Publications

Atanassov, K, Shannon, A, Sotirova, E, Vasilev, V & Sotirov, S 2022, 'Generalized Net Model for Collecting, Evaluating and Including of Facts in the Educational Content' in Advances in Intelligent Systems Research and Innovation, Springer International Publishing, pp. 341-348.
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Cetindamar Kozanoglu, D & Pugalia, S 2022, 'Engaging the Technology Management for Gender Equality' in Daim, T, Dabic, M & Yu-chan, S (eds), Routledge Companion to Technology Management, Routledge, New Yourk, pp. 1-17.
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Inspired by the United Nations (UN) 2030 Agenda Millennium Development Goals, this chapter is an exercise to understand how the technology management (TM) discipline could reduce gender inequality in technology industries. We conduct a literature review of the TM field in the top 10 journals and then present the studies examining women’s role in technology-based industries as managers or entrepreneurs. The findings underline how gender equality is almost a non-existing topic in TM. Hence, this chapter actively calls researchers in developing data and knowledge on gender equality issues for women in technology industries. By owning the problem, TM discipline could produce research that will lay the ground for strategies and policies related to women’s employment and leadership in technology industries.

Hakim, G & Braun, R 2022, 'Wireless Sensor Network Routing for Energy Efficiency' in Advances in Systems Engineering, Springer International Publishing, pp. 329-343.
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Hussain, W & Merigó, JM 2022, 'Centralised Quality of Experience and Service Framework Using PROMETHEE-II for Cloud Provider Selection' in EAI/Springer Innovations in Communication and Computing, Springer International Publishing, pp. 79-94.
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Li, W, Dong, W & Shah, SP 2022, 'Piezoresistivity of Carbon Black/Cement-Based Sensor Enhanced with Polypropylene Fibre' in RILEM Bookseries, Springer International Publishing, pp. 889-899.
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Mirdad, A & Hussain, FK 2022, 'Blockchain-Based Pharmaceutical Supply Chain: A Literature Review' in Advances on P2P, Parallel, Grid, Cloud and Internet Computing, Springer International Publishing, pp. 106-115.
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Recently, blockchain technology was introduced to the public in order to provide a secure environment that is immutable, consensus-based and transparent in the finance technology world. However, there have been many efforts to apply blockchain to other fields where trust and transparency are a requirement. The ability to reliably share pharmaceutical information between various stakeholders is essential. The use of blockchain technology adds traceability and visibility to supply chains such as pharmaceuticals to provide all the information from end to end. Currently, the data is stored and managed by large manufacturers and pharmacy retailers using their own centralized systems. Several existing approaches and methods that allow pharmaceutical information to be stored and shared between the healthcare provider and other stakeholders in a centralized manner have been discussed in the literature. Due to the lack of comprehensive literature review studies that focus on pharmaceutical supply chain using blockchain technology, thus this paper highlights and addresses this gap. This paper reviews several studies which have applied blockchain technology in pharmaceutical supply chains. This paper overviews the knowledge on blockchain technology, discusses and explores the most recent and relevant studies that adopt blockchain technology in the field of pharmaceutical supply chains, describes the challenges associated with blockchain technology, and presents some ideas for future work.

Nimbalkar, S & Meena, NK 2022, 'Static and Seismic Assessment of Soil Arching in Piled Embankments' in Springer Tracts in Civil Engineering, Springer Singapore, pp. 263-281.
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Tawk, M, Indraratna, B, Rujikiatkamjorn, C & Heitor, A 2022, 'A Mixture of Waste Materials as a Construction Fill in Transportation Infrastructure' in Lecture Notes in Civil Engineering, Springer International Publishing, pp. 565-577.
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The reuse of waste materials in engineering projects is a sustainable concept that is being increasingly adopted in civil projects including the transportation infrastructure sector. A mixture of coal wash (CW), a waste produced by the coal mining industry, and rubber crumbs (RC), produced by shredding waste rubber tyres, is a possible alternative to natural quarried rock aggregates that are traditionally used in sublayers of transportation corridors. These materials have different properties than traditional aggregates and their geotechnical behaviour must be investigated before they can be confidently used in transportation infrastructure projects. This study particularly addresses the compaction characteristics of four CWRC mixtures (i.e. 0, 5, 10 and 15% rubber content) with a focus on the energy absorbing nature of rubber. The compaction energy must be increased to compensate for the energy absorbed by rubber and produce a compact packing of particles whilst keeping the breakage levels to a minimum. The strength and deformation properties of the mixture are also addressed through static triaxial tests under three confining pressures to simulate field conditions (i.e. 25, 50 and 75 kPa). The preliminary results showed that the mixture has sufficient strength for transportation sublayers that are not the main load bearing layers such as the subbase layer in roads and the capping/subballast layer in railways. Despite the compressibility of rubber which induces higher settlements, for the loads applied in practice at the level of a subbase or a capping layer, the expected settlements are within the allowable limits.

