications, vol. 187, pp. 115956-115956.
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Nama, S, Sharma, S, Saha, AK & Gandomi, AH 2022, 'A quantum mutation-based backtracking search algorithm', Artificial Intelligence Review, vol. 55, no. 4, pp. 3019-3073.
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Namdarpour, F, Mesbah, M, Gandomi, AH & Assemi, B 2022, 'Using genetic programming on GPS trajectories for travel mode detection', IET Intelligent Transport Systems, vol. 16, no. 1, pp. 99-113.
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Nan, Y, Huang, X & Guo, YJ 2022, '3-D Millimeter-Wave Helical Imaging', IEEE Transactions on Microwave Theory and Techniques, vol. 70, no. 4, pp. 2499-2511.
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Nan, Y, Huang, X & Guo, YJ 2022, 'A Panoramic Synthetic Aperture Radar', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13.
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Nan, Y, Huang, X & Guo, YJ 2022, 'An Universal Circular Synthetic Aperture Radar', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15.
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Nascimben, M, Wang, Y-K, King, J-T, Jung, T-P, Touryan, J, Lance, BJ & Lin, C-T 2022, 'Alpha Correlates of Practice During Mental Preparation for Motor Imagery', IEEE Transactions on Cognitive and Developmental Systems, vol. 14, no. 1, pp. 146-155.
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IEEE In this study we quantified performance variations of motor imagery (MI)-based brain-computer interface (BCI) systems induced by practice. Two experimental sessions were recorded from ten healthy subjects while playing a BCI-oriented videogame for two weeks. The analysis focused on the exploration of electroencephalographic changes during mental preparation between novice and practiced subjects. EEG changes were quantified using global field power (GFP), dynamic time warping (TW) and mutual information (MutInf): GFP represents the strength of the electric field, TW measures signal similarities and MutInf signals inter-dependency. Each metric was selected to relate insights extracted from mental preparation to the three experimental hypotheses associating practice with BCI performance. Significant results were identified in lower alpha for GFP and upper alpha for TW and MutInf. GFP in lower alpha during mental preparation assessed not only novice vs practiced variations but also “intra-session” differences. Findings suggest that EEG changes during mental preparation provide a quantitative measure of practice level. These metrics extracted before motor intention could be applied to BCI models targeting MI to monitor a user’s degree of training.
Nasir, AA, D. Tuan, H, Dutkiewicz, E & Hanzo, L 2022, 'Finite-Resolution Digital Beamforming for Multi-user Millimeter-wave Networks', IEEE Transactions on Vehicular Technology, pp. 1-16.
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Nasir, AA, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2022, 'Relay-Aided Multi-User OFDM Relying on Joint Wireless Power Transfer and Self-Interference Recycling', IEEE Transactions on Communications, vol. 70, no. 1, pp. 291-305.
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Naveed Arif, M, Waqas, A, Ahmed Butt, F, Mahmood, M, Hussain Khoja, A, Ali, M, Ullah, K, Mujtaba, MA & Kalam, MA 2022, 'Techno-economic assessment of solar water heating systems for sustainable tourism in northern Pakistan', Alexandria Engineering Journal, vol. 61, no. 7, pp. 5485-5499.
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Nayak, RP, Sethi, S, Bhoi, SK, Mohapatra, D, Sahoo, RR, Sharma, PK & Puthal, D 2022, 'TFMD-SDVN: a trust framework for misbehavior detection in the edge of software-defined vehicular network', The Journal of Supercomputing, vol. 78, no. 6, pp. 7948-7981.
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Neha, B, Panda, SK, Sahu, PK, Sahoo, KS & Gandomi, AH 2022, 'A Systematic Review on Osmotic Computing', ACM Transactions on Internet of Things, vol. 3, no. 2, pp. 1-30.
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Osmotic computing in association with related computing paradigms (cloud, fog, and edge) emerges as a promising solution for handling bulk of security-critical as well as latency-sensitive data generated by the digital devices. It is a growing research domain that studies deployment, migration, and optimization of applications in the form of microservices across cloud/edge infrastructure. It presents dynamically tailored microservices in technology-centric environments by exploiting edge and cloud platforms. Osmotic computing promotes digital transformation and furnishes benefits to transportation, smart cities, education, and healthcare. In this article, we present a comprehensive analysis of osmotic computing through a systematic literature review approach. To ensure high-quality review, we conduct an advanced search on numerous digital libraries to extracting related studies. The advanced search strategy identifies 99 studies, from which 29 relevant studies are selected for a thorough review. We present a summary of applications in osmotic computing build on their key features. On the basis of the observations, we outline the research challenges for the applications in this research field. Finally, we discuss the security issues resolved and unresolved in osmotic computing.
Newsom, ET, Sadeghpour, A, Entezari, A, Vinzons, JLU, Stanford, RE, Mirkhalaf, M, Chon, D, Dunstan, CR & Zreiqat, H 2022, 'Design and evaluation of 3D-printed Sr-HT-Gahnite bioceramic for FDA regulatory submission: A Good Laboratory Practice sheep study', Acta Biomaterialia.
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Ng, BYS, Ong, HC, Lau, HLN, Ishak, NS, Elfasakhany, A & Lee, HV 2022, 'Production of sustainable two-stroke engine biolubricant ester base oil from palm fatty acid distillate', Industrial Crops and Products, vol. 175, pp. 114224-114224.
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Ngo, T, Indraratna, B & Ferreira, F 2022, 'Influence of synthetic inclusions on the degradation and deformation of ballast under heavy-haul cyclic loading', International Journal of Rail Transportation, vol. 10, no. 4, pp. 413-435.
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Nguyen, AQ, Nguyen, LN, Johir, MAH, Ngo, HH & Nghiem, LD 2022, 'Linking endogenous decay and sludge bulking in the microbial community to membrane fouling at sub-critical flux', Journal of Membrane Science Letters, vol. 2, no. 1, pp. 100023-100023.
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Nguyen, AQ, Nguyen, LN, McDonald, JA, Nghiem, LD, Leusch, FDL, Neale, PA & Khan, SJ 2022, 'Chiral inversion of 2-arylpropionoic acid (2-APA) enantiomers during simulated biological wastewater treatment', Water Research, vol. 209, pp. 117871-117871.
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Nguyen, AQ, Nguyen, LN, Xu, Z, Luo, W & Nghiem, LD 2022, 'New insights to the difference in microbial composition and interspecies interactions between fouling layer and mixed liquor in a membrane bioreactor', Journal of Membrane Science, vol. 643, pp. 120034-120034.
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Nguyen, BP, Nguyen, T, Nguyen, THY & Tran, TD 2022, 'Performance of composite PVD-soil cement column foundation under embankment through plane-strain numerical analysis', International Journal of Geomechanics, vol. 22, no. 8.
Nguyen, CHP, Hoang, NH, Lee, S & Vu, HL 2022, 'A System Optimal Speed Advisory Framework for a Network of Connected and Autonomous Vehicles', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 6, pp. 5727-5739.
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Nguyen, CT, Van Huynh, N, Chu, NH, Saputra, YM, Hoang, DT, Nguyen, DN, Pham, Q-V, Niyato, D, Dutkiewicz, E & Hwang, W-J 2022, 'Transfer Learning for Wireless Networks: A Comprehensive Survey', Proceedings of the IEEE, pp. 1-43.
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Nguyen, CT, Walker, PD, Zhou, S & Zhang, N 2022, 'Optimal sizing and energy management of an electric vehicle powertrain equipped with two motors and multi-gear ratios', Mechanism and Machine Theory, vol. 167, pp. 104513-104513.
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Nguyen, D-A, Tran, X-T, Dang, KN & Iacopi, F 2022, 'A low-power, high-accuracy with fully on-chip ternary weight hardware architecture for Deep Spiking Neural Networks', Microprocessors and Microsystems, vol. 90, pp. 104458-104458.
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Nguyen, HAD & Ha, QP 2022, 'Wireless Sensor Network Dependable Monitoring for Urban Air Quality', IEEE Access, vol. 10, pp. 40051-40062.
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Nguyen, KT, Navidpour, AH, Ahmed, MB, Mojiri, A, Huang, Y & Zhou, JL 2022, 'Adsorption and desorption behavior of arsenite and arsenate at river sediment-water interface', Journal of Environmental Management, vol. 317, pp. 115497-115497.
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Nguyen, LN, Vu, HP, Fu, Q, Abu Hasan Johir, M, Ibrahim, I, Mofijur, M, Labeeuw, L, Pernice, M, Ralph, PJ & Nghiem, LD 2022, 'Synthesis and evaluation of cationic polyacrylamide and polyacrylate flocculants for harvesting freshwater and marine microalgae', Chemical Engineering Journal, vol. 433, pp. 133623-133623.
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Nguyen, NHT, Perry, S, Bone, D, Thanh, HL, Xu, M & Nguyen, T 2022, 'Combination of Images and Point clouds in a Generative Adversarial Network for Upsampling Crack Point Clouds', IEEE Access, pp. 1-1.
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Nguyen, QA, Vu, HP, McDonald, JA, Nguyen, LN, Leusch, FDL, Neale, PA, Khan, SJ & Nghiem, LD 2022, 'Chiral Inversion of 2-Arylpropionic Acid Enantiomers under Anaerobic Conditions', Environmental Science & Technology, vol. 56, no. 12, pp. 8197-8208.
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Nguyen, QD, Afroz, S, Zhang, Y, Kim, T, Li, W & Castel, A 2022, 'Autogenous and total shrinkage of limestone calcined clay cement (LC3) concretes', Construction and Building Materials, vol. 314, pp. 125720-125720.
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Nguyen, T, Indraratna, B & Leroueil, S 2022, 'Localized Behaviour of Fluidized Subgrade Soil Subjected to Cyclic Loading', Canadian Geotechnical Journal, vol. 59, no. 5.
Nguyen, TAH, Le, TV, Ngo, HH, Guo, WS, Vu, ND, Tran, TTT, Nguyen, THH, Nguyen, XC, Nguyen, VH & Pham, TT 2022, 'Hybrid use of coal slag and calcined ferralsol as wetland substrate for improving phosphorus removal from wastewater', Chemical Engineering Journal, vol. 428, pp. 132124-132124.
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Nguyen, TG, Phan, TV, Hoang, DT, Nguyen, HH & Le, DT 2022, 'DeepPlace: Deep reinforcement learning for adaptive flow rule placement in Software-Defined IoT Networks', Computer Communications, vol. 181, pp. 156-163.
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Nguyen, TH, Ryu, S, Loganathan, P, Kandasamy, J, Nguyen, TV & Vigneswaran, S 2022, 'Arsenic adsorption by low-cost laterite column: Long-term experiments and dynamic column modeling', Process Safety and Environmental Protection, vol. 160, pp. 868-875.
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Nguyen, TH, Tran, HN, Nguyen, TV, Vigneswaran, S, Trinh, VT, Nguyen, TD, Ha Nguyen, TH, Mai, TN & Chao, H-P 2022, 'Single-step removal of arsenite ions from water through oxidation-coupled adsorption using Mn/Mg/Fe layered double hydroxide as catalyst and adsorbent', Chemosphere, vol. 295, pp. 133370-133370.
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Nguyen, TK, Nguyen, HH, Tuan, HD & Ngo, HQ 2022, 'Improved Pilot Designs for Enhancing Connectivity in Multicarrier Massive MIMO Systems', IEEE Wireless Communications Letters, vol. 11, no. 5, pp. 1057-1061.
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Nguyen, TT & Indraratna, B 2022, 'Fluidization of soil under increasing seepage flow: an energy perspective through CFD-DEM coupling', Granular Matter, vol. 24, no. 3.
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AbstractIncreasing seepage flow causes soil particles to migrate, i.e., from local piping to complete fluidization, resulting in reduced effectives stress and degraded shear stiffness of the soil foundation. This process has received considerable attention in the past years, however, majority of them concentrate on macro-aspects such as the internal erosion and soil deformation, while there is a lack of fundamental studies addressing the energy transport at micro-scale of fluid-soil systems during soil approaching fluidization. In this regard, the current study presents an assessment of the energy evolution in soil fluidization based on the discrete element method (DEM) coupled with computation fluid dynamics (CFD). In this paper, an upward seepage flow of fluid is modelled by CFD based on the modified Navier–Stokes equations, while soil particles are governed by DEM with their mutual interactions being computed through fluid-particle force models. The energy transformation from the potential state to kinetic forms during fluid flowing is discussed with respect to numerical (CFD-DEM) results and the energy conservation concepts. The results show that majority of the potential energy induced by fluid flows has lost due to frictional mechanisms, while only a small amount of energy is needed to cause the soil to fluidize completely. The contribution of rotational and translational components to the total kinetic energy of particles, and their changing roles during soil fluidization is also presented. The effect of boundary condition on the energy transformation and fluidization of soil is also investigated and discussed.
Graphical abstract
Nguyen, TT & Indraratna, B 2022, 'Rail track degradation under mud pumping evaluated through site and laboratory investigations', International Journal of Rail Transportation, vol. 10, no. 1, pp. 44-71.
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Nguyen, TT, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Nguyen, CT, Zhang, J, Liang, S, Bui, XT & Hoang, NB 2022, 'A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm', Science of The Total Environment, vol. 833, pp. 155066-155066.
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Nguyen, TT, Pham, TD, Nguyen, CT, Delfos, J, Archibald, R, Dang, KB, Hoang, NB, Guo, W & Ngo, HH 2022, 'A novel intelligence approach based active and ensemble learning for agricultural soil organic carbon prediction using multispectral and SAR data fusion', Science of The Total Environment, vol. 804, pp. 150187-150187.
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Nguyen, TV 2022, 'Personalised assessment of fracture risk: Which tool to use?', Australian Journal of General Practice, vol. 51, no. 3, pp. 189-190.
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Nguyen, XC, Nguyen, TTH, Le, QV, Le, PC, Srivastav, AL, Pham, QB, Nguyen, PM, La, DD, Rene, ER, Ngo, HH, Chang, SW & Nguyen, DD 2022, 'Developing a new approach for design support of subsurface constructed wetland using machine learning algorithms', Journal of Environmental Management, vol. 301, pp. 113868-113868.
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Ni, Q, Ji, JC, Feng, K & Halkon, B 2022, 'A fault information-guided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis', Mechanical Systems and Signal Processing, vol. 164, pp. 108216-108216.
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Ni, Z, Zhang, JA, Yang, K, Huang, X & Tsiftsis, TA 2022, 'Multi-Metric Waveform Optimization for Multiple-Input Single-Output Joint Communication and Radar Sensing', IEEE Transactions on Communications, vol. 70, no. 2, pp. 1276-1289.
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Nie, X, Zhang, A, Chen, J, Qu, Y & Yu, S 2022, 'Blockchain-Empowered Secure and Privacy-Preserving Health Data Sharing in Edge-Based IoMT', Security and Communication Networks, vol. 2022, pp. 1-16.
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Health data sharing, as a booming demand, enables the patients with similar symptoms to connect with each other and doctors to obtain the medical history of patients. Health data are usually collected from edge-based Internet of medical things (IoMT) with devices such as smart wearable devices, smart watches, and smartphones. Since health data are highly private and have great financial value, adversaries ceaselessly launch diverse attacks to obtain private information. All these issues pose great challenges to health data sharing in edge-based IoMT scenarios. Existing research either lacks comprehensive consideration of privacy and security protection or fails to provide a proper incentive mechanism, which expels users from sharing data. In this study, we propose a novel blockchain-assisted data sharing scheme, which allows secure and privacy-preserving profile matching. A bloom filter with hash functions is designed to verify the authenticity of keyword ciphertext. Key-policy attribute-based encryption (KP-ABE) algorithm and smart contracts are employed to achieve secure profile matching. To incentivize users actively participating in profile matching, we devise an incentive mechanism and construct a two-phase Stackelberg game to address pricing problems for data owners and accessing problems of data requesters. The optimal pricing mechanism is specially designed for encouraging more users to participate in health data sharing and maximizing users’ profit. Moreover, security analysis illustrates that the proposed protocol is capable of satisfying various security goals, while performance evaluation shows high scalability and feasibility of the proposed scheme in edge-based IoMT scenarios.
Nimmy, SF, Hussain, OK, Chakrabortty, RK, Hussain, FK & Saberi, M 2022, 'Explainability in supply chain operational risk management: A systematic literature review', Knowledge-Based Systems, vol. 235, pp. 107587-107587.
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It is important to manage operational disruptions to ensure the success of supply chain operations. To achieve this aim, researchers have developed techniques that determine the occurrence of operational risk events which assists supply chain operational risk managers develop plans to manage them by detection/monitoring, mitigation/management, or optimization techniques. Various artificial intelligence (AI) approaches have been used to develop such techniques in the broad activities of operational risk management. However, all of these techniques are black box in their working nature. This means that the chosen technique cannot explain why it has given that output and whether it is correct and free from bias. To address this, researchers argue the need for supply chain management professionals to move towards using explainable AI methods for operational risk management. In this paper, we conduct a systematic literature review on the techniques used to determine operational risks and analyse whether they satisfy the requirement of them being explainable. The findings highlight the shortcomings and inspires directions for future research. From a managerial perspective, the paper encourages risk managers to choose techniques for supply chain operational risk management that can be auditable as this will ensure that the risk managers know why they should take a particular risk management action rather than just what they should do to manage the operational risks.
Niu, K, Lu, Y, Peng, X & Zeng, J 2022, 'Fusion of sequential visits and medical ontology for mortality prediction', Journal of Biomedical Informatics, vol. 127, pp. 104012-104012.
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Norouzian-Maleki, P, Izadbakhsh, H, Saberi, M, Hussain, O, Jahangoshai Rezaee, M & GhanbarTehrani, N 2022, 'An integrated approach to system dynamics and data envelopment analysis for determining efficient policies and forecasting travel demand in an urban transport system', Transportation Letters, vol. 14, no. 2, pp. 157-173.
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Nuruzzaman, M, Liu, Y, Ren, J, Rahman, MM, Zhang, H, Hasan Johir, MA, Shon, HK & Naidu, R 2022, 'Capability of Organically Modified Montmorillonite Nanoclay as a Carrier for Imidacloprid Delivery', ACS Agricultural Science & Technology, vol. 2, no. 1, pp. 57-68.
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O’Connor, J, Bolan, NS, Kumar, M, Nital, AS, Ahmed, MB, Vithanage, M, Rinklebe, J, Mukhopadhyay, R, Srivastava, P, Sarkar, B, Bhatnagar, A, Wang, H, Siddique, KHM & Kirkham, MB 2022, 'Distribution, transformation and remediation of poly- and per-fluoroalkyl substances (PFAS) in wastewater sources', Process Safety and Environmental Protection.
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Olszak, CM, Zurada, J & Cetindamar, D 2022, 'Business Intelligence & Big Data for Innovative and Sustainable Development of Organizations', Information Systems Management, vol. 39, no. 1, pp. 2-2.
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Omar, KR, Fatahi, B & Nguyen, LD 2022, 'Impacts of Pre-contamination Moisture Content on Mechanical Properties of High-Plasticity Clay Contaminated with Used Engine Oil', Journal of Testing and Evaluation, vol. 50, no. 6, pp. 20210477-20210477.
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Ortega, JS, Corrales-Orovio, R, Ralph, P, Egaña, JT & Gentile, C 2022, 'Photosynthetic microorganisms for the oxygenation of advanced 3D bioprinted tissues', Acta Biomaterialia.
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Ouchchen, M, Boutaleb, S, Abia, EH, El Azzab, D, Miftah, A, Dadi, B, Echogdali, FZ, Mamouch, Y, Pradhan, B, Santosh, M & Abioui, M 2022, 'Exploration targeting of copper deposits using staged factor analysis, geochemical mineralization prospectivity index, and fractal model (Western Anti-Atlas, Morocco)', Ore Geology Reviews, vol. 143, pp. 104762-104762.
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Pan, T, Jiang, Z, Han, J, Wen, S, Men, A & Wang, H 2022, 'Taylor saves for later: Disentanglement for video prediction using Taylor representation', Neurocomputing, vol. 472, pp. 166-174.
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Pang, G, Shen, C, Cao, L & Hengel, AVD 2022, 'Deep Learning for Anomaly Detection', ACM Computing Surveys, vol. 54, no. 2, pp. 1-38.
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Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e.,
deep anomaly detection
, has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.
Peellage, WH, Fatahi, B & Rasekh, H 2022, 'Experimental investigation for vibration characteristics of jointed rocks using cyclic triaxial tests', Soil Dynamics and Earthquake Engineering, vol. 160, pp. 107377-107377.
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Peng, Y, Azeem, M, Li, R, Xing, L, Li, Y, Zhang, Y, Guo, Z, Wang, Q, Ngo, HH, Qu, G & Zhang, Z 2022, 'Zirconium hydroxide nanoparticle encapsulated magnetic biochar composite derived from rice residue: Application for As(III) and As(V) polluted water purification', Journal of Hazardous Materials, vol. 423, pp. 127081-127081.
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Finding a low-cost and suitable adsorbent is still in urgent need for efficient decontamination of As(III) and As(V) elements from the polluted waters. A novel zirconium hydroxide nanoparticle encapsulated magnetic biochar composite (ZBC) derived from rice residue was synthesized for the adsorptive capture of As(III) and As(V) from aqueous solutions. The results revealed that ZBC showed an acceptable magnet separation ability and its surface was encapsulated with lots of hydrous zirconium oxide nanoparticles. Compared to As(III), the adsorption of As(V) onto ZBC was mainly dependent on the pH of the solution. The intraparticle diffusion model described the adsorption process. ZBC showed satisfactory adsorption performances to As(III) and As(V) with the highest adsorption quantity of 107.6 mg/g and 40.8 mg/g at pH 6.5 and 8.5, respectively. The adsorption of As(III) and As(V) on ZBC was almost impervious with the ionic strength while the presence of coexisting ions, especially phosphate, significantly affected the adsorption process. The processes of complexation reaction and electrostatic attraction contributed to the adsorption of As(III) and As(V) onto ZBC. ZBC prepared from kitchen rice residue was found to be a low cost environmentally friendly promising adsorbent with high removal capacity for As(III) and As(V) and could be recycled easily from contaminated waters.
Peng, Y, Lin, X, Zhang, Y, Zhang, W & Qin, L 2022, 'Answering reachability and K-reach queries on large graphs with label constraints', The VLDB Journal, vol. 31, no. 1, pp. 101-127.
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The purpose of this paper is to examine the problem of label-constrained reachability (LCR) and K-reach (LCKR) queries, which are fundamental in a wide variety of applications using directed edge-labeled graphs. While reachability and K-reach queries have been extensively researched, LCR and LCKR queries are much more challenging due to the fact that the number of potential label-constraint sets is exponential to the size of the labels. We note that existing techniques for LCR queries only build a partial index and that their worse-case query time could be comparable to that of an online breadth-first search (BFS). This paper proposes a new label-constrained 2-hop indexing method with innovative pruning rules and order strategies. Our work demonstrates that the worst query time could be bounded by the number of in-out index entries. Extensive experiments demonstrate that the proposed methods substantially outperform the state-of-the-art approach in terms of the query response time (up to 5 orders of magnitude speedup), index size, and the index construction time. More precisely, the method we present can response LCR queries across billion-scale networks within microseconds on a single machine. We formally define the problem of LCKR queries and discuss critical applications for addressing it. To tackle the difficulties presented by label and hop constraints, an efficient upper and lower bound is suggested based on a search method. Using all of these techniques, extensive experiments on synthetic and real-world networks demonstrate that our algorithm outperforms the baseline by about three to four orders of magnitude while maintaining competitive indexing time and size.
Peng, Y, Liu, Y, Li, M, Liu, H & Guo, YJ 2022, 'Synthesizing Circularly Polarized Multi-Beam Planar Dipole Arrays With Sidelobe and Cross-Polarization Control by Two-Step Element Rotation and Phase Optimization', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4379-4391.
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Pérez-Escamilla, B, Benrimoj, SI, Martínez-Martínez, F, Gastelurrutia, MÁ, Varas-Doval, R, Musial-Gabrys, K & Garcia-Cardenas, V 2022, 'Using network analysis to explore factors moderating the implementation of a medication review service in community pharmacy', Research in Social and Administrative Pharmacy, vol. 18, no. 3, pp. 2432-2443.
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Background
Implementation factors are hypothesised to moderate the implementation of innovations. Although individual barriers and facilitators have been identified for the implementation of different evidence-based services in pharmacy, relationships between implementation factors are usually not considered.Objectives
To examine how a network of implementation factors and the position of each factor within this network structure influences the implementation of a medication review service in community pharmacy.Methods
A mixed methods approach was used. Medication review with follow-up service was the innovation to be implemented over 12 months in community pharmacies. A network analysis to model relationships between implementation factors was undertaken. Two networks were created.Results
Implementation factors hindering the service implementation with the highest centrality measures were time, motivation, recruitment, individual identification with the organization and personal characteristics of the pharmacists. Three hundred and sixty-nine different interrelationships between implementation factors were identified. Important causal relationships between implementation factors included: workflow-time; characteristics of the pharmacy-time; personal characteristics of the pharmacists-motivation. Implementation factors facilitating the implementation of the service with highest centrality scores were motivation, individual identification with the organization, beliefs, adaptability, recruitment, external support and leadership. Four hundred and fifty-six different interrelationships were identified. The important causal relationships included: motivation-external support; structure-characteristics of the pharmacy; demographics-location of the pharmacy.Conclusion
Network analysis has proven to be a useful technique to explore networks of factors moderating the implementation of a pharmacy service. Relationships were complex ...
Perrin, R & Halkon, B 2022, 'Sacred Geometry in the English Church Bell', IMA Journal of Applied Mathematics.
Pham, DX, Phung, AHT, Nguyen, HD, Bui, TD, Mai, LD, Tran, BNH, Tran, TS, Nguyen, TV & Ho-Pham, LT 2022, 'Trends in colorectal cancer incidence in Ho Chi Minh City, Vietnam (1996–2015): Joinpoint regression and age–period–cohort analyses', Cancer Epidemiology, vol. 77, pp. 102113-102113.
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Pham, HN, Dang, KB, Nguyen, TV, Tran, NC, Ngo, XQ, Nguyen, DA, Phan, TTH, Nguyen, TT, Guo, W & Ngo, HH 2022, 'A new deep learning approach based on bilateral semantic segmentation models for sustainable estuarine wetland ecosystem management', Science of The Total Environment, vol. 838, pp. 155826-155826.
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Poostchi, H & Piccardi, M 2022, 'BiLSTM-SSVM: Training the BiLSTM with a Structured Hinge Loss for Named-Entity Recognition', IEEE Transactions on Big Data, vol. 8, no. 1, pp. 203-212.
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Building on the achievements of the BiLSTM-CRF in named-entity recognition (NER), this paper introduces the BiLSTM-SSVM, an equivalent neural model where training is performed using a structured hinge loss. The typical loss functions used for evaluating NER are entity-level variants of the F1 score such as the CoNLL and MUC losses. Unfortunately, the common loss function used for training NER - the cross entropy - is only loosely related to the evaluation losses. For this reason, in this paper we propose a training approach for the BiLSTM-CRF that leverages a hinge loss bounding the CoNLL loss from above. In addition, we present a mixed hinge loss that bounds either the CoNLL loss or the Hamming loss based on the density of entity tokens in each sentence. The experimental results over four benchmark languages (English, German, Spanish and Dutch) show that training with the mixed hinge loss has led to small but consistent improvements over the cross entropy across all languages and four different evaluation measures
Poursafar, N, Taghizadeh, S, J. Hossain, M & M. Guerrero, J 2022, 'An Optimized Distributed Cooperative Control to Improve the Charging Performance of Battery Energy Storage in a Multiphotovoltaic Islanded DC Microgrid', IEEE Systems Journal, vol. 16, no. 1, pp. 1170-1181.
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Pradhan, S, Dyson, L & Lama, S 2022, 'The Nexus between Cultural Tourism and Social Entrepreneurship: A pathway to sustainable community development in Nepal', Journal of Heritage Tourism.
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Cultural tourism offers a pathway to community development and poverty eradication, particularly in developing countries and poor rural communities. In order to ensure that the benefits are spread equitably across the community and that cultural and environmental integrity is maintained over time, active participation of community members supported by outside actors is essential. This paper explores the potential for community-based cultural tourism initiatives in three different regions of Nepal through a series of interviews with 18 experts in the Nepalese tourism industry. The list of tourism programs suggested by the interviewees were interpreted through a community-based entrepreneurship model, focussing on the processes required to produce a sustainable cultural tourism product or service. The research furthers our understanding of the tourism industry in Nepal as well as providing guidance for the implementation of sustainable cultural tourism initiatives using community-based entrepreneurship.
Pugalia, S & Cetindamar, D 2022, 'Insights on the glass ceiling for immigrant women entrepreneurs in the technology sector', International Journal of Gender and Entrepreneurship, vol. 14, no. 1, pp. 44-68.
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PurposeTechnology sector is the pivotal element for innovation and economic development of any country. Hence, the present article explores past researches looking into challenges faced by immigrant women entrepreneurs in technology sector and their corresponding response strategies.Design/methodology/approachThis study employs a systematic literature review (SLR) technique to collate all the relevant literature looking into the challenges and strategies from immigrant women entrepreneur's perspective and provide a comprehensive picture. Overall, 49 research articles are included in this SLR.FindingsFindings indicate that immigrant status further escalates the human, financial and network disadvantages faced by women who want to start a technology-based venture.Originality/valueThis paper contributes to the literature by categorizing the barriers and strategies on a 3 × 2 matrix reflecting the origins of the barrier or strategy (taking place at the individual, firm or institutional level) versus the type of the barrier or strategy (arising from being an immigrant woman and being a woman in the technology sector). After underlining the dearth of studies in the literature about the complex phenomenon of immigrant WEs in the technology sector, the paper points out several neglected themes for future research.
Punitha, S, Stephan, T & Gandomi, AH 2022, 'A Novel Breast Cancer Diagnosis Scheme With Intelligent Feature and Parameter Selections', Computer Methods and Programs in Biomedicine, vol. 214, pp. 106432-106432.
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Puthal, D, Wilson, S, Nanda, A, Liu, M, Swain, S, Sahoo, BPS, Yelamarthi, K, Pillai, P, El-Sayed, H & Prasad, M 2022, 'Decision tree based user-centric security solution for critical IoT infrastructure', Computers and Electrical Engineering, vol. 99, pp. 107754-107754.
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Qi, Y & Indraratna, B 2022, 'Influence of Rubber Inclusion on the Dynamic Response of Rail Track', Journal of Materials in Civil Engineering, vol. 34, no. 2.
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Qian, H, Li, J, Pan, Y, Zong, Z & Wu, C 2022, 'Numerical derivation of P-I diagrams for shallow buried RC box structures', Tunnelling and Underground Space Technology, vol. 124, pp. 104454-104454.
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Qin, L, Yang, G, Li, D, Ou, K, Zheng, H, Fu, Q & Sun, Y 2022, 'High area energy density of all-solid-state supercapacitor based on double-network hydrogel with high content of graphene/PANI fiber', Chemical Engineering Journal, vol. 430, pp. 133045-133045.
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Qin, P-Y, Song, L-Z & Guo, YJ 2022, 'Conformal Transmitarrays for Unmanned Aerial Vehicles Aided 6G Networks', IEEE Communications Magazine, vol. 60, no. 1, pp. 14-20.
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Qiu, N, Zhang, J, Yuan, F, Jin, Z, Zhang, Y & Fang, J 2022, 'Mechanical performance of triply periodic minimal surface structures with a novel hybrid gradient fabricated by selective laser melting', Engineering Structures, vol. 263, pp. 114377-114377.
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Qu, F, Li, W, Guo, Y, Zhang, S, Zhou, JL & Wang, K 2022, 'Chloride-binding capacity of cement-GGBFS-nanosilica composites under seawater chloride-rich environment', Construction and Building Materials, vol. 342, pp. 127890-127890.
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Qu, X, Zou, Z, Su, X, Zhou, P, Wei, W, Wen, S & Wu, D 2022, 'Attend to Where and When: Cascaded Attention Network for Facial Expression Recognition', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 3, pp. 580-592.
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Qu, Z, Lau, CW, Simoff, SJ, Kennedy, PJ, Nguyen, QV & Catchpoole, DR 2022, 'Review of Innovative Immersive Technologies for Healthcare Applications', Innovations in Digital Health, Diagnostics, and Biomarkers, vol. 2, no. 2022, pp. 27-39.
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ABSTRACT
Immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), can connect people using enhanced data visualizations to better involve stakeholders as integral members of the process. Immersive technologies have started to change the research on multidimensional genomic data analysis for disease diagnostics and treatments. Immersive technologies are highlighted in some research for health and clinical needs, especially for precision medicine innovation. The use of immersive technology for genomic data analysis has recently received attention from the research community. Genomic data analytics research seeks to integrate immersive technologies to build more natural human-computer interactions that allow better perception engagements. Immersive technologies, especially VR, help humans perceive the digital world as real and give learning output with lower performance errors and higher accuracy. However, there are limited reviews about immersive technologies used in healthcare and genomic data analysis with specific digital health applications. This paper contributes a comprehensive review of using immersive technologies for digital health applications, including patient-centric applications, medical domain education, and data analysis, especially genomic data visual analytics. We highlight the evolution of a visual analysis using VR as a case study for how immersive technologies step, can by step, move into the genomic data analysis domain. The discussion and conclusion summarize the current immersive technology applications' usability, innovation, and future work in the healthcare domain, and digital health data visual analytics.
R., J, Gurunathan, B, K, S, Varjani, S, Ngo, HH & Gnansounou, E 2022, 'Advancements in heavy metals removal from effluents employing nano-adsorbents: Way towards cleaner production', Environmental Research, vol. 203, pp. 111815-111815.
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Rad, HS, Shiravand, Y, Radfar, P, Ladwa, R, Perry, C, Han, X, Warkiani, ME, Adams, MN, Hughes, BGM, O'Byrne, K & Kulasinghe, A 2022, 'Understanding the tumor microenvironment in head and neck squamous cell carcinoma', Clinical & Translational Immunology, vol. 11, no. 6.
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Rad, MA, Mahmodi, H, Filipe, EC, Cox, TR, Kabakova, I & Tipper, JL 2022, 'Micromechanical characterisation of 3D bioprinted neural cell models using Brillouin microspectroscopy', Bioprinting, vol. 25, pp. e00179-e00179.
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Radfar, P, Aboulkheyr Es, H, Salomon, R, Kulasinghe, A, Ramalingam, N, Sarafraz-Yazdi, E, Thiery, JP & Warkiani, ME 2022, 'Single-cell analysis of circulating tumour cells: enabling technologies and clinical applications', Trends in Biotechnology.
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Rafique, S, Nizami, MSH, Irshad, UB, Hossain, MJ & Mukhopadhyay, SC 2022, 'A two-stage multi-objective stochastic optimization strategy to minimize cost for electric bus depot operators', Journal of Cleaner Production, vol. 332, pp. 129856-129856.
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Rafique, S, Nizami, MSH, Irshad, UB, Hossain, MJ & Mukhopadhyay, SC 2022, 'EV Scheduling Framework for Peak Demand Management in LV Residential Networks', IEEE Systems Journal, vol. 16, no. 1, pp. 1520-1528.
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Rahman, M, Zhao, M, Islam, MS, Dong, K & Saha, SC 2022, 'Numerical study of nano and micro pollutant particle transport and deposition in realistic human lung airways', Powder Technology, vol. 402, pp. 117364-117364.
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Raj, C & Meel, P 2022, 'ARCNN framework for multimodal infodemic detection', Neural Networks, vol. 146, pp. 36-68.
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Raj, C & Meel, P 2022, 'People lie, actions Don't! Modeling infodemic proliferation predictors among social media users', Technology in Society, vol. 68, pp. 101930-101930.
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Rajamohan, D, Kim, J, Garratt, M & Pickering, M 2022, 'Image based Localization under large perspective difference between Sfm and SLAM using split sim(3) optimization', Autonomous Robots, vol. 46, no. 3, pp. 437-449.
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AbstractImage based Localization (IbL) uses both Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM) data for accurate pose estimation. However, under conditions where there is a large perspective difference between the SfM images and SLAM keyframes, the SfM-SLAM co-visibility graph becomes sparse. As a result, the scale drift can increase especially when using monocular SLAM as part of the IbL framework. The drift rarely gets corrected at loop closure due to its large magnitude. We propose a split affine transformation approach that uses SfM-SLAM information along with Sim(3) optimization to minimize the scale drift. Experiments are performed using an image dataset collected in a campus environment with different trajectories, showing the improvement in scale drift correction with the proposed method. The SLAM data was collected close to plainly textured structures like buildings while SfM images were captured from a larger distance from the building facade which leads to a challenging navigation scenario in the context of IbL. Localizing mobile platforms moving close to buildings is an example of such a case. The paper positively impacts the widespread use of small autonomous robotic platforms, which is to perform an accurate outdoor localization under urban conditions using only a monocular camera.
Ramachandran, M, Patan, R, Kumar, A, Hosseini, S & Gandomi, AH 2022, 'Mutual Informative MapReduce and Minimum Quadrangle Classification for Brain Tumor Big Data', IEEE Transactions on Engineering Management, pp. 1-12.
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Ran, H, Sun, L, Cheng, S, Ma, Y, Yan, S, Meng, S, Shi, K & Wen, S 2022, 'A novel cooperative searching architecture for multi‐unmanned aerial vehicles under restricted communication', Asian Journal of Control, vol. 24, no. 2, pp. 510-516.
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Ran, H, Wen, S, Li, Q, Yang, Y, Shi, K, Feng, Y, Zhou, P & Huang, T 2022, 'Memristor-Based Edge Computing of Blaze Block for Image Recognition', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 5, pp. 2121-2131.
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In this article, a novel edge computing system is proposed for image recognition via memristor-based blaze block circuit, which includes a memristive convolutional neural network (MCNN) layer, two single-memristive blaze blocks (SMBBs), four double-memristive blaze blocks (DMBBs), a global Avg-pooling (GAP) layer, and a memristive full connected (MFC) layer. SMBBs and DMBBs mainly utilize the depthwise separable convolution neural network (DwCNN) that can be implemented with a much smaller memristor crossbar (MC). In the backward propagation, we use batch normalization (BN) layers to accelerate the convergence. In the forward propagation, this circuit combines DwCNN layers/CNN layers with nonseparate BN layers, which means that the required number of operational amplifiers is cut by half as long as the greatly reduced power consumption. A diode is added after the rectified linear unit (ReLU) layer to limit the output of the circuit below the threshold voltage Vt of the memristor; thus, the circuit is more stable. Experiments show that the proposed memristor-based circuit achieves an accuracy of 84.38% on the CIFAR-10 data set with advantages in computing resources, calculation time, and power consumption. Experiments also show that, when the number of multistate conductance is 2⁸ and the quantization bit of the data is 8, the circuit can achieve its best balance between power consumption and production cost.
Rana, AK, Thakur, MK, Saini, AK, Mokhta, SK, Moradi, O, Rydzkowski, T, Alsanie, WF, Wang, Q, Grammatikos, S & Thakur, VK 2022, 'Recent developments in microbial degradation of polypropylene: Integrated approaches towards a sustainable environment', Science of The Total Environment, vol. 826, pp. 154056-154056.
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Rani, P, Mishra, AR, Krishankumar, R, Ravichandran, KS & Gandomi, AH 2022, 'A New Pythagorean Fuzzy Based Decision Framework for Assessing Healthcare Waste Treatment', IEEE Transactions on Engineering Management, pp. 1-15.
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Rao, A, Elder, E, Center, JR, Tran, T, Pocock, N & Elder, GJ 2022, 'Improving Bone Mineral Density Screening by Using Digital X‐Radiogrammetry Combined With Mammography', JBMR Plus, vol. 6, no. 5.
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Rao, P, Xiang, Y, Ouyang, P, Nimbalkar, S & Chen, Q 2022, 'Finite Element Analysis of Electro-Thermal Coupling of Sandstone Under Lightning Currents', Geotechnical and Geological Engineering, vol. 40, no. 5, pp. 2593-2604.
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Rasal, AS, Yadav, S, Kashale, AA, Altaee, A & Chang, J-Y 2022, 'Stability of quantum dot-sensitized solar cells: A review and prospects', Nano Energy, vol. 94, pp. 106854-106854.
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Rasouli, H & Fatahi, B 2022, 'Liquefaction and post-liquefaction resistance of sand reinforced with recycled geofibre', Geotextiles and Geomembranes, vol. 50, no. 1, pp. 69-81.
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Raza, MA, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2022, 'Statistical Learning-Based Grant-Free Access for Delay-Sensitive Internet of Things Applications', IEEE Transactions on Vehicular Technology, vol. 71, no. 5, pp. 5492-5506.
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Razzak, I, Xu, G & Khan, MK 2022, 'Guest Editorial: Privacy-Preserving Federated Machine Learning Solutions for Enhanced Security of Critical Energy Infrastructures', IEEE Transactions on Industrial Informatics, vol. 18, no. 5, pp. 3449-3451.
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Razzaq, L, Mujtaba, MA, Shahbaz, MA, Nawaz, S, Mahmood Khan, H, Hussain, A, Ishtiaq, U, Kalam, MA, M. Soudagar, ME, Ismail, KA, Elfasakhany, A & Rizwan, HM 2022, 'Effect of biodiesel-dimethyl carbonate blends on engine performance, combustion and emission characteristics', Alexandria Engineering Journal, vol. 61, no. 7, pp. 5111-5121.
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Rees, N, Thiyagarajan, K, Wickramanayake, S & Kodagoda, S 2022, 'Ground-Penetrating Radar Signal Characterization for Non-destructive Evaluation of Low-Range Concrete Sub-surface Boundary Conditions', IEEE Sensors Letters, vol. 6, no. 4, pp. 1-4.
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Ren, J, Zhang, B, Zhu, X & Li, S 2022, 'Damaged cable identification in cable-stayed bridge from bridge deck strain measurements using support vector machine', Advances in Structural Engineering, vol. 25, no. 4, pp. 754-771.
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A new two-step approach is developed for damaged cable identification in a cable-stayed bridge from deck bending strain responses using Support Vector Machine. A Damaged Cable Identification Machine (DCIM) based on support vector classification is constructed to determine the damaged cable and a Damage Severity Identification Machine (DSIM) based on support vector regression is built to estimate the damage severity. A field cable-stayed bridge with a long-term monitoring system is used to verify the proposed method. The three-dimensional Finite Element Model (FEM) of the cable-stayed bridge is established using ANSYS, and the model is validated using the field testing results, such as the mode shape, natural frequencies and its bending strain responses of the bridge under a moving vehicle. Then the validated FEM is used to simulate the bending strain responses of the longitude deck near the cable anchors when the vehicle is passing over the bridge. Different damage scenarios are simulated for each cable with various severities. Based on damage indexes vector, the training datasets and testing datasets are acquired, including single damaged cable scenarios and double damaged cable scenarios. Eventually, DCIM is trained using Support Vector Classification Machine and DSIM is trained using Support Vector Regression Machine. The testing datasets are input in DCIM and DSIM to check their accuracy and generalization capability. Different noise levels including 5%, 10%, and 20% are considered to study their anti-noise capability. The results show that DCIM and DSIM both have good generalization capability and anti-noise capability.
Ren, P, Xiao, Y, Chang, X, Huang, P-Y, Li, Z, Chen, X & Wang, X 2022, 'A Comprehensive Survey of Neural Architecture Search', ACM Computing Surveys, vol. 54, no. 4, pp. 1-34.
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Deep learning has made substantial breakthroughs in many fields due to its powerful automatic representation capabilities. It has been proven that neural architecture design is crucial to the feature representation of data and the final performance. However, the design of the neural architecture heavily relies on the researchers’ prior knowledge and experience. And due to the limitations of humans’ inherent knowledge, it is difficult for people to jump out of their original thinking paradigm and design an optimal model. Therefore, an intuitive idea would be to reduce human intervention as much as possible and let the algorithm automatically design the neural architecture.
Neural Architecture Search
(
NAS
) is just such a revolutionary algorithm, and the related research work is complicated and rich. Therefore, a comprehensive and systematic survey on the NAS is essential. Previously related surveys have begun to classify existing work mainly based on the key components of NAS: search space, search strategy, and evaluation strategy. While this classification method is more intuitive, it is difficult for readers to grasp the challenges and the landmark work involved. Therefore, in this survey, we provide a new perspective: beginning with an overview of the characteristics of the earliest NAS algorithms, summarizing the problems in these early NAS algorithms, and then providing solutions for subsequent related research work. In addition, we conduct a detailed and comprehensive analysis, comparison, and summary of these works. Finally, we provide some possible future research directions.
Ren, P, Xiao, Y, Chang, X, Huang, P-Y, Li, Z, Gupta, BB, Chen, X & Wang, X 2022, 'A Survey of Deep Active Learning', ACM Computing Surveys, vol. 54, no. 9, pp. 1-40.
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Active learning (AL) attempts to maximize a model’s performance gain while annotating the fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize a massive number of parameters if the model is to learn how to extract high-quality features. In recent years, due to the rapid development of internet technology, we have entered an era of information abundance characterized by massive amounts of available data. As a result, DL has attracted significant attention from researchers and has been rapidly developed. Compared with DL, however, researchers have a relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples, meaning that early AL is rarely according the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to a large number of publicly available annotated datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, making it unfeasible in fields that require high levels of expertise (such as speech recognition, information extraction, medical images, etc.). Therefore, AL is gradually coming to receive the attention it is due.
It is therefore natural to investigate whether AL can be used to reduce the cost of sample annotation while retaining the powerful learning capabilities of DL. As a result of such investigations, deep active learning (DeepAL) has emerged. Although research on this topic is quite abundant, there has not yet been a comprehensive survey of DeepAL-related works; accordingly, this article aims to fill this gap. We provide a formal classification method for the existing work, along with a comprehensive and systematic overview. In addition, we also analyze and summarize the development of DeepAL from an application perspective. Finally, we discuss the confusion and p...
Ren, S, Guo, B, Li, K, Wang, Q, Yu, Z & Cao, L 2022, 'CoupledMUTS: Coupled Multivariate Utility Time Series Representation and Prediction', IEEE Internet of Things Journal, pp. 1-1.
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Ren, Z, Zhang, X, Huang, Z, Hu, J, Li, Y, Zheng, S, Gao, M, Pan, H & Liu, Y 2022, 'Controllable synthesis of 2D TiH2 nanoflakes with superior catalytic activity for low-temperature hydrogen cycling of NaAlH4', Chemical Engineering Journal, vol. 427, pp. 131546-131546.
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Rene, ER, Kennes, C, Nghiem, LD & Varjani, S 2022, 'New insights in biodegradation of organic pollutants', Bioresource Technology, vol. 347, pp. 126737-126737.
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Rezazadegan, D, Berkovsky, S, Quiroz, JC, Kocaballi, AB, Wang, Y, Laranjo, L & Coiera, E 2022, 'Symbolic and Statistical Learning Approaches to Speech Summarization: A Scoping Review', Computer Speech & Language, vol. 72, pp. 101305-101305.
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Roberts, AGK, Catchpoole, DR & Kennedy, PJ 2022, 'Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability', NAR Genomics and Bioinformatics, vol. 4, no. 1.
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ABSTRACT
There is increasing evidence that changes in the variability or overall distribution of gene expression are important both in normal biology and in diseases, particularly cancer. Genes whose expression differs in variability or distribution without a difference in mean are ignored by traditional differential expression-based analyses. Using a Bayesian hierarchical model that provides tests for both differential variability and differential distribution for bulk RNA-seq data, we report here an investigation into differential variability and distribution in cancer. Analysis of eight paired tumour–normal datasets from The Cancer Genome Atlas confirms that differential variability and distribution analyses are able to identify cancer-related genes. We further demonstrate that differential variability identifies cancer-related genes that are missed by differential expression analysis, and that differential expression and differential variability identify functionally distinct sets of potentially cancer-related genes. These results suggest that differential variability analysis may provide insights into genetic aspects of cancer that would not be revealed by differential expression, and that differential distribution analysis may allow for more comprehensive identification of cancer-related genes than analyses based on changes in mean or variability alone.
Rodriguez, J, Garcia, C, Mora, A, Flores-Bahamonde, F, Acuna, P, Novak, M, Zhang, Y, Tarisciotti, L, Davari, SA, Zhang, Z, Wang, F, Norambuena, M, Dragicevic, T, Blaabjerg, F, Geyer, T, Kennel, R, Khaburi, DA, Abdelrahem, M, Zhang, Z, Mijatovic, N & Aguilera, RP 2022, 'Latest Advances of Model Predictive Control in Electrical Drives—Part I: Basic Concepts and Advanced Strategies', IEEE Transactions on Power Electronics, vol. 37, no. 4, pp. 3927-3942.
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Rodríguez-Antón, JM, Rubio-Andrada, L, Celemín-Pedroche, MS & Ruíz-Peñalver, SM 2022, 'From the circular economy to the sustainable development goals in the European Union: an empirical comparison', International Environmental Agreements: Politics, Law and Economics, vol. 22, no. 1, pp. 67-95.
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AbstractThe European Union (EU) is trying to accelerate the transition from the current linear economy to a circular economy (CE). In fact, the CE is considered a tool to attain sustainable development goals (SDGs). In this sense, this paper aims at analysing the interaction between the CE and SDGs in the context of the new 2030 Agenda and the European CE strategy; thus contributing to the scarce empirical literature that links the potential of the European CE strategy to the achievement of the SDGs set by the 2030 Agenda. Three specific research questions have been formulated. First, could the objectives defined in the 2030 Agenda be considered homogeneous, and could they uniquely measure the concept of sustainability? Second, are there significant correlations between the implementation of a CE in the EU and the SDGs? Finally, is the behaviour of the 28 countries that make up the EU homogeneous in terms of the results of the initiatives aimed at the implementation of a CE? From these questions, nine hypotheses are put forward concerning the possible relationships between a CE implementation and the fulfilment of SDGs in the EU. Using a correlation analysis, an exploratory factor analysis, and a cluster analysis, it has been demonstrated that (a) SDGs do not univocally measure the concept of sustainability; (b) there are significant relationships between CE and SDGs in the EU; (c) the behaviour of these European countries is not homogeneous.
Romeijn, T, Behrens, M, Paul, G & Wei, D 2022, 'Experimental analysis of water and slurry flows in gravity-driven helical mineral separators', Powder Technology, vol. 405, pp. 117538-117538.
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Roslan, MF, Hannan, MA, Ker, PJ, Mannan, M, Muttaqi, KM & Mahlia, TMI 2022, 'Microgrid control methods toward achieving sustainable energy management: A bibliometric analysis for future directions', Journal of Cleaner Production, vol. 348, pp. 131340-131340.
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Rout, JK, Dalmia, A, Rath, SK, Mohanta, BK, Ramasubbareddy, S & H. Gandomi, A 2022, 'Detecting Product Review Spammers Using Principles of Big Data', IEEE Transactions on Engineering Management, pp. 1-12.
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Rout, RK, Hassan, SS, Sheikh, S, Umer, S, Sahoo, KS & Gandomi, AH 2022, 'Feature-extraction and analysis based on spatial distribution of amino acids for SARS-CoV-2 Protein sequences', Computers in Biology and Medicine, vol. 141, pp. 105024-105024.
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Rozina, Ahmad, M, Asif, S, Klemeš, JJ, Mubashir, M, Bokhari, A, Sultana, S, Mukhtar, A, Zafar, M, Bazmi, AA, Ullah, S, Khan, MS, Koyande, AK, Mofijur, M & Show, P-L 2022, 'Conversion of the toxic and hazardous Zanthoxylum armatum seed oil into methyl ester using green and recyclable silver oxide nanoparticles', Fuel, vol. 310, pp. 122296-122296.
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Rush, A, Catchpoole, DR, Reaiche-Miller, G, Gilbert, T, Ng, W, Watson, PH & Byrne, JA 2022, 'What Do Biomedical Researchers Want from Biobanks? Results of an Online Survey', Biopreservation and Biobanking, vol. 20, no. 3, pp. 271-282.
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Sadeghian, F, Jahandari, S, Haddad, A, Rasekh, H & Li, J 2022, 'Effects of variations of voltage and pH value on the shear strength of soil and durability of different electrodes and piles during electrokinetic phenomenon', Journal of Rock Mechanics and Geotechnical Engineering, vol. 14, no. 2, pp. 625-636.
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Saha, SC, Francis, I, Huang, X & Paul, AR 2022, 'Heat transfer and fluid flow analysis in a realistic 16-generation lung', Physics of Fluids, vol. 34, no. 6, pp. 061906-061906.
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Heat transfer between inhaled hot/cool air and the lung surface within the human respiratory system is an intriguing topic that has not received enough attention. The lung can be considered an in vivo heat exchanger, balancing the inhaled air temperature by lowering the hot air temperature and increasing the cool air temperature. The current work studies the unsteady and incompressible airflow motion and heat transfer during inhalation between the surface of the lungs (37 °C) and the inhaled cool air (25 °C) in one case and inhaled hot air (43 °C) in another. Computerized tomography scan (CT-scan) images of the lung of a 39-year-old male patient were processed to generate the airway geometry consisting of 16 generations. The geometry was further modified in UG NX 12.0, and the mesh generation was carried out using Ansys Meshing. The shear stress transport (SST) [Formula: see text] turbulent model was employed in Ansys Fluent 20.2 to model the air/lung convective volume heat transfer utilizing a realistic breathing velocity profile. Temperature streamlines, lung volume temperatures, surface heat flux, and surface temperatures on all 16 generations were produced for both cases during the breathing cycle of 4.75 s. Several conclusions were made by studying and comparing the two cases. First, heat transfer between inhaled hot or cool air and the lung surface mainly occurred in the first few generations. Second, airflow temperature patterns are dependent on the inlet breathing velocity profile. Third, the lung volume temperature change directly correlates with the temperature difference between air and the lung surface. Finally, the surface heat flux strongly depended on the heat transfer coefficient. The density, viscosity, thermal conductivity, and specific heat of hot/cool air affected the Reynolds number, Nusselt number, heat transfer coefficient, and surface heat flux.
Saki, M, Abolhasan, M, Lipman, J & Jamalipour, A 2022, 'Mobility Model for Contact-Aware Data Offloading Through Train-to-Train Communications in Rail Networks', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 1, pp. 597-609.
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Samy, I, Han, X, Lazos, L, Li, M, Xiao, Y & Krunz, M 2022, 'Misbehavior Detection in Wi-Fi/LTE Coexistence over Unlicensed Bands', IEEE Transactions on Mobile Computing, pp. 1-1.
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Sanahuja-Embuena, V, Lim, S, Górecki, R, Trzaskus, K, Hélix-Nielsen, C & Kyong Shon, H 2022, 'Enhancing selectivity of novel outer-selective hollow fiber forward osmosis membrane by polymer nanostructures', Chemical Engineering Journal, vol. 433, pp. 133634-133634.
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Santra, SB, Chatterjee, D & Siwakoti, YP 2022, 'Coupled Inductor Based Soft Switched High Gain Bidirectional DC-DC Converter with Reduced Input Current Ripple', IEEE Transactions on Industrial Electronics, pp. 1-1.
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Saputra, YM, Nguyen, D, Dinh, HT, Pham, Q-V, Dutkiewicz, E & Hwang, W-J 2022, 'Federated Learning Framework with Straggling Mitigation and Privacy-Awareness for AI-based Mobile Application Services', IEEE Transactions on Mobile Computing, pp. 1-1.
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Saravanakumar, A, Chen, W-H, Arunachalam, KD, Park, Y-K & Chyuan Ong, H 2022, 'Pilot-scale study on downdraft gasification of municipal solid waste with mass and energy balance analysis', Fuel, vol. 315, pp. 123287-123287.
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Sarker, PC, Guo, Y, Lu, H & Zhu, JG 2022, 'Improvement on parameter identification of modified Jiles-Atherton model for iron loss calculation', Journal of Magnetism and Magnetic Materials, vol. 542, pp. 168602-168602.
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Scherer, S, Agrawal, V, Best, G, Cao, C, Cujic, K, Darnley, R, DeBortoli, R, Dexheimer, E, Drozd, B, Garg, R & others 2022, 'Resilient and modular subterranean exploration with a team of roving and flying robots', Field Robotics, vol. 2, pp. 678-734.
Senanayake, S & Pradhan, B 2022, 'Predicting soil erosion susceptibility associated with climate change scenarios in the Central Highlands of Sri Lanka', Journal of Environmental Management, vol. 308, pp. 114589-114589.
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Senanayake, S, Pradhan, B, Huete, A & Brennan, J 2022, 'Spatial modeling of soil erosion hazards and crop diversity change with rainfall variation in the Central Highlands of Sri Lanka', Science of The Total Environment, vol. 806, pp. 150405-150405.