Tong, C, Zhang, S & Sheng, D 2022, 'Particle Breakage Observed in Both Transitional and Non-transitional Carbonate Sands' in Lecture Notes in Civil Engineering, Springer International Publishing, pp. 935-943.
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Zhou, J & Chen, F 2022, 'Towards Humanity-in-the-Loop in AI Lifecycle' in Humanity Driven AI, Springer International Publishing, pp. 3-13.
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Abdollahzadeh, M, Bayatsarmadi, B, Vepsäläinen, M, Razmjou, A & Asadnia, M 2022, 'Highly stable Li+ selective electrode with metal-organic framework as ion-to-electron transducer', Sensors and Actuators B: Chemical, vol. 350, pp. 130799-130799.
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Detection of Li+ ions is vital due to their applications as therapeutic drugs in medicine. In addition, finding Li+ in geothermal brines is gaining interest because of its application in energy storage systems. Monitoring Li+ levels in an aqueous environment can be achieved using chemical sensors. Solid-contact sensors (SCS) have attracted significant attention because of their portability, high sensitivity and lack of requirement of calibration. However, most solid-contact sensors suffer from low stability, especially in the long term. As a result, ion-to-electron transducers have been utilized to mitigate this problem. Recently Metal-Organic Frameworks (MOFs) have proven to be excellent candidates for use as ion-to-electron transducers in SCSs. In this study, we have used Ni-HAB MOF to produce a highly stable Li+ selective electrode. Increasing the thickness of the MOF to 3.28 µm enhanced the sensor's capacitance 100-fold leading to the lowest drift in Li+ SCSs reported in the literature, 1.15×10−6 mV/h with a low limit of detection (LOD) of 9.94×10−7M and a 57.6 mV/dec sensitivity. The sensor exhibited a linear output and a fast response time of less than 1 s. In addition, the sensor developed was used in a real brine to detect the concentration of Li+ ions, where the obtained results were in good agreement with the actual concentration of Li+ ions. This paper offers a solution for the persistent issues of solid-contact sensors such as drift, response time, and limit of detection and paves the way for the miniaturization of sensors to be used in real-life applications.

Abolpour Moshizi, S, Moradi, H, Wu, S, Han, ZJ, Razmjou, A & Asadnia, M 2022, 'Biomimetic Ultraflexible Piezoresistive Flow Sensor Based on Graphene Nanosheets and PVA Hydrogel', Advanced Materials Technologies, vol. 7, no. 1, pp. 2100783-2100783.
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Flow sensors play a critical role in monitoring flow parameters, including rate, velocity, direction, and rotation frequency. In this paper, inspired by biological hair cells in the human vestibular system, an innovative flow sensor is developed based on polyvinyl alcohol (PVA) hydrogel nanocomposites with a maze-like network of vertically grown graphene nanosheets (VGNs). The VGNs/PVA hydrogel absorbs a copious amount of water when immersed in water, making the sensor highly sensitive to tiny stimuli underwater. The sensor demonstrates a high sensitivity (5.755 mV (mm s−1)−1) and extremely low velocity detection (0.022 mm s−1). It also reveals outstanding performance in detecting low-frequency oscillatory flows down to 0.1 Hz, which make it suitable for many biomedical applications. As one of the potential applications of the sensor, it exhibits excellent performance in mimicking various physiological conditions of vestibular hair cells. To explain the experimental results, a complete finite element simulation is developed to model the piezoresistive effect of VGNs/PVA thin film structure. This is the first attempt to develop hydrogel–graphene nanosheet-based flow sensors, which creates the closest artificial sensor to vestibular hair cells. This miniaturized hair cell sensor paves the way for utilizing hydrogels to develop next-generation of ultrasensitive flow sensors for biomedical applications.