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Septiadi, WN, Iswari, GA, Sudarsana, PB, Putra, GJP, Febraldo, D, Putra, N & Mahlia, TMI 2022, 'Effect of Al2O3 and TiO2 nano-coated wick on the thermal performance of heat pipe', Journal of Thermal Analysis and Calorimetry, vol. 147, no. 11, pp. 6193-6205.
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A heat pipe is a passive two-phase heat exchanger technology, as a capillary-driven structure that allows heat transport by maintaining temperature difference. Heat pipe performance can be determined from the value of heat resistance, and nanoparticle can be applied to increase heat pipe performance. This research uses Al2O3 and TiO2 as a coating material for the heat pipe. The methods used in this research were by giving the heat pipe a nano-coating treatment using the electrophoretic deposition process and doing a pool boiling experiment by giving the heat pipe some heat loads. The main data of this research are temperature and bubble growth data. Based on the result of the research, the use of nanoparticles can improve heat pipe performance. The temperature difference between the evaporator and condenser area was calculated 2.38 °C on Al2O3 coating and 3.92 °C on TiO2 coating. Al2O3 nanoparticle coating was able to provide a heat transfer coefficient 480% superior to sample without nanoparticle coating, and 174% better than TiO2 nanoparticle coating.
Shadman, S, Chin, CMM, Sakundarini, N, Yap, EH, Fairuz, S, Wong, XY, Khalid, PA, Karimi, F, Karaman, C, Mofijur, M, Koyande, AK & Show, PL 2022, 'A system dynamics approach to pollution remediation and mitigation based on increasing the share of renewable resources', Environmental Research, vol. 205, pp. 112458-112458.
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Shafaghat, A & Khabbaz, H 2022, 'Recent advances and past discoveries on tapered pile foundations: a review', Geomechanics and Geoengineering, vol. 17, no. 2, pp. 455-484.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. The growing tendency to study the behaviour of tapered piles in the last two decades has made it necessary to gain a deeper insight into this specific kind of deep foundation. Tapered piles have been investigated through analytical, experimental, and numerical studies. These piles have revealed different behaviour under various loading conditions. Hence, reviewing and assessing these efforts to comprehend their response can be of great significance. In this paper firstly, it is attempted to go over experimental studies, conducted on tapered piles. Then, the proposed mathematical and numerical solutions, employed to calculate the bearing capacity of single tapered piles, are compared to have a better vision of how these piles behave. In the third section, the optimum tapering angles of tapered piles in loose, medium, and dense sand are discussed. All the efforts are investigated technically to find the advantages, disadvantages, and the research gaps for this specific kind of piles. In addition, another section entitled the directions and ideas for future research on tapered piles is provided comprising the most recent achievements in this area. Moreover, the implementation of tapered piles in a significant project as a case study is discussed.
Shafaghat, A, Khabbaz, H & Fatahi, B 2022, 'Axial and Lateral Efficiency of Tapered Pile Groups in Sand Using Mathematical and Three-Dimensional Numerical Analyses', Journal of Performance of Constructed Facilities, vol. 36, no. 1.
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Shahabuddin, M, Mofijur, M, Rizwanul Fattah, IM, Kalam, MA, Masjuki, HH, Chowdhury, MA & Hossain, N 2022, 'Study on the tribological characteristics of plant oil-based bio-lubricant with automotive liner-piston ring materials', Current Research in Green and Sustainable Chemistry, vol. 5, pp. 100262-100262.
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Shanableh, A, Al-Ruzouq, R, Hamad, K, Gibril, MBA, Khalil, MA, Khalifa, I, El Traboulsi, Y, Pradhan, B, Jena, R, Alani, S, Alhosani, M, Stietiya, MH, Al Bardan, M & AL-Mansoori, S 2022, 'Effects of the COVID-19 lockdown and recovery on People's mobility and air quality in the United Arab Emirates using satellite and ground observations', Remote Sensing Applications: Society and Environment, vol. 26, pp. 100757-100757.
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Shao, R, Wu, C & Li, J 2022, 'A comprehensive review on dry concrete: Application, raw material, preparation, mechanical, smart and durability performance', Journal of Building Engineering, vol. 55, pp. 104676-104676.
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Shao, R, Wu, C, Li, J & Liu, Z 2022, 'Development of sustainable steel fibre-reinforced dry ultra-high performance concrete (DUHPC)', Journal of Cleaner Production, vol. 337, pp. 130507-130507.
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Sharari, N, Fatahi, B, Hokmabadi, A & Xu, R 2022, 'Seismic resilience of extra-large LNG tank built on liquefiable soil deposit capturing soil-pile-structure interaction', Bulletin of Earthquake Engineering, vol. 20, no. 7, pp. 3385-3441.
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AbstractAssessment of seismic resilience of critical infrastructure such as liquefied natural gas (LNG) storage tanks, is essential to ensure availability and security of services during and after occurrence of large earthquakes. In many projects, it is preferred to build energy storage facilities in coastal areas for the ease of sea transportation, where weak soils such as soft clay and loose sand with liquefaction potential may be present. In this study, three-dimensional finite element model is implemented to examine the seismic response of a 160,000 m3 full containment LNG tank supported by 289 reinforced concrete piles constructed on liquefiable soil overlaying the soft clay deposit. The seismic soil-structure interaction analysis was conducted through direct method in the time domain subjected to the 1999 Chi-Chi and the 1968 Hachinohe earthquakes, scaled to Safe Shutdown Earthquake hazard level for design of LNG tanks. The analyses considered different thicknesses of the liquified soil deposit varying from zero (no liquefaction) to 15 m measured from the ground surface. The key design parameters inspected for the LNG tank include the acceleration profile for both inner and outer tanks, the axial, hoop and shear forces as well as the von Mises stresses in the inner tank wall containing the LNG, in addition to the pile response in terms of lateral displacements, shear forces and bending moments. The results show that the seismic forces generated in the superstructure decreased with increasing the liquefied soil depth. In particular, the von Mises stresses in the inner steel tank exceeded the yield stress for non-liquefied soil deposit, and the elastic–plastic buckling was initiated in the upper section of the tank where plastic deformations were detected as a result of excessive von Mises stresses. However, when soil liquefaction occurred, although von Mises stresses in the inner tank shell remained below...
Sharari, N, Fatahi, B, Hokmabadi, AS & Xu, R 2022, 'Impacts of Pile Foundation Arrangement on Seismic Response of LNG Tanks Considering Soil–Foundation–Structure Interaction', Journal of Performance of Constructed Facilities, vol. 36, no. 1.
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Sharif, O, Islam, MR, Hasan, MZ, Kabir, MA, Hasan, ME, AlQahtani, SA & Xu, G 2022, 'Analyzing the Impact of Demographic Variables on Spreading and Forecasting COVID-19', Journal of Healthcare Informatics Research, vol. 6, no. 1, pp. 72-90.
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Sharma, M, Kumar, A, Luthra, S, Joshi, S & Upadhyay, A 2022, 'The impact of environmental dynamism on low‐carbon practices and digital supply chain networks to enhance sustainable performance: An empirical analysis', Business Strategy and the Environment, vol. 31, no. 4, pp. 1776-1788.
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Sharma, M, Luthra, S, Joshi, S & Joshi, H 2022, 'Challenges to agile project management during COVID-19 pandemic: an emerging economy perspective', Operations Management Research.
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Sharma, M, Luthra, S, Joshi, S & Kumar, A 2022, 'Analysing the impact of sustainable human resource management practices and industry 4.0 technologies adoption on employability skills', International Journal of Manpower, vol. 43, no. 2, pp. 463-485.
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PurposeThe study aims to examine the influence of Sustainable Human Resource Management (SHRM) practices and Industry 4.0 Technologies (I4Te) adoption on the Employability Skills (ES) of the employees. The study has undertaken four major SHRM practices – Training (TR), Flexibility (FL), Employee Participation (EP) and Employee Empowerment (EE) to measure its impact on the ES along with I4Te.Design/methodology/approachA survey approach method was designed on the identified constructs from existing literature based on SHRM, I4Te and ES. The survey resulted into 198 valid responses. The study used confirmatory factor analysis (CFA) and structural equation modelling (SEM) using SPSS 25.0 and AMOS 25.0 for constructs validation and hypothesis testing.FindingsThe current study reveals that all the four SHRM practices (TR, FL, EP and EE) along with I4Te directly influence ES in the organisation. The I4Te along with the SHRM practices may bring enhancement in the skills and competencies of the employees that is the requirement of future organisations.Practical implicationsConsidering the results, the SHRM practices aligned with I4Te may directly influence the employee's ES including core skills, IT skills and personal attributes. The SHRM practices in the organisation will enhance the opportunities for the employees and bring long-term association with the employees.Social implicationsFor the development of the economy and the individual, the SHRM practices need to conduct themselves in more socially responsib...
Sharma, M, Luthra, S, Joshi, S & Kumar, A 2022, 'Developing a framework for enhancing survivability of sustainable supply chains during and post-COVID-19 pandemic', International Journal of Logistics Research and Applications, vol. 25, no. 4-5, pp. 433-453.
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The pandemic has created a restrictive working system including remote working, and flexible hours for the firms and employees all around the globe, thus transforming into a platform economy may reduce unemployment and enhance job opportunities. Therefore, firms are now trying to identify the ways for enhancing survivability of Sustainable Supply Chains (SSCs). This study has made an effort to develop a framework for enhancing survivability of SSCs to survive in and post-COVID-19 pandemic. This study has utilised Stepwise Weight Assessment Ratio Analysis (SWARA) method for identifying the significant factors for enhancing survivability of SSCs to be focused in pandemic situation. The study revealed that ‘Supply Chain Network Viability (SCV)’ is the main criterion for managing buyer–supplier relationship and enhancing survivability of SSCs during and post-COVID-19 situation. This study is helpful for firms, suppliers, and other stakeholders to focus on the identified factors for healthier future.
Sharma, P, Gaur, VK, Gupta, S, Varjani, S, Pandey, A, Gnansounou, E, You, S, Ngo, HH & Wong, JWC 2022, 'Trends in mitigation of industrial waste: Global health hazards, environmental implications and waste derived economy for environmental sustainability', Science of The Total Environment, vol. 811, pp. 152357-152357.
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Sharma, P, Liu Chung Ming, C, Wang, X, Bienvenu, LA, Beck, D, Figtree, G, Boyle, A & Gentile, C 2022, 'Biofabrication of advanced in vitro 3D models to study ischaemic and doxorubicin-induced myocardial damage', Biofabrication, vol. 14, no. 2, pp. 025003-025003.
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Abstract
Current preclinical in vitro and in vivo models of cardiac injury typical of myocardial infarction (MI, or heart attack) and drug induced cardiotoxicity mimic only a few aspects of these complex scenarios. This leads to a poor translation of findings from the bench to the bedside. In this study, we biofabricated for the first time advanced in vitro models of MI and doxorubicin (DOX) induced injury by exposing cardiac spheroids (CSs) to pathophysiological changes in oxygen (O2) levels or DOX treatment. Then, contractile function and cell death was analyzed in CSs in control verses I/R and DOX CSs. For a deeper dig into cell death analysis, 3D rendering analyses and mRNA level changes of cardiac damage-related genes were compared in control verses I/R and DOX CSs. Overall, in vitro CSs recapitulated major features typical of the in vivo MI and drug induced cardiac damages, such as adapting intracellular alterations to O2 concentration changes and incubation with cardiotoxic drug, mimicking the contraction frequency and fractional shortening and changes in mRNA expression levels for genes regulating sarcomere structure, calcium transport, cell cycle, cardiac remodelling and signal transduction. Taken together, our study supports the use of I/R and DOX CSs as advanced in vitro models to study MI and DOX-induced cardiac damge by recapitulating their complex in vivo scenario.
Shehab, M, Abualigah, L, Shambour, Q, Abu-Hashem, MA, Shambour, MKY, Alsalibi, AI & Gandomi, AH 2022, 'Machine learning in medical applications: A review of state-of-the-art methods', Computers in Biology and Medicine, vol. 145, pp. 105458-105458.
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Shen, S, Zhu, T, Wu, D, Wang, W & Zhou, W 2022, 'From distributed machine learning to federated learning: In the view of data privacy and security', Concurrency and Computation: Practice and Experience, vol. 34, no. 16.
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Federated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server. The server becomes more like an assistant coordinating clients to work together rather than micromanaging the workforce as in traditional DML. One of the greatest advantages of federated learning is the additional privacy and security guarantees it affords. Federated learning architecture relies on smart devices, such as smartphones and IoT sensors, that collect and process their own data, so sensitive information never has to leave the client device. Rather, clients train a submodel locally and send an encrypted update to the central server for aggregation into the global model. These strong privacy guarantees make federated learning an attractive choice in a world where data breaches and information theft are common and serious threats. This survey outlines the landscape and latest developments in data privacy and security for federated learning. We identify the different mechanisms used to provide privacy and security, such as differential privacy, secure multiparty computation and secure aggregation. We also survey the current attack models, identifying the areas of vulnerability and the strategies adversaries use to penetrate federated systems. The survey concludes with a discussion on the open challenges and potential directions of future work in this increasingly popular learning paradigm.
Shen, S, Zhu, T, Ye, D, Wang, M, Zuo, X & Zhou, A 2022, 'A novel differentially private advising framework in cloud server environment', Concurrency and Computation: Practice and Experience, vol. 34, no. 7.
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Due to the rapid development of the cloud computing environment, it is widely accepted that cloud servers are important for users to improve work efficiency. Users need to know servers' capabilities and make optimal decisions on selecting the best available servers for users' tasks. We consider the process of learning servers' capabilities by users as a multiagent reinforcement learning process. The learning speed and efficiency in reinforcement learning can be improved by sharing the learning experience among learning agents which is defined as advising. However, existing advising frameworks are limited by the requirement that during advising all learning agents in a reinforcement learning environment must have exactly the same actions. To address the above limitation, this article proposes a novel differentially private advising framework for multiagent reinforcement learning. Our proposed approach can significantly improve the application of conventional advising frameworks when agents have one different action. The approach can also widen the applicable field of advising and speed up reinforcement learning by triggering more potential advising processes among agents with different actions.
Shen, X, Dong, G, Zheng, Y, Lan, L, Tsang, I & Sun, Q-S 2022, 'Deep Co-Image-Label Hashing for Multi-Label Image Retrieval', IEEE Transactions on Multimedia, vol. 24, pp. 1116-1126.
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Shenbagamuthuraman, V, Patel, A, Khanna, S, Banerjee, E, Parekh, S, Karthick, C, Ashok, B, Velvizhi, G, Nanthagopal, K & Ong, HC 2022, 'State of art of valorising of diverse potential feedstocks for the production of alcohols and ethers: Current changes and perspectives', Chemosphere, vol. 286, pp. 131587-131587.
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Sheu, A, Bliuc, D, Tran, T, White, CP & Center, JR 2022, 'Fractures in type 2 diabetes confer excess mortality: The Dubbo osteoporosis epidemiology study', Bone, vol. 159, pp. 116373-116373.
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Shi, J, Fan, K, Yan, L, Fan, Z, Li, F, Wang, G, Liu, H, Liu, P, Yu, H, Li, JJ & Wang, B 2022, 'Cost Effectiveness of Pharmacological Management for Osteoarthritis: A Systematic Review', Applied Health Economics and Health Policy, vol. 20, no. 3, pp. 351-370.
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Shi, J, Li, X, Zhang, S, Sharma, E, Sivakumar, M, Sherchan, SP & Jiang, G 2022, 'Enhanced decay of coronaviruses in sewers with domestic wastewater', Science of The Total Environment, vol. 813, pp. 151919-151919.
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Shi, Q, Wu, N, Nguyen, DN, Huang, X, Wang, H & Hanzo, L 2022, 'Low-Complexity Iterative Detection for Dual-Mode Index Modulation in Dispersive Nonlinear Satellite Channels', IEEE Transactions on Communications, vol. 70, no. 2, pp. 1261-1275.
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Shi, T, Tang, M-C, Chai, R & Ziolkowski, RW 2022, 'Multipole-based Electrically Small Unidirectional Antenna with Exceptionally High Realized Gain', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Shi, Y, Campbell, DJ, Yu, X & Li, H 2022, 'Geometry-Guided Street-View Panorama Synthesis from Satellite Imagery', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1.
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Shi, Z, Zhang, JA, Xu, RY & Cheng, Q 2022, 'Environment-Robust Device-Free Human Activity Recognition With Channel-State-Information Enhancement and One-Shot Learning', IEEE Transactions on Mobile Computing, vol. 21, no. 2, pp. 540-554.
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Shon, HS, Choi, ES, Cho, Y-S, Cha, EJ, Kang, T-G & Kim, KA 2022, 'Machine learning-based risk factor analysis for periodontal disease from a Korean National Survey', Journal of Biomedical Translational Research, vol. 23, no. 1, pp. 17-28.
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Shu, Y, Li, Q, Xu, C, Liu, S & Xu, G 2022, 'V-SVR+: Support Vector Regression With Variational Privileged Information', IEEE Transactions on Multimedia, vol. 24, pp. 876-889.
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Many regression tasks encounter an asymmetric distribution of information between training and testing phases where the additional information available in training, the so-called privileged information, is often inaccessible in testing. In practice, the privileged information (PI) in training data might be expressed in different formats, such as continuous, ordinal, or binary values. However, most of the existing learning using privileged information (LUPI) paradigms mainly deal with the continuous form of PI, resulting in their incapability to handle variational PI, which motivates this research. Therefore, in this paper, we propose a unified framework to tackle the aforementioned three forms of privileged information systematically. The proposed method (called V-SVR+) integrates the continuous, ordinal, and binary PI into the learning process of support vector regression (SVR) via the proposed three losses. Specifically, for continuous privileged information, we define a linear correcting (slack) function in the privileged information space to estimate the slack variables in the standard SVR method using privileged information. For the ordinal relations of privileged information, we first rank the privileged information. Then, we regard this ordinal privileged information as auxiliary information applied in the learning process of the SVR model. For the binary or Boolean form of privileged information, we infer a probabilistic dependency between the privileged information and labels from the summarized privileged information knowledge. Then, we transfer the privileged information knowledge to constraints and form a constrained optimization problem. We evaluate the proposed method on three applications: music emotion recognition from songs with the help of implicit information about music elements judged by composers; multiple object recognition from images with the help of implicit information about the object's importance conveyed by the list of manually anno...
Sick, N & Bröring, S 2022, 'Exploring the research landscape of convergence from a TIM perspective: A review and research agenda', Technological Forecasting and Social Change, vol. 175, pp. 121321-121321.
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Siddiki, SYA, Mofijur, M, Kumar, PS, Ahmed, SF, Inayat, A, Kusumo, F, Badruddin, IA, Khan, TMY, Nghiem, LD, Ong, HC & Mahlia, TMI 2022, 'Microalgae biomass as a sustainable source for biofuel, biochemical and biobased value-added products: An integrated biorefinery concept', Fuel, vol. 307, pp. 121782-121782.
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Silvani, G, Bradbury, P, Basirun, C, Mehner, C, Zalli, D, Poole, K & Chou, J 2022, 'Testing 3D printed biological platform for advancing simulated microgravity and space mechanobiology research', npj Microgravity, vol. 8, no. 1.
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AbstractThe advancement of microgravity simulators is helping many researchers better understanding the impact of the mechanically unloaded space environment on cellular function and disfunction. However, performing microgravity experiments on Earth, using simulators such as the Random Positioning Machine, introduces some unique practical challenges, including air bubble formation and leakage of growth medium from tissue culture flask and plates, all of which limit research progress. Here, we developed an easy-to-use hybrid biological platform designed with the precision of 3D printing technologies combined with PDMS microfluidic fabrication processes to facilitate reliable and reproducible microgravity cellular experiments. The system has been characterized for applications in the contest of brain cancer research by exposing glioblastoma and endothelial cells to 24 h of simulated microgravity condition to investigate the triggered mechanosensing pathways involved in cellular adaptation to the new environment. The platform demonstrated compatibility with different biological assays, i.e., proliferation, viability, morphology, protein expression and imaging of molecular structures, showing advantages over the conventional usage of culture flask. Our results indicated that both cell types are susceptible when the gravitational vector is disrupted, confirming the impact that microgravity has on both cancer and healthy cells functionality. In particular, we observed deactivation of Yap-1 molecule in glioblastoma cells and the remodeling of VE-Cadherin junctional protein in endothelial cells. The study provides support for the application of the proposed biological platform for advancing space mechanobiology research, also highlighting perspectives and strategies for developing next generation of brain cancer molecular therapies, including targeted drug delivery strategies.
Singh, K, Afzal, MU & Esselle, KP 2022, 'Accurate optimization technique for phase-gradient metasurfaces used in compact near-field meta-steering systems', Scientific Reports, vol. 12, no. 1.
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AbstractNear-Field Meta-Steering (NFMS) is a constantly evolving and progressively emerging novel antenna beam-steering technology that involves an elegant assembly of a base antenna and a pair of Phase-Gradient Metasurfaces (PGMs) placed in the near-field region of the antenna aperture. The upper PGM in an NFMS system receives an oblique incidence from the lower PGM at all times, a fact that is ignored in the traditional design process of upper metasurfaces. This work proposes an accurate optimization method for metasurfaces in NFMS systems to reduce signal leakage by suppressing the grating lobes and side lobes that are innate artifacts of beam-steering. We detail the design and optimization approach for both upper and lower metasurface. Compared to the conventionally optimized compact 2D steering system, the proposed system exhibits higher directivity and lower side-lobe and grating lobe levels within the entire scanning range. The broadside directivity is 1.4 dB higher, and the side-lobe level is 4 dB lower in comparison. The beam-steering patterns for the proposed 2D compact design are experimentally validated, and the measured and predicted results are in excellent concurrence. The versatile compatibility of truncated PGMs with a low gain antenna makes it a compelling technology for wireless backhaul mesh networks and future antenna hardware.
Singh, SK, Taylor, RW, Pradhan, B, Shirzadi, A & Pham, BT 2022, 'Predicting sustainable arsenic mitigation using machine learning techniques', Ecotoxicology and Environmental Safety, vol. 232, pp. 113271-113271.
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Skarding, J, Hellmich, M, Gabrys, B & Musial, K 2022, 'A Robust Comparative Analysis of Graph Neural Networks on Dynamic Link Prediction', IEEE Access, vol. 10, pp. 64146-64160.
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Smith, CM & Hutvagner, G 2022, 'A comparative analysis of single cell small RNA sequencing data reveals heterogeneous isomiR expression and regulation', Scientific Reports, vol. 12, no. 1.
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AbstractMicroRNAs (miRNAs) are non-coding small RNAs which play a critical role in the regulation of gene expression in cells. It is known that miRNAs are often expressed as multiple isoforms, called isomiRs, which may have alternative regulatory functions. Despite the recent development of several single cell small RNA sequencing protocols, these methods have not been leveraged to investigate isomiR expression and regulation to better understand their role on a single cell level. Here we integrate sequencing data from three independent studies and find substantial differences in isomiR composition that suggest that cell autonomous mechanisms may drive isomiR processing. We also find evidence of altered regulatory functions of different classes of isomiRs, when compared to their respective wild-type miRNA, which supports a biological role for many of the isomiRs that are expressed.
Sobhi, P & Far, H 2022, 'Impact of structural pounding on structural behaviour of adjacent buildings considering dynamic soil-structure interaction', Bulletin of Earthquake Engineering, vol. 20, no. 7, pp. 3515-3547.
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Song, D, Zhang, W, Ren, T & Chang, X 2022, 'Editorial paper for pattern recognition letters VSI on multi-view representation learning and multi-modal information representation', Pattern Recognition Letters, vol. 159, pp. 165-166.
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Song, K, Li, Z, Li, L, Zhao, X, Deng, M, Zhou, X, Xu, Y, Peng, L, Li, R & Wang, Q 2022, 'Methane production from peroxymonosulfate pretreated algae biomass: Insights into microbial mechanisms, microcystin detoxification and heavy metal partitioning behavior', Science of The Total Environment, vol. 834, pp. 155500-155500.
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Song, L-Z, Wang, X & Qin, P-Y 2022, 'Single-Feed Multi-Beam Conformal Transmitarrays with Phase and Amplitude Modulations', IEEE Antennas and Wireless Propagation Letters, pp. 1-1.
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Song, Y, Zhang, Z, Wu, J, Wang, Y, Zhao, L & Huang, S 2022, 'A Right Invariant Extended Kalman Filter for Object Based SLAM', IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 1316-1323.
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Song, Z, Ji, J, Zhang, R & Cao, L 2022, 'Development of a test equipment for rating front to rear-end collisions based on C-NCAP-2018', International Journal of Crashworthiness, vol. 27, no. 2, pp. 522-532.
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Soomro, MHAA, Indraratna, B & Karekal, S 2022, 'Critical shear strain and sliding potential of rock joint under cyclic loading', Transportation Geotechnics, vol. 32, pp. 100708-100708.
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Stone, B, Mitchell, S, Halkon, B, Peirce, N & Harland, A 2022, 'The Dynamic Properties of Cricket and Cricket Training Balls', Sports Engineering (SPEN).
Su, G, Mohd Zulkifli, NW, Ong, HC, Ibrahim, S, Bu, Q & Zhu, R 2022, 'Pyrolysis of oil palm wastes for bioenergy in Malaysia: A review', Renewable and Sustainable Energy Reviews, vol. 164, pp. 112554-112554.
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Su, G, Ong, HC, Fattah, IMR, Ok, YS, Jang, J-H & Wang, C-T 2022, 'State-of-the-art of the pyrolysis and co-pyrolysis of food waste: Progress and challenges', Science of The Total Environment, vol. 809, pp. 151170-151170.
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Su, G, Ong, HC, Gan, YY, Chen, W-H, Chong, CT & Ok, YS 2022, 'Co-pyrolysis of microalgae and other biomass wastes for the production of high-quality bio-oil: Progress and prospective', Bioresource Technology, vol. 344, pp. 126096-126096.
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Su, G, Ong, HC, Mofijur, M, Mahlia, TMI & Ok, YS 2022, 'Pyrolysis of waste oils for the production of biofuels: A critical review', Journal of Hazardous Materials, vol. 424, pp. 127396-127396.
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Su, G, Ong, HC, Mohd Zulkifli, NW, Ibrahim, S, Chen, WH, Chong, CT & Ok, YS 2022, 'Valorization of animal manure via pyrolysis for bioenergy: A review', Journal of Cleaner Production, vol. 343, pp. 130965-130965.
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Sun, G, Wang, Y, Luo, Q & Li, Q 2022, 'Vibration-based damage identification in composite plates using 3D-DIC and wavelet analysis', Mechanical Systems and Signal Processing, vol. 173, pp. 108890-108890.
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Sun, G, Wei, Y, Huo, X, Luo, Q & Li, Q 2022, 'On quasi-static large deflection of single lap joints under transverse loading', Thin-Walled Structures, vol. 170, pp. 108572-108572.
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Sun, N, Dou, P, Zhai, W, He, H, Nghiem, LD, Vatanpour, V, Zhang, Y, Liu, C & He, T 2022, 'Polyethylene separator supported thin-film composite forward osmosis membranes for concentrating lithium enriched brine', Water Research, vol. 216, pp. 118297-118297.
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Sun, S, Hou, Y-N, Wei, W, Sharif, HMA, Huang, C, Ni, B-J, Li, H, Song, Y, Lu, C, Han, Y & Guo, J 2022, 'Perturbation of clopyralid on bio-denitrification and nitrite accumulation: Long-term performance and biological mechanism', Environmental Science and Ecotechnology, vol. 9, pp. 100144-100144.
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Sun, X, Feng, L, Zhu, Z, Lei, G, Diao, K, Guo, Y & Zhu, J 2022, 'Optimal Design of Terminal Sliding Mode Controller for Direct Torque Control of SRMs', IEEE Transactions on Transportation Electrification, vol. 8, no. 1, pp. 1445-1453.
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A nonsingular terminal sliding mode controller (NTSMC) based on a direct torque control is presented for a switched reluctance motor (SRM) in this paper. To guarantee dynamic stability, the nonsingular terminal sliding mode based on an improved reaching law is employed to design the speed controller. The torque ripple of the system can be suppressed, and the disturbance caused by uncertainties like load disturbance and parameter perturbation can be suppressed by the proposed NTSMC. Moreover, the gray wolf optimization algorithm is applied to automatically adjust the parameters of the controllers and the value of given flux, thereby acquiring a satisfactory result. The NTSMC is validated by both simulation and experimental results with a six-phase 12/10 SRM. Compared with PI and conventional sliding mode control, NTSMC improves the convergence rate of state and exhibits better performance in torque ripple reduction and anti-disturbance ability. The robustness and dynamic performance of the system can be ensured.
Sun, X, Li, T, Tian, X & Zhu, J 2022, 'Fault-Tolerant Operation of a Six-Phase Permanent Magnet Synchronous Hub Motor Based on Model Predictive Current Control With Virtual Voltage Vectors', IEEE Transactions on Energy Conversion, vol. 37, no. 1, pp. 337-346.
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Sun, X, Tang, X, Tian, X, Lei, G, Guo, Y & Zhu, J 2022, 'Sensorless Control With Fault-Tolerant Ability for Switched Reluctance Motors', IEEE Transactions on Energy Conversion, vol. 37, no. 2, pp. 1272-1281.
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Sun, X, Tang, X, Tian, X, Wu, J & Zhu, J 2022, 'Position Sensorless Control of Switched Reluctance Motor Drives Based on a New Sliding Mode Observer Using Fourier Flux Linkage Model', IEEE Transactions on Energy Conversion, vol. 37, no. 2, pp. 978-988.
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Sun, X, Zhang, Y, Lei, G, Guo, Y & Zhu, J 2022, 'An Improved Deadbeat Predictive Stator Flux Control With Reduced-Order Disturbance Observer for In-Wheel PMSMs', IEEE/ASME Transactions on Mechatronics, vol. 27, no. 2, pp. 690-700.
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In this paper, an improved deadbeat predictive stator flux control (DPSFC) based on disturbance observer is proposed to improve the control performance of in-wheel permanent magnet synchronous motors (PMSMs) with parameter mismatch and disturbance. First, the sensitivity of conventional deadbeat predictive current control to the parameter variation, including flux linkage, stator resistance and stator inductance, is analyzed. Then, a reduced-order observer based on additional disturbance state variables is designed to predict the future stator flux and observe the system disturbance caused by parameter mismatch. The proposed DPSFC method is able to enhance the robustness of the drive performance effectively via the compensations of one-step delay and stator voltage. Finally, the performance of the proposed control method is validated by simulations and experiments on a prototype of an in-wheel PMSM drive.
Sun, X, Zhang, Y, Tian, X, Cao, J & Zhu, J 2022, 'Speed Sensorless Control for IPMSMs Using a Modified MRAS With Gray Wolf Optimization Algorithm', IEEE Transactions on Transportation Electrification, vol. 8, no. 1, pp. 1326-1337.
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This paper represents modified model reference adaptive system (MRAS) method for speed sensorless control of interior permanent magnet synchronous motor (IPMSM), which can estimate rotor speed and position information. In order to suppress the adverse effect of parameter variation on control performance, the stator resistance and the permanent magnet flux linkage are estimated and continuously updated in reference and adjustable model. Then, proportional-integral (PI) controller parameters of speed adaptive law obtained by model reference adaptive system are optimized by grey wolf optimization (GWO) algorithm. In order to get the smallest possible speed following error and reference current error, the objective function is designed with discretized rotor speed error and current error as variables. The simulation results show the better performance of rotor speed estimation is obtained with GWO algorithm.
Sylvester, DE, Chen, Y, Grima, N, Saletta, F, Padhye, B, Bennetts, B, Wright, D, Krivanek, M, Graf, N, Zhou, L, Catchpoole, D, Kirk, J, Latchoumanin, O, Qiao, L, Ballinger, M, Thomas, D, Jamieson, R, Dalla‐Pozza, L & Byrne, JA 2022, 'Rare germline variants in childhood cancer patients suspected of genetic predisposition to cancer', Genes, Chromosomes and Cancer, vol. 61, no. 2, pp. 81-93.
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Tabandeh, A & Hossain, MJ 2022, 'Hybrid Scenario-IGDT-Based Congestion Management Considering Uncertain Demand Response Firms and Wind Farms', IEEE Systems Journal, vol. 16, no. 2, pp. 3108-3119.
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Tabandeh, M, Cheng, CK, Centi, G, Show, PL, Chen, W-H, Ling, TC, Ong, HC, Ng, E-P, Juan, JC & Lam, SS 2022, 'Recent advancement in deoxygenation of fatty acids via homogeneous catalysis for biofuel production', Molecular Catalysis, vol. 523, pp. 111207-111207.
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Tahmassebi, A, Motamedi, M, Alavi, AH & Gandomi, AH 2022, 'An explainable prediction framework for engineering problems: case studies in reinforced concrete members modeling', Engineering Computations, vol. 39, no. 2, pp. 609-626.
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PurposeEngineering design and operational decisions depend largely on deep understanding of applications that requires assumptions for simplification of the problems in order to find proper solutions. Cutting-edge machine learning algorithms can be used as one of the emerging tools to simplify this process. In this paper, we propose a novel scalable and interpretable machine learning framework to automate this process and fill the current gap.Design/methodology/approachThe essential principles of the proposed pipeline are mainly (1) scalability, (2) interpretibility and (3) robust probabilistic performance across engineering problems. The lack of interpretibility of complex machine learning models prevents their use in various problems including engineering computation assessments. Many consumers of machine learning models would not trust the results if they cannot understand the method. Thus, the SHapley Additive exPlanations (SHAP) approach is employed to interpret the developed machine learning models.FindingsThe proposed framework can be applied to a variety of engineering problems including seismic damage assessment of structures. The performance of the proposed framework is investigated using two case studies of failure identification in reinforcement concrete (RC) columns and shear walls. In addition, the reproducibility, reliability and generalizability of the results were validated and the results of the framework were compared to the benchmark studies. The results of the proposed framework outperformed the benchmark results with high statistical significance.Originality/valueAlthough, the...
Tang, M-C, Guo, P, Li, D, Hu, K-Z, Li, M & Ziolkowski, RW 2022, 'Vertically Polarized, High-Performance, Electrically Small Monopole Filtennas', IEEE Transactions on Antennas and Propagation, vol. 70, no. 2, pp. 1488-1493.
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Tang, Z, Li, W, Peng, Q, Tam, VWY & Wang, K 2022, 'Study on the failure mechanism of geopolymeric recycled concrete using digital image correlation method', Journal of Sustainable Cement-Based Materials, vol. 11, no. 2, pp. 113-126.
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Tanko, D, Barua, PD, Dogan, S, Tuncer, T, Palmer, E, Ciaccio, EJ & Acharya, UR 2022, 'EPSPatNet86: eight-pointed star pattern learning network for detection ADHD disorder using EEG signals', Physiological Measurement, vol. 43, no. 3, pp. 035002-035002.
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Abstract
Objective. The main objective of this work is to present a hand-modelled one-dimensional signal classification system to detect Attention-Deficit Hyperactivity Disorder (ADHD) disorder using electroencephalography (EEG) signals. Approach. A novel handcrafted feature extraction method is presented in this research. Our proposed method uses a directed graph and an eight-pointed star pattern (EPSPat). Also, tunable q wavelet transforms (TQWT), wavelet packet decomposition (WPD), statistical extractor, iterative Chi2 (IChi2) selector, and the k-nearest neighbors (kNN) classifier have been utilized to develop the EPSPat based learning model. This network uses two wavelet decomposition methods (TQWT and WPD), and 85 wavelet coefficient bands are extracted. The proposed EPSPat and statistical feature creator generate features from the 85 wavelet coefficient bands and the original EEG signal. The learning network is termed EPSPatNet86. The main purpose of the presented EPSPatNet86 is to detect abnormalities of the EEG signals. Therefore, 85 wavelet subbands have been generated to extract features. The created 86 feature vectors have been evaluated using the Chi2 selector and the kNN classifier in the loss value calculation phase. The final features vector is created by employing a minimum loss-valued eight feature vectors. The IChi2 selector selects the best feature vector, which is fed to the kNN classifier. An EEG signal dataset has been used to demonstrate the presented model’s EEG signal classification ability. We have used an ADHD EEG dataset since ADHD is a commonly seen brain-related ailment. Main results. Our developed EPSPatNet86 model can detect the ADHD EEG signals with 97.19% and 87.60% accuracy using 10-fold cross and subject-wise validations, respectively. Significance. The ca...
Tanveer, M, Ganaie, MA, Bhattacharjee, A & Lin, CT 2022, 'Intuitionistic Fuzzy Weighted Least Squares Twin SVMs', IEEE Transactions on Cybernetics, pp. 1-10.
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Tao, X, Zhang, D-P, Ma, W, Hou, Z, Lu, Z & Adak, C 2022, 'Unsupervised Anomaly Detection for Surface Defects with Dual-Siamese Network', IEEE Transactions on Industrial Informatics, pp. 1-1.
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Tashima, T, Takashima, H, Schell, AW, Tran, TT, Aharonovich, I & Takeuchi, S 2022, 'Hybrid device of hexagonal boron nitride nanoflakes with defect centres and a nano-fibre Bragg cavity', Scientific Reports, vol. 12, no. 1.
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AbstractSolid-state quantum emitters coupled with a single mode fibre are of interest for photonic and quantum applications. In this context, nanofibre Bragg cavities (NFBCs), which are microcavities fabricated in an optical nanofibre, are promising devices because they can efficiently couple photons emitted from the quantum emitters to the single mode fibre. Recently, we have realized a hybrid device of an NFBC and a single colloidal CdSe/ZnS quantum dot. However, colloidal quantum dots exhibit inherent photo-bleaching. Thus, it is desired to couple an NFBC with hexagonal boron nitride (hBN) as stable quantum emitters. In this work, we realize a hybrid system of an NFBC and ensemble defect centres in hBN nanoflakes. In this experiment, we fabricate NFBCs with a quality factor of 807 and a resonant wavelength at around 573 nm, which matches well with the fluorescent wavelength of the hBN, using helium-focused ion beam (FIB) system. We also develop a manipulation system to place hBN nanoflakes on a cavity region of the NFBCs and realize a hybrid device with an NFBC. By exciting the nanoflakes via an objective lens and collecting the fluorescence through the NFBC, we observe a sharp emission peak at the resonant wavelength of the NFBC.
Tavakoli, J & Tipper, JL 2022, 'Detailed mechanical characterization of the transition zone: New insight into the integration between the annulus and nucleus of the intervertebral disc', Acta Biomaterialia, vol. 143, pp. 87-99.
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Telikani, A, Gandomi, AH, Choo, K-KR & Shen, J 2022, 'A Cost-Sensitive Deep Learning-Based Approach for Network Traffic Classification', IEEE Transactions on Network and Service Management, vol. 19, no. 1, pp. 661-670.
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Telikani, A, Tahmassebi, A, Banzhaf, W & Gandomi, AH 2022, 'Evolutionary Machine Learning: A Survey', ACM Computing Surveys, vol. 54, no. 8, pp. 1-35.
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Evolutionary Computation (EC) approaches are inspired by nature and solve optimization problems in a stochastic manner. They can offer a reliable and effective approach to address complex problems in real-world applications. EC algorithms have recently been used to improve the performance of Machine Learning (ML) models and the quality of their results. Evolutionary approaches can be used in all three parts of ML: preprocessing (e.g., feature selection and resampling), learning (e.g., parameter setting, membership functions, and neural network topology), and postprocessing (e.g., rule optimization, decision tree/support vectors pruning, and ensemble learning). This article investigates the role of EC algorithms in solving different ML challenges. We do not provide a comprehensive review of evolutionary ML approaches here; instead, we discuss how EC algorithms can contribute to ML by addressing conventional challenges of the artificial intelligence and ML communities. We look at the contributions of EC to ML in nine sub-fields: feature selection, resampling, classifiers, neural networks, reinforcement learning, clustering, association rule mining, and ensemble methods. For each category, we discuss evolutionary machine learning in terms of three aspects: problem formulation, search mechanisms, and fitness value computation. We also consider open issues and challenges that should be addressed in future work.
Tian, Z, Li, S & Li, Y 2022, 'Enhanced sensing performance of cement-based composites achieved via magnetically aligned nickel particle network', Composites Communications, vol. 29, pp. 101006-101006.
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Tian, Z, Li, Y, Li, S, Vute, S & Ji, J 2022, 'Influence of particle morphology and concentration on the piezoresistivity of cement-based sensors with magneto-aligned nickel fillers', Measurement, vol. 187, pp. 110194-110194.
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Cement-based sensors with magneto-aligned nickel fillers have the proven capability to significantly enhance piezoresistivity compared with the sensors with randomized fillers. In this paper, the influence of particle morphology and concentration of nickel particles on the piezoresistive and mechanical properties of cement-based sensors, treated with and without magnetic field intervention, are investigated experimentally. Five categories of nickel particles with different average diameters are type N50 (50 nm), N500 (0.5 μm), F(1 μm × 20 μm flake), T (5 μm) and U (25 μm). The obtained results indicate that the application of magnetic field enhances most of the piezoresistive performance and yields best piezoresistivity for the samples with type T nickel powder. Anisotropic piezoresistivity can be achieved under a very low filler content (0.1 vol%) in N50 nano-scale nickel powder and cement composite, followed by the N500 and T nickel particles in 5 vol% content. Small particles with lower content have similar piezoresistive performance to the samples with large particles and higher concentration. One half of the samples can achieve high giant gauge factor (GF) of over 500, two-thirds of which are aligned by magnetic field with anisotropic piezoresistive property. Samples with 5 vol% type T nickel content has the highest GF value, followed by the sample with 5 vol% type F nickel flakes and 10 vol% type U nickel powder. It is also found that mechanical strength decreases with the increase of particle concentration.
Tianqing, Z, Zhou, W, Ye, D, Cheng, Z & Li, J 2022, 'Resource Allocation in IoT Edge Computing via Concurrent Federated Reinforcement Learning', IEEE Internet of Things Journal, vol. 9, no. 2, pp. 1414-1426.
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Tiwary, K, Patro, SK, Gandomi, AH & Sahoo, KS 2022, 'Model updating using causal information: a case study in coupled slab', Structural and Multidisciplinary Optimization, vol. 65, no. 2.
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AbstractProblems like improper sampling (sampling on unnecessary variables) and undefined prior distribution (or taking random priors) often occur in model updating. Any such limitations on model parameters can lead to lower accuracy and higher experimental costs (due to more iterations) of structural optimisation. In this work, we explored the effective dimensionality of the model updating problem by leveraging the causal information. In order to utilise the causal structure between the parameters, we used Causal Bayesian Optimisation (CBO), a recent variant of Bayesian Optimisation, to integrate observational and intervention data. We also employed generative models to generate synthetic observational data, which helps in creating a better prior for surrogate models. This case study of a coupled slab structure in a recreational building resulted in the modal updated frequencies which were extracted from the finite element of the structure and compared to measured frequencies from ambient vibration tests found in the literature. The results of mode shapes between experimental and predicted values were also compared using modal assurance criterion (MAC) percentages. The updated frequency and MAC number that was obtained using the proposed model was found in least number of iterations (impacts experimental budget) as compared to previous approaches which optimise the same parameters using same data. This also shows how the causal information has impact on experimental budget.
Tong, C-X, Dong, Z-L, Sun, Q, Zhang, S, Zheng, J-X & Sheng, D 2022, 'On compression behavior and particle breakage of carbonate silty sands', Engineering Geology, vol. 297, pp. 106492-106492.
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Tong, C-X, Zhai, M-Y, Li, H-C, Zhang, S & Sheng, D 2022, 'Particle breakage of granular soils: changing critical state line and constitutive modelling', Acta Geotechnica, vol. 17, no. 3, pp. 755-768.
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Tran, D-T, Pham, T-D, Dang, V-C, Pham, T-D, Nguyen, M-V, Dang, N-M, Ha, M-N, Nguyen, V-N & Nghiem, LD 2022, 'A facile technique to prepare MgO-biochar nanocomposites for cationic and anionic nutrient removal', Journal of Water Process Engineering, vol. 47, pp. 102702-102702.
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Truong, DQ, Loganathan, P, Tran, LM, Vu, DL, Nguyen, TV, Vigneswaran, S & Naidu, G 2022, 'Removing ammonium from contaminated water using Purolite C100E: batch, column, and household filter studies', Environmental Science and Pollution Research, vol. 29, no. 12, pp. 16959-16972.
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Tuan Tran, H, Lin, C, Bui, X-T, Ky Nguyen, M, Dan Thanh Cao, N, Mukhtar, H, Giang Hoang, H, Varjani, S, Hao Ngo, H & Nghiem, LD 2022, 'Phthalates in the environment: characteristics, fate and transport, and advanced wastewater treatment technologies', Bioresource Technology, vol. 344, pp. 126249-126249.
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Uddin Murad, MA, Cetindamar, D & Chakraborty, S 2022, 'Identifying the Key Big Data Analytics Capabilities in Bangladesh’s Healthcare Sector', Sustainability, vol. 14, no. 12, pp. 7077-7077.
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The study explores the crucial big data analytics capabilities (BDAC) for healthcare in Bangladesh. After a rigorous and extensive literature review, we list a wide range of BDAC and empirically examine their applicability in Bangladesh’s healthcare sector by consulting 51 experts with ample domain knowledge. The study adopted the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method. Findings highlighted 11 key BDAC, such as using advanced analytical techniques that could be critical in managing big data in the healthcare sector. The paper ends with a summary and puts forward suggestions for future studies.
Ullah, MA, Keshavarz, R, Abolhasan, M, Lipman, J, Esselle, KP & Shariati, N 2022, 'A Review on Antenna Technologies for Ambient RF Energy Harvesting and Wireless Power Transfer: Designs, Challenges and Applications', IEEE Access, vol. 10, pp. 17231-17267.
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Ung, HTT, Leu, BT, Tran, HTH, Nguyen, LN, Nghiem, LD, Hoang, NB, Pham, HT & Duong, HC 2022, 'Combining flowform cascade with constructed wetland to enhance domestic wastewater treatment', Environmental Technology & Innovation, vol. 27, pp. 102537-102537.
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Unhelkar, B, Joshi, S, Sharma, M, Prakash, S, Mani, AK & Prasad, M 2022, 'Enhancing supply chain performance using RFID technology and decision support systems in the industry 4.0–A systematic literature review', International Journal of Information Management Data Insights, vol. 2, no. 2, pp. 100084-100084.
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Valipour, M, Yousefi, S, Jahangoshai Rezaee, M & Saberi, M 2022, 'A clustering-based approach for prioritizing health, safety and environment risks integrating fuzzy C-means and hybrid decision-making methods', Stochastic Environmental Research and Risk Assessment, vol. 36, no. 3, pp. 919-938.
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Van Huynh, N, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2022, 'Joint Coding and Scheduling Optimization for Distributed Learning Over Wireless Edge Networks', IEEE Journal on Selected Areas in Communications, vol. 40, no. 2, pp. 484-498.
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Van Huynh, N, Nguyen, DN, Hoang, DT, Vu, TX, Dutkiewicz, E & Chatzinotas, S 2022, 'Defeating Super-Reactive Jammers With Deception Strategy: Modeling, Signal Detection, and Performance Analysis', IEEE Transactions on Wireless Communications, pp. 1-1.
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Vanda, S, Nikoo, MR, Hashempour Bakhtiari, P, Al-Wardy, M, Franklin Adamowski, J, Šimůnek, J & Gandomi, AH 2022, 'Reservoir operation under accidental MTBE pollution: A graph-based conflict resolution framework considering spatial-temporal-quantitative uncertainties', Journal of Hydrology, vol. 605, pp. 127313-127313.
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Vazquez, S, Zafra, E, Aguilera, RP, Geyer, T, Leon, JI & Franquelo, LG 2022, 'Prediction Model With Harmonic Load Current Components for FCS-MPC of an Uninterruptible Power Supply', IEEE Transactions on Power Electronics, vol. 37, no. 1, pp. 322-331.
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Veitch, D, Mani, SK, Cao, Y & Barford, P 2022, 'iHorology: Lowering the Barrier to Microsecond-Level Internet Time', IEEE/ACM Transactions on Networking, pp. 1-15.
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Veza, I, Afzal, A, Mujtaba, MA, Hoang, AT, Balasubramanian, D, Sekar, M, Fattah, IMR, Soudagar, MEM, EL-Seesy, A, Djamari, DW, Hananto, AL, Putra, NR & Tamaldin, N 2022, 'Review of artificial neural networks for gasoline, diesel and homogeneous charge compression ignition engine', ALEXANDRIA ENGINEERING JOURNAL, vol. 61, no. 11, pp. 8363-8391.
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Veza, I, Afzal, A, Mujtaba, MA, Tuan Hoang, A, Balasubramanian, D, Sekar, M, Fattah, IMR, Soudagar, MEM, EL-Seesy, AI, Djamari, DW, Hananto, AL, Putra, NR & Tamaldin, N 2022, 'Review of artificial neural networks for gasoline, diesel and homogeneous charge compression ignition engine', Alexandria Engineering Journal, vol. 61, no. 11, pp. 8363-8391.
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Vo, HNP, Nguyen, TMH, Ngo, HH, Guo, W & Shukla, P 2022, 'Biochar sorption of perfluoroalkyl substances (PFASs) in aqueous film-forming foams-impacted groundwater: Effects of PFASs properties and groundwater chemistry', Chemosphere, vol. 286, pp. 131622-131622.
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The widespread use of per- and polyfluoroalkyl substances (PFASs)-related products such as aqueous film-forming foams (AFFF) has led to increasing contamination of groundwater systems. The concentration of PFASs in AFFF-impacted groundwater can be several orders of magnitude higher than the drinking water standard. There is a need for a sustainable and effective sorbent to remove PFASs from groundwater. This work aims to investigate the sorption of PFASs in groundwater by biochar column. The specific objectives are to understand the influences of PFASs properties and groundwater chemistry to PFASs sorption by biochar. The PFASs-spiked Milli-Q water (including 19 PFASs) and four aqueous film-forming foams (AFFF)-impacted groundwater were used. The partitioning coefficients (log Kd) of long chain PFASs ranged from 0.77 to 4.63 while for short chain PFASs they remained below 0.68. For long chain PFASs (C ≥ 7), log Kd increased by 0.5 and 0.8 for each CF2 moiety of PFCAs and PFSAs, respectively. Dissolved organic matter (DOM) was the most influential factor in PFASs sorption over pH, salinity, and specific ultraviolet absorbance (SUVA). DOM contained hydrophobic compounds and metal ions which can form DOM-PFASs complexes to provide more sorption sites for PFASs. The finding is useful for executing PFASs remediation by biochar filtration column, especially legacy long chain PFASs, for groundwater remediation.
Vo, NNY, Vu, QT, Vu, NH, Vu, TA, Mach, BD & Xu, G 2022, 'Domain-specific NLP system to support learning path and curriculum design at tech universities', Computers and Education: Artificial Intelligence, vol. 3, pp. 100042-100042.
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Vosoughi, F, Nikoo, MR, Rakhshandehroo, G, Adamowski, JF & Gandomi, AH 2022, 'Downstream semi-circular obstacles' influence on floods arising from the failure of dams with different levels of reservoir silting', Physics of Fluids, vol. 34, no. 1, pp. 013312-013312.
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Vu, HP, Nguyen, LN, Wang, Q, Ngo, HH, Liu, Q, Zhang, X & Nghiem, LD 2022, 'Hydrogen sulphide management in anaerobic digestion: A critical review on input control, process regulation, and post-treatment', Bioresource Technology, vol. 346, pp. 126634-126634.
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Vu, L, Cao, VL, Nguyen, QU, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2022, 'Learning Latent Representation for IoT Anomaly Detection', IEEE Transactions on Cybernetics, vol. 52, no. 5, pp. 3769-3782.
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Vu, L, Nguyen, QU, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2022, 'Deep Generative Learning Models for Cloud Intrusion Detection Systems', IEEE Transactions on Cybernetics, pp. 1-13.
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Vu, MT, Nguyen, LN, Mofijur, M, Johir, MAH, Ngo, HH, Mahlia, TMI & Nghiem, LD 2022, 'Simultaneous nutrient recovery and algal biomass production from anaerobically digested sludge centrate using a membrane photobioreactor', Bioresource Technology, vol. 343, pp. 126069-126069.
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This study aims to evaluate the performance of C. vulgaris microalgae to simultaneously recover nutrients from sludge centrate and produce biomass in a membrane photobioreactor (MPR). Microalgae growth and nutrient removal were evaluated at two different nutrient loading rates (sludge centrate). The results show that C. vulgaris microalgae could thrive in sludge centrate. Nutrient loading has an indiscernible impact on biomass growth and a notable impact on nutrient removal efficiency. Nutrient removal increased as the nutrient loading rate decreased and hydraulic retention time increased. There was no membrane fouling observed in the MPR and the membrane water flux was fully restored by backwashing using only water. However, the membrane permeability varies with the hydraulic retention time (HRT) and biomass concentration in the reactor. Longer HRT offers higher permeability. Therefore, it is recommended to operate the MPR system in lower HRT to improve the membrane resistance and energy consumption.
Wali, SB, Hannan, MA, Ker, PJ, Rahman, MSA, Mansor, M, Muttaqi, KM, Mahlia, TMI & Begum, RA 2022, 'Grid-connected lithium-ion battery energy storage system: A bibliometric analysis for emerging future directions', Journal of Cleaner Production, vol. 334, pp. 130272-130272.
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Walker, P, Li, T, Khonasty, R, Ponnanna, KM, Kuo, A, Zhao, L & Huang, S 2022, 'Proof of concept study for using UR10 robot to help total hip replacement', The International Journal of Medical Robotics and Computer Assisted Surgery, vol. 18, no. 2.
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Wan Mahari, WA, Kee, SH, Foong, SY, Amelia, TSM, Bhubalan, K, Man, M, Yang, Y, Ong, HC, Vithanage, M, Lam, SS & Sonne, C 2022, 'Generating alternative fuel and bioplastics from medical plastic waste and waste frying oil using microwave co-pyrolysis combined with microbial fermentation', Renewable and Sustainable Energy Reviews, vol. 153, pp. 111790-111790.
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Wan, S, Pan, S, Zhong, P, Chang, X, Yang, J & Gong, C 2022, 'Dual Interactive Graph Convolutional Networks for Hyperspectral Image Classification', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-14.
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Wang, B, Liu, W, Li, JJ, Chai, S, Xing, D, Yu, H, Zhang, Y, Yan, W, Xu, Z, Zhao, B, Du, Y & Jiang, Q 2022, 'A low dose cell therapy system for treating osteoarthritis: In vivo study and in vitro mechanistic investigations', Bioactive Materials, vol. 7, pp. 478-490.
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Wang, B, Lu, J, Li, T, Yan, Z & Zhang, G 2022, 'A quantile fusion methodology for deep forecasting', Neurocomputing, vol. 483, pp. 286-298.
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Wang, C, Pan, S, Yu, CP, Hu, R, Long, G & Zhang, C 2022, 'Deep neighbor-aware embedding for node clustering in attributed graphs', Pattern Recognition, vol. 122, pp. 108230-108230.
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Wang, C, Park, MJ, Gonzales, RR, Phuntsho, S, Matsuyama, H, Drioli, E & Shon, HK 2022, 'Novel organic solvent nanofiltration membrane based on inkjet printing-assisted layer-by-layer assembly', Journal of Membrane Science, vol. 655, pp. 120582-120582.
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Wang, C, Park, MJ, Seo, DH, Phuntsho, S, Gonzales, RR, Matsuyama, H, Drioli, E & Shon, HK 2022, 'Inkjet printed polyelectrolyte multilayer membrane using a polyketone support for organic solvent nanofiltration', Journal of Membrane Science, vol. 642, pp. 119943-119943.
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Wang, C, Wang, S, Wang, L, Cao, C, Sun, G, Li, C & Yang, Y 2022, 'Framework of nacelle inverse design method based on improved generative adversarial networks', Aerospace Science and Technology, vol. 121, pp. 107365-107365.
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Wang, C, Wei, W, Dai, X & Ni, B-J 2022, 'Calcium peroxide significantly enhances volatile solids destruction in aerobic sludge digestion through improving sludge biodegradability', Bioresource Technology, vol. 346, pp. 126655-126655.
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Wang, C, Wei, W, Dai, X & Ni, B-J 2022, 'Zero valent iron greatly improves sludge destruction and nitrogen removal in aerobic sludge digestion', Chemical Engineering Journal, vol. 433, pp. 134459-134459.
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Wang, C, Wei, W, Mannina, G, Dai, X & Ni, B-J 2022, 'Unveiling the distinctive role of titanium dioxide nanoparticles in aerobic sludge digestion', Science of The Total Environment, vol. 813, pp. 151872-151872.