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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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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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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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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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, 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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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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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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Asadniaye Fardjahromi, M, Nazari, H, Ahmadi Tafti, SM, Razmjou, A, Mukhopadhyay, S & Warkiani, ME 2022, 'Metal-organic framework-based nanomaterials for bone tissue engineering and wound healing', Materials Today Chemistry, vol. 23, pp. 100670-100670.
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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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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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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.

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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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.

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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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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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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Cai, S, Wang, D, Wang, H, Lyu, Y, Xu, G, Zheng, X & Vasilakos, AV 2022, 'DynaComm: Accelerating Distributed CNN Training Between Edges and Clouds Through Dynamic Communication Scheduling', IEEE Journal on Selected Areas in Communications, vol. 40, no. 2, pp. 611-625.
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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.

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.

Chakraborty, S 2022, 'TOPSIS and Modified TOPSIS: A comparative analysis', Decision Analytics Journal, vol. 2, pp. 100021-100021.
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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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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, 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, 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.

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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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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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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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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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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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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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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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, 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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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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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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Fang, G, Lu, H, Al-Nakashli, R, Chapman, R, Zhang, Y, Ju, LA, Lin, G, Stenzel, MH & Jin, D 2022, 'Enabling peristalsis of human colon tumor organoids on microfluidic chips', Biofabrication, vol. 14, no. 1, pp. 015006-015006.
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Abstract Peristalsis in the digestive tract is crucial to maintain physiological functions. It remains challenging to mimic the peristaltic microenvironment in gastrointestinal organoid culture. Here, we present a method to model the peristalsis for human colon tumor organoids on a microfluidic chip. The chip contains hundreds of lateral microwells and a surrounding pressure channel. Human colon tumor organoids growing in the microwell were cyclically contracted by pressure channel, mimicking the in vivo mechano-stimulus by intestinal muscles. The chip allows the control of peristalsis amplitude and rhythm and the high throughput culture of organoids simultaneously. By applying 8% amplitude with 8 ∼ 10 times min−1, we observed the enhanced expression of Lgr5 and Ki67. Moreover, ellipticine-loaded polymeric micelles showed reduced uptake in the organoids under peristalsis and resulted in compromised anti-tumor efficacy. The results indicate the importance of mechanical stimuli mimicking the physiological environment when using in vitro models to evaluate nanoparticles. This work provides a method for attaining more reliable and representative organoids models in nanomedicine.

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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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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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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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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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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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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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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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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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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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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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, Alhathal Alanezi, A, Ghaffour, N, Déon, S, Subbiah, S & Altaee, A 2022, 'Development in Forward Osmosis-Membrane Distillation Hybrid System for Wastewater Treatment', Separation and Purification Technology, pp. 120498-120498.
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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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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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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, 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.

Joshi, S 2022, 'Assessing Effectiveness of Humanitarian Activities against COVID-19 Disruption: The Role of Blockchain enabled Digital Humanitarian Network (BT-DHN)', Sustainability.

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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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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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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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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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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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, F, Dong, S, Leier, A, Han, M, Guo, X, Xu, J, Wang, X, Pan, S, Jia, C, Zhang, Y, Webb, GI, Coin, LJM, Li, C & Song, J 2022, 'Positive-unlabeled learning in bioinformatics and computational biology: a brief review', Briefings in Bioinformatics, vol. 23, no. 1.
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Abstract Conventional supervised binary classification algorithms have been widely applied to address significant research questions using biological and biomedical data. This classification scheme requires two fully labeled classes of data (e.g. positive and negative samples) to train a classification model. However, in many bioinformatics applications, labeling data is laborious, and the negative samples might be potentially mislabeled due to the limited sensitivity of the experimental equipment. The positive unlabeled (PU) learning scheme was therefore proposed to enable the classifier to learn directly from limited positive samples and a large number of unlabeled samples (i.e. a mixture of positive or negative samples). To date, several PU learning algorithms have been developed to address various biological questions, such as sequence identification, functional site characterization and interaction prediction. In this paper, we revisit a collection of 29 state-of-the-art PU learning bioinformatic applications to address various biological questions. Various important aspects are extensively discussed, including PU learning methodology, biological application, classifier design and evaluation strategy. We also comment on the existing issues of PU learning and offer our perspectives for the future development of PU learning applications. We anticipate that our work serves as an instrumental guideline for a better understanding of the PU learning framework in bioinformatics and further developing next-generation PU learning frameworks for critical biological applications.