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Wang, C, Wei, W, Zhang, Y-T, Dai, X & Ni, B-J 2022, 'Different sizes of polystyrene microplastics induced distinct microbial responses of anaerobic granular sludge', Water Research, vol. 220, pp. 118607-118607.
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Wang, D, Zhang, J, Li, J, Wang, W, Shon, HK, Huang, H, Zhao, Y & Wang, Z 2022, 'Inorganic scaling in the treatment of shale gas wastewater by fertilizer drawn forward osmosis process', Desalination, vol. 521, pp. 115396-115396.
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Wang, F, Long, G, Bai, M, Wang, J, Yang, Z, Zhou, X & Zhou, JL 2022, 'Cleaner and safer disposal of electrolytic manganese residues in cement-based materials using direct electric curing', Journal of Cleaner Production, vol. 356, pp. 131842-131842.
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Wang, F, Long, G, Bai, M, Wang, J, Zhou, JL & Zhou, X 2022, 'Application of electrolytic manganese residues in cement products through pozzolanic activity motivation and calcination', Journal of Cleaner Production, vol. 338, pp. 130629-130629.
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Wang, F, Long, G, He, J, Xie, Y, Tang, Z, Zhou, X, Bai, M & Zhou, JL 2022, 'Fabrication of Energy-Efficient Carbonate-Based Cementitious Material Using Sodium Meta-Aluminate Activated Limestone Powder', ACS Sustainable Chemistry & Engineering, vol. 10, no. 20, pp. 6559-6572.
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Wang, G, Choi, K-S, Teoh, JY-C & Lu, J 2022, 'Deep Cross-Output Knowledge Transfer Using Stacked-Structure Least-Squares Support Vector Machines', IEEE Transactions on Cybernetics, vol. 52, no. 5, pp. 3207-3220.
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This article presents a new deep cross-output knowledge transfer approach based on least-squares support vector machines, called DCOT-LS-SVMs. Its aim is to improve the generalizability of least-squares support vector machines (LS-SVMs) while avoiding the complicated parameter tuning process that occurs in many kernel machines. The proposed approach has two significant characteristics: 1) DCOT-LS-SVMs is inspired by a stacked hierarchical architecture that combines several layer-by-layer LS-SVMs modules. The module that forms the higher layer has additional input features that consider the predictions from all previous modules and 2) cross-output knowledge transfer is used to leverage knowledge from the predictions of the previous module to improve the learning process in the current module. With this approach, the model's parameters, such as a tradeoff parameter C and a kernel width δ, can be randomly assigned to each module in order to greatly simplify the learning process. Moreover, DCOT-LS-SVMs is able to autonomously and quickly decide the extent of the cross-output knowledge transfer between adjacent modules through a fast leave-one-out cross-validation strategy. In addition, we present an imbalanced version of DCOT-LS-SVMs, called IDCOT-LS-SVMs, given that imbalanced datasets are common in real-world scenarios. The effectiveness of the proposed approaches is demonstrated through a comparison with five comparative methods on UCI datasets and with a case study on the diagnosis of prostate cancer.
Wang, G, Weng, L, Huang, Y, Ling, Y, Zhen, Z, Lin, Z, Hu, H, Li, C, Guo, J, Zhou, JL, Chen, S, Jia, Y & Ren, L 2022, 'Microbiome-metabolome analysis directed isolation of rhizobacteria capable of enhancing salt tolerance of Sea Rice 86', Science of The Total Environment, pp. 156817-156817.
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Wang, G, Xing, D, Liu, W, Zhu, Y, Liu, H, Yan, L, Fan, K, Liu, P, Yu, B, Li, JJ & Wang, B 2022, 'Preclinical studies and clinical trials on mesenchymal stem cell therapy for knee osteoarthritis: A systematic review on models and cell doses', International Journal of Rheumatic Diseases, vol. 25, no. 5, pp. 532-562.
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Wang, G, Zhou, T, Choi, K-S & Lu, J 2022, 'A Deep-Ensemble-Level-Based Interpretable Takagi–Sugeno–Kang Fuzzy Classifier for Imbalanced Data', IEEE Transactions on Cybernetics, vol. 52, no. 5, pp. 3805-3818.
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Existing research reveals that the misclassification rate for imbalanced data depends heavily on the problematic areas due to the existence of small disjoints, class overlap, borderline, and rare data samples. In this study, by stacking zero-order Takagi-Sugeno-Kang (TSK) fuzzy subclassifiers on the minority class and its problematic areas in the deep ensemble, a novel deep-ensemble-level-based TSK fuzzy classifier (IDE-TSK-FC) for imbalanced data classification tasks is presented to achieve both promising classification performance and high interpretability of zero-order TSK fuzzy classifiers. Simultaneously, according to the stacked generalization principle, the proposed classifier lifts up oversampling from the data level to the deep ensemble level with a guarantee of enhanced generalization capability for class imbalance learning. In the structure of IDE-TSK-FC, the first interpretable zero-order TSK fuzzy subclassifier is built on the original training dataset. After that, several successive zero-order TSK fuzzy subclassifiers are stacked layer by layer on the newly identified problematic areas from the original training dataset plus the corresponding interpretable predictions obtained by the averaging strategy on all previous layers. IDE-TSK-FC simply takes the classical K-nearest neighboring algorithm at each layer to identify its problematic area that consists of the minority samples and its surrounding K majority neighbors. After randomly neglecting certain input features and randomly selecting the five Gaussian membership functions for all the chosen input features and the augmented feature in the premise of each fuzzy rule, each subclassifier can be quickly obtained by using the least learning machine to determine the consequent part of each fuzzy rule. The experimental results on both the public datasets and a real-world healthcare dataset demonstrate IDE-TSK-FC's superiority in class imbalanced learning.
Wang, H, Ding, S, Yang, S, Liu, C, Yu, S & Zheng, X 2022, 'Guided Activity Prediction for Minimally Invasive Surgery Safety Improvement in the Internet of Medical Things', IEEE Internet of Things Journal, vol. 9, no. 6, pp. 4758-4768.
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Wang, H, Lian, D, Liu, W, Wen, D, Chen, C & Wang, X 2022, 'Powerful graph of graphs neural network for structured entity analysis', World Wide Web, vol. 25, no. 2, pp. 609-629.
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Wang, H, Obeidy, P, Wang, Z, Zhao, Y, Wang, Y, Su, QP, Cox, CD & Ju, LA 2022, 'Fluorescence-coupled micropipette aspiration assay to examine calcium mobilization caused by red blood cell mechanosensing', European Biophysics Journal, vol. 51, no. 2, pp. 135-146.
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AbstractMechanical stimuli such as tension, compression, and shear stress play critical roles in the physiological functions of red blood cells (RBCs) and their homeostasis, ATP release, and rheological properties. Intracellular calcium (Ca2+) mobilization reflects RBC mechanosensing as they transverse the complex vasculature. Emerging studies have demonstrated the presence of mechanosensitive Ca2+ permeable ion channels and their function has been implicated in the regulation of RBC volume and deformability. However, how these mechanoreceptors trigger Ca2+ influx and subsequent cellular responses are still unclear. Here, we introduce a fluorescence-coupled micropipette aspiration assay to examine RBC mechanosensing at the single-cell level. To achieve a wide range of cell aspirations, we implemented and compared two negative pressure adjusting apparatuses: a homemade water manometer (− 2.94 to 0 mmH2O) and a pneumatic high-speed pressure clamp (− 25 to 0 mmHg). To visualize Ca2+ influx, RBCs were pre-loaded with an intensiometric probe Cal-520 AM, then imaged under a confocal microscope with concurrent bright-field and fluorescent imaging at acquisition rates of 10 frames per second. Remarkably, we observed the related changes in intracellular Ca2+ levels immediately after aspirating individual RBCs in a pressure-dependent manner. The RBC aspirated by the water manometer only displayed 1.1-fold increase in fluorescence intensity, whereas the RBC aspirated by the pneumatic clamp showed up to threefold increase. These results demonstrated the water manometer as a gentle tool for cell manipulation with minimal pre-activation, while the high-speed pneumatic clamp as a much stronger pressure actuator to examine cell mechanosensing directly. Together, this multimodal platform enables us to precisely control aspi...
Wang, J, An, Y, Li, Z & Lu, H 2022, 'A novel combined forecasting model based on neural networks, deep learning approaches, and multi-objective optimization for short-term wind speed forecasting', Energy, vol. 251, pp. 123960-123960.
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Wang, J, Li, Q, Ma, X & Lu, H 2022, 'Distribution parameter-determining method comparison for airborne wind energy potential assessment in the eastern coastal area of China', Sustainable Energy Technologies and Assessments, vol. 52, pp. 102161-102161.
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Wang, J, Wang, S, Zeng, B & Lu, H 2022, 'A novel ensemble probabilistic forecasting system for uncertainty in wind speed', Applied Energy, vol. 313, pp. 118796-118796.
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Wang, K, Bao, G, Fan, Q, Zhu, L, Yang, L, Liu, T, Zhang, Z, Li, G, Chen, X, Xu, X, Xu, X, He, B & Zheng, Y 2022, 'Feasibility evaluation of a Cu-38 Zn alloy for intrauterine devices: In vitro and in vivo studies', Acta Biomaterialia, vol. 138, pp. 561-575.
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The existing adverse effects of copper in copper-containing intrauterine devices (Cu-IUDs) have raised concerns regarding their use. These adverse effects include burst release of cupric ions (Cu2+) at the initial stage and an increasingly rough surface of the Cu-IUDs. In this study, we investigated the use of two copper alloys, Cu-38Zn and H62 as the new upgrading or alternative material for IUDs. Their corrosive properties were studied in simulated uterine fluid (SUF) by using electrochemical methods, with pure Cu as a control. We studied the in vitro long-term corrosion behaviors in SUF, cytotoxicity to uterine cells (human endometrial epithelial cells and human endometrial stromal cells), in vivo biocompatibility and contraceptive efficacy of pure Cu, H62, and Cu-38Zn. In the first month, the burst release rate of Cu2+ in the Cu-38Zn group was significantly lower than those in the pure Cu and H62 groups. The in vitro cytocompatibility Cu-38Zn was better than that of pure Cu and H62. Moreover, Cu-38Zn showed improved tissue biocompatibility in vivo experiments. Therefore, the contraceptive efficacy of the Cu-38Zn is still maintained as high as the pure Cu while the adverse effects are significantly eased, suggesting that Cu-38Zn can be a suitable potential candidate material for IUDs. STATEMENT OF SIGNIFICANCE: The existing adverse effects associated with the intrinsic properties of copper materials for copper-containing intrauterine devices (Cu-IUD) are of concern in their employment. Such as, burst release of cupric ions (Cu2+) at the initial stage and an increasingly rough surface of the Cu-IUD. In this work, Cu alloyed with a high amount of bioactive Zn was used for a Cu-IUD. The Cu-38Zn alloy exhibited reduced burst release of Cu2+ within the first month compared with the pure Cu and H62. Furthermore, the Cu-38Zn alloy displayed significantly improved biocompatibility and a much smoother surface. Therefore, high...
Wang, K, Lu, J, Liu, A, Song, Y, Xiong, L & Zhang, G 2022, 'Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation', Neurocomputing, vol. 491, pp. 288-304.
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Wang, K, Wang, J, Zeng, B & Lu, H 2022, 'An integrated power load point-interval forecasting system based on information entropy and multi-objective optimization', Applied Energy, vol. 314, pp. 118938-118938.
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Wang, L, Wu, C, Fan, L & Wang, M 2022, 'Effective velocity of reflected wave in rock mass with different wave impedances of normal incidence of stress wave', International Journal for Numerical and Analytical Methods in Geomechanics, vol. 46, no. 9, pp. 1607-1619.
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Wang, N, Liu, ZX, Ding, C, Zhang, J-N, Sui, G-R, Jia, H-Z & Gao, X-M 2022, 'High Efficiency Thermoelectric Temperature Control System With Improved Proportional Integral Differential Algorithm Using Energy Feedback Technique', IEEE Transactions on Industrial Electronics, vol. 69, no. 5, pp. 5225-5234.
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Wang, N, Zhang, J-N, Ni, H, Jia, H-Z & Ding, C 2022, 'Improved MPPT System Based on FTSMC for Thermoelectric Generator Array Under Dynamic Temperature and Impedance', IEEE Transactions on Industrial Electronics, vol. 69, no. 10, pp. 10715-10723.
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Wang, P, Li, L, Wang, R, Zheng, X, He, J & Xu, G 2022, 'Learning persona-driven personalized sentimental representation for review-based recommendation', Expert Systems with Applications, vol. 203, pp. 117317-117317.
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Wang, P, Li, L, Xie, Q, Wang, R & Xu, G 2022, 'Social dual-effect driven group modeling for neural group recommendation', Neurocomputing, vol. 481, pp. 258-269.
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Wang, Q, Feng, Y, Wu, D, Yang, C, Yu, Y, Li, G, Beer, M & Gao, W 2022, 'Polyphase uncertainty analysis through virtual modelling technique', Mechanical Systems and Signal Processing, vol. 162, pp. 108013-108013.
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Wang, Q, Liu, D, G. Carmichael, M, Aldini, S & Lin, C-T 2022, 'Computational Model of Robot Trust in Human Co-Worker for Physical Human-Robot Collaboration', IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 3146-3153.
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Wang, Q, Zhou, Y, Cao, Z & Zhang, W 2022, 'M2SPL: Generative multiview features with adaptive meta-self-paced sampling for class-imbalance learning', Expert Systems with Applications, vol. 189, pp. 115999-115999.
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Class-imbalance learning is an important research area and draws continued attention in various real-world applications for many years. Undersampling is a key method of class-imbalance learning in order to obtain a balanced class distribution, but it may discard potentially crucial samples and may be influenced by outliers or noises in imbalanced data. Multiview learning methods have shown that models trained on different views can help each other to improve their performances and robustness, but the existing imbalance learning approaches rely only on single-view samples. In this paper, we propose a multiview feature imbalance sampling method via meta self-paced learning (M2SPL) to effectively choose high-quality samples and separate adjacent features to improve the robustness of the trained model. There are two advantages of our proposed method: (1) An adaptive reweight generation process acts as a pivotal part in our M2SPL. The adaptive density-based reweight samples learning mechanism considers noisy and intractable samples to improve the robustness of model. (2) The multiview feature learning can avoid the large value of the loss function to learn a robust model from original data, and can enhance the discrimination capability of the model. Comparison with the existing sampling approaches shows that our proposed M2SPL approach significantly improves the classification performance, with increases in the F1-score and G-mean of 15.4% and 12.5%, respectively, on average. Finally, our experimental results pass the Friedman and Holm tests, indicating that our model has a significant improvement over existing methods.
Wang, S, Cao, L, Wang, Y, Sheng, QZ, Orgun, MA & Lian, D 2022, 'A Survey on Session-based Recommender Systems', ACM Computing Surveys, vol. 54, no. 7, pp. 1-38.
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Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized economy. In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs. Different from other RSs such as content-based RSs and collaborative filtering-based RSs that usually model long-term yet static user preferences, SBRSs aim to capture short-term but dynamic user preferences to provide more timely and accurate recommendations sensitive to the evolution of their session contexts. Although SBRSs have been intensively studied, neither unified problem statements for SBRSs nor in-depth elaboration of SBRS characteristics and challenges are available. It is also unclear to what extent SBRS challenges have been addressed and what the overall research landscape of SBRSs is. This comprehensive review of SBRSs addresses the above aspects by exploring in depth the SBRS entities (e.g., sessions), behaviours (e.g., users’ clicks on items), and their properties (e.g., session length). We propose a general problem statement of SBRSs, summarize the diversified data characteristics and challenges of SBRSs, and define a taxonomy to categorize the representative SBRS research. Finally, we discuss new research opportunities in this exciting and vibrant area.
Wang, S, Cao, Y, Chen, X, Yao, L, Wang, X & Sheng, QZ 2022, 'Adversarial Robustness of Deep Reinforcement Learning Based Dynamic Recommender Systems', Frontiers in Big Data, vol. 5.
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Adversarial attacks, e.g., adversarial perturbations of the input and adversarial samples, pose significant challenges to machine learning and deep learning techniques, including interactive recommendation systems. The latent embedding space of those techniques makes adversarial attacks challenging to detect at an early stage. Recent advance in causality shows that counterfactual can also be considered one of the ways to generate the adversarial samples drawn from different distribution as the training samples. We propose to explore adversarial examples and attack agnostic detection on reinforcement learning (RL)-based interactive recommendation systems. We first craft different types of adversarial examples by adding perturbations to the input and intervening on the casual factors. Then, we augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data. Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods. Our extensive experiments show that most adversarial attacks are effective, and both attack strength and attack frequency impact the attack performance. The strategically-timed attack achieves comparative attack performance with only 1/3 to 1/2 attack frequency. Besides, our white-box detector trained with one crafting method has the generalization ability over several other crafting methods.
Wang, S, Li, Z, Cao, Z, Jolfaei, A & Cao, Q 2022, 'Jam-Absorption Driving Strategy for Improving Safety Near Oscillations in a Connected Vehicle Environment Considering Consequential Jams', IEEE Intelligent Transportation Systems Magazine, vol. 14, no. 2, pp. 41-52.
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Wang, S, Lu, J, Li, B, Liu, C, Wang, Y, Lei, G, Guo, Y & Zhu, J 2022, 'Design and analysis of mechanical flux-weakening device of axial flux permanent magnet machines', Journal of Power Electronics, vol. 22, no. 4, pp. 653-663.
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Wang, S, Tao, J, Qiu, X & Burnett, IS 2022, 'A natural ventilation window for transformer noise control based on coiled-up silencers consisting of coupled tubes', Applied Acoustics, vol. 192, pp. 108744-108744.
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Wang, S, Tao, J, Qiu, X & Burnett, IS 2022, 'Improving the performance of an active staggered window with multiple resonant absorbers', The Journal of the Acoustical Society of America, vol. 151, no. 3, pp. 1661-1671.
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The active noise control (ANC) technique has been applied in staggered windows to improve the noise reduction at low frequencies. The control performance of such a system deteriorates significantly at some frequencies where the secondary source cannot radiate effectively due to the reflection at the boundaries of the staggered window. A resonant absorber consisting of a perforated panel and coiled up tubes is proposed to solve the problem. By designing a combination of different absorbers, a proper sound absorption coefficient is achieved around the ineffective frequency. Numerical simulations show that the active sound power reduction increases by 13.5 dB at the frequency with the absorbers attached on one end of the staggered window, and the overall sound power reduction between 100 and 500 Hz increases from 25.9 to 31.2 dB. Attaching the sound absorbers elsewhere in the upstream of the secondary source, for example, on the side walls of the duct also works. The active sound power reduction at 435 Hz increases by 6.3 dB after attaching the absorbers in the experiments, and the noise reduction increment at the evaluation point is 13.6 dB, which agrees with simulation results and demonstrates the feasibility of the proposed sound absorbers.
Wang, T, Zheng, Z, Yan, C, Zhang, J, Sun, Y, Zheng, B & Yang, Y 2022, 'Each Part Matters: Local Patterns Facilitate Cross-View Geo-Localization', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 2, pp. 867-879.
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Wang, W, Zhang, Y, Sui, Y, Wan, Y, Zhao, Z, Wu, J, Yu, PS & Xu, G 2022, 'Reinforcement-Learning-Guided Source Code Summarization Using Hierarchical Attention', IEEE Transactions on Software Engineering, vol. 48, no. 1, pp. 102-119.
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Wang, X, Chen, H-T, Wang, Y-K & Lin, C-T 2022, 'Implicit Robot Control using Error-related Potential-based Brain-Computer Interface', IEEE Transactions on Cognitive and Developmental Systems, pp. 1-1.
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Wang, X, Fei, Z, Zhang, JA & Huang, J 2022, 'Sensing-Assisted Secure Uplink Communications With Full-Duplex Base Station', IEEE Communications Letters, vol. 26, no. 2, pp. 249-253.
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Wang, X, Gao, J, Chen, Z, Chen, H, Zhao, Y, Huang, Y & Chen, Z 2022, 'Evaluation of hydrous ethanol as a fuel for internal combustion engines: A review', Renewable Energy, vol. 194, pp. 504-525.
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Wang, X, Li, Q, Yu, D, Cui, P, Wang, Z & Xu, G 2022, 'Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Wang, X, Li, W, Luo, Z, Wang, K & Shah, SP 2022, 'A critical review on phase change materials (PCM) for sustainable and energy efficient building: Design, characteristic, performance and application', Energy and Buildings, vol. 260, pp. 111923-111923.
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Wang, X, Wang, Y, Cui, Q, Chen, K-C & Ni, W 2022, 'Machine Learning Enables Radio Resource Allocation in the Downlink of Ultra-Low Latency Vehicular Networks', IEEE Access, vol. 10, pp. 44710-44723.
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Wang, X, Xie, G-J, Tian, N, Dang, C-C, Cai, C, Ding, J, Liu, B-F, Xing, D-F, Ren, N-Q & Wang, Q 2022, 'Anaerobic microbial manganese oxidation and reduction: A critical review', Science of The Total Environment, vol. 822, pp. 153513-153513.
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Wang, X, Yu, G, Liu, RP, Zhang, J, Wu, Q, Su, SW, He, Y, Zhang, Z, Yu, L, Liu, T, Zhang, W, Loneragan, P, Dutkiewicz, E, Poole, E & Paton, N 2022, 'Blockchain-Enabled Fish Provenance and Quality Tracking System', IEEE Internet of Things Journal, vol. 9, no. 11, pp. 8130-8142.
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Wang, X, Zhu, L, Tang, S, Fu, H, Li, P, Wu, F, Yang, Y & Zhuang, Y 2022, 'Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images', IEEE Transactions on Image Processing, vol. 31, pp. 1107-1119.
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Wang, Y, Wei, W, Dai, X & Ni, B-J 2022, 'Corncob ash boosts fermentative hydrogen production from waste activated sludge', Science of The Total Environment, vol. 807, pp. 151064-151064.
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With the increasing demand for sustainable development, the recycling and utilization of wastes has received widespread attention. This study proposed a green method of using one waste, corncob ash, to boost microbial the production of hydrogen from another waste, waste activated sludge, during anaerobic fermentation. The corncob ash dosage and the fermentative hydrogen production was positively correlated, and the maximum production of hydrogen reached up to 46.8 ± 1.0 mL/g VS, which was about 3.5 times that of the control group without corncob ash dosage (17.0 ± 0.9 mL/g VS). Mechanistic studies found that corncob ash was beneficial to the solubilization, hydrolysis and acetogenesis processes involved in fermentative hydrogen production process. The microbial community analysis indicated that corncob ash enriched more hydrolytic microorganisms (e.g., Bacteroides sp. and Leptolinea sp.), and has less impact on acidifying microorganisms, compared to the control group. The strategy of using corncob ash to boost the production of hydrogen during anaerobic waste activated sludge fermentation proposed in this study might provide a new waste-control-waste paradigm, making sludge disposal and wastewater treatment more sustainable.
Wang, Y, Zhu, S, Shao, H, Wang, L & Wen, S 2022, 'Trade off analysis between fixed-time stabilization and energy consumption of nonlinear neural networks', Neural Networks, vol. 148, pp. 66-73.
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Wang, Y, Zhuang, J-L, Lu, Q-Q, Cui, C-Z, Liu, Y-D, Ni, B-J & Li, W 2022, 'Halophilic Martelella sp. AD-3 enhanced phenanthrene degradation in a bioaugmented activated sludge system through syntrophic interaction', Water Research, vol. 218, pp. 118432-118432.
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Wang, Z, Jin, X, Kaw, HY, Fatima, Z, Quinto, M, Zhou, JL, Jin, D, He, M & Li, D 2022, 'Tracing historical changes, degradation, and original sources of airborne polycyclic aromatic hydrocarbons (PAHs) in Jilin Province, China, by Abies holophylla and Pinus tabuliformis needle leaves', Environmental Science and Pollution Research, vol. 29, no. 5, pp. 7079-7088.
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Wang, Z, Luo, Q, Li, Q & Sun, G 2022, 'Design optimization of bioinspired helicoidal CFRPP/GFRPP hybrid composites for multiple low-velocity impact loads', International Journal of Mechanical Sciences, vol. 219, pp. 107064-107064.
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Wang, Z, Yuan, B, Huang, Y, Cao, J, Wang, Y & Cheng, X 2022, 'Progress in experimental investigations on evaporation characteristics of a fuel droplet', Fuel Processing Technology, vol. 231, pp. 107243-107243.
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Wang, Z, Zhang, JA, Xiao, F & Xu, M 2022, 'Accurate AoA Estimation for RFID Tag Array with Mutual Coupling', IEEE Internet of Things Journal, pp. 1-1.
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Wang, Z, Zhang, JA, Xu, M & Guo, J 2022, 'Single-Target Real-Time Passive WiFi Tracking', IEEE Transactions on Mobile Computing, pp. 1-1.
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Wei, W, Shi, X, Wu, L, Liu, X & Ni, B-J 2022, 'Calcium peroxide pre-treatment improved the anaerobic digestion of primary sludge and its co-digestion with waste activated sludge', Science of The Total Environment, vol. 828, pp. 154404-154404.
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Wei, W, Zhang, Y-T, Wang, C, Guo, W, Ngo, HH, Chen, X & Ni, B-J 2022, 'Responses of anaerobic hydrogen-producing granules to acute microplastics exposure during biological hydrogen production from wastewater', Water Research, vol. 220, pp. 118680-118680.
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Wen, D, Yang, B, Zhang, Y, Qin, L, Cheng, D & Zhang, W 2022, 'Span-reachability querying in large temporal graphs', The VLDB Journal, vol. 31, no. 4, pp. 629-647.
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Wen, J, Gabrys, B & Musial, K 2022, 'Toward Digital Twin Oriented Modeling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey', IEEE Access, vol. 10, pp. 66886-66923.
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Wen, L, Zhou, J, Huang, W & Chen, F 2022, 'A Survey of Facial Capture for Virtual Reality', IEEE Access, vol. 10, pp. 6042-6052.
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Wen, S, Feng, Z-K, Huang, T & Zhang, N 2022, 'Theoretical analysis of advanced intelligent computing in environmental research', Environmental Research Letters, vol. 17, no. 4, pp. 040401-040401.
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Wen, S, Huang, T, Schuller, BW & Taher Azar, A 2022, 'Guest Editorial: Introduction to the Special Section on Efficient Network Design for Convergence of Deep Learning and Edge Computing', IEEE Transactions on Network Science and Engineering, vol. 9, no. 1, pp. 109-110.
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Wen, S, Li, D, Liu, Y, Chen, C, Wang, F, Zhou, J, Bao, G, Zhang, L & Jin, D 2022, 'Power-Dependent Optimal Concentrations of Tm3+ and Yb3+ in Upconversion Nanoparticles', The Journal of Physical Chemistry Letters, pp. 5316-5323.
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Lanthanide-doped upconversion nanoparticles (UCNPs) have enabled a broad range of emerging nanophotonics and biophotonics applications. Here, we provide a quantitative guide to the optimum concentrations of Yb3+ sensitizer and Tm3+ emitter ions, highly dependent on the excitation power densities. To achieve this, we fabricate the inert-core@active-shell@inert-shell architecture to sandwich the same volume of the optically active section. Our results show that highly doped UCNPs enable an approximately 18-fold enhancement in brightness over that of conventional ones. Increasing the Tm3+ concentration improves the brightness by 6 times and increases the NIR/blue ratio by 11 times, while the increase of Yb3+ concentration enhances the brightness by 3 times and only slightly affects the NIR/blue ratio. Moreover, the optimal doping concentration of Tm3+ varies from 2% to 16%, which is highly dependent on the excitation power density ranging from 102 to 107 W/cm2. This work provides a guideline for designing bright UCNPs under different excitation conditions.
Wen, Y, Liu, B, Ding, M, Xie, R & Song, L 2022, 'IdentityDP: Differential private identification protection for face images', Neurocomputing, vol. 501, pp. 197-211.
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Wen, Y, Qin, P-Y, Wei, G-M & Ziolkowski, RW 2022, 'Circular Array of Endfire Yagi-Uda Monopoles with A Full 3600 Azimuthal Beam Scanning', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Why, ESK, Ong, HC, Lee, HV, Chen, W-H, Asikin-Mijan, N, Varman, M & Loh, WJ 2022, 'Single-step catalytic deoxygenation of palm feedstocks for the production of sustainable bio-jet fuel', Energy, vol. 239, pp. 122017-122017.
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Whyte, T, Lind, E, Richards, A, Eager, D, Bilston, LE & Brown, J 2022, 'Neck Loads During Head-First Entries into Trampoline Dismount Foam Pits: Considerations for Trampoline Park Safety', Annals of Biomedical Engineering, vol. 50, no. 6, pp. 691-702.
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AbstractSerious cervical spine injuries have been documented from falls into foam pits at trampoline parks. To address the lack of evidence on how foam pits should be designed for mitigating neck injury risk, this study aimed to quantify neck loads during head-first entry into varying foam pit designs. An instrumented Hybrid III anthropomorphic test device was dropped head-first from a height of up to 1.5 m into three differently constructed foam pits, each using a different mechanism to prevent direct contact between the falling person and the floor (foam slab, trampoline or net bed). Measured neck loads were compared to published injury reference values. In the simplest, foam-only pit design, increasing foam depth tended to reduce peak compressive force. At least one injury assessment reference metric was exceeded in all pit conditions tested for 1.5 m falls, most commonly the time-dependent neck compression criterion. The results highlight the importance of adequate foam depth in combination with appropriate pit design in minimizing injury risk. The risk of cervical spine injury may not be reduced sufficiently with current foam pit designs.
Wickramanayake, S, Thiyagarajan, K & Kodagoda, S 2022, 'Deep Learning for Estimating Low-Range Concrete Sub-Surface Boundary Depths Using Ground Penetrating Radar Signals', IEEE Sensors Letters, vol. 6, no. 3, pp. 1-4.
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Williams, P, Kirby, R & Karimi, M 2022, 'Sound power radiated from acoustically thick, fluid loaded, axisymmetric pipes excited by a central monopole', Journal of Sound and Vibration, vol. 527, pp. 116843-116843.
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Wisanmongkol, J, Taparugssanagorn, A, Tran, LC, Le, AT, Huang, X, Ritz, C, Dutkiewicz, E & Phung, SL 2022, 'An ensemble approach to deep‐learning‐based wireless indoor localization', IET Wireless Sensor Systems, vol. 12, no. 2, pp. 33-55.
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Wooster, EIF, Fleck, R, Torpy, F, Ramp, D & Irga, PJ 2022, 'Urban green roofs promote metropolitan biodiversity: A comparative case study', Building and Environment, vol. 207, pp. 108458-108458.
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Wu, C, Xia, Y & Bi, K 2022, 'Guest editorial', Advances in Structural Engineering, vol. 25, no. 7, pp. 1371-1372.
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Wu, H-L, Chong, Y-H, Ong, H-C & Shu, C-M 2022, 'Thermal stability of modified lithium-ion battery electrolyte by flame retardant, tris (2,2,2-trifluoroethyl) phosphite', Journal of Thermal Analysis and Calorimetry, vol. 147, no. 6, pp. 4245-4252.
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With the increasing awareness of green energy, electric vehicles have become the future trend, with lithium-ion batteries (LIBs) regarded as the most suitable energy storage carrier. Therefore, more and more research topics are focused on LIBs, and all parties are working hard to improve the performance of LIBs. Yet, the safety concerns caused by the failure of LIBs cannot be ignored. LIBs themselves are energetic materials, and the causes of accidents often go through multistage irreversible reactions. Several studies have also pointed out that the electrolyte has a significant correlation with the response characteristics because, in the process of LIBs thermal runaway, the electrolyte participating in the oxidation of the entire battery leads to a considerable amount of heat and even runaway reaction as well. Accordingly, it is necessary to obtain a safer electrolyte by modification. In this study, a significant flame retardant (FR) additive, tris (2,2,2-trifluoroethyl) phosphite (TTFP), is used to suppress lithium-ion battery fires or even explosions and maintain typical battery performance. The performance of the electrolyte was tested by differential scanning calorimetry and thermogravimetric analyzer, and the electrolysis was examined on liquid flash point (FP), self-extinguishing time (SET), and conductivity. During the heating process, adding TTFP to the electrolyte effectively delayed the exothermic peak, reduced the amount of heat, improved the FP, and curtailed the SET. The hazard degree of the electrolyte under high-temperature environment was much lower than before adding the additives, and the additives were finally obtained. It can conclusively prove the safety of lithium batteries without lessening the practical performance of the batteries.
Wu, K, Andrew Zhang, J, Huang, X & Jay Guo, Y 2022, 'Integrating Secure Communications into Frequency Hopping MIMO Radar with Improved Data Rate', IEEE Transactions on Wireless Communications, pp. 1-1.
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Wu, K, Zhang, JA, Huang, X & Guo, YJ 2022, 'Frequency-Hopping MIMO Radar-Based Communications: An Overview', IEEE Aerospace and Electronic Systems Magazine, vol. 37, no. 4, pp. 42-54.
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Wu, K, Zhang, JA, Huang, X & Guo, YJ 2022, 'Integrating Low-Complexity and Flexible Sensing Into Communication Systems', IEEE Journal on Selected Areas in Communications, vol. 40, no. 6, pp. 1873-1889.
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Wu, L, Wang, L, Wei, W, Song, L & Ni, B 2022, 'Sulfur‐driven autotrophic denitrification of nitric oxide for efficient nitrous oxide recovery', Biotechnology and Bioengineering, vol. 119, no. 1, pp. 257-267.
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Wu, L, Wang, L-K, Wei, W & Ni, B-J 2022, 'Autotrophic denitrification of NO for effectively recovering N2O through using thiosulfate as sole electron donor', Bioresource Technology, vol. 347, pp. 126681-126681.
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Wu, S-L, Wei, W, Wang, Y, Song, L & Ni, B-J 2022, 'Transforming waste activated sludge into medium chain fatty acids in continuous two-stage anaerobic fermentation: Demonstration at different pH levels', Chemosphere, vol. 288, pp. 132474-132474.
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Bioenergy recovery in the form of medium-chain fatty acids (MCFAs) from waste activated sludge (WAS) is increasingly attractive, which are valuable building blocks for fuel production. This study experimentally demonstrated the long-term MCFAs (C6–C8) production from WAS in two-stage anaerobic sludge fermentation at different pH conditions, using continuously operated bench-scale anaerobic reactors. The WAS was continuously converted to short chain fatty acids (SCFAs, 3500–3800 mg chemical oxygen demand (COD)/L) at the first stage via alkaline anaerobic fermentation, which was directly fed into the second stage as both substrates and inoculum for MCFAs production through chain elongation (CE). The productions of MCFAs at the second stage were continuously studied under three different pH conditions (i.e., 10, 7 and 5.5). The results demonstrated that there was no significant MCFAs production at pH 10 during the steady state, whereas the MCFAs productions were clearly observed at both pH 7 and pH 5.5, with much higher MCFAs production from WAS at pH 7 (i.e., 10.32 g COD/L MCFAs) than that at pH 5.5 (i.e., 8.73 g COD/L MCFAs) during the steady state. A higher MCFAs selectivity of 62.3% was also achieved at pH 7. The relatively lower MCFAs production and selectivity at pH 5.5 was likely due to the higher undissociated MCFAs generated at pH 5.5, which would pose toxicity impact on CE microbes and thus inhibit the CE process. Microbial community analysis confirmed that the relative abundances of CE related microbes (e.g., Clostridium sensu stricto 12 sp. and Clostridium sensu stricto 1) increased at pH 7 compared to those at pH 5.5, which enabled more efficient MCFAs production from WAS.
Wu, Y, Jiang, L & Yang, Y 2022, 'Switchable Novel Object Captioner', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1.
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Wu, Z, Khalilpour, K & Hämäläinen, RP 2022, 'A decision support tool for multi-attribute evaluation of demand-side commercial battery storage products', Sustainable Energy Technologies and Assessments, vol. 50, pp. 101723-101723.
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Wu, Z, Tang, M-C & Ziolkowski, RW 2022, 'Broadside Radiating, Low-Profile, Electrically Small, Huygens Dipole Filtenna', IEEE Antennas and Wireless Propagation Letters, vol. 21, no. 3, pp. 556-560.
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Wu, Z-Y, Xu, J, Wu, L & Ni, B-J 2022, 'Three-dimensional biofilm electrode reactors (3D-BERs) for wastewater treatment', Bioresource Technology, vol. 344, pp. 126274-126274.
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Xia, J, Zhang, H, Wen, S, Yang, S & Xu, M 2022, 'An efficient multitask neural network for face alignment, head pose estimation and face tracking', Expert Systems with Applications, vol. 205, pp. 117368-117368.
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Xiao, B, Walker, PD, Zhou, S, Yang, W & Zhang, N 2022, 'A Power Consumption and Total Cost of Ownership Analysis of Extended Range System for a Logistics Van', IEEE Transactions on Transportation Electrification, vol. 8, no. 1, pp. 72-81.
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Xiao, J, Zhong, S & Wen, S 2022, 'Unified Analysis on the Global Dissipativity and Stability of Fractional-Order Multidimension-Valued Memristive Neural Networks With Time Delay', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-10.
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The unified criteria are analyzed on the global dissipativity and stability for the delayed fractional-order systems of multidimension-valued memristive neural networks (FSMVMNNs) in this article. First, based on the comprehensive knowledge about multidimensional algebra, fractional derivatives, and nonsmooth analysis, we establish the unified model for the studied FSMVMNNs in order to propose a more uniform method to analyze the dynamic behaviors of multidimensional neural networks. Then, by mainly applying the Lyapunov method, employing several new lemmas, and solving some mathematical difficulties, without any separation, we acquire the unified and concise criteria. The derived criteria have many advantages in a smaller calculation, lower conservatism, more diversity, and higher flexibility. Finally, we provide two numerical examples to express the availability and improvements of the theoretical results.
Xiao, M, Li, H, Huang, Q, Yu, S & Susilo, W 2022, 'Attribute-Based Hierarchical Access Control With Extendable Policy', IEEE Transactions on Information Forensics and Security, vol. 17, pp. 1868-1883.
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Xiao, T, Halkon, B & Oberst, S 2022, 'Error Sensors in Active Noise Control at Openings and Use of Refracto-vibrometry'.
Xie, J, Liu, C-H, Huang, Y & Mok, W-C 2022, 'Effect of sampling duration on the estimate of pollutant concentration behind a heavy-duty vehicle: A large-eddy simulation', Environmental Pollution, vol. 305, pp. 119132-119132.
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Xing, L, Yang, J, Ni, B-J, Yang, C, Yuan, C & Li, A 2022, 'Insight into the generation and consumption mechanism of tightly bound and loosely bound extracellular polymeric substances by mathematical modeling', Science of The Total Environment, vol. 811, pp. 152359-152359.
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Xiong, H, Huang, X, Yang, M, Wang, L & Yu, S 2022, 'Unbounded and Efficient Revocable Attribute-Based Encryption With Adaptive Security for Cloud-Assisted Internet of Things', IEEE Internet of Things Journal, vol. 9, no. 4, pp. 3097-3111.
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Xiong, H, Yao, T, Wang, H, Feng, J & Yu, S 2022, 'A Survey of Public-Key Encryption With Search Functionality for Cloud-Assisted IoT', IEEE Internet of Things Journal, vol. 9, no. 1, pp. 401-418.
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Xu, B, Qiu, W, Du, J, Wan, Z, Zhou, JL, Chen, H, Liu, R, Magnuson, JT & Zheng, C 2022, 'Translocation, bioaccumulation, and distribution of perfluoroalkyl and polyfluoroalkyl substances (PFASs) in plants', iScience, vol. 25, no. 4, pp. 104061-104061.
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Xu, B-H, Indraratna, B, Rujikiatkamjorn, C & Trung Nguyen, T 2022, 'A large-strain radial consolidation model incorporating soil destructuration and isotache concept', Computers and Geotechnics, vol. 147, pp. 104761-104761.
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Xu, C, Jia, W, Cui, T, Wang, R, Zhang, Y-F & He, X 2022, 'Arbitrary-shape Scene Text Detection via Visual-Relational Rectification and Contour Approximation', IEEE Transactions on Multimedia, pp. 1-1.
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Xu, J, Zhang, B, Wang, Z, Wang, Y, Chen, F, Gao, J & Feng, DD 2022, 'Affective Audio Annotation of Public Speeches with Convolutional Clustering Neural Network', IEEE Transactions on Affective Computing, vol. 13, no. 1, pp. 238-249.
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Xu, L, Cao, X, Yao, J & Yan, Z 2022, 'Orthogonal Super Greedy Learning for Sparse Feedforward Neural Networks', IEEE Transactions on Network Science and Engineering, vol. 9, no. 1, pp. 161-170.
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Xu, Q, Su, Z, Yu, S & Wang, Y 2022, 'Trust Based Incentive Scheme to Allocate Big Data Tasks with Mobile Social Cloud', IEEE Transactions on Big Data, vol. 8, no. 1, pp. 113-124.
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Xu, R-Z, Cao, J-S, Feng, G, Luo, J-Y, Feng, Q, Ni, B-J & Fang, F 2022, 'Fast identification of fluorescent components in three-dimensional excitation-emission matrix fluorescence spectra via deep learning', Chemical Engineering Journal, vol. 430, pp. 132893-132893.
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Xu, R-Z, Cao, J-S, Luo, J-Y, Feng, Q, Ni, B-J & Fang, F 2022, 'Integrating mechanistic and deep learning models for accurately predicting the enrichment of polyhydroxyalkanoates accumulating bacteria in mixed microbial cultures', Bioresource Technology, vol. 344, pp. 126276-126276.
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Xu, S, Yang, Y, Wu, C & Liu, K 2022, 'Electromagnetic wave absorption performance of UHPC incorporated with carbon black and carbon fiber', Archives of Civil and Mechanical Engineering, vol. 22, no. 2.
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Xu, S, Zhang, J, Qin, X, Xiao, Y, Qian, J, Bo, L, Zhang, H, Li, H & Zhong, Z 2022, 'Deep retinex decomposition network for underwater image enhancement', Computers and Electrical Engineering, vol. 100, pp. 107822-107822.
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Xu, T, Li, Y & Leng, D 2022, 'Mitigating jacket offshore platform vibration under earthquake and ocean waves utilizing tuned inerter damper', Bulletin of Earthquake Engineering.
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AbstractThe unwanted vibrations of offshore structures induced by wave or earthquake loads can lead to the reduction of the service life and fatigue failure of the offshore platforms. This paper introduces tuned inerter damper (TID) to a jacket offshore platform as passive control device for mitigating the excessive vibrations of platform structure induced by wave and earthquake loads. An analytical design method is proposed for jacket platforms and the influence of installation location on the modal response is investigated. The proposed design method can determine the optimal installation position and obtain the optimal design parameters by transform the original multi-degree of freedom (MDOF) system to a single DOF (SDOF) modal system. Two sets of closed-form solutions of which corresponding to wave and earthquake excitations are derived based on the $${\mathrm{H}}_{2}$$
H
2
optimization criterion. Further, a practical 90 (m) high and 80 (m) deep in-water jacket offshore platform is used in numerical simulation and the wave forces are modeled using Morison’s equation. The case study finds that the optimal installation location of TID is deck level for both wave and earthquake loads. The proposed design method is validated by the numerical example and the results demonstrate that TID system can effectively mitigate the maximum, minimum, and RMS responses of jacket platforms. Besides, the TID is more effective when the jacket platform is under the action of waves and the tuning of TID according to earthquake load is more reliable when the jacket platform subjected to both wave and seismic loads.
Xu, T, Yang, G, Li, Y & Lai, T 2022, 'Influence of inerter‐based damper installations on control efficiency of building structures', Structural Control and Health Monitoring, vol. 29, no. 5.
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Xu, X, Xu, G, Chen, J, Liu, Z, Chen, X, Zhang, Y, Fang, J & Gao, Y 2022, 'Multi-objective design optimization using hybrid search algorithms with interval uncertainty for thin-walled structures', Thin-Walled Structures, vol. 175, pp. 109218-109218.
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Xu, X, Zhang, Y, Fang, J, Chen, X, Liu, Z, Xu, Y & Gao, Y 2022, 'Axial mechanical properties and robust optimization of foam-filled hierarchical structures', Composite Structures, vol. 289, pp. 115501-115501.
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Xu, Y, Yu, X, Zhang, J, Zhu, L & Wang, D 2022, 'Weakly Supervised RGB-D Salient Object Detection With Prediction Consistency Training and Active Scribble Boosting', IEEE Transactions on Image Processing, vol. 31, pp. 2148-2161.
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Xu, Z, Li, J, Qian, H & Wu, C 2022, 'Blast resistance of hybrid steel and polypropylene fibre reinforced ultra-high performance concrete after exposure to elevated temperatures', Composite Structures, vol. 294, pp. 115771-115771.
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Xu, Z, Ren, H, Liang, W, Xia, Q, Zhou, W, Zhou, P, Xu, W, Wu, G & Li, M 2022, 'Near Optimal Learning-Driven Mechanisms for Stable NFV Markets in Multitier Cloud Networks', IEEE/ACM Transactions on Networking, pp. 1-15.
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Xu, Z, Zhou, L, Dai, H, Liang, W, Zhou, W, Zhou, P, Xu, W & Wu, G 2022, 'Energy-Aware Collaborative Service Caching in a 5G-Enabled MEC With Uncertain Payoffs', IEEE Transactions on Communications, vol. 70, no. 2, pp. 1058-1071.
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Yadav, S, Ibrar, I, Al-Juboori, RA, Singh, L, Ganbat, N, Kazwini, T, Karbassiyazdi, E, Samal, AK, Subbiah, S & Altaee, A 2022, 'Updated review on emerging technologies for PFAS contaminated water treatment', Chemical Engineering Research and Design, vol. 182, pp. 667-700.
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Yadav, S, Ibrar, I, Altaee, A, Samal, AK & Zhou, J 2022, 'Surface modification of nanofiltration membrane with kappa-carrageenan/graphene oxide for leachate wastewater treatment', Journal of Membrane Science, pp. 120776-120776.
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Yadav, S, Ibrar, I, Altaee, A, Samal, AK, Karbassiyazdi, E, Zhou, J & Bartocci, P 2022, 'High-Performance mild annealed CNT/GO-PVA composite membrane for brackish water treatment', Separation and Purification Technology, vol. 285, pp. 120361-120361.
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Yadav, S, Ibrar, I, Samal, AK, Altaee, A, Déon, S, Zhou, J & Ghaffour, N 2022, 'Preparation of fouling resistant and highly perm-selective novel PSf/GO-vanillin nanofiltration membrane for efficient water purification', Journal of Hazardous Materials, vol. 421, pp. 126744-126744.
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To meet the rising global demand for water, it is necessary to develop membranes capable of efficiently purifying contaminated water sources. Herein, we report a series of novel polysulfone (PSf)/GO-vanillin nanofiltration membranes highly permeable, selective, and fouling resistant. The membranes are composed of two-dimensional (2D) graphite oxide (GO) layers embedded with vanillin as porogen and PSf as the base polymer. There is a growing interest in addressing the synergistic effect of GO and vanillin on improving the permeability and antifouling characteristics of membranes. Various spectroscopic and microscopic techniques were used to perform detailed physicochemical and morphological analyses. The optimized PSf16/GO0.15-vanillin0.8 membrane demonstrated 92.5% and 25.4% rejection rate for 2000 ppm magnesium sulphate (MgSO4) and sodium chloride (NaCl) solutions respectively. Antifouling results showed over 99% rejection for BSA and 93.57% flux recovery ratio (FRR). Experimental work evaluated the antifouling characteristics of prepared membranes to treat landfill leachate wastewater. The results showed 84-90% rejection for magnesium (Mg+2) and calcium (Ca+2) with 90.32 FRR. The study experimentally demonstrated that adding GO and vanillin to the polymeric matrix significantly improves fouling resistance and membrane performance. Future research will focus on molecular sieving for industrial separations and other niche applications using mixed matrix membranes.
Yan, C, Chang, X, Luo, M, Liu, H, Zhang, X & Zheng, Q 2022, 'Semantics-Guided Contrastive Network for Zero-Shot Object detection', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1.
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Yan, Y, Zhuang, N, Ni, B, Zhang, J, Xu, M, Zhang, Q, Zhang, Z, Cheng, S, Tian, Q, Xu, Y, Yang, X & Zhang, W 2022, 'Fine-Grained Video Captioning via Graph-based Multi-Granularity Interaction Learning', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 2, pp. 666-683.
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Yang, C, Wang, X, Yao, L, Long, G, Jiang, J & Xu, G 2022, 'Attentional Gated Res2Net for Multivariate Time Series Classification', Neural Processing Letters.
Yang, G, Guan, R, Zhen, H, Ou, K, Fang, J, Li, D-S, Fu, Q & Sun, Y 2022, 'Tunable Size of Hierarchically Porous Alumina Ceramics Based on DIW 3D Printing Supramolecular Gel', ACS Applied Materials & Interfaces, vol. 14, no. 8, pp. 10998-11005.
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Yang, G, Qin, L, Li, M, Ou, K, Fang, J, Fu, Q & Sun, Y 2022, 'Shear-induced alignment in 3D-printed nitrile rubber-reinforced glass fiber composites', Composites Part B: Engineering, vol. 229, pp. 109479-109479.
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Yang, H, Chen, L, Pan, S, Wang, H & Zhang, P 2022, 'Discrete embedding for attributed graphs', Pattern Recognition, vol. 123, pp. 108368-108368.
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Yang, K, Lu, J, Wan, W, Zhang, G & Hou, L 2022, 'Transfer learning based on sparse Gaussian process for regression', Information Sciences, vol. 605, pp. 286-300.
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Yang, M, Sharma, D, Shi, X, Mamaril, K, Jiang, H & Candlin, A 2022, 'Power connectivity in the Greater Mekong Subregion (GMS) – The need for a wider discourse', Energy Policy, vol. 165, pp. 112994-112994.
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Yang, M, Shi, X, Zhou, Y, Xiang, J & Zhang, R 2022, 'Deepening regional power connectivity: Beyond the industry-centric perspective', Energy Research & Social Science, vol. 90, pp. 102614-102614.
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Yang, M, Zhang, X, Yang, Y, Liu, Q, Nghiem, LD, Guo, W & Ngo, HH 2022, 'Effective destruction of perfluorooctanoic acid by zero-valent iron laden biochar obtained from carbothermal reduction: Experimental and simulation study', Science of The Total Environment, vol. 805, pp. 150326-150326.
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Yang, T, Miro, JV, Wang, Y & Xiong, R 2022, 'Optimal Task-Space Tracking With Minimum Manipulator Reconfiguration', IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 5079-5086.
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Yang, W, Wang, S, Yin, X, Wang, X & Hu, J 2022, 'A Review on Security Issues and Solutions of the Internet of Drones', IEEE Open Journal of the Computer Society, pp. 1-15.
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Yang, X, Liu, W & Liu, W 2022, 'Tensor Canonical Correlation Analysis Networks for Multi-View Remote Sensing Scene Recognition', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 6, pp. 2948-2961.
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Yang, X, Zhang, X, Ngo, HH, Guo, W, Huo, J, Du, Q, Zhang, Y, Li, C & Yang, F 2022, 'Sorptive removal of ibuprofen from water by natural porous biochar derived from recyclable plane tree leaf waste', Journal of Water Process Engineering, vol. 46, pp. 102627-102627.
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Yang, Y, Chen, P & Sun, H 2022, 'Incorporating pixel proximity into answer aggregation for crowdsourced image segmentation', CCF Transactions on Pervasive Computing and Interaction, vol. 4, no. 2, pp. 172-187.
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Yang, Y, Liu, S, Dong, Z, Huang, Z, Lu, C, Wu, Y, Gao, M, Liu, Y & Pan, H 2022, 'Hierarchical conformal coating enables highly stable microparticle Si anodes for advanced Li-ion batteries', Applied Materials Today, vol. 26, pp. 101403-101403.
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Yang, Y, Wu, C, Liu, Z & Zhang, H 2022, '3D-printing ultra-high performance fiber-reinforced concrete under triaxial confining loads', Additive Manufacturing, vol. 50, pp. 102568-102568.
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Yang, Y, Wu, C, Liu, Z, Li, J, Yang, T & Jiang, X 2022, 'Characteristics of 3D-printing ultra-high performance fibre-reinforced concrete under impact loading', International Journal of Impact Engineering, vol. 164, pp. 104205-104205.
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Yang, Y, Wu, C, Liu, Z, Wang, H & Ren, Q 2022, 'Mechanical anisotropy of ultra-high performance fibre-reinforced concrete for 3D printing', Cement and Concrete Composites, vol. 125, pp. 104310-104310.
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Yang, Y, Zhang, X, Ngo, HH, Guo, W, Li, Z, Wang, X, Zhang, J & Long, T 2022, 'A new spent coffee grounds based biochar - Persulfate catalytic system for enhancement of urea removal in reclaimed water for ultrapure water production', Chemosphere, vol. 288, pp. 132459-132459.
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Yang, Y, Zhao, L & Liu, X 2022, 'Iterative Zero-Shot Localization via Semantic-Assisted Location Network', IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 5974-5981.
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Yao, H, Liu, C, Zhang, P, Wu, S, Jiang, C & Yu, S 2022, 'Identification of Encrypted Traffic Through Attention Mechanism Based Long Short Term Memory', IEEE Transactions on Big Data, vol. 8, no. 1, pp. 241-252.
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Yao, L, Kusakunniran, W, Wu, Q, Xu, J & Zhang, J 2022, 'Collaborative Feature Learning for Gait Recognition Under Cloth Changes', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 6, pp. 3615-3629.
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Since gait can be utilized to identify individuals from a far distance without their interaction and coordination, recently many gait recognition methods have been proposed. However, due to a real-world scenario of clothing changes, a degradation occurs for most of these methods. Thus in this paper, a more efficient gait recognition method is proposed to address the problem of clothing variances. First, part-based gait features are formulated from two different perspectives, i.e., the separated body parts that are more robust to clothing changes and the estimated human skeleton key-point regions. It is reasonable to formulate such features for cloth-changing gait recognition, because these two perspectives are both less vulnerable to clothing changes. Given that each feature has its own advantages and disadvantages, a more efficient gait feature is generated in this paper by assembling these two features together. Moreover, since local features are more discriminative than global features, in this paper more attention is focused on the local short-range features. Also, unlike most methods, in our method we treat the estimated key-point features as a set of word embeddings, and a transformer encoder is specifically used to learn the dependence of each correlative key-points. The robustness and effectiveness of our proposed method are certified by experiments on CASIA Gait Dataset B, and it has achieved the state-of-the-art performance on this dataset.
Yao, L, Kusakunniran, W, Wu, Q, Xu, J & Zhang, J 2022, 'Recognizing Gaits Across Walking and Running Speeds', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 18, no. 3, pp. 1-22.
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For decades, very few methods were proposed for cross-mode (i.e., walking vs. running) gait recognition. Thus, it remains largely unexplored regarding how to recognize persons by the way they walk and run. Existing cross-mode methods handle the walking-versus-running problem in two ways, either by exploring the generic mapping relation between walking and running modes or by extracting gait features which are non-/less vulnerable to the changes across these two modes. However, for the first approach, a mapping relation fit for one person may not be applicable to another person. There is no generic mapping relation given that walking and running are two highly self-related motions. The second approach does not give more attention to the disparity between walking and running modes, since mode labels are not involved in their feature learning processes. Distinct from these existing cross-mode methods, in our method, mode labels are used in the feature learning process, and a mode-invariant gait descriptor is hybridized for cross-mode gait recognition to handle this walking-versus-running problem. Further research is organized in this article to investigate the disparity between walking and running. Running is different from walking not only in the speed variances but also, more significantly, in prominent gesture/motion changes. According to these rationales, in our proposed method, we give more attention to the differences between walking and running modes, and a robust gait descriptor is developed to hybridize the mode-invariant spatial and temporal features. Two multi-task learning-based networks are proposed in this method to explore these mode-invariant features. Spatial features describe the body parts non-/less affected by mode changes, and temporal features depict the instinct motion relation of each person. Mode labels are also adopted in the training phase to guide the network to give more attention to the disparity across walking and running modes...
Yazdani, D, Yazdani, D, Branke, J, Omidvar, MN, Gandomi, AH & Yao, X 2022, 'Robust Optimization Over Time by Estimating Robustness of Promising Regions', IEEE Transactions on Evolutionary Computation, pp. 1-1.
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Ye, D, Shen, S, Zhu, T, Liu, B & Zhou, W 2022, 'One Parameter Defense—Defending Against Data Inference Attacks via Differential Privacy', IEEE Transactions on Information Forensics and Security, vol. 17, pp. 1466-1480.