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, 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, 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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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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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 & 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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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, 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, 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-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, X, Deng, RH, Miao, Y & Vasilakos, AV 2022, 'Guest Editorial: 5G-Enabled Intelligent Application for Distributed Industrial Internet-of-Thing System', IEEE Transactions on Industrial Informatics, vol. 18, no. 4, pp. 2807-2810.
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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, 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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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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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, 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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Ma, J, Li, J, Du, B, Wu, J, Wan, J & Xiao, Y 2022, 'Robust face alignment by dual-attentional spatial-aware capsule networks', Pattern Recognition, vol. 122, pp. 108297-108297.
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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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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...

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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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.

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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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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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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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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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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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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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, 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, 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, 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, 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, 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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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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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.

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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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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Parsa, K, Hassall, M & Naderpour, M 2022, 'Enhancing Alarm Prioritization in the Alarm Management Lifecycle', IEEE Access, vol. 10, pp. 99-111.
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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.

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

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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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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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, 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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Raj, C & Meel, P 2022, 'ARCNN framework for multimodal infodemic detection', Neural Networks, vol. 146, pp. 36-68.
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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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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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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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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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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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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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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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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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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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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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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, pp. 130507-130507.
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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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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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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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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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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, 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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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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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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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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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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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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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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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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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, 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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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, 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, 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, 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, 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, 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, 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, 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, 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, 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.

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, 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, 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.

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.

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, 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, 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.

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/GO_0.15-vanillin_0.8 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.

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, 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, 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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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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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, 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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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, 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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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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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, 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, 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, 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, SS, Ke, Y, Chen, E, Biscaia, H & Li, WG 2022, 'Effect of load distribution on the behaviour of RC beams strengthened in flexure with near-surface mounted (NSM) FRP', Composite Structures, vol. 279, pp. 114782-114782.
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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, 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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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, 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, 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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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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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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Ahmed, M, Cetindamar, D & Chakraborty, S 1970, 'Public Procurement, Big Data Analytics Capabilities, and Healthcare Supply Chain Sustainability', Hawaii International Conference on System Sciences, Hawai.

Alharbi, M & Hussain, FK 1970, 'Blockchain-Based Identity Management for Personal Data: A Survey', Springer International Publishing, pp. 167-178.
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Bhandari, S, Fatahi, B, Khabbaz, H, Lee, J, Xu, Z & Zhong, J 1970, 'Evaluating the Influence of Soil Plasticity on the Vibratory Roller—Soil Interaction for Intelligent Compaction', Lecture Notes in Civil Engineering, pp. 247-260.
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Use of intelligent compaction (IC) is a growing technique for compaction in the field of construction. It provides an efficient way of evaluating the soil compaction level with a higher degree of certainty than traditional quality control methods. IC involves the interpretation of measured values received through the accelerometer and other sensors attached to the roller. The key objective of this paper is to analyse the dynamic roller–soil interaction via a three-dimensional nonlinear finite element model, capturing soil nonlinear response and damping in both small and large strain ranges as a result of dynamic load applied via the vibratory roller. In particular, the impact of soil plasticity index (PI) on the response of a typical vibratory roller is assessed. Indeed, the soil plasticity impacts stiffness degradation with shear strain influencing the soil stiffness during compaction and the roller response. The numerical predictions exhibit that the soil plasticity can significantly influence the response of the roller and the ground settlement level; hence, practising engineers can consider the soil plasticity index as an influencing factor to interpret the intelligent compaction results and optimize the compaction process.

Boyd-Weetman, B, Thomas, P, Sirivivatnanon, V & Nairn, J 1970, 'Alkali thresholds in concrete; the balanced alkali approach in ASRmitigation', Proceedings of the 16th International Conference on Alkali-Aggregate Reaction in Concrete, International Conference on Alkali-Aggregate Reaction in Concrete, LABORATÓRIO NACIONAL DE ENGENHARIA CIVIL, I. P., Lisbon, pp. 611-616.
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The Alkali Silica Reaction (ASR) is a deleterious reaction in concrete that poses significant durability concern worldwide. As a preventative measure, total alkali content of general purpose Portland cements can be limited. In Australia, like in other countries, general purpose cement is limited with a conservative alkali content of 0.6%, this may be unnecessary, as low risk non-reactive aggregates and SCM blends are effective in reducing ASR prevalence. Indeed there is a growing argument to transition to risk assessed methods in choosing cement alkali levels. ASTM in the USA has employed a prescriptive approach to selecting preventative measures that incorporates a variety of cement alkali contents without compromising on safety. Similar balanced alkali approaches such as those recommended in Europe, Canada and New Zealand may be applicable in Australia. Raising alkali limits to a level greater than 0.6% in cement used in conjunction with alternative mitigation techniques would reduce the economic and environmental impact associated with alkali removal during cement production. This literature review discusses the Australian approach to alkali limits in contrast to the methods used around the world and explores the continuing research into alkali’s mechanistic contribution to ASR.