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Ye, D, Zhu, T, Cheng, Z, Zhou, W & Yu, PS 2022, 'Differential Advising in Multiagent Reinforcement Learning', IEEE Transactions on Cybernetics, vol. 52, no. 6, pp. 5508-5521.
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Ye, D, Zhu, T, Zhu, C, Zhou, W & Yu, PS 2022, 'Model-Based Self-Advising for Multi-Agent Learning', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12.
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Ye, K & Ji, JC 2022, 'An origami inspired quasi-zero stiffness vibration isolator using a novel truss-spring based stack Miura-ori structure', Mechanical Systems and Signal Processing, vol. 165, pp. 108383-108383.
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Ye, Q, Bie, H, Li, K-C, Fan, X, Gong, L, He, X & Fang, G 2022, 'EdgeLoc: A Robust and Real-Time Localization System Toward Heterogeneous IoT Devices', IEEE Internet of Things Journal, vol. 9, no. 5, pp. 3865-3876.
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Indoor localization has become an essential demand driven by indoor location-based services (ILBSs) for mobile users. With the rising of Internet of Things (IoT), heterogeneous smartphones and wearables have become ubiquitous. However, the ILBSs for heterogeneous IoT devices confront significant challenges, such as received signal strength (RSS) variances caused by hardware heterogeneity, multipath reflections from complex environments and localization time restricted by computation resources. This paper proposes EdgeLoc, a robust and real-time indoor localization system towards heterogeneous IoT devices to solve the above challenges. In particular, the RSS fingerprinting data of Wi-Fi is employed for localization and tackling the heterogeneity of IoT devices in two folds. First, feature-level and signal-level solutions are presented to address the random RSS variances. At the feature level, this works proposes a novel capsule neural network model to efficiently extract incremental features from RSS fingerprinting data. At the signal level, a multi-step dataflow is further devised to process RSS fingerprints into image-like data, which utilizes the feature matrix to reduce absolute sensing errors introduced by hardware heterogeneity. Second, an edge-IoT framework is designed to utilize the edge server to train the deep learning model and further supports real-time localization for heterogeneous IoT devices. Extensive field experiments with over 33,600 data points are conducted to validate the effectiveness of EdgeLoc with a large-scale Wi-Fi fingerprint dataset. The results show that EdgeLoc outperforms the state-of-the-art SAE-CNN method in localization accuracy by up to 14.4%, with an average error of 0.68 m and an average positioning time of 2.05 ms.
Ye, Q, Fan, X, Bie, H, Puthal, D, Wu, T, Song, X & Fang, G 2022, 'SE-Loc: Security-Enhanced Indoor Localization with Semi-Supervised Deep Learning', IEEE Transactions on Network Science and Engineering, pp. 1-1.
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Ye, Y, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Varjani, S, Liu, Q, Bui, XT & Hoang, NB 2022, 'Bio-membrane integrated systems for nitrogen recovery from wastewater in circular bioeconomy', Chemosphere, vol. 289, pp. 133175-133175.
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Ying, M, Zhou, L, Li, Y & Feng, Y 2022, 'A proof system for disjoint parallel quantum programs', Theoretical Computer Science, vol. 897, pp. 164-184.
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Youssef, AM, Pradhan, B, Dikshit, A, Al-Katheri, MM, Matar, SS & Mahdi, AM 2022, 'Landslide susceptibility mapping using CNN-1D and 2D deep learning algorithms: comparison of their performance at Asir Region, KSA', Bulletin of Engineering Geology and the Environment, vol. 81, no. 4.
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Yu, E, Ma, J, Sun, J, Chang, X, Zhang, H & Hauptmann, AG 2022, 'Deep Discrete Cross-Modal Hashing with Multiple Supervision', Neurocomputing, vol. 486, pp. 215-224.
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Yu, E, Song, Y, Zhang, G & Lu, J 2022, 'Learn-to-adapt: Concept drift adaptation for hybrid multiple streams', Neurocomputing, vol. 496, pp. 121-130.
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Yu, H, Li, W, Chen, C, Liang, J, Gui, W, Wang, M & Chen, H 2022, 'Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis', Engineering with Computers, vol. 38, no. S1, pp. 743-771.
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The Fruit Fly Optimization Algorithm (FOA) is a recent algorithm inspired by the foraging behavior of fruit fly populations. However, the original FOA easily falls into the local optimum in the process of solving practical problems, and has a high probability of escaping from the optimal solution. In order to improve the global search capability and the quality of solutions, a dynamic step length mechanism, abandonment mechanism and Gaussian bare-bones mechanism are introduced into FOA, termed as BareFOA. Firstly, the random and ambiguous behavior of fruit flies during the olfactory phase is described using the abandonment mechanism. The search range of fruit fly populations is automatically adjusted using an update strategy with dynamic step length. As a result, the convergence speed and convergence accuracy of FOA have been greatly improved. Secondly, the Gaussian bare-bones mechanism that overcomes local optimal constraints is introduced, which greatly improves the global search capability of the FOA. Finally, 30 benchmark functions for CEC2017 and seven engineering optimization problems are experimented with and compared to the best-known solutions reported in the literature. The computational results show that the BareFOA not only significantly achieved the superior results on the benchmark problems than other competitive counterparts, but also can offer better results on the engineering optimization design problems.
Yu, H, Lu, J & Zhang, G 2022, 'Continuous Support Vector Regression for Nonstationary Streaming Data', IEEE Transactions on Cybernetics, vol. 52, no. 5, pp. 3592-3605.
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Quadratic programming is the process of solving a special type of mathematical optimization problem. Recent advances in online solutions for quadratic programming problems (QPPs) have created opportunities to widen the scope of applications for support vector regression (SVR). In this vein, efforts to make SVR compatible with streaming data have been met with substantial success. However, streaming data with concept drift remain problematic because the trained prediction function in SVR tends to drift as the data distribution drifts. Aiming to contribute a solution to this aspect of SVR's advancement, we have developed continuous SVR (C-SVR) to solve regression problems with nonstationary streaming data, that is, data where the optimal input-output prediction function can drift over time. The basic idea of C-SVR is to continuously learn a series of input-output functions over a series of time windows to make predictions about different periods. However, strikingly, the learning process in different time windows is not independent. An additional similarity term in the QPP, which is solved incrementally, threads the various input-output functions together by conveying some learned knowledge through consecutive time windows. How much learned knowledge is transferred is determined by the extent of the concept drift. Experimental evaluations with both synthetic and real-world datasets indicate that C-SVR has better performance than most existing methods for nonstationary streaming data regression.
Yu, H, Lu, J & Zhang, G 2022, 'Topology Learning-Based Fuzzy Random Neural Networks for Streaming Data Regression', IEEE Transactions on Fuzzy Systems, vol. 30, no. 2, pp. 412-425.
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IEEE As a type of evolving-fuzzy system, the evolving-fuzzy-neuro (EFN) system uses the structure inspired by neural networks to determine its parameters (fuzzy sets and fuzzy rules), so EFN system can inherit the advantages of neural networks. However, for streaming data regression, EFN systems still have several drawbacks: 1) determining fuzzy sets is not robust to data sequence; 2) determining fuzzy rules is complex due to subspaces that can approximate to a Takagi-Sugeno-Kang (TSK) rule need to be obtained, and many parameters need to be optimized; 3) it is difficult to detect and adapt to changes in the data distribution, i.e., concept drift, if the output is a continuous variable. Hence, in this paper, a novel evolving-fuzzy-neuro system, called the topology learning-based fuzzy random neural network (TLFRNN), is proposed. In TLFRNN, an online topology learning algorithm is designed to self-organize each layer of TLFRNN. Different from current EFN systems, TLFRNN learns multiple fuzzy sets to reduce the impact of noises on each fuzzy set, and a randomness layer is designed, which assigning the probability of each fuzzy set. Also, TLFRNN does not utilize TSK rules instead uses a simple inference which considering fuzzy and random information of data simultaneously. More importantly, in TLFRNN, concept drift can be detected and adapted easily and rapidly. The experiments demonstrate that TLFRNN achieves superior performance compared to other EFSs.
Yu, H, Lu, J, Liu, A, Wang, B, Li, R & Zhang, G 2022, 'Real-Time Prediction System of Train Carriage Load Based on Multi-Stream Fuzzy Learning', IEEE Transactions on Intelligent Transportation Systems, pp. 1-11.
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Yu, H, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2022, 'Maximizing the Geometric Mean of User-Rates to Improve Rate-Fairness: Proper vs. Improper Gaussian Signaling', IEEE Transactions on Wireless Communications, vol. 21, no. 1, pp. 295-309.
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Yu, H, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2022, 'RIS-aided Zero-Forcing and Regularized Zero-Forcing Beamfoming in Integrated Information and Energy Delivery', IEEE Transactions on Wireless Communications, pp. 1-1.
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Yu, H, Tuan, HD, Nasir, AA, Debbah, M & Fang, Y 2022, 'New generalized zero forcing beamforming for serving more users in energy-harvesting enabled networks', Physical Communication, vol. 50, pp. 101500-101500.
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Yu, L, Li, Z, Xu, M, Gao, Y, Luo, J & Zhang, J 2022, 'Distribution-Aware Margin Calibration for Semantic Segmentation in Images', International Journal of Computer Vision, vol. 130, no. 1, pp. 95-110.
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Yu, L, Zhang, J & Wu, Q 2022, 'Dual Attention on Pyramid Feature Maps for Image Captioning', IEEE Transactions on Multimedia, vol. 24, pp. 1775-1786.
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Yu, N & Zhou, L 2022, 'Comments on and Corrections to “When Is the Chernoff Exponent for Quantum Operations Finite?”', IEEE Transactions on Information Theory, vol. 68, no. 6, pp. 3989-3990.
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Yu, Y, Tang, M-C, Yi, D, Hong, D, Shi, T & Ziolkowski, RW 2022, 'Electrically Small Antenna With a Significantly Enhanced Gain-Bandwidth Product', IEEE Transactions on Antennas and Propagation, vol. 70, no. 5, pp. 3153-3162.
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Yuan, D, Chang, X, Li, Z & He, Z 2022, 'Learning Adaptive Spatial-Temporal Context-Aware Correlation Filters for UAV Tracking', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 18, no. 3, pp. 1-18.
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Tracking in the unmanned aerial vehicle (UAV) scenarios is one of the main components of target-tracking tasks. Different from the target-tracking task in the general scenarios, the target-tracking task in the UAV scenarios is very challenging because of factors such as small scale and aerial view. Although the discriminative correlation filter (DCF)-based tracker has achieved good results in tracking tasks in general scenarios, the boundary effect caused by the dense sampling method will reduce the tracking accuracy, especially in UAV-tracking scenarios. In this work, we propose learning an adaptive spatial-temporal context-aware (ASTCA) model in the DCF-based tracking framework to improve the tracking accuracy and reduce the influence of boundary effect, thereby enabling our tracker to more appropriately handle UAV-tracking tasks. Specifically, our ASTCA model can learn a spatial-temporal context weight, which can precisely distinguish the target and background in the UAV-tracking scenarios. Besides, considering the small target scale and the aerial view in UAV-tracking scenarios, our ASTCA model incorporates spatial context information within the DCF-based tracker, which could effectively alleviate background interference. Extensive experiments demonstrate that our ASTCA method performs favorably against state-of-the-art tracking methods on some standard UAV datasets.
Yuan, D, Shu, X, Fan, N, Chang, X, Liu, Q & He, Z 2022, 'Accurate bounding-box regression with distance-IoU loss for visual tracking', Journal of Visual Communication and Image Representation, vol. 83, pp. 103428-103428.
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Yusoff, MNAM, Zulkifli, NWM, Sukiman, NL, Kalam, MA, Masjuki, HH, Syahir, AZ, Awang, MSN, Mujtaba, MA, Milano, J & Shamsuddin, AH 2022, 'Microwave irradiation-assisted transesterification of ternary oil mixture of waste cooking oil – Jatropha curcas – Palm oil: Optimization and characterization', Alexandria Engineering Journal, vol. 61, no. 12, pp. 9569-9582.
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Zamani, H, Nadimi-Shahraki, MH & Gandomi, AH 2022, 'Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization', Computer Methods in Applied Mechanics and Engineering, vol. 392, pp. 114616-114616.
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Zandavi, SM, Koch, FC, Vijayan, A, Zanini, F, Mora, FV, Ortega, DG & Vafaee, F 2022, 'Disentangling single-cell omics representation with a power spectral density-based feature extraction', Nucleic Acids Research, vol. 50, no. 10, pp. 5482-5492.
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Abstract
Emerging single-cell technologies provide high-resolution measurements of distinct cellular modalities opening new avenues for generating detailed cellular atlases of many and diverse tissues. The high dimensionality, sparsity, and inaccuracy of single cell sequencing measurements, however, can obscure discriminatory information, mask cellular subtype variations and complicate downstream analyses which can limit our understanding of cell function and tissue heterogeneity. Here, we present a novel pre-processing method (scPSD) inspired by power spectral density analysis that enhances the accuracy for cell subtype separation from large-scale single-cell omics data. We comprehensively benchmarked our method on a wide range of single-cell RNA-sequencing datasets and showed that scPSD pre-processing, while being fast and scalable, significantly reduces data complexity, enhances cell-type separation, and enables rare cell identification. Additionally, we applied scPSD to transcriptomics and chromatin accessibility cell atlases and demonstrated its capacity to discriminate over 100 cell types across the whole organism and across different modalities of single-cell omics data.
Zdarta, J, Jankowska, K, Strybel, U, Marczak, Ł, Nguyen, LN, Oleskowicz-Popiel, P & Jesionowski, T 2022, 'Bioremoval of estrogens by laccase immobilized onto polyacrylonitrile/polyethersulfone material: Effect of inhibitors and mediators, process characterization and catalytic pathways determination', Journal of Hazardous Materials, vol. 432, pp. 128688-128688.
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Zdarta, J, Jesionowski, T, Pinelo, M, Meyer, AS, Iqbal, HMN, Bilal, M, Nguyen, LN & Nghiem, LD 2022, 'Free and immobilized biocatalysts for removing micropollutants from water and wastewater: Recent progress and challenges', Bioresource Technology, vol. 344, pp. 126201-126201.
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Zdarta, J, Nguyen, LN, Jankowska, K, Jesionowski, T & Nghiem, LD 2022, 'A contemporary review of enzymatic applications in the remediation of emerging estrogenic compounds', Critical Reviews in Environmental Science and Technology, vol. 52, no. 15, pp. 2661-2690.
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Zhang, D, Peng, C, Chang, X & Xia, F 2022, 'The Effect of Facial Perception and Academic Performance on Social Centrality', IEEE Transactions on Computational Social Systems, pp. 1-12.
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Zhang, G, Liu, B, Zhu, T, Ding, M & Zhou, W 2022, 'Label-Only Membership Inference Attacks and Defenses In Semantic Segmentation Models', IEEE Transactions on Dependable and Secure Computing, pp. 1-1.
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Zhang, G, Niwa, K & Kleijn, WB 2022, 'Revisiting the Primal-Dual Method of Multipliers for Optimisation Over Centralised Networks', IEEE Transactions on Signal and Information Processing over Networks, vol. 8, pp. 228-243.
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Zhang, H & Xu, M 2022, 'Multiscale Emotion Representation Learning for Affective Image Recognition', IEEE Transactions on Multimedia, pp. 1-1.
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Zhang, H & Xu, M 2022, 'Recognition of Emotions in User-generated Videos through Frame-level Adaptation and Emotion Intensity Learning', IEEE Transactions on Multimedia, pp. 1-1.
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Zhang, H, An, N & Zhu, X 2022, 'Structural dynamic reliability analysis of super large-scale lattice domes during earthquakes using the stochastic finite element method', Soil Dynamics and Earthquake Engineering, vol. 153, pp. 107076-107076.
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Zhang, J, Hanjalic, A, Jain, R, Hua, X, Satoh, S, Yao, Y & Zeng, D 2022, 'Guest Editorial: Learning From Noisy Multimedia Data', IEEE Transactions on Multimedia, vol. 24, pp. 1247-1252.
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Zhang, JA, Rahman, ML, Wu, K, Huang, X, Guo, YJ, Chen, S & Yuan, J 2022, 'Enabling Joint Communication and Radar Sensing in Mobile Networks—A Survey', IEEE Communications Surveys & Tutorials, vol. 24, no. 1, pp. 306-345.
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Mobile network is evolving from a communication-only network towards one with joint communication and radar/radio sensing (JCAS) capabilities, that we call perceptive mobile network (PMN). Radio sensing here refers to information retrieval from received mobile signals for objects of interest in the environment surrounding the radio transceivers, and it may go beyond the functions of localization, tracking, and object recognition of traditional radar. In PMNs, JCAS integrates sensing into communications, sharing a majority of system modules and the same transmitted signals. The PMN is expected to provide a ubiquitous radio sensing platform and enable a vast number of novel smart applications, whilst providing non-compromised communications. In this paper, we present a broad picture of the motivation, methodologies, challenges, and research opportunities of realizing PMN, by providing a comprehensive survey for systems and technologies developed mainly in the last ten years. Beginning by reviewing the work on coexisting communication and radar systems, we highlight their limits on addressing the interference problem, and then introduce the JCAS technology. We then set up JCAS in the mobile network context and envisage its potential applications. We continue to provide a brief review of three types of JCAS systems, with particular attention to their differences in design philosophy. We then introduce a framework of PMN, including the system platform and infrastructure, three types of sensing operations, and signals usable for sensing. Subsequently, we discuss required system modifications to enable sensing on current communication-only infrastructure. Within the context of PMN, we review stimulating research problems and potential solutions, organized under nine topics: performance bounds, waveform optimization, antenna array design, clutter suppression, sensing parameter estimation, resolution of sensing ambiguity, pattern analysis, networked sensing under cellular...
Zhang, K, Fu, Y, Hao, D, Guo, J, Ni, B-J, Jiang, B, Xu, L & Wang, Q 2022, 'Fabrication of CN75/NH2-MIL-53(Fe) p-n heterojunction with wide spectral response for efficiently photocatalytic Cr(VI) reduction', Journal of Alloys and Compounds, vol. 891, pp. 161994-161994.
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Zhang, K, Song, X, Zhang, C & Yu, S 2022, 'Challenges and future directions of secure federated learning: a survey', Frontiers of Computer Science, vol. 16, no. 5.
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Zhang, L, Chang, X, Liu, J, Luo, M, Li, Z, Yao, L & Hauptmann, A 2022, 'TN-ZSTAD: Transferable Network for Zero-Shot Temporal Activity Detection', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-14.
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Zhang, L, He, Z, Yang, Y, Wang, L & Gao, X 2022, 'Tasks Integrated Networks: Joint Detection and Retrieval for Image Search', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 1, pp. 456-473.
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The traditional object (person) retrieval (re-identification) task aims to learn a discriminative feature representation or metric on the cropped objects. However, in many real-world scenarios, the objects are seldom accurately annotated. Therefore, object-level retrieval becomes intractable without annotation, which leads to a new but challenging topic, i.e. image search with joint detection and retrieval. To address the image search issue, we introduce an end-to-end Integrated Net, which has four merits: 1) A Siamese architecture and an on-line pairing strategy for similar and dissimilar objects in the given images are designed. 2) A novel on-line pairing (OLP) loss is introduced with a dynamic feature dictionary, which alleviates the multi-task training stagnation problem, by automatically generating a number of negative pairs to restrict the positives. 3) Two modules are tailored to handle different tasks separately in the integrated framework, such that the task specification is guaranteed. 4) A class-center guided HEP loss (C2HEP) by exploiting the stored class centers is proposed, such that the intra-similarity and inter-dissimilarity can be captured. Extensive experiments on the CUHK-SYSU and PRW datasets for person search and the large-scale WebTattoo dataset for tattoo search, demonstrate that the proposed model outperforms the state-of-the-art image search models.
Zhang, L, Huang, S & Liu, W 2022, 'Learning sequentially diversified representations for fine-grained categorization', Pattern Recognition, vol. 121, pp. 108219-108219.
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Zhang, L, Shen, J, Zhang, J, Xu, J, Li, Z, Yao, Y & Yu, L 2022, 'Multimodal Marketing Intent Analysis for Effective Targeted Advertising', IEEE Transactions on Multimedia, vol. 24, pp. 1830-1843.
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Zhang, L, Sun, J, Zhang, Z, Peng, Z, Dai, X & Ni, B-J 2022, 'Polyethylene terephthalate microplastic fibers increase the release of extracellular antibiotic resistance genes during sewage sludge anaerobic digestion', Water Research, vol. 217, pp. 118426-118426.
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Zhang, L, Wang, S, Chang, X, Liu, J, Ge, Z & Zheng, Q 2022, 'Auto-FSL: Searching the Attribute Consistent Network for Few-Shot Learning', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 3, pp. 1213-1223.
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Zhang, L, Wang, S, Liu, J, Lin, Q, Chang, X, Wu, Y & Zheng, Q 2022, 'MuL-GRN: Multi-Level Graph Relation Network for Few-Shot Node Classification', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Zhang, L, Xu, J, Gong, Y, Yu, L, Zhang, J & Shen, J 2022, 'Unsupervised Image and Text Fusion for Travel Information Enhancement', IEEE Transactions on Multimedia, vol. 24, pp. 1415-1425.
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Zhang, L, Zhu, T, Xiong, P, Zhou, W & Yu, P 2022, 'A Robust Game-theoretical Federated Learning Framework with Joint Differential Privacy', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Zhang, L, Zhu, T, Xiong, P, Zhou, W & Yu, PS 2022, 'More than Privacy', ACM Computing Surveys, vol. 54, no. 7, pp. 1-37.
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The vast majority of artificial intelligence solutions are founded on game theory, and differential privacy is emerging as perhaps the most rigorous and widely adopted privacy paradigm in the field. However, alongside all the advancements made in both these fields, there is not a single application that is not still vulnerable to privacy violations, security breaches, or manipulation by adversaries. Our understanding of the interactions between differential privacy and game theoretic solutions is limited. Hence, we undertook a comprehensive review of literature in the field, finding that differential privacy has several advantageous properties that can make more of a contribution to game theory than just privacy protection. It can also be used to build heuristic models for game-theoretic solutions, to avert strategic manipulations, and to quantify the cost of privacy protection. With a focus on mechanism design, the aim of this article is to provide a new perspective on the currently held impossibilities in game theory, potential avenues to circumvent those impossibilities, and opportunities to improve the performance of game-theoretic solutions with differentially private techniques.
Zhang, R, Xu, L, Yu, Z, Shi, Y, Mu, C & Xu, M 2022, 'Deep-IRTarget: An Automatic Target Detector in Infrared Imagery Using Dual-Domain Feature Extraction and Allocation', IEEE Transactions on Multimedia, vol. 24, pp. 1735-1749.
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Recently, convolutional neural networks (CNNs) have brought impressive improvements for object detection. However, detecting targets in infrared images still remains challenging, because the poor texture information, low resolution and high noise levels of the thermal imagery restrict the feature extraction ability of CNNs. In order to deal with these difficulties in the feature extraction, we propose a novel backbone network named Deep-IRTarget, composing of a frequency feature extractor, a spatial feature extractor and a dual-domain feature resource allocation model. Hypercomplex Infrared Fourier Transform is developed to calculate the infrared intensity saliency by designing hypercomplex representations in the frequency domain, while a convolutional neural network is invoked to extract feature maps in the spatial domain. Features from the frequency domain and spatial domain are stacked to construct Dual-domain features. To efficiently integrate and recalibrate them, we propose a Resource Allocation model for Features (RAF). The well-designed channel attention block and position attention block are used in RAF to respectively extract interdependent relationships among channel and position dimensions, and capture channel-wise and position-wise contextual information. Extensive experiments are conducted on three challenging infrared imagery databases. We achieve 10.14%, 9.1% and 8.05% improvement on mAP scores, compared to the current state of the art method on MWIR, BITIR and WCIR respectively.
Zhang, S, Lan, P, Li, HC, Tong, CX & Sheng, D 2022, 'Physics-informed neural networks for consolidation of soils', Engineering Computations (Swansea, Wales).
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Purpose: Prediction of excess pore water pressure and estimation of soil parameters are the two key interests for consolidation problems, which can be mathematically quantified by a set of partial differential equations (PDEs). Generally, there are challenges in solving these two issues using traditional numerical algorithms, while the conventional data-driven methods require massive data sets for training and exhibit negative generalization potential. This paper aims to employ the physics-informed neural networks (PINNs) for solving both the forward and inverse problems. Design/methodology/approach: A typical consolidation problem with continuous drainage boundary conditions is firstly considered. The PINNs, analytical, and finite difference method (FDM) solutions are compared for the forward problem, and the estimation of the interface parameters involved in the problem is discussed for the inverse problem. Furthermore, the authors also explore the effects of hyperparameters and noisy data on the performance of forward and inverse problems, respectively. Finally, the PINNs method is applied to the more complex consolidation problems. Findings: The overall results indicate the excellent performance of the PINNs method in solving consolidation problems with various drainage conditions. The PINNs can provide new ideas with a broad application prospect to solve PDEs in the field of geotechnical engineering, and also exhibit a certain degree of noise resistance for estimating the soil parameters. Originality/value: This study presents the potential application of PINNs for the consolidation of soils. Such a machine learning algorithm helps to obtain remarkably accurate solutions and reliable parameter estimations with fewer and average-quality data, which is beneficial in engineering practice.
Zhang, S, Li, X, Shi, J, Sivakumar, M, Luby, S, O'Brien, J & Jiang, G 2022, 'Analytical performance comparison of four SARS-CoV-2 RT-qPCR primer-probe sets for wastewater samples', Science of The Total Environment, vol. 806, pp. 150572-150572.
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Zhang, S, Sun, W-L, Song, H-L, Zhang, T, Yin, M, Wang, Q & Zuo, X 2022, 'Effects of voltage on the emergence and spread of antibiotic resistance genes in microbial electrolysis cells: From mutation to horizontal gene transfer', Chemosphere, vol. 291, pp. 132703-132703.
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Zhang, T, Jin, B & Jia, W 2022, 'An anchor-free object detector based on soften optimized bi-directional FPN', Computer Vision and Image Understanding, vol. 218, pp. 103410-103410.
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Zhang, T, Zhu, T, Li, J, Han, M, Zhou, W & Yu, PS 2022, 'Fairness in Semi-Supervised Learning: Unlabeled Data Help to Reduce Discrimination', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 4, pp. 1763-1774.
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Zhang, T, Zhu, T, Liu, R & Zhou, W 2022, 'Correlated data in differential privacy: Definition and analysis', Concurrency and Computation: Practice and Experience, vol. 34, no. 16.
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Zhang, W, Liu, T, Brown, A, Ueland, M, Forbes, SL & Su, SW 2022, 'The Use of Electronic Nose for the Classification of Blended and Single Malt Scotch Whisky', IEEE Sensors Journal, vol. 22, no. 7, pp. 7015-7021.
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Zhang, X & Far, H 2022, 'Effects of dynamic soil-structure interaction on seismic behaviour of high-rise buildings', Bulletin of Earthquake Engineering, vol. 20, no. 7, pp. 3443-3467.
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Zhang, X, Huang, J, Cheng, X, Chen, H, Liu, Q, Yao, P, Ngo, HH & Nghiem, LD 2022, 'Mitigation of reverse osmosis membrane fouling by electrochemical-microfiltration- activated carbon pretreatment', Journal of Membrane Science, vol. 656, pp. 120615-120615.
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Zhang, X, Li, Z-X, Shi, Y, Wu, C & Li, J 2022, 'Fragility analysis for performance-based blast design of FRP-strengthened RC columns using artificial neural network', Journal of Building Engineering, vol. 52, pp. 104364-104364.
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Zhang, X, Xia, W, Wang, X, Liu, J, Cui, Q, Tao, X & Liu, RP 2022, 'The Block Propagation in Blockchain-Based Vehicular Networks', IEEE Internet of Things Journal, vol. 9, no. 11, pp. 8001-8011.
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Zhang, X, Xu, Z, Fan, L, Yu, S & Qu, Y 2022, 'Near-Optimal Energy-Efficient Algorithm for Virtual Network Function Placement', IEEE Transactions on Cloud Computing, vol. 10, no. 1, pp. 553-567.
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Zhang, X, Zhang, S, Teng, J, Ye, X & Sheng, D 2022, 'Study on crystallization law of expansive sodium sulfate saline soils based on NMR', Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology), vol. 53, no. 4, pp. 1352-1361.