Dang, CC, Dang, LC & Khabbaz, H 1970, 'Predicting the Stability of Riverbank Slope Reinforced with Columns Under Various River Water Conditions', Lecture Notes in Civil Engineering, pp. 513-523.
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A numerical analysis on the stability of soil–cement column-reinforced riverbank along a river delta region in Viet Nam is presented in this paper. The numerical analyses based on the limit equilibrium method (LEM) were performed to assess the safety factor of the column-reinforced riverbank system under various river water level (RWL) conditions. Several factors influencing the riverbank slope stability including the position, length, quantity of soil–cement columns, and RWL changes were investigated. The simulated results showed that the riverbank stability is improved with an increase in the column quantity and the column length when subjected to a constant RWL. Moreover, the predicted results by LEM indicated that the column location and the RWL change significantly influence the stability of riverbank with column reinforcement. The column location between the middle and the slope toe had a significant improvement of the riverbank slope stability, where an initial drawdown of RWL resulted in a notable reduction of the riverbank slope safety factor. These factors should be taken into consideration in the design of a riverbank slope, reinforced with columns, under variable RWL. It is worth mentioning that the use of soil–cement column-reinforced riverbank could be a practical and possible engineering countermeasure to prevent a steep riverbank slope under RWL variations from sliding failure.

Doan, S, Fatahi, B, Khabbaz, H & Rasekh, H 1970, 'Analytical Solution for Plane Strain Consolidation of Soft Soil Stabilised by Stone Columns', Springer International Publishing, pp. 753-767.
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Indraratna, B, Ngo, T, Qi, Y & Rujikiatkamjorn, C 1970, 'Track Geomechanics for Future Railways: Use of Artificial Inclusions', Lecture Notes in Civil Engineering, 4th International Conference on Transportation Geotechnics (ICGT2020), Chicago, Illinois, USA, pp. 139-154.
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Given that current ballasted tracks cannot support faster and heavier Australian heavy freight trains, the need to develop innovative and sustainable ballasted tracks for transport infrastructure is crucial. This paper reviews and discusses the use of artificial inclusions such as recycled rubber mats, end-of-life tyres, and geogrids to stabilize ballasted rail tracks overlying soft formation soil. It also presents a novel solution for increasing the stability and resiliency of track structure by energy-absorbing recycled rubber tyres. This study confirms that a capping layer confined by tyres will actively reduce ballast breakage within the track substructure. Numerical simulations employing discrete element method (DEM) are also carried out to study the micro-mechanical aspects of ballast aggregates and the interaction between the particles and inclusions. This study shows that waste rubber products and geosynthetics will eliminate the need for a capping layer in certain terrain and help to decrease the thickness of the ballast layer. The outcomes of this study will lead to a better understanding of the performance of ballast tracks reinforced with artificial inclusions and also help to improve the design and cost-effectiveness of ballasted tracks, with a view to enhancing passenger comfort and safety.

Marjanovic, O, Ariyachandra, T & Dinter, B 1970, 'Looking Ahead: Business Intelligence & Analytics Research in the Post-Pandemic New Normal', https://hicss.hawaii.edu/, HICSS 2022 : Hawaii International Conference on System Sciences, Virtual Conference.

Punetha, P & Nimbalkar, S 1970, 'Mathematical Modeling of the Short-Term Performance of Railway Track Under Train-Induced Loading', Lecture Notes in Civil Engineering, pp. 15-24.
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The accurate prediction of the track deformation under train-induced repetitive loading is inevitable to assess the efficiency of a railway track. This paper presents an analytical technique to calculate the transient deformations in a railway track subjected to train-induced loading. The method considers the track substructure as multilayered media in which the behavior of an individual track layer is simulated using a mass-spring-dashpot model. Unlike existing approaches to model the track substructure as an equivalent single or double layer, the proposed analytical approach considers all the three layers of the ballasted track (i.e., ballast, capping or subballast and subgrade). The accuracy of the proposed technique is investigated by comparing the predicted values of track displacement with the published data available in the literature. The predicted results are found to be in good agreement with past studies. A parametric study on the substructure behavior revealed that the elastic modulus of track layers significantly influences the track response.

Vizcarra, GC, Muniz, L, Gonçalves, T & Nimbalkar, S 1970, 'Railway Subgrade Characterization Through Repeated Loading Triaxial Testing', Lecture Notes in Civil Engineering, pp. 327-335.
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Currently, the improvement of means of transportation is a great challenge. Brazil has a large ore production, which will continue in the next decades, and seeks to reduce the transportation times between production and export centers, as well as reduce the emission of contaminants to the environment. In this sense, railways are a more efficient and environmentally friendly means of land transportation, and their proper conservation and operability affect the net gains that Brazil receives from the export of commodities. The implementation of this program proposed in the engineering practice would allow taking more precise decisions regarding the activities of maintenance of railroads, generating significant savings. The first step of the research is the analysis and interpretation of results of repeated load triaxial tests carried out in Brazil on railway subgrade soils. An engineering methodology is presented considering the geotechnical properties of the foundation soil obtained through field and laboratory tests for performing of geotechnical analysis. To ensure the railway stability, criteria of bearing capacity, elastic deflection and permanent deformation for the railway substructure must be met. A prediction model of permanent deformation is used, as well as the influence of moisture on the behavior of the foundation soil. This study aims to contribute to the finding of a comprehensive methodology for evaluating the useful service life of the track substructure so that the most appropriate material can be selected for use as a railroad formation material in order to limit stresses on the railway subgrade, which in turn cause progressive loss of geometric profile of the railway, and to maintain a safe operation of the trains. This will allow significant savings in the periodic maintenance of the substructure, which are one of the activities to restore the track geometry of railways.

Wang, X, Yao, L, Wang, X, Paik, H-Y & Wang, S 2021, 'Global Convolutional Neural Processes', IEEE International Conference on Data Mining, Auckland, New Zealand.

West, N, Syberg, M & Deuse, J 1970, 'A Holistic Methodology for Successive Bottleneck Analysis in Dynamic Value Streams of Manufacturing Companies', Springer International Publishing, pp. 612-619.
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Xu, Z, Khabbaz, H, Fatahi, B, Lee, J & Bhandari, S 1970, 'Numerical Assessment of Impacts of Vibrating Roller Characteristics on Acceleration Response of Drum Used for Intelligent Compaction', Lecture Notes in Civil Engineering, pp. 231-245.
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Intelligent compaction (IC) is an emerging technology for efficient and optimized ground compaction. IC combines the roller-integrated measurements with the Global Positioning System (GPS), which performs the real-time quality control and assurance during the compaction work. Indeed, IC technology is proven to be capable of providing a detailed control system for compaction process with real-time feedback and adjustment on full-area of compaction. Although roller manufacturers offer typical recommended settings for rollers in various soils, there are still some areas needing further improvement, particularly on the selection of vibration frequency and amplitude of the roller in soils experiencing significant nonlinearity and plasticity during compaction. In this paper, the interaction between the road subgrade and the vibrating roller is simulated, using the three-dimensional finite element method capturing the dynamic responses of the soil and the roller. The developed numerical model is able to simulate the nonlinear behavior of soil subjected to dynamic loading, particularly variations of soil stiffness and damping with the cyclic shear strain induced by the applied load. In this study, the dynamic load of the roller is explicitly applied to the simulated cylindrical roller drum. Besides, the impact of the frequency and amplitude on the level of subgrade compaction is discussed based on the detailed numerical analysis. The adopted constitutive model allows to assess the progressive settlement of ground subjected to cyclic loading. The results based on the numerical modeling reveal that the roller vibration characteristics can impact the influence depth as well as the level of soil compaction and its variations with depth. The results of this study can be used as a potential guidance by practicing engineers and construction teams on selecting the best choice of roller vibration frequency and amplitude to achieve high-quality compaction.

Ye, K, Ji, JC & Hu, D 1970, 'Dynamic Analysis of a Novel Zero-Stiffness Vibration Isolator by Considering Frictional Force Involved', Springer Singapore, pp. 544-553.
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Gandomi, AH 2022, 'Evolutionary Data-Driven Modelling and Optimization of Engineering Systems'.
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Keynote Speak at 2021 International Conference on Modal Analysis (IMAC-XL), February 7-10, 2022, Orlando, FL, USA