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In order to study the salt-frost heave characteristics of unsaturated sodium sulfate saline soils with high salt mass fraction, the crystallization of silty clay, silt and red clay with different initial sodium sulfate mass fractions during cooling were analyzed by nuclear magnetic resonance(NMR). The results show that the initial precipitation temperature of mirabilite in the same soil is directly proportional to the mass fraction of initial sodium sulfate. In the precipitation stage of mirabilite, the silty clay and silt containing sodium sulfate experiences two stages, i.e., rapid stage and slow stage of mirabilite precipitation and the red clay containing sodium sulfate experience three stages, i.e., slow stage, explosive stage and slow stage of mirabilite precipitation. Sodium sulfate can significantly reduce the freezing temperature and increase the mass fraction of unfrozen water of red clay, but this phenomenon is not obvious in silty clay and silt. The amount of ice crystals and mirabilite precipitated from silty clay and silt containing sodium sulfate after freezing is less than that of red clay. The mass fraction of fine particles in the soil has great influence on the amount of mirabilite crystallization, the pore size has great influence on the freezing temperature of the soil, and the pore size and plastic limit have great influence on the mass fraction of unfrozen water in the soil.
Zhang, X, Zhang, W, Zhang, L, Huang, Z, Hu, J, Gao, M, Pan, H & Liu, Y 2022, 'Single-pot solvothermal strategy toward support-free nanostructured LiBH4 featuring 12 wt% reversible hydrogen storage at 400 °C', Chemical Engineering Journal, vol. 428, pp. 132566-132566.
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Zhang, Y, Huang, R, Zhou, Y, Zhou, T, Tao, C, Huang, Y & Qian, Y 2022, 'Effects of turbulence intensity and n-pentanol concentration on droplet evaporation and auto-ignition', Fuel, vol. 322, pp. 124177-124177.
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Zhang, Y, Li, B, Wu, J, Liu, B, Chen, R & Chang, J 2022, 'Efficient and Privacy-preserving Blockchain-based Multi-factor Device Authentication Protocol for Cross-domain IIoT', IEEE Internet of Things Journal, pp. 1-1.
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Zhang, Y, Xu, X, Fang, J, Huang, W & Wang, J 2022, 'Load characteristics of triangular honeycomb structures with self-similar hierarchical features', Engineering Structures, vol. 257, pp. 114114-114114.
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Zhang, Y, Yu, X, Lu, X & Liu, P 2022, 'Pro-UIGAN: Progressive Face Hallucination From Occluded Thumbnails', IEEE Transactions on Image Processing, vol. 31, pp. 3236-3250.
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Zhang, Y, Yuen, K-V, Mousavi, M & Gandomi, AH 2022, 'Timber damage identification using dynamic broad network and ultrasonic signals', Engineering Structures, vol. 263, pp. 114418-114418.
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Zhang, Y-F, Zheng, J, Jia, W, Huang, W, Li, L, Liu, N, Li, F & He, X 2022, 'Deep RGB-D Saliency Detection Without Depth', IEEE Transactions on Multimedia, vol. 24, pp. 755-767.
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IEEE The existing saliency detection models based on RGB colors only leverage appearance cues to detect salient objects. Depth information also plays a very important role in visual saliency detection and can supply complementary cues for saliency detection. Although many RGB-D saliency models have been proposed, they require to acquire depth data, which is expensive and not easy to get. In this paper, we propose to estimate depth information from monocular RGB images and leverage the intermediate depth features to enhance the saliency detection performance in a deep neural network framework. Specically, we rst use an encoder network to extract common features from each RGB image and then build two decoder networks for depth estimation and saliency detection, respectively. The depth decoder features can be fused with the RGB saliency features to enhance their capability. Furthermore, we also propose a novel dense multiscale fusion model to densely fuse multiscale depth and RGB features based on the dense ASPP model. A new global context branch is also added to boost the multiscale features. Experimental results demonstrate that the added depth cues and the proposed fusion model can both improve the saliency detection performance. Finally, our model not only outperforms state-of-the-art RGB saliency models, but also achieves comparable results compared with state-of-the-art RGB-D saliency models.
Zhang, Z, Jiang, S, Huang, C & Da Xu, RY 2022, 'Unsupervised Clothing Change Adaptive Person ReID', IEEE Signal Processing Letters, vol. 29, pp. 304-308.
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Zhang, Z, Li, H, Zhang, H, Zhang, J, Zhong, Z & Xu, W 2022, 'Model-free predictive control of nonlinear systems under False Data Injection attacks', Computers and Electrical Engineering, vol. 100, pp. 107977-107977.
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Zhang, Z, Li, X, Liu, H, Zamyadi, A, Guo, W, Wen, H, Gao, L, Nghiem, LD & Wang, Q 2022, 'Advancements in detection and removal of antibiotic resistance genes in sludge digestion: A state-of-art review', Bioresource Technology, vol. 344, pp. 126197-126197.
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Zhang, Z, Ni, B-J, Zhang, L, Liu, Z, Fu, W, Dai, X & Sun, J 2022, 'Medium-chain fatty acids production from carbohydrates-rich wastewater through two-stage yeast biofilm processes without external electron donor addition: Biofilm development and pH impact', Science of The Total Environment, vol. 828, pp. 154428-154428.
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Zhang, Z, Wang, L, Wang, Y, Zhou, L, Zhang, J & Chen, F 2022, 'Dataset-driven Unsupervised Object Discovery for Region-based Instance Image Retrieval', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1.
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Zhang, Z, Wu, Q, Wang, Y & Chen, F 2022, 'Exploring Pairwise Relationships Adaptively From Linguistic Context in Image Captioning', IEEE Transactions on Multimedia, vol. 24, pp. 3101-3113.
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Zhao, E, Walker, PD & Surawski, NC 2022, 'Emissions life cycle assessment of diesel, hybrid and electric buses', Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 236, no. 6, pp. 1233-1245.
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This paper applies a case study approach for Australia and calculates the equipment life cycle assessment of diesel, hybrid and electric buses. This study prepared the assessment according to the procedures and methodologies outlined in the ISO 14040:2006 Environmental Management – Life Cycle Assessment. The authors have chosen three bus models currently in service in the Australian bus fleet to serve as a baseline model for comparison. The amount of greenhouse gas emissions were calculated from the production, assembly, transportation, maintenance and disposal phases. The results in this study show that the electric bus has a higher total environmental impact than the diesel and hybrid bus, mainly due to the manufacturing of the lithium-ion battery. The results also show that the electric bus has a higher environmental impact than the diesel and hybrid bus (18.2% and 14.7% higher, respectively), albeit specific to the product life cycle and without including operation emissions. However, there are many opportunities to reduce product life cycle emissions, such as improvement in manufacturing efficiency, developing new battery technology and production in regions with low carbon-intense grid-mixes.
Zhao, F, Cao, S, Luo, Q, Li, L & Ji, J 2022, 'Practical design of the QZS isolator with one pair of oblique bars by considering pre-compression and low-dynamic stiffness', Nonlinear Dynamics, vol. 108, no. 4, pp. 3313-3330.
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Zhao, H, Hu, Y, Tang, Z, Wang, K, Li, Y & Li, W 2022, 'Deterioration of concrete under coupled aggressive actions associated with load, temperature and chemical attacks: A comprehensive review', Construction and Building Materials, vol. 322, pp. 126466-126466.
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Zhao, J, Li, H, Qu, L, Zhang, Q, Sun, Q, Huo, H & Gong, M 2022, 'DCFGAN: An adversarial deep reinforcement learning framework with improved negative sampling for session-based recommender systems', Information Sciences, vol. 596, pp. 222-235.
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Zhao, J, Zhang, JA, Li, Q, Zhang, H & Wang, X 2022, 'Recursive constrained generalized maximum correntropy algorithms for adaptive filtering', Signal Processing, vol. 199, pp. 108611-108611.
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Zhao, J, Zhang, JA, Li, Q, Zhang, H & Wang, X 2022, 'Recursive Maximum Correntropy Algorithms for Second-Order Volterra Filtering', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 4, pp. 2336-2340.
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As a special case of the Volterra system, the second-order Volterra (SOV) filter is very efficient for nonlinear system identification. The improved correntorpy based on the generalized Gaussian density function has been proven robust against impulsive noise. In this brief, we propose several SOV filters based on a recursive maximum correntropy (RMC) algorithm for nonlinear system identification. We first introduce a basic RMC algorithm, which faces a trade-off between filtering accuracy and tracking capability due to the use of a fixed forgetting factor (FFF). Two RMCs with variable FF (VFF) are further proposed to enhance the tracking ability. Simulation results demonstrate that our proposed algorithms outperform existing ones in impulsive noise environments and/or in time-varying systems.
Zhao, J, Zhang, JA, Zhang, H & Li, Q 2022, 'Generalized correntropy induced metric based total least squares for sparse system identification', Neurocomputing, vol. 467, pp. 66-72.
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The total least squares (TLS) method has been successfully applied to system identification in the errors-in-variables (EIV) model, which can efficiently describe systems where input–output pairs are contaminated by noise. In this paper, we propose a new gradient-descent TLS filtering algorithm based on the generalized correntropy induced metric (GCIM), called as GCIM-TLS, for sparse system identification. By introducing GCIM as a penalty term to the TLS problem, we can achieve improved accuracy of sparse system identification. We also characterize the convergence behaviour analytically for GCIM-TLS. To reduce computational complexity, we use the first-order Taylor series expansion and further derive a simplified version of GCIM-TLS. Simulation results verify the effectiveness of our proposed algorithms in sparse system identification.
Zhao, L, He, Y, Zhao, G, Chen, X, Huang, G-L & Lin, W 2022, 'Scanning Angle Extension of a Millimeter Wave Antenna Array using Electromagnetic Band Gap Ground', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Zhao, L-H, Wen, S, Xu, M, Shi, K, Zhu, S & Huang, T 2022, 'PID Control for Output Synchronization of Multiple Output Coupled Complex Networks', IEEE Transactions on Network Science and Engineering, vol. 9, no. 3, pp. 1553-1566.
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Zhao, S & Burnett, IS 2022, 'Evolutionary array optimization for multizone sound field reproduction', The Journal of the Acoustical Society of America, vol. 151, no. 4, pp. 2791-2801.
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Multizone sound field reproduction aims to generate personal sound zones in a shared space with multiple loudspeakers. Traditional multizone sound field reproduction methods have focused on optimizing the source strengths given a preset array configuration. Recently, however, various methods have explored optimization of the loudspeaker locations. These can be categorized into sparse regularization and iterative methods with existing studies based on numerical simulations and mostly aiming at single-zone sound field reproduction. In this paper, unique experiments compare the state-of-the-art loudspeaker placement optimization methods by selecting a smaller number of loudspeakers from the candidates uniformly placed along a circle. An evolutionary array optimization scheme is proposed and shown to outperform the best existing methods in terms of mean square error in the bright zone and acoustic contrast between the bright and dark zones at frequencies below 1 kHz. The proposed evolutionary optimization scheme is simple, flexible, and can be extended to broadband optimization and other cost functions.
Zhao, Y, Chen, J, Zhang, J, Wu, D, Blumenstein, M & Yu, S 2022, 'Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks', Concurrency and Computation: Practice and Experience, vol. 34, no. 7.
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Zhao, Y, Liu, B, Zhu, T, Ding, M & Zhou, W 2022, 'Private‐encoder: Enforcing privacy in latent space for human face images', Concurrency and Computation: Practice and Experience, vol. 34, no. 3.
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Zheng, G, Zhang, L, Wang, E, Yao, R, Luo, Q, Li, Q & Sun, G 2022, 'Investigation into multiaxial mechanical behaviors of Kelvin and Octet-B polymeric closed-cell foams', Thin-Walled Structures, vol. 177, pp. 109405-109405.
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Zheng, H, Guan, R, Liu, Q, Ou, K, Li, D-S, Fang, J, Fu, Q & Sun, Y 2022, 'A flexible supercapacitor with high capacitance retention at an ultra-low temperature of -65.0°C', Electrochimica Acta, vol. 424, pp. 140644-140644.
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Zheng, Y, Luo, Z, Wang, Y, Li, Z, Qu, J & Zhang, C 2022, 'Optimized high thermal insulation by the topological design of hierarchical structures', International Journal of Heat and Mass Transfer, vol. 186, pp. 122448-122448.
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Zheng, Y, Yu, X, Liu, M & Zhang, S 2022, 'Single-Image Deraining via Recurrent Residual Multiscale Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 3, pp. 1310-1323.
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Zhong, J & Qiu, X 2022, 'On the spherical expansion for calculating the sound radiated by a baffled circular piston', Journal of Theoretical and Computational Acoustics, vol. 30, no. 01, pp. 2050026-2050026.
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An efficient and accurate method for calculating the sound radiated by a baffled circular rigid piston is using spherical harmonics, and the solution is a series containing the integral of spherical Bessel functions. The integral is usually calculated with the generalized hypergeometric functions in existing literatures, which shows poor convergence at middle and high frequencies due to the overflow and the loss of significant figures. A rigorous and closed form solution of the integral is derived in this paper based on the recurrence method, which is accurate in the whole frequency range and thousands of times faster than the existing methods. It is shown that the proposed method can be extended for the calculation of the sound radiated by a baffled piston and an unbaffled resilient disk with axisymmetric velocity and pressure profiles, respectively, and some baffled rotating sources where the velocity profile is asymmetric.
Zhong, J, Kirby, R, Karimi, M, Zou, H & Qiu, X 2022, 'Scattering by a rigid sphere of audio sound generated by a parametric array loudspeaker', The Journal of the Acoustical Society of America, vol. 151, no. 3, pp. 1615-1626.
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This work investigates the scattering by a rigid sphere of audio sound generated by a parametric array loudspeaker (pal). A computationally efficient method utilizing a spherical harmonic expansion is developed to calculate the quasilinear solution of audio sound fields based on both Kuznetsov and Westervelt equations. The accuracy of using the Westervelt equation is examined, and the rigid sphere scattering effects are simulated with the proposed method. It is found the results obtained using the Westervelt equation are inaccurate near the sphere at low frequencies. Contrary to conventional loudspeakers, the directivity of the audio sound generated by a pal severely deteriorates behind a sphere, as the ultrasounds maintaining the directivity of the audio sound are almost completely blocked by the sphere. Instead, the ultrasounds are reflected and generate audio sound on the front side of the sphere. It means that a listener in front of the pal will hear the audio sound scattered back after introducing the sphere as if it is reflected by the sphere. The experiment results are also presented to validate the numerical results.
Zhong, J, Zhuang, T, Kirby, R, Karimi, M, Zou, H & Qiu, X 2022, 'Quiet zone generation in an acoustic free field using multiple parametric array loudspeakers', The Journal of the Acoustical Society of America, vol. 151, no. 2, pp. 1235-1245.
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This paper investigates the feasibility of remotely generating a quiet zone in an acoustic free field using multiple parametric array loudspeakers (PALs). A primary sound field is simulated using point monopoles located randomly in a two-dimensional plane, or three-dimensional (3D) space, whereas the secondary sound field is generated by multiple PALs uniformly distributed around the circumference of a circle sitting on the same plane as the primary sources, or on the surface of a sphere for 3D space. A quiet zone size is defined as the diameter of the maximal circular zone within which the noise reduction is greater than 10 dB. The size of this quiet zone is found to be proportional to 0.19 λN for N secondary sources with a wavelength λ when the primary and secondary sources are in the same plane, whereas it is found to be 0.55 λN1/2 for the 3D case. The size of the quiet zones generated by PALs is similar to that observed with traditional omnidirectional loudspeakers; however, the effects of using PALs on the sound field outside the target zone is much smaller due to their sharp radiation directivity and slow decay rate along the propagation distance. Experimental results are also presented to validate these numerical simulations.
Zhou, F, Xiao, W-Z, Zhou, K-Y, Zhuang, J-L, Zhang, X, Liu, Y-D, Ni, B-J, Shapleigh, JP, Zhou, M, Luo, X-Z & Li, W 2022, 'Performance characteristics and community analysis of a single-stage partial nitritation, anammox and denitratation (SPANADA) integrated process for treating low C/N ratio wastewater', Chemical Engineering Journal, vol. 433, pp. 134452-134452.
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Zhou, I, Lipman, J, Abolhasan, M & Shariati, N 2022, 'Minute-wise frost prediction: An approach of recurrent neural networks', Array, vol. 14, pp. 100158-100158.
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Zhou, S, Halkon, B, Guo, Z & Eager, D 2022, 'Transient waveform replication for flexible structures in shaker testing using time-domain convolution techniques'.
Zhou, T, Li, X, Zhang, Q, Dong, S, Liu, H, Liu, Y, Chaves, AV, Ralph, PJ, Ruan, R & Wang, Q 2022, 'Ecotoxicological response of Spirulina platensis to coexisted copper and zinc in anaerobic digestion effluent', Science of The Total Environment, vol. 837, pp. 155874-155874.
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Zhou, Y, Fu, Y, Luo, Z, Hu, M, Wu, D, Sheng, QZ & Yu, S 2022, 'The Role of Communication Time in the Convergence of Federated Edge Learning', IEEE Transactions on Vehicular Technology, vol. 71, no. 3, pp. 3241-3254.
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Zhou, Y, Shang, Y, Cao, Y, Li, Q, Zhou, C & Xu, G 2022, 'API-GNN: attribute preserving oriented interactive graph neural network', World Wide Web, vol. 25, no. 1, pp. 239-258.
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AbstractAttributed graph embedding aims to learn node representation based on the graph topology and node attributes. The current mainstream GNN-based methods learn the representation of the target node by aggregating the attributes of its neighbor nodes. These methods still face two challenges: (1) In the neighborhood aggregation procedure, the attributes of each node would be propagated to its neighborhoods which may cause disturbance to the original attributes of the target node and cause over-smoothing in GNN iteration. (2) Because the representation of the target node is derived from the attributes and topology of its neighbors, the attributes and topological information of each neighbor have different effects on the representation of the target node. However, this different contribution has not been considered by the existing GNN-based methods. In this paper, we propose a novel GNN model named API-GNN (Attribute Preserving Oriented Interactive Graph Neural Network). API-GNN can not only reduce the disturbance of neighborhood aggregation to the original attribute of target node, but also explicitly model the different impacts of attribute and topology on node representation. We conduct experiments on six public real-world datasets to validate API-GNN on node classification and link prediction. Experimental results show that our model outperforms several strong baselines over various graph datasets on multiple graph analysis tasks.
Zhou, Y, Wang, J, Lu, H & Zhao, W 2022, 'Short-term wind power prediction optimized by multi-objective dragonfly algorithm based on variational mode decomposition', Chaos, Solitons & Fractals, vol. 157, pp. 111982-111982.
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Zhu, C, Xiao, F & Cao, Z 2022, 'A generalized Rényi divergence for multi-source information fusion with its application in EEG data analysis', Information Sciences, vol. 605, pp. 225-243.
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Zhu, C, Ye, D, Zhu, T & Zhou, W 2022, 'Time-optimal and privacy preserving route planning for carpool policy', World Wide Web, vol. 25, no. 3, pp. 1151-1168.
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AbstractTo alleviate the traffic congestion caused by the sharp increase in the number of private cars and save commuting costs, taxi carpooling service has become the choice of many people. Current research on taxi carpooling services has focused on shortening the detour distances. While with the development of intelligent cities, efficiently match passengers and vehicles and planning routes become urgent. And the privacy between passengers in the taxi carpooling service also needs to be considered. In this paper, we propose a time-optimal and privacy-preserving carpool route planning system via deep reinforcement learning. This system uses the traffic information around the carpooling vehicle to optimize passengers’ travel time, not only to efficiently match passengers and vehicles but also to generate detailed route planning for carpooling vehicles. We conducted experiments on an Internet of Vehicles simulator CARLA, and the results demonstrate that our method is better than other advanced methods and has better performance in complex environments.
Zhu, H, Zhang, T & Guo, YJ 2022, 'Wideband Hybrid Couplers With Unequal Power Division/Arbitrary Output Phases and Applications to Miniaturized Nolen Matrices', IEEE Transactions on Microwave Theory and Techniques, vol. 70, no. 6, pp. 3040-3053.
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Zhu, J, Li, J, Chapman, EC, Shi, H, Ciocan, CM, Chen, K, Shi, X, Zhou, J, Sun, P, Zheng, Y & Rotchell, JM 2022, 'Gonadal Atresia, Estrogen-Responsive, and Apoptosis-Specific mRNA Expression in Marine Mussels from the East China Coast: A Preliminary Study', Bulletin of Environmental Contamination and Toxicology, vol. 108, no. 6, pp. 1111-1117.
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AbstractThis preliminary survey analysed mussel atresia incidences, estrogen-responsive and apoptotic-specific molecular end points, and aqueous and gonadal levels of selected estrogens from the East China coast. Estrogen levels were low (e.g. < LOD-28.36 ng/L, < LOD-3.88 ng/g wet weight of tissue for BPA) relative to worldwide freshwater environments, but high oocyte follicle atresia incidences (up to 26.6%) occurred at selected sites. Expression of estrogen-responsive ER2 was significantly increased in males relative to females at sites with high atresia incidences in females. A second estrogen-responsive gene, V9, was significantly increased at two sites in April in females relative to males; the opposite was true for the remaining two sites. Apoptosis-specific genes (Bcl-2, fas) showed elevated expression in males relative to females at the site with the highest atresia incidence. These results provide coastal estrogen levels and the utility of several estrogen-specific molecular-level markers for marine mussels.
Zhu, J, Yang, Y, Hou, Z, Liao, S & Xue, Q 2022, 'Dual-Band Aperture-Shared High Gain Antenna for Millimeter-Wave Multi-Beam and Sub-6 GHz Communication Applications', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4848-4853.
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Zhu, J, Yang, Y, Liao, S & Xue, Q 2022, 'Dual-Band Antenna Hybridizing Folded Transmitarray and Folded Reflectarray', IEEE Transactions on Antennas and Propagation, vol. 70, no. 4, pp. 3070-3075.
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Zhu, J, Yang, Y, Liao, S, Li, S & Xue, Q 2022, 'Dual-Band Aperture-Shared Fabry–Perot Cavity-Integrated Patch Antenna for Millimeter-Wave/Sub-6 GHz Communication Applications', IEEE Antennas and Wireless Propagation Letters, vol. 21, no. 5, pp. 868-872.
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Zhu, L, Fan, H, Luo, Y, Xu, M & Yang, Y 2022, 'Temporal Cross-Layer Correlation Mining for Action Recognition', IEEE Transactions on Multimedia, vol. 24, pp. 668-676.
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Neighboring frames are more correlated compared to frames from further temporal distances. In this paper, we aim to explore the temporal correlations among neighboring frames and exploit cross-layer multi-scale features for action recognition. We present a Temporal Cross-Layer Correlation (TCLC) framework for temporal correlation learning. First, we introduce a context-aware reconstruction block to enable the exploration of neighboring context. This prediction block aims to reconstruct the past frame and the future frame in a unified way. It learns to mine frame correlations and aggregate long sequences at the same time. We demonstrate that this neighborhood mining process enhances the discriminative ability of the network. Second, we propose a novel cross-layer attention and a center-guided attention mechanism to integrate features with contextual knowledge from multiple scales. Our method is a two-stage process for effective cross-layer feature learning. The first stage incorporates the cross-layer attention module to decide the importance weight of the convolutional layers. The second stage leverages the center-guided attention mechanism to aggregate local features from each layer for the generation of a final video representation. We leverage global centers to extract shared semantic knowledge among videos. We evaluate TCLC on three action recognition datasets, i.e., UCF-101, HMDB-51 and Kinetics. Our experimental results demonstrate the superiority of our proposed temporal correlation mining method.
Zhu, P, Pan, X, Shen, Y, Huang, X, Yu, F, Wu, D, Feng, Q, Zhou, J & Li, X 2022, 'Biodegradation and potential effect of ranitidine during aerobic composting of human feces', Chemosphere, vol. 296, pp. 134062-134062.
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Zhu, P, Shen, Y, Li, X, Liu, X, Qian, G & Zhou, J 2022, 'Feeding preference of insect larvae to waste electrical and electronic equipment plastics', Science of The Total Environment, vol. 807, pp. 151037-151037.
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Zhu, Q & Ha, Q 2022, 'A Bidirectional Self-Rectifying Network with Bayesian Modelling for Vision-Based Crack Detection', IEEE Transactions on Industrial Informatics, pp. 1-1.
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Zhu, T, Li, J, Hu, X, Xiong, P & Zhou, W 2022, 'The Dynamic Privacy-Preserving Mechanisms for Online Dynamic Social Networks', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 6, pp. 2962-2974.
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Zhu, W, Hoang, TD, Dutkiewicz, E & Hanzo, L 2022, 'Collaborative Beamforming Aided Fog Radio Access Networks', IEEE Transactions on Vehicular Technology, pp. 1-1.
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Zhu, Y, Shen, S, Ouyang, L, Liu, J, Wang, H, Huang, Z & Zhu, M 2022, 'Effective synthesis of magnesium borohydride via B-O to B-H bond conversion', Chemical Engineering Journal, vol. 432, pp. 134322-134322.
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Zhu, Y, Zhang, H, Li, H, Zhang, J & Zhang, D 2022, 'Optimal Jamming Strategy Against Two-State Switched System', IEEE Communications Letters, vol. 26, no. 3, pp. 706-710.
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Zhu, Y, Zhang, Q, Qin, L, Chang, L & Yu, JX 2022, 'Cohesive Subgraph Search Using Keywords in Large Networks', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 1, pp. 178-191.
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Zhu, Y-Y, Liu, Y, Xu, J & Ni, B-J 2022, 'Three-dimensional excitation-emission matrix (EEM) fluorescence approach to probing the binding interactions of polystyrene microplastics to bisphenol A', Journal of Hazardous Materials Advances, vol. 5, pp. 100046-100046.
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Zhuang, J-L, Sun, X, Zhao, W-Q, Zhang, X, Zhou, J-J, Ni, B-J, Liu, Y-D, Shapleigh, JP & Li, W 2022, 'The anammox coupled partial-denitrification process in an integrated granular sludge and fixed-biofilm reactor developed for mainstream wastewater treatment: Performance and community structure', Water Research, vol. 210, pp. 117964-117964.
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Ziolkowski, RW 2022, 'Mixtures of Multipoles—Should They Be in Your EM Toolbox?', IEEE Open Journal of Antennas and Propagation, vol. 3, pp. 154-188.
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Zogan, H, Razzak, I, Wang, X, Jameel, S & Xu, G 2022, 'Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media', World Wide Web, vol. 25, no. 1, pp. 281-304.
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AbstractThe ability to explain why the model produced results in such a way is an important problem, especially in the medical domain. Model explainability is important for building trust by providing insight into the model prediction. However, most existing machine learning methods provide no explainability, which is worrying. For instance, in the task of automatic depression prediction, most machine learning models lead to predictions that are obscure to humans. In this work, we propose explainable Multi-Aspect Depression Detection with Hierarchical Attention Network MDHAN, for automatic detection of depressed users on social media and explain the model prediction. We have considered user posts augmented with additional features from Twitter. Specifically, we encode user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words’ importance, and capture semantic sequence features from the user timelines (posts). Our hierarchical attention model is developed in such a way that it can capture patterns that leads to explainable results. Our experiments show that MDHAN outperforms several popular and robust baseline methods, demonstrating the effectiveness of combining deep learning with multi-aspect features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. MDHAN achieves excellent performance and ensures adequate evidence to explain the prediction.
Zohurul Islam, M, Nath Mondal, R & C. Saha, S 2022, 'Impacts of Rotation on Unsteady Fluid Flow and Energy Distribution through a Bending Duct with Rectangular Cross Section', Energy Engineering, vol. 119, no. 2, pp. 453-472.
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Zong, T, Zhao, J, Xiang, Y, Natgunanathan, I, Gao, L & Zhou, W 2022, 'Desynchronization-attack-resilient audio watermarking mechanism for stereo signals using the linear correlation between channels', World Wide Web, vol. 25, no. 1, pp. 357-379.
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Zou, S, Wang, W, Ni, W, Wang, L & Tang, Y 2022, 'Efficient Orchestration of Virtualization Resource in RAN Based on Chemical Reaction Optimization and Q-Learning', IEEE Internet of Things Journal, vol. 9, no. 5, pp. 3383-3396.
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Zuo, S, Wang, D, Zhang, Y & Luo, Q 2022, 'Design and testing of a parabolic cam-roller quasi-zero-stiffness vibration isolator', International Journal of Mechanical Sciences, vol. 220, pp. 107146-107146.
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